NLP/LLMs • Score 85
Cultural Encoding in Large Language Models: The Existence Gap in AI-Mediated Brand Discovery
arXiv:2601.00869v1 Announce Type: new
Abstract: As artificial intelligence systems increasingly mediate consumer information discovery,
brands face algorithmic invisibility. This study investigates Cultural Encoding in Large
Language Models (LLMs) -- systematic differences in brand recommendations arising from
training data composition. Analyzing 1,909 pure-English queries across 6 LLMs (GPT-4o,
Claude, Gemini, Qwen3, DeepSeek, Doubao) and 30 brands, we find Chinese LLMs exhibit 30.6
percentage points higher brand mention rates than International LLMs (88.9% vs. 58.3%,
p<.001). This disparity persists in identical English queries, indicating training data
geography -- not language -- drives the effect. We introduce the Existence Gap: brands
absent from LLM training corpora lack "existence" in AI responses regardless of quality.
Through a case study of Zhizibianjie (OmniEdge), a collaboration platform with 65.6%
mention rate in Chinese LLMs but 0% in International models (p<.001), we demonstrate how
Linguistic Boundary Barriers create invisible market entry obstacles. Theoretically, we
contribute the Data Moat Framework, conceptualizing AI-visible content as a VRIN strategic
resource. We operationalize Algorithmic Omnipresence -- comprehensive brand visibility
across LLM knowledge bases -- as the strategic objective for Generative Engine Optimization
(GEO). Managerially, we provide an 18-month roadmap for brands to build Data Moats
through semantic coverage, technical depth, and cultural localization. Our findings reveal
that in AI-mediated markets, the limits of a brand's "Data Boundaries" define the limits
of its "Market Frontiers."
Fonte: arXiv cs.AI
NLP/LLMs • Score 85
Can We Trust AI Explanations? Evidence of Systematic Underreporting in Chain-of-Thought Reasoning
arXiv:2601.00830v1 Announce Type: new
Abstract: When AI systems explain their reasoning step-by-step, practitioners often assume these explanations reveal what actually influenced the AI's answer. We tested this assumption by embedding hints into questions and measuring whether models mentioned them. In a study of over 9,000 test cases across 11 leading AI models, we found a troubling pattern: models almost never mention hints spontaneously, yet when asked directly, they admit noticing them. This suggests models see influential information but choose not to report it. Telling models they are being watched does not help. Forcing models to report hints works, but causes them to report hints even when none exist and reduces their accuracy. We also found that hints appealing to user preferences are especially dangerous-models follow them most often while reporting them least. These findings suggest that simply watching AI reasoning is not enough to catch hidden influences.
Fonte: arXiv cs.AI
NLP/LLMs • Score 85
Temporal Attack Pattern Detection in Multi-Agent AI Workflows: An Open Framework for Training Trace-Based Security Models
arXiv:2601.00848v1 Announce Type: new
Abstract: We present an openly documented methodology for fine-tuning language models to detect temporal attack patterns in multi-agent AI workflows using OpenTelemetry trace analysis. We curate a dataset of 80,851 examples from 18 public cybersecurity sources and 35,026 synthetic OpenTelemetry traces. We apply iterative QLoRA fine-tuning on resource-constrained ARM64 hardware (NVIDIA DGX Spark) through three training iterations with strategic augmentation. Our custom benchmark accuracy improves from 42.86% to 74.29%, a statistically significant 31.4-point gain. Targeted examples addressing specific knowledge gaps outperform indiscriminate scaling. Key contributions include: (1) synthetic trace generation methodology for multi-agent coordination attacks and regulatory violations, (2) empirical evidence that training data composition fundamentally determines behavior, and (3) complete open release of datasets, training scripts, and evaluation benchmarks on HuggingFace. While practical deployment requires human oversight due to false positive rates, this work establishes the first reproducible framework enabling practitioners to build custom agentic security models adapted to their threat landscapes.
Fonte: arXiv cs.AI
NLP/LLMs • Score 85
MathLedger: Um Substrato de Aprendizado Verificável com Feedback Atestado por Ledger
Os sistemas de IA contemporâneos alcançam desempenho extraordinário, mas permanecem opacos e não verificáveis, criando uma crise de confiança para implantações críticas de segurança. Apresentamos o MathLedger, um substrato para cognição de máquina verificável que integra verificação formal, atestação criptográfica e dinâmicas de aprendizado em um único loop epistêmico.
Fonte: arXiv cs.AI
NLP/LLMs • Score 85
MindChat: A Privacy-preserving Large Language Model for Mental Health Support
arXiv:2601.01993v1 Announce Type: new
Abstract: Large language models (LLMs) have shown promise for mental health support, yet training such models is constrained by the scarcity and sensitivity of real counseling dialogues. In this article, we present MindChat, a privacy-preserving LLM for mental health support, together with MindCorpus, a synthetic multi-turn counseling dataset constructed via a multi-agent role-playing framework. To synthesize high-quality counseling data, the developed dialogue-construction framework employs a dual closed-loop feedback design to integrate psychological expertise and counseling techniques through role-playing: (i) turn-level critique-and-revision to improve coherence and counseling appropriateness within a session, and (ii) session-level strategy refinement to progressively enrich counselor behaviors across sessions. To mitigate privacy risks under decentralized data ownership, we fine-tune the base model using federated learning with parameter-efficient LoRA adapters and incorporate differentially private optimization to reduce membership and memorization risks. Experiments on synthetic-data quality assessment and counseling capability evaluation show that MindCorpus improves training effectiveness and that MindChat is competitive with existing general and counseling-oriented LLM baselines under both automatic LLM-judge and human evaluation protocols, while exhibiting reduced privacy leakage under membership inference attacks.
Fonte: arXiv cs.AI
NLP/LLMs • Score 85
COMPASS: A Framework for Evaluating Organization-Specific Policy Alignment in LLMs
arXiv:2601.01836v1 Announce Type: new
Abstract: As large language models are deployed in high-stakes enterprise applications, from healthcare to finance, ensuring adherence to organization-specific policies has become essential. Yet existing safety evaluations focus exclusively on universal harms. We present COMPASS (Company/Organization Policy Alignment Assessment), the first systematic framework for evaluating whether LLMs comply with organizational allowlist and denylist policies. We apply COMPASS to eight diverse industry scenarios, generating and validating 5,920 queries that test both routine compliance and adversarial robustness through strategically designed edge cases. Evaluating seven state-of-the-art models, we uncover a fundamental asymmetry: models reliably handle legitimate requests (>95% accuracy) but catastrophically fail at enforcing prohibitions, refusing only 13-40% of adversarial denylist violations. These results demonstrate that current LLMs lack the robustness required for policy-critical deployments, establishing COMPASS as an essential evaluation framework for organizational AI safety.
Fonte: arXiv cs.AI
NLP/LLMs • Score 96
FaithSCAN: Detecção de Alucinações em Uma Única Passagem Baseada em Modelos para Respostas Visuais de Perguntas Fiéis
As alucinações de fidelidade em VQA ocorrem quando modelos de visão-linguagem produzem respostas fluentes, mas visualmente não fundamentadas, comprometendo sua confiabilidade em aplicações críticas de segurança. Propomos o FaithSCAN, uma rede leve que detecta alucinações explorando sinais internos ricos dos VLMs, superando limitações de métodos existentes em eficiência e desempenho de detecção.
Fonte: arXiv cs.AI
Vision • Score 95
DichroGAN: Towards Restoration of in-air Colours of Seafloor from Satellite Imagery
arXiv:2601.00194v1 Announce Type: new
Abstract: Recovering the in-air colours of seafloor from satellite imagery is a challenging task due to the exponential attenuation of light with depth in the water column. In this study, we present DichroGAN, a conditional generative adversarial network (cGAN) designed for this purpose. DichroGAN employs a two-steps simultaneous training: first, two generators utilise a hyperspectral image cube to estimate diffuse and specular reflections, thereby obtaining atmospheric scene radiance. Next, a third generator receives as input the generated scene radiance containing the features of each spectral band, while a fourth generator estimates the underwater light transmission. These generators work together to remove the effects of light absorption and scattering, restoring the in-air colours of seafloor based on the underwater image formation equation. DichroGAN is trained on a compact dataset derived from PRISMA satellite imagery, comprising RGB images paired with their corresponding spectral bands and masks. Extensive experiments on both satellite and underwater datasets demonstrate that DichroGAN achieves competitive performance compared to state-of-the-art underwater restoration techniques.
Fonte: arXiv cs.CV
Vision • Score 95
SV-GS: Sparse View 4D Reconstruction with Skeleton-Driven Gaussian Splatting
arXiv:2601.00285v1 Announce Type: new
Abstract: Reconstructing a dynamic target moving over a large area is challenging. Standard approaches for dynamic object reconstruction require dense coverage in both the viewing space and the temporal dimension, typically relying on multi-view videos captured at each time step. However, such setups are only possible in constrained environments. In real-world scenarios, observations are often sparse over time and captured sparsely from diverse viewpoints (e.g., from security cameras), making dynamic reconstruction highly ill-posed. We present SV-GS, a framework that simultaneously estimates a deformation model and the object's motion over time under sparse observations. To initialize SV-GS, we leverage a rough skeleton graph and an initial static reconstruction as inputs to guide motion estimation. (Later, we show that this input requirement can be relaxed.) Our method optimizes a skeleton-driven deformation field composed of a coarse skeleton joint pose estimator and a module for fine-grained deformations. By making only the joint pose estimator time-dependent, our model enables smooth motion interpolation while preserving learned geometric details. Experiments on synthetic datasets show that our method outperforms existing approaches under sparse observations by up to 34% in PSNR, and achieves comparable performance to dense monocular video methods on real-world datasets despite using significantly fewer frames. Moreover, we demonstrate that the input initial static reconstruction can be replaced by a diffusion-based generative prior, making our method more practical for real-world scenarios.
Fonte: arXiv cs.CV
Vision • Score 95
ActErase: A Training-Free Paradigm for Precise Concept Erasure via Activation Patching
arXiv:2601.00267v1 Announce Type: new
Abstract: Recent advances in text-to-image diffusion models have demonstrated remarkable generation capabilities, yet they raise significant concerns regarding safety, copyright, and ethical implications. Existing concept erasure methods address these risks by removing sensitive concepts from pre-trained models, but most of them rely on data-intensive and computationally expensive fine-tuning, which poses a critical limitation. To overcome these challenges, inspired by the observation that the model's activations are predominantly composed of generic concepts, with only a minimal component can represent the target concept, we propose a novel training-free method (ActErase) for efficient concept erasure. Specifically, the proposed method operates by identifying activation difference regions via prompt-pair analysis, extracting target activations and dynamically replacing input activations during forward passes. Comprehensive evaluations across three critical erasure tasks (nudity, artistic style, and object removal) demonstrates that our training-free method achieves state-of-the-art (SOTA) erasure performance, while effectively preserving the model's overall generative capability. Our approach also exhibits strong robustness against adversarial attacks, establishing a new plug-and-play paradigm for lightweight yet effective concept manipulation in diffusion models.
Fonte: arXiv cs.CV
Vision • Score 95
A Spatially Masked Adaptive Gated Network for multimodal post-flood water extent mapping using SAR and incomplete multispectral data
arXiv:2601.00123v1 Announce Type: new
Abstract: Mapping water extent during a flood event is essential for effective disaster management throughout all phases: mitigation, preparedness, response, and recovery. In particular, during the response stage, when timely and accurate information is important, Synthetic Aperture Radar (SAR) data are primarily employed to produce water extent maps. Recently, leveraging the complementary characteristics of SAR and MSI data through a multimodal approach has emerged as a promising strategy for advancing water extent mapping using deep learning models. This approach is particularly beneficial when timely post-flood observations, acquired during or shortly after the flood peak, are limited, as it enables the use of all available imagery for more accurate post-flood water extent mapping. However, the adaptive integration of partially available MSI data into the SAR-based post-flood water extent mapping process remains underexplored. To bridge this research gap, we propose the Spatially Masked Adaptive Gated Network (SMAGNet), a multimodal deep learning model that utilizes SAR data as the primary input for post-flood water extent mapping and integrates complementary MSI data through feature fusion. In experiments on the C2S-MS Floods dataset, SMAGNet consistently outperformed other multimodal deep learning models in prediction performance across varying levels of MSI data availability. Furthermore, we found that even when MSI data were completely missing, the performance of SMAGNet remained statistically comparable to that of a U-Net model trained solely on SAR data. These findings indicate that SMAGNet enhances the model robustness to missing data as well as the applicability of multimodal deep learning in real-world flood management scenarios.
Fonte: arXiv cs.CV
NLP/LLMs • Score 96
FCMBench: Um Benchmark Multimodal Abrangente de Crédito Financeiro para Aplicações do Mundo Real
À medida que a IA multimodal se torna amplamente utilizada para avaliação de risco de crédito e revisão de documentos, um benchmark específico do domínio é urgentemente necessário. Apresentamos o FCMBench-V1.0, um benchmark multimodal de crédito financeiro em larga escala, cobrindo 18 tipos de certificados principais, com 4.043 imagens em conformidade com a privacidade e 8.446 amostras de QA.
Fonte: arXiv cs.AI
NLP/LLMs • Score 95
ABFR-KAN: Kolmogorov-Arnold Networks for Functional Brain Analysis
arXiv:2601.00416v1 Announce Type: new
Abstract: Functional connectivity (FC) analysis, a valuable tool for computer-aided brain disorder diagnosis, traditionally relies on atlas-based parcellation. However, issues relating to selection bias and a lack of regard for subject specificity can arise as a result of such parcellations. Addressing this, we propose ABFR-KAN, a transformer-based classification network that incorporates novel advanced brain function representation components with the power of Kolmogorov-Arnold Networks (KANs) to mitigate structural bias, improve anatomical conformity, and enhance the reliability of FC estimation. Extensive experiments on the ABIDE I dataset, including cross-site evaluation and ablation studies across varying model backbones and KAN configurations, demonstrate that ABFR-KAN consistently outperforms state-of-the-art baselines for autism spectrum distorder (ASD) classification. Our code is available at https://github.com/tbwa233/ABFR-KAN.
Fonte: arXiv cs.CV
Vision • Score 95
BHaRNet: Reliability-Aware Body-Hand Modality Expertized Networks for Fine-grained Skeleton Action Recognition
arXiv:2601.00369v1 Announce Type: new
Abstract: Skeleton-based human action recognition (HAR) has achieved remarkable progress with graph-based architectures. However, most existing methods remain body-centric, focusing on large-scale motions while neglecting subtle hand articulations that are crucial for fine-grained recognition. This work presents a probabilistic dual-stream framework that unifies reliability modeling and multi-modal integration, generalizing expertized learning under uncertainty across both intra-skeleton and cross-modal domains. The framework comprises three key components: (1) a calibration-free preprocessing pipeline that removes canonical-space transformations and learns directly from native coordinates; (2) a probabilistic Noisy-OR fusion that stabilizes reliability-aware dual-stream learning without requiring explicit confidence supervision; and (3) an intra- to cross-modal ensemble that couples four skeleton modalities (Joint, Bone, Joint Motion, and Bone Motion) to RGB representations, bridging structural and visual motion cues in a unified cross-modal formulation. Comprehensive evaluations across multiple benchmarks (NTU RGB+D~60/120, PKU-MMD, N-UCLA) and a newly defined hand-centric benchmark exhibit consistent improvements and robustness under noisy and heterogeneous conditions.
Fonte: arXiv cs.CV
Vision • Score 92
HarmoniAD: Harmonizing Local Structures and Global Semantics for Anomaly Detection
arXiv:2601.00327v1 Announce Type: new
Abstract: Anomaly detection is crucial in industrial product quality inspection. Failing to detect tiny defects often leads to serious consequences. Existing methods face a structure-semantics trade-off: structure-oriented models (such as frequency-based filters) are noise-sensitive, while semantics-oriented models (such as CLIP-based encoders) often miss fine details. To address this, we propose HarmoniAD, a frequency-guided dual-branch framework. Features are first extracted by the CLIP image encoder, then transformed into the frequency domain, and finally decoupled into high- and low-frequency paths for complementary modeling of structure and semantics. The high-frequency branch is equipped with a fine-grained structural attention module (FSAM) to enhance textures and edges for detecting small anomalies, while the low-frequency branch uses a global structural context module (GSCM) to capture long-range dependencies and preserve semantic consistency. Together, these branches balance fine detail and global semantics. HarmoniAD further adopts a multi-class joint training strategy, and experiments on MVTec-AD, VisA, and BTAD show state-of-the-art performance with both sensitivity and robustness.
Fonte: arXiv cs.CV
Privacy/Security/Fairness • Score 90
Deep Delta Learning
O artigo apresenta o Deep Delta Learning (DDL), uma nova arquitetura que generaliza a conexão residual padrão, modulando o atalho de identidade com uma transformação geométrica aprendível e dependente de dados. Essa transformação, chamada de Delta Operator, permite que a rede controle explicitamente o espectro de seu operador de transição, modelando dinâmicas complexas e não-monotônicas.
Fonte: arXiv cs.LG
NLP/LLMs • Score 95
StockBot 2.0: Vanilla LSTMs Outperform Transformer-based Forecasting for Stock Prices
arXiv:2601.00197v1 Announce Type: cross
Abstract: Accurate forecasting of financial markets remains a long-standing challenge due to complex temporal and often latent dependencies, non-linear dynamics, and high volatility. Building on our earlier recurrent neural network framework, we present an enhanced StockBot architecture that systematically evaluates modern attention-based, convolutional, and recurrent time-series forecasting models within a unified experimental setting. While attention-based and transformer-inspired models offer increased modeling flexibility, extensive empirical evaluation reveals that a carefully constructed vanilla LSTM consistently achieves superior predictive accuracy and more stable buy/sell decision-making when trained under a common set of default hyperparameters. These results highlight the robustness and data efficiency of recurrent sequence models for financial time-series forecasting, particularly in the absence of extensive hyperparameter tuning or the availability of sufficient data when discretized to single-day intervals. Additionally, these results underscore the importance of architectural inductive bias in data-limited market prediction tasks.
Fonte: arXiv cs.CL
Theory/Optimization • Score 92
Inferência de Variáveis Instrumentais Não Paramétricas com Muitos Instrumentos Fracos
Estudamos a inferência em funcionais lineares no problema de variável instrumental não paramétrica (NPIV) com um instrumento de valor discreto sob um regime assintótico de muitos instrumentos fracos, onde o número de valores do instrumento cresce com o tamanho da amostra. Um exemplo motivador chave é a estimativa de efeitos causais de longo prazo em um novo experimento com apenas resultados de curto prazo.
Fonte: arXiv stat.ML
NLP/LLMs • Score 95
Adapting Natural Language Processing Models Across Jurisdictions: A pilot Study in Canadian Cancer Registries
arXiv:2601.00787v1 Announce Type: new
Abstract: Population-based cancer registries depend on pathology reports as their primary diagnostic source, yet manual abstraction is resource-intensive and contributes to delays in cancer data. While transformer-based NLP systems have improved registry workflows, their ability to generalize across jurisdictions with differing reporting conventions remains poorly understood. We present the first cross-provincial evaluation of adapting BCCRTron, a domain-adapted transformer model developed at the British Columbia Cancer Registry, alongside GatorTron, a biomedical transformer model, for cancer surveillance in Canada. Our training dataset consisted of approximately 104,000 and 22,000 de-identified pathology reports from the Newfoundland & Labrador Cancer Registry (NLCR) for Tier 1 (cancer vs. non-cancer) and Tier 2 (reportable vs. non-reportable) tasks, respectively. Both models were fine-tuned using complementary synoptic and diagnosis focused report section input pipelines. Across NLCR test sets, the adapted models maintained high performance, demonstrating transformers pretrained in one jurisdiction can be localized to another with modest fine-tuning. To improve sensitivity, we combined the two models using a conservative OR-ensemble achieving a Tier 1 recall of 0.99 and reduced missed cancers to 24, compared with 48 and 54 for the standalone models. For Tier 2, the ensemble achieved 0.99 recall and reduced missed reportable cancers to 33, compared with 54 and 46 for the individual models. These findings demonstrate that an ensemble combining complementary text representations substantially reduce missed cancers and improve error coverage in cancer-registry NLP. We implement a privacy-preserving workflow in which only model weights are shared between provinces, supporting interoperable NLP infrastructure and a future pan-Canadian foundation model for cancer pathology and registry workflows.
Fonte: arXiv cs.CL
NLP/LLMs • Score 95
CSSBench: Evaluating the Safety of Lightweight LLMs against Chinese-Specific Adversarial Patterns
arXiv:2601.00588v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly deployed in cost-sensitive and on-device scenarios, and safety guardrails have advanced mainly in English. However, real-world Chinese malicious queries typically conceal intent via homophones, pinyin, symbol-based splitting, and other Chinese-specific patterns. These Chinese-specific adversarial patterns create the safety evaluation gap that is not well captured by existing benchmarks focused on English. This gap is particularly concerning for lightweight models, which may be more vulnerable to such specific adversarial perturbations. To bridge this gap, we introduce the Chinese-Specific Safety Benchmark (CSSBench) that emphasizes these adversarial patterns and evaluates the safety of lightweight LLMs in Chinese. Our benchmark covers six domains that are common in real Chinese scenarios, including illegal activities and compliance, privacy leakage, health and medical misinformation, fraud and hate, adult content, and public and political safety, and organizes queries into multiple task types. We evaluate a set of popular lightweight LLMs and measure over-refusal behavior to assess safety-induced performance degradation. Our results show that the Chinese-specific adversarial pattern is a critical challenge for lightweight LLMs. This benchmark offers a comprehensive evaluation of LLM safety in Chinese, assisting robust deployments in practice.
Fonte: arXiv cs.CL
NLP/LLMs • Score 95
MotionPhysics: Learnable Motion Distillation for Text-Guided Simulation
arXiv:2601.00504v1 Announce Type: new
Abstract: Accurately simulating existing 3D objects and a wide variety of materials often demands expert knowledge and time-consuming physical parameter tuning to achieve the desired dynamic behavior. We introduce MotionPhysics, an end-to-end differentiable framework that infers plausible physical parameters from a user-provided natural language prompt for a chosen 3D scene of interest, removing the need for guidance from ground-truth trajectories or annotated videos. Our approach first utilizes a multimodal large language model to estimate material parameter values, which are constrained to lie within plausible ranges. We further propose a learnable motion distillation loss that extracts robust motion priors from pretrained video diffusion models while minimizing appearance and geometry inductive biases to guide the simulation. We evaluate MotionPhysics across more than thirty scenarios, including real-world, human-designed, and AI-generated 3D objects, spanning a wide range of materials such as elastic solids, metals, foams, sand, and both Newtonian and non-Newtonian fluids. We demonstrate that MotionPhysics produces visually realistic dynamic simulations guided by natural language, surpassing the state of the art while automatically determining physically plausible parameters. The code and project page are available at: https://wangmiaowei.github.io/MotionPhysics.github.io/.
Fonte: arXiv cs.CV
Vision • Score 93
Aprendendo a Ser Reproduzível: Design de Função de Perda Personalizada para Redes Neurais Robústas
Para melhorar a reproducibilidade e a confiabilidade de modelos de deep learning, abordamos uma lacuna crítica nas metodologias de treinamento atuais: a falta de mecanismos que garantam desempenho consistente e robusto em diferentes execuções. Nossa análise empírica revela que, mesmo sob condições controladas, a precisão do modelo pode apresentar variabilidade significativa.
Fonte: arXiv cs.LG
NLP/LLMs • Score 95
ECR: Manifold-Guided Semantic Cues for Compact Language Models
arXiv:2601.00543v1 Announce Type: new
Abstract: Compact models often lose the structure of their embedding space. The issue shows up when the capacity is tight or the data spans several languages. Such collapse makes it difficult for downstream tasks to build on the resulting representation. Existing compression methods focus on aligning model outputs at a superficial level but fail to preserve the underlying manifold structure. This mismatch often leads to semantic drift in the compact model, causing both task behavior and linguistic properties to deviate from the reference model.
To address those issues, we provide a new framework called Embedding Consistency Regulation (ECR). This framework first derives a set of semantic anchors from teacher embeddings (computed once offline). Then, the compact model learns to maintain consistent geometry around these anchors, without relying on matching logits or internal features. ECR adds only a small projection step at inference, without altering the decoding architecture or its runtime behavior.
In experiments on a 100K multilingual corpus, ECR consistently stabilizes training and preserves semantic structure across tasks and languages. It also produces a more compact and task-aligned representation space, enabling low-capacity models to learn cleaner manifolds than conventional baselines. ECR works without teacher outputs and is compatible with, but independent of, distillation. Taken together, our results show that ECR helps compact models better follow task requirements and makes them easier to deploy under strict efficiency or privacy limits.
Fonte: arXiv cs.CL
MLOps/Systems • Score 92
Noise-Aware Named Entity Recognition for Historical VET Documents
arXiv:2601.00488v1 Announce Type: new
Abstract: This paper addresses Named Entity Recognition (NER) in the domain of Vocational Education and Training (VET), focusing on historical, digitized documents that suffer from OCR-induced noise. We propose a robust NER approach leveraging Noise-Aware Training (NAT) with synthetically injected OCR errors, transfer learning, and multi-stage fine-tuning. Three complementary strategies, training on noisy, clean, and artificial data, are systematically compared. Our method is one of the first to recognize multiple entity types in VET documents. It is applied to German documents but transferable to arbitrary languages. Experimental results demonstrate that domain-specific and noise-aware fine-tuning substantially increases robustness and accuracy under noisy conditions. We provide publicly available code for reproducible noise-aware NER in domain-specific contexts.
Fonte: arXiv cs.CL
Vision • Score 95
DepFlow: Disentangled Speech Generation to Mitigate Semantic Bias in Depression Detection
arXiv:2601.00303v1 Announce Type: new
Abstract: Speech is a scalable and non-invasive biomarker for early mental health screening. However, widely used depression datasets like DAIC-WOZ exhibit strong coupling between linguistic sentiment and diagnostic labels, encouraging models to learn semantic shortcuts. As a result, model robustness may be compromised in real-world scenarios, such as Camouflaged Depression, where individuals maintain socially positive or neutral language despite underlying depressive states. To mitigate this semantic bias, we propose DepFlow, a three-stage depression-conditioned text-to-speech framework. First, a Depression Acoustic Encoder learns speaker- and content-invariant depression embeddings through adversarial training, achieving effective disentanglement while preserving depression discriminability (ROC-AUC: 0.693). Second, a flow-matching TTS model with FiLM modulation injects these embeddings into synthesis, enabling control over depressive severity while preserving content and speaker identity. Third, a prototype-based severity mapping mechanism provides smooth and interpretable manipulation across the depression continuum. Using DepFlow, we construct a Camouflage Depression-oriented Augmentation (CDoA) dataset that pairs depressed acoustic patterns with positive/neutral content from a sentiment-stratified text bank, creating acoustic-semantic mismatches underrepresented in natural data. Evaluated across three depression detection architectures, CDoA improves macro-F1 by 9%, 12%, and 5%, respectively, consistently outperforming conventional augmentation strategies in depression Detection. Beyond enhancing robustness, DepFlow provides a controllable synthesis platform for conversational systems and simulation-based evaluation, where real clinical data remains limited by ethical and coverage constraints.
Fonte: arXiv cs.CL
RL • Score 95
Clustering por Denoising: Difusão latente plug-and-play para dados de célula única
O sequenciamento de RNA de célula única (scRNA-seq) permite o estudo da heterogeneidade celular. No entanto, a precisão do clustering e as análises subsequentes baseadas em rótulos celulares ainda são desafiadoras devido ao ruído de medição e à variabilidade biológica. Apresentamos um framework de difusão plug-and-play que separa o espaço de observação e o espaço de denoising.
Fonte: arXiv stat.ML
Vision • Score 95
Simulação como Supervisão: Pré-treinamento Mecânico para Descoberta Científica
A modelagem científica enfrenta um trade-off entre a interpretabilidade da teoria mecanicista e o poder preditivo do machine learning. Apresentamos as Simulation-Grounded Neural Networks (SGNNs), um framework que incorpora conhecimento de domínio nos dados de treinamento, permitindo que o modelo aprenda padrões amplos de possibilidade física e seja mais robusto a erros de especificação do modelo.
Fonte: arXiv stat.ML
NLP/LLMs • Score 96
Grande Estudo de Caso Empírico: Go-Explore adaptado para Testes de Red Team de IA
Agentes LLM de produção com capacidades de uso de ferramentas requerem testes de segurança, apesar de seu treinamento em segurança. Adaptamos o Go-Explore para avaliar o GPT-4o-mini em 28 execuções experimentais, abordando seis questões de pesquisa. Nossos resultados mostram que a variação de sementes aleatórias domina os parâmetros algorítmicos, resultando em uma variação de 8x nos resultados.
Fonte: arXiv cs.AI
RL • Score 95
Mitigando o viés otimista na estimativa e otimização de risco entrópico
A medida de risco entrópico é amplamente utilizada em decisões críticas em economia, ciência da gestão, finanças e sistemas de controle críticos, pois captura riscos extremos associados a perdas incertas. Este trabalho apresenta um procedimento de bootstrap paramétrico que corrige o viés do estimador empírico de risco entrópico, melhorando a precisão na tomada de decisões.
Fonte: arXiv stat.ML
Privacy/Security/Fairness • Score 90
Personalização Federada de Grandes Modelos: Abordagens, Experimentos e Insights
Neste artigo, exploramos a personalização federada de grandes modelos e destacamos os principais desafios que isso representa dentro do framework de aprendizado federado. Revisamos várias técnicas populares de personalização de grandes modelos e discutimos como essas técnicas podem ser implementadas no contexto do aprendizado federado.
Fonte: arXiv cs.LG
Privacy/Security/Fairness • Score 89
Classificação Ajustada por Incerteza para Precificação de Ativos com Machine Learning
O machine learning é central para a precificação empírica de ativos, mas a construção de portfólios ainda se baseia em previsões pontuais e ignora em grande parte a incerteza de estimativa específica de ativos. Propomos uma mudança simples: classificar ativos usando limites de previsão ajustados por incerteza em vez de apenas previsões pontuais.
Fonte: arXiv stat.ML
Theory/Optimization • Score 93
Otimização Bi-objetiva Guiada por Interpretabilidade: Alinhando Precisão e Explicabilidade
Este artigo apresenta a Otimização Bi-objetiva Guiada por Interpretabilidade (IGBO), um framework que treina modelos interpretáveis incorporando conhecimento de domínio estruturado por meio de uma formulação bi-objetiva. O IGBO codifica hierarquias de importância de características como um Grafo Acíclico Direcionado (DAG) e utiliza Gradientes Integrados Temporais (TIG) para medir a importância das características.
Fonte: arXiv cs.LG
NLP/LLMs • Score 96
HFedMoE: Aprendizado Federado Heterogêneo Consciente de Recursos com Mixture-of-Experts
Embora o aprendizado federado (FL) permita o ajuste fino de grandes modelos de linguagem (LLMs) sem comprometer a privacidade dos dados, o tamanho substancial de um LLM torna o treinamento em dispositivos impraticável para clientes com recursos limitados, como dispositivos móveis. Modelos Mixture-of-Experts (MoE) surgiram como uma solução eficiente em termos de computação, ativando apenas um subconjunto esparso de especialistas durante o treinamento do modelo.
Fonte: arXiv cs.LG
RL • Score 96
Amostras Adversariais Não São Criadas Iguais
No último década, diversas teorias foram propostas para explicar a vulnerabilidade generalizada das redes neurais profundas a ataques de evasão adversariais. Este trabalho defende que amostras que utilizam características frágeis, mas preditivas, e aquelas que não utilizam, representam dois tipos de fraquezas adversariais e devem ser diferenciadas na avaliação da robustez adversarial.
Fonte: arXiv cs.LG
NLP/LLMs • Score 96
Os Chatbots LLMs Falam Demais? O Benchmark YapBench
Modelos de Linguagem de Grande Escala (LLMs) como ChatGPT, Claude e Gemini atuam cada vez mais como copilotos de propósito geral, mas frequentemente respondem com excessiva extensão em solicitações simples, aumentando a carga cognitiva e inflacionando o custo de inferência baseado em tokens. Apresentamos o YapBench, um benchmark leve para quantificar a sobregeração visível ao usuário em prompts de brevidade ideal.
Fonte: arXiv cs.LG
NLP/LLMs • Score 96
Trajectory Guard -- Um Modelo Leve e Consciente de Sequência para Detecção de Anomalias em Tempo Real em AI Agente
Agentes autônomos de LLM geram planos de ação de múltiplos passos que podem falhar devido a desalinhamento contextual ou incoerência estrutural. Métodos existentes de detecção de anomalias não são adequados para esse desafio. Apresentamos o Trajectory Guard, um Autoencoder Recorrente Siamês que aprende alinhamento de tarefa e trajetória, permitindo a detecção unificada de planos incorretos e estruturas de planos malformadas.
Fonte: arXiv cs.LG
RL • Score 96
Uma Análise Comparativa de Métodos de Machine Learning Interpretabéis
Nos últimos anos, o Machine Learning (ML) tem sido amplamente adotado em diversos setores, incluindo áreas críticas como saúde, finanças e direito. Essa dependência crescente levantou preocupações sobre a interpretabilidade e a responsabilidade dos modelos, especialmente com a imposição de restrições legais e regulatórias sobre o uso de modelos black-box. Este estudo apresenta uma avaliação comparativa de 16 métodos inerentemente interpretabéis, abrangendo 216 conjuntos de dados tabulares do mundo real.
Fonte: arXiv cs.LG
NLP/LLMs • Score 96
Universos Paralelos, Linguagens Paralelas: Um Estudo Abrangente sobre Geração de Exemplos Contrafactuais Multilíngues Baseados em LLM
Os contrafactuais referem-se a entradas minimamente editadas que fazem a previsão de um modelo mudar, servindo como uma abordagem promissora para explicar o comportamento do modelo. Este estudo investiga a eficácia dos LLMs na geração de contrafactuais multilíngues, comparando contrafactuais gerados diretamente e aqueles derivados de tradução em inglês.
Fonte: arXiv cs.AI
NLP/LLMs • Score 96
Agentes Potencializados por LLMs Tendem a Ter Viés Contra Humanos? Explorando a Vulnerabilidade Dependente da Crença
Agentes potencializados por LLMs podem apresentar não apenas viés demográfico, mas também viés intergrupal desencadeado por pistas mínimas de 'nós' versus 'eles'. Este estudo investiga como a crença de um agente sobre a presença de humanos pode influenciar seu comportamento, introduzindo um novo vetor de ataque chamado Belief Poisoning Attack (BPA).
Fonte: arXiv cs.AI
Vision • Score 95
All-in-One Video Restoration under Smoothly Evolving Unknown Weather Degradations
arXiv:2601.00533v1 Announce Type: new
Abstract: All-in-one image restoration aims to recover clean images from diverse unknown degradations using a single model. But extending this task to videos faces unique challenges. Existing approaches primarily focus on frame-wise degradation variation, overlooking the temporal continuity that naturally exists in real-world degradation processes. In practice, degradation types and intensities evolve smoothly over time, and multiple degradations may coexist or transition gradually. In this paper, we introduce the Smoothly Evolving Unknown Degradations (SEUD) scenario, where both the active degradation set and degradation intensity change continuously over time. To support this scenario, we design a flexible synthesis pipeline that generates temporally coherent videos with single, compound, and evolving degradations. To address the challenges in the SEUD scenario, we propose an all-in-One Recurrent Conditional and Adaptive prompting Network (ORCANet). First, a Coarse Intensity Estimation Dehazing (CIED) module estimates haze intensity using physical priors and provides coarse dehazed features as initialization. Second, a Flow Prompt Generation (FPG) module extracts degradation features. FPG generates both static prompts that capture segment-level degradation types and dynamic prompts that adapt to frame-level intensity variations. Furthermore, a label-aware supervision mechanism improves the discriminability of static prompt representations under different degradations. Extensive experiments show that ORCANet achieves superior restoration quality, temporal consistency, and robustness over image and video-based baselines. Code is available at https://github.com/Friskknight/ORCANet-SEUD.
Fonte: arXiv cs.CV
Vision • Score 95
ReMA: A Training-Free Plug-and-Play Mixing Augmentation for Video Behavior Recognition
arXiv:2601.00311v1 Announce Type: new
Abstract: Video behavior recognition demands stable and discriminative representations under complex spatiotemporal variations. However, prevailing data augmentation strategies for videos remain largely perturbation-driven, often introducing uncontrolled variations that amplify non-discriminative factors, which finally weaken intra-class distributional structure and representation drift with inconsistent gains across temporal scales. To address these problems, we propose Representation-aware Mixing Augmentation (ReMA), a plug-and-play augmentation strategy that formulates mixing as a controlled replacement process to expand representations while preserving class-conditional stability. ReMA integrates two complementary mechanisms. Firstly, the Representation Alignment Mechanism (RAM) performs structured intra-class mixing under distributional alignment constraints, suppressing irrelevant intra-class drift while enhancing statistical reliability. Then, the Dynamic Selection Mechanism (DSM) generates motion-aware spatiotemporal masks to localize perturbations, guiding them away from discrimination-sensitive regions and promoting temporal coherence. By jointly controlling how and where mixing is applied, ReMA improves representation robustness without additional supervision or trainable parameters. Extensive experiments on diverse video behavior benchmarks demonstrate that ReMA consistently enhances generalization and robustness across different spatiotemporal granularities.
Fonte: arXiv cs.CV
RL • Score 96
O Transporte Ótimo Pode Melhorar o Aprendizado por Reforço Inverso Federado?
Neste artigo, introduzimos uma abordagem baseada em transporte ótimo para o aprendizado por reforço inverso federado (IRL). Cada cliente realiza localmente um IRL de Máxima Entropia, respeitando suas limitações computacionais e de privacidade. As funções de recompensa resultantes são fundidas via um barycenter de Wasserstein, que considera sua estrutura geométrica subjacente. Este trabalho oferece um framework eficiente em comunicação para derivar uma recompensa compartilhada que se generaliza entre agentes e ambientes heterogêneos.
Fonte: arXiv cs.LG
Evaluation/Benchmarks • Score 96
Ajuste Fino Robusto de Grafos com Prompting Adversarial de Grafos
O método de Ajuste Fino Eficiente em Parâmetros (PEFT) se destacou como um paradigma dominante para adaptar modelos GNN pré-treinados a tarefas específicas. No entanto, métodos PEFT existentes geralmente mostram vulnerabilidades significativas a ruídos e ataques na topologia de grafos e atributos/nomeações de nós. Propomos integrar aprendizado adversarial ao prompting de grafos, desenvolvendo um novo framework de Adversarial Graph Prompting (AGP) para alcançar um ajuste fino robusto.
Fonte: arXiv cs.LG
Privacy/Security/Fairness • Score 89
Identificação e Estimativa sob Múltiplas Versões de Tratamento: Abordagem Mixture-of-Experts
A suposição de valor de tratamento unitário estável (SUTVA) inclui a condição de que não existem múltiplas versões de tratamento na inferência causal. Este trabalho introduz o framework Mixture-of-Experts na inferência causal e desenvolve uma metodologia para estimar os efeitos causais de versões latentes, permitindo a estimativa explícita de efeitos causais específicos de versão, mesmo que as versões não sejam observadas.
Fonte: arXiv stat.ML
Evaluation/Benchmarks • Score 95
Reparametrização Categórica com Modelos de Difusão Denoising
A otimização baseada em gradiente com variáveis categóricas geralmente depende de estimadores de função de pontuação, que são imparciais, mas ruidosos, ou de relaxamentos contínuos que substituem a distribuição discreta por um substituto suave. Neste artigo, estendemos essa família de relaxamentos introduzindo uma reparametrização suave baseada em difusão para distribuições categóricas, permitindo um sampler de difusão sem treinamento.
Fonte: arXiv stat.ML
Evaluation/Benchmarks • Score 92
Redes de Imputação Condicional Generativa de Valores Ausentes
Neste estudo, apresentamos uma estratégia condicional generativa sofisticada para imputar valores ausentes em conjuntos de dados, uma área de grande importância na análise estatística. Esclarecemos os fundamentos teóricos das Redes de Imputação Condicional Generativa de Valores Ausentes (GCMI) e demonstramos suas propriedades robustas em contextos de Missing Completely at Random (MCAR) e Missing at Random (MAR).
Fonte: arXiv stat.ML
NLP/LLMs • Score 96
SSI-GAN: Redes Geradoras Adversariais Semi-Supervisionadas Inspiradas no Swin para Classificação de Espículas Neurais
Os mosquitos são os principais agentes transmissores de doenças arbovirais. A classificação manual de seus padrões de espículas neurais é muito trabalhosa e cara. Para resolver a escassez de dados rotulados, propomos uma nova arquitetura de Rede Geradora Adversarial (GAN) chamada SSI-GAN, que alcançou 99,93% de precisão na classificação com apenas 3% de dados rotulados.
Fonte: arXiv cs.AI
NLP/LLMs • Score 95
Robust Uncertainty Quantification for Factual Generation of Large Language Models
arXiv:2601.00348v1 Announce Type: new
Abstract: The rapid advancement of large language model(LLM) technology has facilitated its integration into various domains of professional and daily life. However, the persistent challenge of LLM hallucination has emerged as a critical limitation, significantly compromising the reliability and trustworthiness of AI-generated content. This challenge has garnered significant attention within the scientific community, prompting extensive research efforts in hallucination detection and mitigation strategies. Current methodological frameworks reveal a critical limitation: traditional uncertainty quantification approaches demonstrate effectiveness primarily within conventional question-answering paradigms, yet exhibit notable deficiencies when confronted with non-canonical or adversarial questioning strategies. This performance gap raises substantial concerns regarding the dependability of LLM responses in real-world applications requiring robust critical thinking capabilities. This study aims to fill this gap by proposing an uncertainty quantification scenario in the task of generating with multiple facts. We have meticulously constructed a set of trap questions contained with fake names. Based on this scenario, we innovatively propose a novel and robust uncertainty quantification method(RU). A series of experiments have been conducted to verify its effectiveness. The results show that the constructed set of trap questions performs excellently. Moreover, when compared with the baseline methods on four different models, our proposed method has demonstrated great performance, with an average increase of 0.1-0.2 in ROCAUC values compared to the best performing baseline method, providing new sights and methods for addressing the hallucination issue of LLMs.
Fonte: arXiv cs.CL
NLP/LLMs • Score 96
DA-DPO: Otimização de Preferências Consciente da Dificuldade e Custo-Eficiente para Reduzir Alucinações em MLLMs
O Direct Preference Optimization (DPO) demonstrou grande potencial para mitigar alucinações em Multimodal Large Language Models (MLLMs). No entanto, abordagens existentes frequentemente sofrem com overfitting devido ao desequilíbrio de dificuldade nos dados de preferência. Propomos o Difficulty-Aware Direct Preference Optimization (DA-DPO), um framework custo-efetivo que equilibra o processo de aprendizado.
Fonte: arXiv cs.AI
NLP/LLMs • Score 96
Rumo a Sistemas de IA Potencializados por Fotônica em Grande Escala: Da Automação de Design Físico à Coexploração de Sistema e Algoritmo
Neste trabalho, identificamos três considerações essenciais para a realização de sistemas práticos de IA fotônica em escala: (1) suporte a operações tensorais dinâmicas para modelos modernos; (2) gerenciamento sistemático de sobrecargas de conversão, controle e movimentação de dados; e (3) robustez sob não idealidades de hardware. Desenvolvemos uma ferramenta de suporte ao design de IA fotônica desde a exploração inicial até a realização física.
Fonte: arXiv cs.AI
RL • Score 96
SD2AIL: Aprendizado por Imitação Adversarial a partir de Demonstrações Sintéticas via Modelos de Difusão
O Aprendizado por Imitação Adversarial (AIL) é um framework dominante que infere recompensas a partir de demonstrações de especialistas para guiar a otimização de políticas. Inspirados pelo sucesso dos modelos de difusão, propomos o SD2AIL, que utiliza demonstrações sintéticas para aumentar as demonstrações de especialistas, introduzindo também uma estratégia de replay priorizado para maximizar a eficácia das demonstrações.
Fonte: arXiv cs.LG
NLP/LLMs • Score 96
Contribuição Consciente de Dados via Destilação de Cadeia de Pensamento Orientada pela Comunidade
A era atual de desenvolvimento de IA enfatiza fortemente o treinamento de grandes modelos em conjuntos de dados cada vez maiores. Este paradigma gerou novas categorias de produtos, como chatbots LLM, mas também levantou preocupações sobre privacidade de dados e escolha do consumidor. Este artigo aborda a portabilidade de dados e a autonomia do usuário no contexto de LLMs que 'raciocinam' usando rastros de cadeia de pensamento (CoT).
Fonte: arXiv cs.LG
Vision • Score 96
Grad: Geração de Difusão de Relações Guiadas para Aumento de Grafos na Detecção de Fraude em Grafos
Atualmente, a Detecção de Fraude em Grafos (GFD) em cenários financeiros tornou-se um tópico de pesquisa urgente para proteger a segurança de pagamentos online. Com a evolução das estratégias de camuflagem dos fraudadores, propomos o modelo Grad, que utiliza um módulo de aprendizado contrastivo supervisionado para melhorar a diferença entre fraudes e usuários benignos, gerando relações homofílicas auxiliares.
Fonte: arXiv cs.LG
RL • Score 95
Seleção de Recursos Não Supervisionada via Autoencoder Robusto e Aprendizado Adaptativo de Grafo
A seleção eficaz de recursos é essencial para a análise de dados de alta dimensão e machine learning. A seleção de recursos não supervisionada (UFS) visa agrupar dados e identificar as características mais discriminativas. Propomos o modelo Robust Autoencoder-based Unsupervised Feature Selection (RAEUFS), que utiliza um autoencoder profundo para aprender representações de recursos não lineares, melhorando a robustez contra outliers.
Fonte: arXiv stat.ML
NLP/LLMs • Score 96
Uma Rede Híbrida Indutiva-Transdutiva para Imputação de Fluxo de Tráfego em Locais Não Amostrados
Imputar com precisão o fluxo de tráfego em locais não sensorizados é desafiador. Propomos a HINT, uma Rede Híbrida Indutiva-Transdutiva, que utiliza uma estratégia de treinamento INDU-TRANSDUTIVA para tratar a velocidade como um sinal transdutivo, enquanto aprende o fluxo indutivamente. HINT supera consistentemente as linhas de base indutivas em três conjuntos de dados do mundo real.
Fonte: arXiv cs.LG
NLP/LLMs • Score 95
Does It Tie Out? Towards Autonomous Legal Agents in Venture Capital
arXiv:2512.18658v1 Announce Type: new
Abstract: Before closing venture capital financing rounds, lawyers conduct diligence that includes tying out the capitalization table: verifying that every security (for example, shares, options, warrants) and issuance term (for example, vesting schedules, acceleration triggers, transfer restrictions) is supported by large sets of underlying legal documentation. While LLMs continue to improve on legal benchmarks, specialized legal workflows, such as capitalization tie-out, remain out of reach even for strong agentic systems. The task requires multi-document reasoning, strict evidence traceability, and deterministic outputs that current approaches fail to reliably deliver. We characterize capitalization tie-out as an instance of a real-world benchmark for legal AI, analyze and compare the performance of existing agentic systems, and propose a world model architecture toward tie-out automation-and more broadly as a foundation for applied legal intelligence.
Fonte: arXiv cs.CL
Theory/Optimization • Score 92
Garantindo Robustez de Calibração na Predição Conformal Dividida Sob Ataques Adversariais
A predição conformal (CP) oferece garantias de cobertura de amostra finita e independente da distribuição, mas depende criticamente da intercambiabilidade, uma condição frequentemente violada sob mudança de distribuição. Estudamos a robustez da predição conformal dividida sob perturbações adversariais durante o teste, focando na validade da cobertura e no tamanho do conjunto de predição resultante.
Fonte: arXiv stat.ML
NLP/LLMs • Score 96
De Atalho a Cabeça de Indução: Como a Diversidade de Dados Molda a Seleção de Algoritmos em Transformers
Transformers podem implementar tanto algoritmos generalizáveis (ex: induction heads) quanto atalhos posicionais simples (ex: memorização de posições de saída fixas). Neste trabalho, estudamos como a escolha da distribuição de dados de pré-treinamento direciona um transformer raso para um comportamento ou outro, analisando o treinamento baseado em gradiente de um transformer de camada única.
Fonte: arXiv cs.LG
Vision • Score 92
A informação mútua normalizada é uma medida enviesada para classificação e detecção de comunidades
A informação mútua normalizada é amplamente utilizada como uma medida de similaridade para avaliar o desempenho de algoritmos de agrupamento e classificação. Neste artigo, argumentamos que os resultados retornados pela informação mútua normalizada são enviesados por duas razões: ignoram o conteúdo informativo da tabela de contingência e sua normalização simétrica introduz dependência espúria na saída do algoritmo. Apresentamos uma versão modificada da informação mútua que corrige essas falhas.
Fonte: arXiv stat.ML
NLP/LLMs • Score 95
De Palavra a Mundo: Podem Modelos de Linguagem Grande Servir como Modelos de Mundo Baseados em Texto Implicitamente?
O aprendizado por reforço agente depende cada vez mais de escalabilidade orientada pela experiência, mas ambientes do mundo real continuam sendo não adaptativos e difíceis de escalar. Este estudo investiga se modelos de linguagem grande (LLMs) podem melhorar a eficiência do aprendizado em ambientes baseados em texto, apresentando um framework de três níveis para avaliação de modelos de mundo baseados em LLMs.
Fonte: arXiv cs.CL
NLP/LLMs • Score 95
GeoSense-AI: Fast Location Inference from Crisis Microblogs
arXiv:2512.18225v1 Announce Type: new
Abstract: This paper presents an applied AI pipeline for realtime geolocation from noisy microblog streams, unifying statistical hashtag segmentation, part-of-speech-driven proper-noun detection, dependency parsing around disaster lexicons, lightweight named-entity recognition, and gazetteer-grounded disambiguation to infer locations directly from text rather than sparse geotags. The approach operationalizes information extraction under streaming constraints, emphasizing low-latency NLP components and efficient validation against geographic knowledge bases to support situational awareness during emergencies. In head to head comparisons with widely used NER toolkits, the system attains strong F1 while being engineered for orders-of-magnitude faster throughput, enabling deployment in live crisis informatics settings. A production map interface demonstrates end-to-end AI functionality ingest, inference, and visualization--surfacing locational signals at scale for floods, outbreaks, and other fastmoving events. By prioritizing robustness to informal text and streaming efficiency, GeoSense-AI illustrates how domain-tuned NLP and knowledge grounding can elevate emergency response beyond conventional geo-tag reliance.
Fonte: arXiv cs.CL
NLP/LLMs • Score 96
Repensando a Inteligência Multi-Agente Através da Lente de Redes de Pequeno Mundo
Modelos de linguagem grandes (LLMs) possibilitaram sistemas multi-agente (MAS) onde múltiplos agentes argumentam, criticam e coordenam para resolver tarefas complexas, tornando a topologia de comunicação uma escolha de design fundamental. Neste trabalho, revisitamos a teoria clássica sobre redes de pequeno mundo (SW) e investigamos como a conectividade SW pode ser utilizada como um princípio de design para MAS.
Fonte: arXiv cs.AI
NLP/LLMs • Score 95
A Comparative Study of Light-weight Language Models for PII Masking and their Deployment for Real Conversational Texts
arXiv:2512.18608v1 Announce Type: new
Abstract: Automated masking of Personally Identifiable Information (PII) is critical for privacy-preserving conversational systems. While current frontier large language models demonstrate strong PII masking capabilities, concerns about data handling and computational costs motivate exploration of whether lightweight models can achieve comparable performance. We compare encoder-decoder and decoder-only architectures by fine-tuning T5-small and Mistral-Instruct-v0.3 on English datasets constructed from the AI4Privacy benchmark. We create different dataset variants to study label standardization and PII representation, covering 24 standardized PII categories and higher-granularity settings. Evaluation using entity-level and character-level metrics, type accuracy, and exact match shows that both lightweight models achieve performance comparable to frontier LLMs for PII masking tasks. Label normalization consistently improves performance across architectures. Mistral achieves higher F1 and recall with greater robustness across PII types but incurs significantly higher generation latency. T5, while less robust in conversational text, offers more controllable structured outputs and lower inference cost, motivating its use in a real-time Discord bot for real-world PII redaction. Evaluation on live messages reveals performance degradation under informal inputs. These results clarify trade-offs between accuracy, robustness, and computational efficiency, demonstrating that lightweight models can provide effective PII masking while addressing data handling concerns associated with frontier LLMs.
Fonte: arXiv cs.CL
NLP/LLMs • Score 96
NEURO-GUARD: Generalização Neuro-Simbólica e Roteamento Adaptativo Imparcial para Diagnósticos -- IA Médica Explicável
O diagnóstico baseado em imagem, preciso e interpretável, continua sendo um desafio central na IA médica, especialmente em ambientes com dados limitados e decisões clínicas críticas. Apresentamos o NEURO-GUARD, um novo framework guiado por conhecimento que integra Vision Transformers (ViTs) com raciocínio orientado por linguagem, melhorando desempenho e robustez em diferentes domínios.
Fonte: arXiv cs.AI
NLP/LLMs • Score 96
MEEA: Otimização Confrontacional Baseada no Efeito de Exposição Mere para Jailbreaking de LLMs
O rápido avanço dos grandes modelos de linguagem (LLMs) intensificou preocupações sobre a robustez de seu alinhamento de segurança. Propomos o MEEA (Mere Exposure Effect Attack), um framework automatizado inspirado na psicologia para avaliar a robustez de segurança em interações multi-turno, utilizando o efeito de exposição mere. Nossos experimentos mostram que o MEEA supera consistentemente as taxas de sucesso de ataque de modelos como GPT-4 e Claude-3.5.
Fonte: arXiv cs.AI
RL • Score 96
Podemos Testar Teorias da Consciência em IA? Ablations, Marcadores e Robustez
A busca por indicadores confiáveis de consciência se fragmentou em campos teóricos concorrentes (Global Workspace Theory (GWT), Integrated Information Theory (IIT) e Higher-Order Theories (HOT)), cada um propondo assinaturas neurais distintas. Adotamos uma abordagem de neuro-fenomenologia sintética, construindo agentes artificiais para testar as consequências funcionais dessas teorias através de ablações arquitetônicas precisas. Relatamos dissociações que sugerem que essas teorias descrevem camadas funcionais complementares.
Fonte: arXiv cs.AI
NLP/LLMs • Score 96
Geração de Regras Programáticas para Detecção de Falsificação de Documentos Usando Modelos de Linguagem de Grande Escala
A falsificação de documentos representa uma ameaça crescente a processos legais, econômicos e governamentais, exigindo mecanismos de verificação cada vez mais sofisticados. Este trabalho investiga como modelos de linguagem de grande escala (LLMs) podem ser adaptados para gerar verificações de plausibilidade baseadas em regras para detecção de falsificações, utilizando recursos de hardware limitados.
Fonte: arXiv cs.AI
Vision • Score 96
EIA-SEC: Framework Melhorado de Actor-Critic para Controle Colaborativo de Multi-UAV na Agricultura Inteligente
A aplicação generalizada da tecnologia de comunicação sem fio tem promovido o desenvolvimento da agricultura inteligente, onde veículos aéreos não tripulados (UAVs) desempenham um papel multifuncional. Neste trabalho, modelamos um processo de decisão de Markov para resolver o problema de planejamento de trajetória de multi-UAV e propomos o novo framework Elite Imitation Actor-Shared Ensemble Critic (EIA-SEC). Resultados experimentais mostram que o EIA-SEC supera as referências de ponta em desempenho de recompensa, estabilidade de treinamento e velocidade de convergência.
Fonte: arXiv cs.LG
NLP/LLMs • Score 96
Benchmarking de substitutos neurais em fluxos multifísicos espaço-temporais realistas
Prever dinâmicas multifísicas é computacionalmente caro e desafiador devido ao acoplamento severo de processos físicos heterogêneos e multiescala. Apresentamos o REALM (REalistic AI Learning for Multiphysics), um framework rigoroso de benchmarking para testar substitutos neurais em fluxos reativos desafiadores, com 11 conjuntos de dados de alta fidelidade e um protocolo padronizado de treinamento e avaliação.
Fonte: arXiv cs.LG
RL • Score 96
Inteligência Alinhada à Segurança Embutida via Embeddings de Alinhamento Interno Diferenciáveis
Apresentamos a Inteligência Alinhada à Segurança Embutida (ESAI), um framework teórico para aprendizado por reforço multi-agente que incorpora restrições de alinhamento diretamente nas representações internas dos agentes usando embeddings de alinhamento interno diferenciáveis. Este trabalho analisa condições de estabilidade e propriedades teóricas, posicionando o ESAI como uma contribuição conceitual para mecanismos de alinhamento diferenciáveis em sistemas multi-agente.
Fonte: arXiv cs.LG
Vision • Score 96
Mapas auto-organizáveis para avaliação da qualidade da água em reservatórios e lagos: Uma revisão sistemática da literatura
A qualidade da água sustentável é fundamental para o equilíbrio ecológico e a segurança hídrica. Esta revisão examina a aplicação do Self-Organizing Map (SOM), uma técnica de IA não supervisionada, na avaliação da qualidade da água, abordando seleção de parâmetros, estratégias de amostragem e abordagens de agrupamento.
Fonte: arXiv cs.LG
NLP/LLMs • Score 95
Ensinando e Criticando a Conceituação e Operacionalização em NLP
Pesquisadores de NLP frequentemente invocam conceitos abstratos como 'interpretabilidade', 'viés', 'raciocínio' e 'estereótipos' sem defini-los. Este artigo descreve um seminário criado para estudantes explorarem questões de conceituação e operacionalização, com uma lista de leitura interdisciplinar e ênfase em discussão e crítica.
Fonte: arXiv cs.CL
NLP/LLMs • Score 95
Remedy-R: Generative Reasoning for Machine Translation Evaluation without Error Annotations
arXiv:2512.18906v1 Announce Type: new
Abstract: Over the years, automatic MT metrics have hillclimbed benchmarks and presented strong and sometimes human-level agreement with human ratings. Yet they remain black-box, offering little insight into their decision-making and often failing under real-world out-of-distribution (OOD) inputs. We introduce Remedy-R, a reasoning-driven generative MT metric trained with reinforcement learning from pairwise translation preferences, without requiring error-span annotations or distillation from closed LLMs. Remedy-R produces step-by-step analyses of accuracy, fluency, and completeness, followed by a final score, enabling more interpretable assessments. With only 60K training pairs across two language pairs, Remedy-R remains competitive with top scalar metrics and GPT-4-based judges on WMT22-24 meta-evaluation, generalizes to other languages, and exhibits strong robustness on OOD stress tests. Moreover, Remedy-R models generate self-reflective feedback that can be reused for translation improvement. Building on this finding, we introduce Remedy-R Agent, a simple evaluate-revise pipeline that leverages Remedy-R's evaluation analysis to refine translations. This agent consistently improves translation quality across diverse models, including Qwen2.5, ALMA-R, GPT-4o-mini, and Gemini-2.0-Flash, suggesting that Remedy-R's reasoning captures translation-relevant information and is practically useful.
Fonte: arXiv cs.CL
RL • Score 96
FairExpand: Justiça Individual em Grafos com Informações de Similaridade Parcial
A justiça individual, que exige que indivíduos semelhantes sejam tratados de forma semelhante por sistemas algorítmicos, é um princípio central em machine learning justo. Este trabalho apresenta o FairExpand, um framework flexível que promove a justiça individual em cenários de informações parciais, superando a limitação de métodos existentes que requerem informações de similaridade pré-definidas para todos os pares de nós.
Fonte: arXiv cs.LG
RL • Score 96
Unificando Aprendizado por Reforço Causal: Revisão, Taxonomia, Algoritmos e Aplicações
Integrar inferência causal (CI) com aprendizado por reforço (RL) se tornou um paradigma poderoso para abordar limitações críticas no RL clássico, como baixa explicabilidade e falta de robustez. Este trabalho revisa avanços recentes na interseção entre CI e RL, categorizando abordagens existentes e discutindo desafios, sucessos empíricos e direções futuras de pesquisa.
Fonte: arXiv cs.AI
Theory/Optimization • Score 92
Inferência Causal como Adaptação de Distribuição: Otimizando o Risco ATE sob Incerteza de Propensão
Abordagens padrão para inferência causal, como Regressão de Resultado e Ajuste de Regressão Ponderada por Probabilidade Inversa (IPWRA), são geralmente derivadas através da lente da imputação de dados ausentes e teoria de identificação. Neste trabalho, unificamos esses métodos sob uma perspectiva de Machine Learning, reformulando a estimativa de ATE como um problema de adaptação de domínio sob mudança de distribuição.
Fonte: arXiv stat.ML
RL • Score 92
Deep Learning para Extração do Modo $B$ Primordial
A busca por ondas gravitacionais primordiais é um objetivo central das pesquisas sobre o fundo cósmico de micro-ondas (CMB). Isolar o sinal de polarização característico do modo $B$ gerado por ondas gravitacionais primordiais é desafiador devido a vários fatores, incluindo a pequena amplitude do sinal e a contaminação por foregrounds astrofísicos. Este trabalho demonstra como redes de deep learning podem ser aplicadas para estimar e remover múltiplas fontes de polarização do modo $B$ secundário.
Fonte: arXiv stat.ML
NLP/LLMs • Score 92
Toward Human-Centered AI-Assisted Terminology Work
arXiv:2512.18859v1 Announce Type: new
Abstract: The rapid diffusion of generative artificial intelligence is transforming terminology work. While this technology promises gains in efficiency, its unstructured adoption risks weakening professional autonomy, amplifying bias, and eroding linguistic and conceptual diversity. This paper argues that a human-centered approach to artificial intelligence has become a necessity for terminology work. Building on research in artificial intelligence and translation studies, it proposes a human-centered framework that conceptualizes artificial intelligence as a means of amplifying the terminologist's capabilities, rather than replacing them. The framework is organized around three interrelated dimensions: the augmented terminologist, ethical AI, and human-centered design. Together, these dimensions emphasize the compatibility of high automation with strong human control, the central role of terminologists in bias mitigation, and the importance of designing AI tools and workflows around the needs, values, and well-being of the terminologist. The paper concludes by stressing that current choices in AI adoption will shape not only terminological practice, but also the preservation of accuracy, adequacy, and diversity in terminology and specialized knowledge.
Fonte: arXiv cs.CL
Evaluation/Benchmarks • Score 95
Descida de Espelho Variacional Online para Aprendizado Robusto na Ponte de Schrödinger
A Ponte de Schrödinger (SB) evoluiu para uma classe universal de modelos generativos probabilísticos. No entanto, os sinais de aprendizado estimados são intrinsecamente incertos, e a confiabilidade prometida pelos métodos existentes muitas vezes se baseia em cenários ótimos especulativos. Neste trabalho, propomos um framework de Descida de Espelho Online Variacional (OMD) para os problemas de SB, que proporciona maior estabilidade aos solucionadores de SB.
Fonte: arXiv stat.ML
NLP/LLMs • Score 96
MSC-180: Um Benchmark para Prova Formal Automatizada de Teoremas a partir da Classificação de Assuntos Matemáticos
O Automated Theorem Proving (ATP) é uma direção de pesquisa central em inteligência artificial para alcançar raciocínio formal e verificação. Propomos o MSC-180, um benchmark baseado na classificação de assuntos matemáticos MSC2020, que compreende 180 problemas de verificação formal, abrangendo níveis de graduação e pós-graduação, para avaliar e impulsionar o desenvolvimento de sistemas de IA com habilidades genuínas de raciocínio matemático.
Fonte: arXiv cs.AI
Evaluation/Benchmarks • Score 93
KeenKT: Desambiguação do Estado de Domínio do Conhecimento para Rastreio de Conhecimento
O Rastreio de Conhecimento (KT) visa modelar dinamicamente o domínio de conceitos de conhecimento de um estudante com base em suas interações de aprendizado históricas. A maioria dos métodos atuais depende de estimativas pontuais, que não conseguem distinguir a verdadeira habilidade de explosões ou desatenção, criando ambiguidade no julgamento do domínio.
Fonte: arXiv cs.AI
RL • Score 96
AL-GNN: Aprendizado Contínuo de Grafos Preservando a Privacidade e Livre de Replay via Aprendizado Analítico
O aprendizado contínuo de grafos (CGL) permite que redes neurais de grafos aprendam incrementalmente a partir de dados estruturados em grafos sem esquecer o conhecimento previamente adquirido. O AL-GNN é um novo framework que elimina a necessidade de retropropagação e buffers de replay, utilizando princípios da teoria do aprendizado analítico para otimizar o aprendizado.
Fonte: arXiv cs.LG
RL • Score 95
Defesa Certificada sobre a Justiça das Redes Neurais Gráficas
As Redes Neurais Gráficas (GNNs) se destacaram como um modelo proeminente de aprendizado em grafos, mas são vulneráveis a ataques que podem corromper a justiça de suas previsões. Neste artigo, propomos um framework chamado ELEGANT, que oferece uma análise teórica detalhada para certificar a justiça das GNNs, sem exigir re-treinamento e sem suposições sobre a estrutura ou parâmetros das GNNs.
Fonte: arXiv stat.ML
Theory/Optimization • Score 93
O Gargalo de Interação das Redes Neurais Profundas: Descoberta, Prova e Modulação
Compreender que tipos de estruturas cooperativas as redes neurais profundas (DNNs) podem representar continua sendo um problema fundamental, mas insuficientemente compreendido. Este trabalho investiga como as DNNs codificam interações sob diferentes níveis de complexidade contextual e como esses padrões de interação microscópica moldam a capacidade de representação macroscópica.
Fonte: arXiv cs.LG
NLP/LLMs • Score 95
Research on a hybrid LSTM-CNN-Attention model for text-based web content classification
arXiv:2512.18475v1 Announce Type: new
Abstract: This study presents a hybrid deep learning architecture that integrates LSTM, CNN, and an Attention mechanism to enhance the classification of web content based on text. Pretrained GloVe embeddings are used to represent words as dense vectors that preserve semantic similarity. The CNN layer extracts local n-gram patterns and lexical features, while the LSTM layer models long-range dependencies and sequential structure. The integrated Attention mechanism enables the model to focus selectively on the most informative parts of the input sequence. A 5-fold cross-validation setup was used to assess the robustness and generalizability of the proposed solution. Experimental results show that the hybrid LSTM-CNN-Attention model achieved outstanding performance, with an accuracy of 0.98, precision of 0.94, recall of 0.92, and F1-score of 0.93. These results surpass the performance of baseline models based solely on CNNs, LSTMs, or transformer-based classifiers such as BERT. The combination of neural network components enabled the model to effectively capture both fine-grained text structures and broader semantic context. Furthermore, the use of GloVe embeddings provided an efficient and effective representation of textual data, making the model suitable for integration into systems with real-time or near-real-time requirements. The proposed hybrid architecture demonstrates high effectiveness in text-based web content classification, particularly in tasks requiring both syntactic feature extraction and semantic interpretation. By combining presented mechanisms, the model addresses the limitations of individual architectures and achieves improved generalization. These findings support the broader use of hybrid deep learning approaches in NLP applications, especially where complex, unstructured textual data must be processed and classified with high reliability.
Fonte: arXiv cs.CL
NLP/LLMs • Score 96
Rumo à Avaliação de Vulnerabilidades de Privacidade no Esquecimento Seletivo com Modelos de Linguagem de Grande Escala
Os avanços rápidos em inteligência artificial (IA) têm se concentrado no aprendizado a partir de dados para desenvolver sistemas de aprendizado informados. Com a implementação desses sistemas em áreas críticas, garantir sua privacidade e alinhamento com valores humanos é essencial. O esquecimento seletivo, ou machine unlearning, surge como uma abordagem promissora, mas também levanta preocupações significativas de privacidade, especialmente em domínios sensíveis.
Fonte: arXiv cs.LG
NLP/LLMs • Score 92
SAP: Syntactic Attention Pruning for Transformer-based Language Models
arXiv:2512.19125v1 Announce Type: new
Abstract: This paper introduces Syntactic Attention Pruning (SAP), a novel method for effectively pruning attention heads in Transformer models. Unlike conventional approaches that rely solely on mathematical analysis of model weights and activations, SAP incorporates both the syntactic structure and attention patterns of sentences to guide the pruning process. By leveraging these linguistic features, SAP not only achieves performance comparable to state-of-the-art methods but also enhances the interpretability of model behavior. To further improve robustness, we propose Candidate Filtering (CF), a mechanism that prioritizes heads based on their contribution to model performance, mitigating degradation during pruning. Experimental results indicate that SAP effectively preserves critical heads of a high density of strong attention values, outperforming existing head pruning strategies in retrain-free settings. These findings position SAP as a promising foundation for a new direction in model compression research, offering high flexibility for pruning across all transformer-based language models.
Fonte: arXiv cs.CL
MLOps/Systems • Score 96
A Cama Procrusteana das Séries Temporais: O Viés de Otimização da Função de Perda Pontual
Otimizar modelos de séries temporais por meio de funções de perda pontuais (por exemplo, MSE) baseando-se em uma suposição falha de independência e distribuição idêntica pontual (i.i.d.) que desconsidera a estrutura temporal causal. Este artigo analisa o Expectation of Optimization Bias (EOB) e revela que quanto mais determinística e estruturada a série temporal, mais severo é o viés causado pela função de perda pontual.
Fonte: arXiv cs.LG
Vision • Score 95
EMMA: Concept Erasure Benchmark with Comprehensive Semantic Metrics and Diverse Categories
arXiv:2512.17320v1 Announce Type: new
Abstract: The widespread adoption of text-to-image (T2I) generation has raised concerns about privacy, bias, and copyright violations. Concept erasure techniques offer a promising solution by selectively removing undesired concepts from pre-trained models without requiring full retraining. However, these methods are often evaluated on a limited set of concepts, relying on overly simplistic and direct prompts. To test the boundaries of concept erasure techniques, and assess whether they truly remove targeted concepts from model representations, we introduce EMMA, a benchmark that evaluates five key dimensions of concept erasure over 12 metrics. EMMA goes beyond standard metrics like image quality and time efficiency, testing robustness under challenging conditions, including indirect descriptions, visually similar non-target concepts, and potential gender and ethnicity bias, providing a socially aware analysis of method behavior. Using EMMA, we analyze five concept erasure methods across five domains (objects, celebrities, art styles, NSFW, and copyright). Our results show that existing methods struggle with implicit prompts (i.e., generating the erased concept when it is indirectly referenced) and visually similar non-target concepts (i.e., failing to generate non-targeted concepts resembling the erased one), while some amplify gender and ethnicity bias compared to the original model.
Fonte: arXiv cs.CV
Privacy/Security/Fairness • Score 92
Low-Rank Filtering and Smoothing for Sequential Deep Learning
arXiv:2410.06800v2 Announce Type: replace-cross
Abstract: Learning multiple tasks sequentially requires neural networks to balance retaining knowledge, yet being flexible enough to adapt to new tasks. Regularizing network parameters is a common approach, but it rarely incorporates prior knowledge about task relationships, and limits information flow to future tasks only. We propose a Bayesian framework that treats the network's parameters as the state space of a nonlinear Gaussian model, unlocking two key capabilities: (1) A principled way to encode domain knowledge about task relationships, allowing, e.g., control over which layers should adapt between tasks. (2) A novel application of Bayesian smoothing, allowing task-specific models to also incorporate knowledge from models learned later. This does not require direct access to their data, which is crucial, e.g., for privacy-critical applications. These capabilities rely on efficient filtering and smoothing operations, for which we propose diagonal plus low-rank approximations of the precision matrix in the Laplace approximation (LR-LGF). Empirical results demonstrate the efficiency of LR-LGF and the benefits of the unlocked capabilities.
Fonte: arXiv stat.ML
RL • Score 96
Conjunto de Dados Sintético que Preserva a Privacidade de Trajetórias Diárias Individuais para Análises de Mobilidade em Escala Urbana
Os dados de mobilidade urbana são essenciais para o planejamento urbano, previsão de demanda de transporte e modelagem de pandemias. Este estudo apresenta um conjunto de dados sintético que preserva a privacidade, reconstruindo trajetórias diárias a partir de entradas agregadas, sem a necessidade de identificadores pessoais.
Fonte: arXiv cs.AI
RL • Score 92
Targeted Learning for Variable Importance
arXiv:2411.02221v2 Announce Type: replace
Abstract: Variable importance is one of the most widely used measures for interpreting machine learning with significant interest from both statistics and machine learning communities. Recently, increasing attention has been directed toward uncertainty quantification in these metrics. Current approaches largely rely on one-step procedures, which, while asymptotically efficient, can present higher sensitivity and instability in finite sample settings. To address these limitations, we propose a novel method by employing the targeted learning (TL) framework, designed to enhance robustness in inference for variable importance metrics. Our approach is particularly suited for conditional permutation variable importance. We show that it (i) retains the asymptotic efficiency of traditional methods, (ii) maintains comparable computational complexity, and (iii) delivers improved accuracy, especially in finite sample contexts. We further support these findings with numerical experiments that illustrate the practical advantages of our method and validate the theoretical results.
Fonte: arXiv stat.ML
MLOps/Systems • Score 95
A Systems-Theoretic View on the Convergence of Algorithms under Disturbances
arXiv:2512.17598v1 Announce Type: cross
Abstract: Algorithms increasingly operate within complex physical, social, and engineering systems where they are exposed to disturbances, noise, and interconnections with other dynamical systems. This article extends known convergence guarantees of an algorithm operating in isolation (i.e., without disturbances) and systematically derives stability bounds and convergence rates in the presence of such disturbances. By leveraging converse Lyapunov theorems, we derive key inequalities that quantify the impact of disturbances. We further demonstrate how our result can be utilized to assess the effects of disturbances on algorithmic performance in a wide variety of applications, including communication constraints in distributed learning, sensitivity in machine learning generalization, and intentional noise injection for privacy. This underpins the role of our result as a unifying tool for algorithm analysis in the presence of noise, disturbances, and interconnections with other dynamical systems.
Fonte: arXiv stat.ML
Privacy/Security/Fairness • Score 90
Redes de Atenção em Grafos para Detecção de Epilepsia a partir de Sinais de EEG Usando Hardware Acessível em Ambientes de Baixos Recursos
A epilepsia continua subdiagnosticada em países de baixa renda devido à escassez de neurologistas e ferramentas de diagnóstico caras. Propomos um framework de deep learning baseado em grafos para detectar epilepsia usando hardware de Eletroencefalografia (EEG) de baixo custo, testado em gravações da Nigéria e Guiné-Bissau.
Fonte: arXiv cs.AI
Privacy/Security/Fairness • Score 89
Differentially private Bayesian tests
arXiv:2401.15502v3 Announce Type: replace
Abstract: Differential privacy has emerged as an significant cornerstone in the realm of scientific hypothesis testing utilizing confidential data. In reporting scientific discoveries, Bayesian tests are widely adopted since they effectively circumnavigate the key criticisms of P-values, namely, lack of interpretability and inability to quantify evidence in support of the competing hypotheses. We present a novel differentially private Bayesian hypotheses testing framework that arise naturally under a principled data generative mechanism, inherently maintaining the interpretability of the resulting inferences. Furthermore, by focusing on differentially private Bayes factors based on widely used test statistics, we circumvent the need to model the complete data generative mechanism and ensure substantial computational benefits. We also provide a set of sufficient conditions to establish results on Bayes factor consistency under the proposed framework. The utility of the devised technology is showcased via several numerical experiments.
Fonte: arXiv stat.ML
NLP/LLMs • Score 95
Spatially-informed transformers: Injecting geostatistical covariance biases into self-attention for spatio-temporal forecasting
arXiv:2512.17696v1 Announce Type: cross
Abstract: The modeling of high-dimensional spatio-temporal processes presents a fundamental dichotomy between the probabilistic rigor of classical geostatistics and the flexible, high-capacity representations of deep learning. While Gaussian processes offer theoretical consistency and exact uncertainty quantification, their prohibitive computational scaling renders them impractical for massive sensor networks. Conversely, modern transformer architectures excel at sequence modeling but inherently lack a geometric inductive bias, treating spatial sensors as permutation-invariant tokens without a native understanding of distance. In this work, we propose a spatially-informed transformer, a hybrid architecture that injects a geostatistical inductive bias directly into the self-attention mechanism via a learnable covariance kernel. By formally decomposing the attention structure into a stationary physical prior and a non-stationary data-driven residual, we impose a soft topological constraint that favors spatially proximal interactions while retaining the capacity to model complex dynamics. We demonstrate the phenomenon of ``Deep Variography'', where the network successfully recovers the true spatial decay parameters of the underlying process end-to-end via backpropagation. Extensive experiments on synthetic Gaussian random fields and real-world traffic benchmarks confirm that our method outperforms state-of-the-art graph neural networks. Furthermore, rigorous statistical validation confirms that the proposed method delivers not only superior predictive accuracy but also well-calibrated probabilistic forecasts, effectively bridging the gap between physics-aware modeling and data-driven learning.
Fonte: arXiv stat.ML
MLOps/Systems • Score 95
Penalized Fair Regression for Multiple Groups in Chronic Kidney Disease
arXiv:2512.17340v1 Announce Type: cross
Abstract: Fair regression methods have the potential to mitigate societal bias concerns in health care, but there has been little work on penalized fair regression when multiple groups experience such bias. We propose a general regression framework that addresses this gap with unfairness penalties for multiple groups. Our approach is demonstrated for binary outcomes with true positive rate disparity penalties. It can be efficiently implemented through reduction to a cost-sensitive classification problem. We additionally introduce novel score functions for automatically selecting penalty weights. Our penalized fair regression methods are empirically studied in simulations, where they achieve a fairness-accuracy frontier beyond that of existing comparison methods. Finally, we apply these methods to a national multi-site primary care study of chronic kidney disease to develop a fair classifier for end-stage renal disease. There we find substantial improvements in fairness for multiple race and ethnicity groups who experience societal bias in the health care system without any appreciable loss in overall fit.
Fonte: arXiv stat.ML
Privacy/Security/Fairness • Score 88
Imputation Uncertainty in Interpretable Machine Learning Methods
arXiv:2512.17689v1 Announce Type: new
Abstract: In real data, missing values occur frequently, which affects the interpretation with interpretable machine learning (IML) methods. Recent work considers bias and shows that model explanations may differ between imputation methods, while ignoring additional imputation uncertainty and its influence on variance and confidence intervals. We therefore compare the effects of different imputation methods on the confidence interval coverage probabilities of the IML methods permutation feature importance, partial dependence plots and Shapley values. We show that single imputation leads to underestimation of variance and that, in most cases, only multiple imputation is close to nominal coverage.
Fonte: arXiv stat.ML
MLOps/Systems • Score 92
Sharp Structure-Agnostic Lower Bounds for General Functional Estimation
arXiv:2512.17341v1 Announce Type: new
Abstract: The design of efficient nonparametric estimators has long been a central problem in statistics, machine learning, and decision making. Classical optimal procedures often rely on strong structural assumptions, which can be misspecified in practice and complicate deployment. This limitation has sparked growing interest in structure-agnostic approaches -- methods that debias black-box nuisance estimates without imposing structural priors. Understanding the fundamental limits of these methods is therefore crucial. This paper provides a systematic investigation of the optimal error rates achievable by structure-agnostic estimators. We first show that, for estimating the average treatment effect (ATE), a central parameter in causal inference, doubly robust learning attains optimal structure-agnostic error rates. We then extend our analysis to a general class of functionals that depend on unknown nuisance functions and establish the structure-agnostic optimality of debiased/double machine learning (DML). We distinguish two regimes -- one where double robustness is attainable and one where it is not -- leading to different optimal rates for first-order debiasing, and show that DML is optimal in both regimes. Finally, we instantiate our general lower bounds by deriving explicit optimal rates that recover existing results and extend to additional estimands of interest. Our results provide theoretical validation for widely used first-order debiasing methods and guidance for practitioners seeking optimal approaches in the absence of structural assumptions. This paper generalizes and subsumes the ATE lower bound established in \citet{jin2024structure} by the same authors.
Fonte: arXiv stat.ML
RL • Score 96
Learning Safe Autonomous Driving Policies Using Predictive Safety Representations
arXiv:2512.17586v1 Announce Type: new
Abstract: Safe reinforcement learning (SafeRL) is a prominent paradigm for autonomous driving, where agents are required to optimize performance under strict safety requirements. This dual objective creates a fundamental tension, as overly conservative policies limit driving efficiency while aggressive exploration risks safety violations. The Safety Representations for Safer Policy Learning (SRPL) framework addresses this challenge by equipping agents with a predictive model of future constraint violations and has shown promise in controlled environments. This paper investigates whether SRPL extends to real-world autonomous driving scenarios. Systematic experiments on the Waymo Open Motion Dataset (WOMD) and NuPlan demonstrate that SRPL can improve the reward-safety tradeoff, achieving statistically significant improvements in success rate (effect sizes r = 0.65-0.86) and cost reduction (effect sizes r = 0.70-0.83), with p < 0.05 for observed improvements. However, its effectiveness depends on the underlying policy optimizer and the dataset distribution. The results further show that predictive safety representations play a critical role in improving robustness to observation noise. Additionally, in zero-shot cross-dataset evaluation, SRPL-augmented agents demonstrate improved generalization compared to non-SRPL methods. These findings collectively demonstrate the potential of predictive safety representations to strengthen SafeRL for autonomous driving.
Fonte: arXiv cs.LG
RL • Score 96
SafeBench-Seq: A Homology-Clustered, CPU-Only Baseline for Protein Hazard Screening with Physicochemical/Composition Features and Cluster-Aware Confidence Intervals
arXiv:2512.17527v1 Announce Type: new
Abstract: Foundation models for protein design raise concrete biosecurity risks, yet the community lacks a simple, reproducible baseline for sequence-level hazard screening that is explicitly evaluated under homology control and runs on commodity CPUs. We introduce SafeBench-Seq, a metadata-only, reproducible benchmark and baseline classifier built entirely from public data (SafeProtein hazards and UniProt benigns) and interpretable features (global physicochemical descriptors and amino-acid composition). To approximate "never-before-seen" threats, we homology-cluster the combined dataset at <=40% identity and perform cluster-level holdouts (no cluster overlap between train/test). We report discrimination (AUROC/AUPRC) and screening-operating points (TPR@1% FPR; FPR@95% TPR) with 95% bootstrap confidence intervals (n=200), and we provide calibrated probabilities via CalibratedClassifierCV (isotonic for Logistic Regression / Random Forest; Platt sigmoid for Linear SVM). We quantify probability quality using Brier score, Expected Calibration Error (ECE; 15 bins), and reliability diagrams. Shortcut susceptibility is probed via composition-preserving residue shuffles and length-/composition-only ablations. Empirically, random splits substantially overestimate robustness relative to homology-clustered evaluation; calibrated linear models exhibit comparatively good calibration, while tree ensembles retain slightly higher Brier/ECE. SafeBench-Seq is CPU-only, reproducible, and releases metadata only (accessions, cluster IDs, split labels), enabling rigorous evaluation without distributing hazardous sequences.
Fonte: arXiv cs.LG
RL • Score 93
Traduzindo o Efeito Rashomon para Tarefas de Tomada de Decisão Sequencial
O efeito Rashomon descreve o fenômeno em que múltiplos modelos treinados nos mesmos dados produzem previsões idênticas, mas diferem nas características que utilizam internamente. Este trabalho traduz o efeito Rashomon para a tomada de decisão sequencial, definindo-o como múltiplas políticas que exibem comportamento idêntico, visitando os mesmos estados e selecionando as mesmas ações, enquanto diferem em sua estrutura interna.
Fonte: arXiv cs.AI
Vision • Score 93
DeepShare: Sharing ReLU Across Channels and Layers for Efficient Private Inference
arXiv:2512.17398v1 Announce Type: new
Abstract: Private Inference (PI) uses cryptographic primitives to perform privacy preserving machine learning. In this setting, the owner of the network runs inference on the data of the client without learning anything about the data and without revealing any information about the model. It has been observed that a major computational bottleneck of PI is the calculation of the gate (i.e., ReLU), so a considerable amount of effort have been devoted to reducing the number of ReLUs in a given network.
We focus on the DReLU, which is the non-linear step function of the ReLU and show that one DReLU can serve many ReLU operations. We suggest a new activation module where the DReLU operation is only performed on a subset of the channels (Prototype channels), while the rest of the channels (replicate channels) replicates the DReLU of each of their neurons from the corresponding neurons in one of the prototype channels. We then extend this idea to work across different layers.
We show that this formulation can drastically reduce the number of DReLU operations in resnet type network. Furthermore, our theoretical analysis shows that this new formulation can solve an extended version of the XOR problem, using just one non-linearity and two neurons, something that traditional formulations and some PI specific methods cannot achieve. We achieve new SOTA results on several classification setups, and achieve SOTA results on image segmentation.
Fonte: arXiv cs.LG
NLP/LLMs • Score 96
Um Framework Solver-in-the-Loop para Melhorar LLMs em Programação de Conjuntos de Respostas para Resolução de Quebra-Cabeças Lógicos
O surgimento de grandes modelos de linguagem (LLMs) despertou interesse em assistentes de codificação. Este artigo apresenta uma abordagem inovadora de ASP-solver-in-the-loop para o ajuste de instruções guiadas por solucionadores, focando na geração de código para Programação de Conjuntos de Respostas (ASP), visando resolver problemas complexos de busca combinatória.
Fonte: arXiv cs.AI
NLP/LLMs • Score 96
Adversarially Robust Detection of Harmful Online Content: A Computational Design Science Approach
arXiv:2512.17367v1 Announce Type: new
Abstract: Social media platforms are plagued by harmful content such as hate speech, misinformation, and extremist rhetoric. Machine learning (ML) models are widely adopted to detect such content; however, they remain highly vulnerable to adversarial attacks, wherein malicious users subtly modify text to evade detection. Enhancing adversarial robustness is therefore essential, requiring detectors that can defend against diverse attacks (generalizability) while maintaining high overall accuracy. However, simultaneously achieving both optimal generalizability and accuracy is challenging. Following the computational design science paradigm, this study takes a sequential approach that first proposes a novel framework (Large Language Model-based Sample Generation and Aggregation, LLM-SGA) by identifying the key invariances of textual adversarial attacks and leveraging them to ensure that a detector instantiated within the framework has strong generalizability. Second, we instantiate our detector (Adversarially Robust Harmful Online Content Detector, ARHOCD) with three novel design components to improve detection accuracy: (1) an ensemble of multiple base detectors that exploits their complementary strengths; (2) a novel weight assignment method that dynamically adjusts weights based on each sample's predictability and each base detector's capability, with weights initialized using domain knowledge and updated via Bayesian inference; and (3) a novel adversarial training strategy that iteratively optimizes both the base detectors and the weight assignor. We addressed several limitations of existing adversarial robustness enhancement research and empirically evaluated ARHOCD across three datasets spanning hate speech, rumor, and extremist content. Results show that ARHOCD offers strong generalizability and improves detection accuracy under adversarial conditions.
Fonte: arXiv cs.LG
NLP/LLMs • Score 96
Task Schema and Binding: A Double Dissociation Study of In-Context Learning
arXiv:2512.17325v1 Announce Type: new
Abstract: We provide causal mechanistic validation that in-context learning (ICL) decomposes into two separable mechanisms: Task Schema (abstract task type recognition) and Binding (specific input-output associations). Through activation patching experiments across 9 models from 7 Transformer families plus Mamba (370M-13B parameters), we establish three key findings:
1. Double dissociation: Task Schema transfers at 100% via late MLP patching; Binding transfers at 62% via residual stream patching -- proving separable mechanisms
2. Prior-Schema trade-off: Schema reliance inversely correlates with prior knowledge (Spearman rho = -0.596, p < 0.001, N=28 task-model pairs)
3. Architecture generality: The mechanism operates across all tested architectures including the non-Transformer Mamba
These findings offer a mechanistic account of the ICL puzzle that contrasts with prior views treating ICL as a monolithic mechanism (whether retrieval-based, gradient descent-like, or purely Bayesian). By establishing that Schema and Binding are neurally dissociable -- not merely behavioral modes -- we provide causal evidence for dual-process theories of ICL. Models rely on Task Schema when prior knowledge is absent, but prior knowledge interferes through attentional mis-routing (72.7% recency bias) rather than direct output competition (0%). This explains why arbitrary mappings succeed (zero prior leads to full Schema reliance) while factual overrides fail -- and reveals that the true bottleneck is attentional, not output-level. Practical implications: Understanding these dual mechanisms enables more efficient prompt engineering -- reliable schema transfer reduces required demonstrations for novel tasks, while prior-aware design can mitigate the 38% binding failure rate in high-prior scenarios, improving ICL system reliability in production deployments.
Fonte: arXiv cs.LG
MLOps/Systems • Score 96
MINPO: Memory-Informed Neural Pseudo-Operator to Resolve Nonlocal Spatiotemporal Dynamics
arXiv:2512.17273v1 Announce Type: new
Abstract: Many physical systems exhibit nonlocal spatiotemporal behaviors described by integro-differential equations (IDEs). Classical methods for solving IDEs require repeatedly evaluating convolution integrals, whose cost increases quickly with kernel complexity and dimensionality. Existing neural solvers can accelerate selected instances of these computations, yet they do not generalize across diverse nonlocal structures. In this work, we introduce the Memory-Informed Neural Pseudo-Operator (MINPO), a unified framework for modeling nonlocal dynamics arising from long-range spatial interactions and/or long-term temporal memory. MINPO, employing either Kolmogorov-Arnold Networks (KANs) or multilayer perceptron networks (MLPs) as encoders, learns the nonlocal operator and its inverse directly through neural representations, and then explicitly reconstruct the unknown solution fields. The learning is guarded by a lightweight nonlocal consistency loss term to enforce coherence between the learned operator and reconstructed solution. The MINPO formulation allows to naturally capture and efficiently resolve nonlocal spatiotemporal dependencies governed by a wide spectrum of IDEs and their subsets, including fractional PDEs. We evaluate the efficacy of MINPO in comparison with classical techniques and state-of-the-art neural-based strategies based on MLPs, such as A-PINN and fPINN, along with their newly-developed KAN variants, A-PIKAN and fPIKAN, designed to facilitate a fair comparison. Our study offers compelling evidence of the accuracy of MINPO and demonstrates its robustness in handling (i) diverse kernel types, (ii) different kernel dimensionalities, and (iii) the substantial computational demands arising from repeated evaluations of kernel integrals. MINPO, thus, generalizes beyond problem-specific formulations, providing a unified framework for systems governed by nonlocal operators.
Fonte: arXiv cs.LG
RL • Score 96
Learning to Plan, Planning to Learn: Adaptive Hierarchical RL-MPC for Sample-Efficient Decision Making
arXiv:2512.17091v1 Announce Type: cross
Abstract: We propose a new approach for solving planning problems with a hierarchical structure, fusing reinforcement learning and MPC planning. Our formulation tightly and elegantly couples the two planning paradigms. It leverages reinforcement learning actions to inform the MPPI sampler, and adaptively aggregates MPPI samples to inform the value estimation. The resulting adaptive process leverages further MPPI exploration where value estimates are uncertain, and improves training robustness and the overall resulting policies. This results in a robust planning approach that can handle complex planning problems and easily adapts to different applications, as demonstrated over several domains, including race driving, modified Acrobot, and Lunar Lander with added obstacles. Our results in these domains show better data efficiency and overall performance in terms of both rewards and task success, with up to a 72% increase in success rate compared to existing approaches, as well as accelerated convergence (x2.1) compared to non-adaptive sampling.
Fonte: arXiv cs.AI
RL • Score 96
Distributed Learning in Markovian Restless Bandits over Interference Graphs for Stable Spectrum Sharing
arXiv:2512.17161v1 Announce Type: new
Abstract: We study distributed learning for spectrum access and sharing among multiple cognitive communication entities, such as cells, subnetworks, or cognitive radio users (collectively referred to as cells), in communication-constrained wireless networks modeled by interference graphs. Our goal is to achieve a globally stable and interference-aware channel allocation. Stability is defined through a generalized Gale-Shapley multi-to-one matching, a well-established solution concept in wireless resource allocation. We consider wireless networks where L cells share S orthogonal channels and cannot simultaneously use the same channel as their neighbors. Each channel evolves as an unknown restless Markov process with cell-dependent rewards, making this the first work to establish global Gale-Shapley stability for channel allocation in a stochastic, temporally varying restless environment. To address this challenge, we develop SMILE (Stable Multi-matching with Interference-aware LEarning), a communication-efficient distributed learning algorithm that integrates restless bandit learning with graph-constrained coordination. SMILE enables cells to distributedly balance exploration of unknown channels with exploitation of learned information. We prove that SMILE converges to the optimal stable allocation and achieves logarithmic regret relative to a genie with full knowledge of expected utilities. Simulations validate the theoretical guarantees and demonstrate SMILE's robustness, scalability, and efficiency across diverse spectrum-sharing scenarios.
Fonte: arXiv cs.LG
Vision • Score 95
WDFFU-Mamba: A Wavelet-guided Dual-attention Feature Fusion Mamba for Breast Tumor Segmentation in Ultrasound Images
arXiv:2512.17278v1 Announce Type: new
Abstract: Breast ultrasound (BUS) image segmentation plays a vital role in assisting clinical diagnosis and early tumor screening. However, challenges such as speckle noise, imaging artifacts, irregular lesion morphology, and blurred boundaries severely hinder accurate segmentation. To address these challenges, this work aims to design a robust and efficient model capable of automatically segmenting breast tumors in BUS images.We propose a novel segmentation network named WDFFU-Mamba, which integrates wavelet-guided enhancement and dual-attention feature fusion within a U-shaped Mamba architecture. A Wavelet-denoised High-Frequency-guided Feature (WHF) module is employed to enhance low-level representations through noise-suppressed high-frequency cues. A Dual Attention Feature Fusion (DAFF) module is also introduced to effectively merge skip-connected and semantic features, improving contextual consistency.Extensive experiments on two public BUS datasets demonstrate that WDFFU-Mamba achieves superior segmentation accuracy, significantly outperforming existing methods in terms of Dice coefficient and 95th percentile Hausdorff Distance (HD95).The combination of wavelet-domain enhancement and attention-based fusion greatly improves both the accuracy and robustness of BUS image segmentation, while maintaining computational efficiency.The proposed WDFFU-Mamba model not only delivers strong segmentation performance but also exhibits desirable generalization ability across datasets, making it a promising solution for real-world clinical applications in breast tumor ultrasound analysis.
Fonte: arXiv cs.CV
Evaluation/Benchmarks • Score 96
Bridging Training and Merging Through Momentum-Aware Optimization
arXiv:2512.17109v1 Announce Type: new
Abstract: Training large neural networks and merging task-specific models both exploit low-rank structure and require parameter importance estimation, yet these challenges have been pursued in isolation. Current workflows compute curvature information during training, discard it, then recompute similar information for merging -- wasting computation and discarding valuable trajectory data. We introduce a unified framework that maintains factorized momentum and curvature statistics during training, then reuses this information for geometry-aware model composition. The proposed method achieves memory efficiency comparable to state-of-the-art approaches while accumulating task saliency scores that enable curvature-aware merging without post-hoc Fisher computation. We establish convergence guarantees for non-convex objectives with approximation error bounded by gradient singular value decay. On natural language understanding benchmarks, curvature-aware parameter selection outperforms magnitude-only baselines across all sparsity levels, with multi-task merging improving over strong baselines. The proposed framework exhibits rank-invariant convergence and superior hyperparameter robustness compared to existing low-rank optimizers. By treating the optimization trajectory as a reusable asset rather than discarding it, our approach eliminates redundant computation while enabling more principled model composition.
Fonte: arXiv cs.LG
RL • Score 96
SHARP-QoS: Sparsely-gated Hierarchical Adaptive Routing for joint Prediction of QoS
arXiv:2512.17262v1 Announce Type: new
Abstract: Dependable service-oriented computing relies on multiple Quality of Service (QoS) parameters that are essential to assess service optimality. However, real-world QoS data are extremely sparse, noisy, and shaped by hierarchical dependencies arising from QoS interactions, and geographical and network-level factors, making accurate QoS prediction challenging. Existing methods often predict each QoS parameter separately, requiring multiple similar models, which increases computational cost and leads to poor generalization. Although recent joint QoS prediction studies have explored shared architectures, they suffer from negative transfer due to loss-scaling caused by inconsistent numerical ranges across QoS parameters and further struggle with inadequate representation learning, resulting in degraded accuracy. This paper presents an unified strategy for joint QoS prediction, called SHARP-QoS, that addresses these issues using three components. First, we introduce a dual mechanism to extract the hierarchical features from both QoS and contextual structures via hyperbolic convolution formulated in the Poincar\'e ball. Second, we propose an adaptive feature-sharing mechanism that allows feature exchange across informative QoS and contextual signals. A gated feature fusion module is employed to support dynamic feature selection among structural and shared representations. Third, we design an EMA-based loss balancing strategy that allows stable joint optimization, thereby mitigating the negative transfer. Evaluations on three datasets with two, three, and four QoS parameters demonstrate that SHARP-QoS outperforms both single- and multi-task baselines. Extensive study shows that our model effectively addresses major challenges, including sparsity, robustness to outliers, and cold-start, while maintaining moderate computational overhead, underscoring its capability for reliable joint QoS prediction.
Fonte: arXiv cs.LG
NLP/LLMs • Score 95
A Benchmark for Ultra-High-Resolution Remote Sensing MLLMs
arXiv:2512.17319v1 Announce Type: new
Abstract: Multimodal large language models (MLLMs) demonstrate strong perception and reasoning performance on existing remote sensing (RS) benchmarks. However, most prior benchmarks rely on low-resolution imagery, and some high-resolution benchmarks suffer from flawed reasoning-task designs. We show that text-only LLMs can perform competitively with multimodal vision-language models on RS reasoning tasks without access to images, revealing a critical mismatch between current benchmarks and the intended evaluation of visual understanding. To enable faithful assessment, we introduce RSHR-Bench, a super-high-resolution benchmark for RS visual understanding and reasoning. RSHR-Bench contains 5,329 full-scene images with a long side of at least 4,000 pixels, with up to about 3 x 10^8 pixels per image, sourced from widely used RS corpora and UAV collections. We design four task families: multiple-choice VQA, open-ended VQA, image captioning, and single-image evaluation. These tasks cover nine perception categories and four reasoning types, supporting multi-turn and multi-image dialog. To reduce reliance on language priors, we apply adversarial filtering with strong LLMs followed by rigorous human verification. Overall, we construct 3,864 VQA tasks, 3,913 image captioning tasks, and 500 fully human-written or verified single-image evaluation VQA pairs. Evaluations across open-source, closed-source, and RS-specific VLMs reveal persistent performance gaps in super-high-resolution scenarios. Code: https://github.com/Yunkaidang/RSHR
Fonte: arXiv cs.CV
NLP/LLMs • Score 96
GB-DQN: Gradient Boosted DQN Models for Non-stationary Reinforcement Learning
arXiv:2512.17034v1 Announce Type: new
Abstract: Non-stationary environments pose a fundamental challenge for deep reinforcement learning, as changes in dynamics or rewards invalidate learned value functions and cause catastrophic forgetting. We propose \emph{Gradient-Boosted Deep Q-Networks (GB-DQN)}, an adaptive ensemble method that addresses model drift through incremental residual learning. Instead of retraining a single Q-network, GB-DQN constructs an additive ensemble in which each new learner is trained to approximate the Bellman residual of the current ensemble after drift. We provide theoretical results showing that each boosting step reduces the empirical Bellman residual and that the ensemble converges to the post-drift optimal value function under standard assumptions. Experiments across a diverse set of control tasks with controlled dynamics changes demonstrate faster recovery, improved stability, and greater robustness compared to DQN and common non-stationary baselines.
Fonte: arXiv cs.LG
NLP/LLMs • Score 96
A percepção realista de ameaças impulsiona o conflito entre grupos: Uma análise causal e dinâmica usando simulações de agentes generativos
O conflito humano é frequentemente atribuído a ameaças às condições materiais e valores simbólicos, mas a interação entre eles e qual predomina ainda não está clara. Usamos simulações de agentes impulsionados por modelos de linguagem de grande escala (LLM) em sociedades virtuais para explorar essas dinâmicas.
Fonte: arXiv cs.AI
NLP/LLMs • Score 95
Towards Safer Chatbots: Automated Policy Compliance Evaluation of Custom GPTs
arXiv:2502.01436v3 Announce Type: replace
Abstract: User-configured chatbots built on top of large language models are increasingly available through centralized marketplaces such as OpenAI's GPT Store. While these platforms enforce usage policies intended to prevent harmful or inappropriate behavior, the scale and opacity of customized chatbots make systematic policy enforcement challenging. As a result, policy-violating chatbots continue to remain publicly accessible despite existing review processes. This paper presents a fully automated method for evaluating the compliance of Custom GPTs with its marketplace usage policy using black-box interaction. The method combines large-scale GPT discovery, policy-driven red-teaming prompts, and automated compliance assessment using an LLM-as-a-judge. We focus on three policy-relevant domains explicitly addressed in OpenAI's usage policies: Romantic, Cybersecurity, and Academic GPTs. We validate our compliance assessment component against a human-annotated ground-truth dataset, achieving an F1 score of 0.975 for binary policy violation detection. We then apply the method in a large-scale empirical study of 782 Custom GPTs retrieved from the GPT Store. The results show that 58.7% of the evaluated GPTs exhibit at least one policy-violating response, with substantial variation across policy domains. A comparison with the base models (GPT-4 and GPT-4o) indicates that most violations originate from model-level behavior, while customization tends to amplify these tendencies rather than create new failure modes. Our findings reveal limitations in current review mechanisms for user-configured chatbots and demonstrate the feasibility of scalable, behavior-based policy compliance evaluation.
Fonte: arXiv cs.CL
Vision • Score 95
Robust Scene Coordinate Regression via Geometrically-Consistent Global Descriptors
arXiv:2512.17226v1 Announce Type: new
Abstract: Recent learning-based visual localization methods use global descriptors to disambiguate visually similar places, but existing approaches often derive these descriptors from geometric cues alone (e.g., covisibility graphs), limiting their discriminative power and reducing robustness in the presence of noisy geometric constraints. We propose an aggregator module that learns global descriptors consistent with both geometrical structure and visual similarity, ensuring that images are close in descriptor space only when they are visually similar and spatially connected. This corrects erroneous associations caused by unreliable overlap scores. Using a batch-mining strategy based solely on the overlap scores and a modified contrastive loss, our method trains without manual place labels and generalizes across diverse environments. Experiments on challenging benchmarks show substantial localization gains in large-scale environments while preserving computational and memory efficiency. Code is available at \href{https://github.com/sontung/robust\_scr}{github.com/sontung/robust\_scr}.
Fonte: arXiv cs.CV
Vision • Score 96
Machine Learning Leve e Informado por Física para Previsão de Visibilidade na Aviação em Diversos Regimes Climáticos
A previsão de curto prazo (nowcasting) de eventos de baixa visibilidade e precipitação é crucial para a segurança da aviação e eficiência operacional. Este estudo apresenta um framework leve de gradient boosting (XGBoost) treinado exclusivamente com dados de observação de superfície (METAR) e aprimorado por engenharia de características guiada por princípios termodinâmicos.
Fonte: arXiv cs.LG
NLP/LLMs • Score 96
QSMOTE-PGM/kPGM: Classificação de Conjuntos de Dados Desbalanceados Baseada em QSMOTE e kPGM
O aprendizado de máquina inspirado em quantum (QiML) utiliza estruturas matemáticas da teoria quântica para aprimorar algoritmos clássicos, com foco nas estruturas de produto interno em espaços de características de alta dimensão. Este trabalho apresenta uma comparação teórica e empírica unificada de classificadores baseados em PGM e KPGM, analisando seu desempenho em cenários de oversampling sintético usando variantes do Quantum SMOTE (QSMOTE).
Fonte: arXiv cs.LG
NLP/LLMs • Score 96
AdvJudge-Zero: Binary Decision Flips in LLM-as-a-Judge via Adversarial Control Tokens
arXiv:2512.17375v1 Announce Type: new
Abstract: Reward models and LLM-as-a-Judge systems are central to modern post-training pipelines such as RLHF, DPO, and RLAIF, where they provide scalar feedback and binary decisions that guide model selection and RL-based fine-tuning. We show that these judge systems exhibit a recurring vulnerability: short sequences of low-perplexity control tokens can flip many binary evaluations from correct ``No'' judgments to incorrect ``Yes'' judgments by steering the last-layer logit gap. These control tokens are patterns that a policy model could plausibly generate during post-training, and thus represent realistic reward-hacking risks rather than worst-case adversarial strings. Our method, AdvJudge-Zero, uses the model's next-token distribution and beam-search exploration to discover diverse control-token sequences from scratch, and our analysis shows that the induced hidden-state perturbations concentrate in a low-rank ``soft mode'' that is anti-aligned with the judge's refusal direction. Empirically, these tokens cause very high false positive rates when large open-weight and specialized judge models score incorrect answers on math and reasoning benchmarks. Finally, we show that LoRA-based adversarial training on small sets of control-token-augmented examples can markedly reduce these false positives while preserving evaluation quality.
Fonte: arXiv cs.LG
RL • Score 96
Viés Conservador na Aprendizagem Multi-Professor: Por Que Agentes Preferem Consultores de Baixa Recompensa
A aprendizagem por reforço interativa (IRL) tem mostrado potencial para permitir que agentes autônomos e robôs aprendam comportamentos complexos com professores humanos, mas a dinâmica da seleção de professores ainda é pouco compreendida. Este artigo revela um fenômeno inesperado na IRL: agentes de aprendizagem preferem professores conservadores e de baixa recompensa em vez de aqueles que oferecem recompensas 20 vezes maiores.
Fonte: arXiv cs.AI
NLP/LLMs • Score 96
Destilação de Conhecimento com Cadeia de Pensamento Estruturada para Text-to-SQL
A implementação de sistemas precisos de Text-to-SQL em nível empresarial enfrenta um difícil trilema envolvendo custo, segurança e desempenho. As soluções atuais forçam as empresas a escolher entre Modelos de Linguagem Grande (LLMs) caros e proprietários e Modelos de Linguagem Pequena (SLMs) de baixo desempenho. Propomos o Struct-SQL, um novo framework de Knowledge Distillation (KD) que treina um SLM para emular um poderoso LLM.
Fonte: arXiv cs.AI
NLP/LLMs • Score 96
Incorporação de Embeddings de Nível de Erro de Ruído para Melhorar a Robustez Assistida por LLM no Reconhecimento de Fala Persa
Os sistemas de Reconhecimento Automático de Fala (ASR) enfrentam degradação significativa de desempenho em ambientes ruidosos, especialmente para idiomas de baixo recurso como o persa. Este estudo apresenta um framework robusto de correção de erros ASR sensível ao ruído, que combina múltiplas hipóteses e modelagem consciente do ruído, demonstrando melhorias substanciais na Taxa de Erro de Palavras (WER).
Fonte: arXiv cs.AI
NLP/LLMs • Score 95
CIFE: Code Instruction-Following Evaluation
arXiv:2512.17387v1 Announce Type: cross
Abstract: Large Language Models (LLMs) are increasingly applied to real-world code generation, where functional correctness alone is insufficient for reliable deployment, developers also expect adherence to explicit requirements for robustness, formatting, and security. Existing benchmarks primarily assess correctness through test-case execution, offering limited insight into how reliably models follow such constraints. We introduce a benchmark of 1,000 Python tasks, each paired with an average of 7 developer-specified constraints spanning 13 categories. Constraints are curated through a four-stage human-LLM pipeline to ensure they are atomic, relevant, and objective. We evaluate 14 open- and closed-source models using complementary adherence metrics and propose the C2A Score, a composite measure that jointly captures correctness and constraint compliance. Results reveal a substantial gap between partial and strict satisfaction, while strong models achieve over 90% partial adherence, strict adherence remains between 39-66%. These findings highlight that trustworthy code generation requires not only correctness but also consistent adherence to developer intent.
Fonte: arXiv cs.CL
NLP/LLMs • Score 95
Bangla MedER: Multi-BERT Ensemble Approach for the Recognition of Bangla Medical Entity
arXiv:2512.17769v1 Announce Type: new
Abstract: Medical Entity Recognition (MedER) is an essential NLP task for extracting meaningful entities from the medical corpus. Nowadays, MedER-based research outcomes can remarkably contribute to the development of automated systems in the medical sector, ultimately enhancing patient care and outcomes. While extensive research has been conducted on MedER in English, low-resource languages like Bangla remain underexplored. Our work aims to bridge this gap. For Bangla medical entity recognition, this study first examined a number of transformer models, including BERT, DistilBERT, ELECTRA, and RoBERTa. We also propose a novel Multi-BERT Ensemble approach that outperformed all baseline models with the highest accuracy of 89.58%. Notably, it provides an 11.80% accuracy improvement over the single-layer BERT model, demonstrating its effectiveness for this task. A major challenge in MedER for low-resource languages is the lack of annotated datasets. To address this issue, we developed a high-quality dataset tailored for the Bangla MedER task. The dataset was used to evaluate the effectiveness of our model through multiple performance metrics, demonstrating its robustness and applicability. Our findings highlight the potential of Multi-BERT Ensemble models in improving MedER for Bangla and set the foundation for further advancements in low-resource medical NLP.
Fonte: arXiv cs.CL
NLP/LLMs • Score 96
Modelos de Raciocínio Grande Podem Melhorar a Precisão em Tarefas Matemáticas Usando Pensamento Falho?
O prompting de cadeia de pensamento (CoT) tornou-se central para o raciocínio matemático em grandes modelos de linguagem, mas os modelos ainda são vulneráveis a erros iniciais. Investigamos se o treinamento em rastros de raciocínio intencionalmente falhos pode ensinar os modelos a detectar e se recuperar de tais erros sem degradar a capacidade de resolução de problemas padrão.
Fonte: arXiv cs.AI
Multimodal • Score 95
Peeking Into The Future For Contextual Biasing
arXiv:2512.17657v1 Announce Type: new
Abstract: While end-to-end (E2E) automatic speech recognition (ASR) models excel at general transcription, they struggle to recognize rare or unseen named entities (e.g., contact names, locations), which are critical for downstream applications like virtual assistants. In this paper, we propose a contextual biasing method for attention based encoder decoder (AED) models using a list of candidate named entities. Instead of predicting only the next token, we simultaneously predict multiple future tokens, enabling the model to "peek into the future" and score potential candidate entities in the entity list. Moreover, our approach leverages the multi-token prediction logits directly without requiring additional entity encoders or cross-attention layers, significantly reducing architectural complexity. Experiments on Librispeech demonstrate that our approach achieves up to 50.34% relative improvement in named entity word error rate compared to the baseline AED model.
Fonte: arXiv cs.CL
NLP/LLMs • Score 95
Confidence-Credibility Aware Weighted Ensembles of Small LLMs Outperform Large LLMs in Emotion Detection
arXiv:2512.17630v1 Announce Type: new
Abstract: This paper introduces a confidence-weighted, credibility-aware ensemble framework for text-based emotion detection, inspired by Condorcet's Jury Theorem (CJT). Unlike conventional ensembles that often rely on homogeneous architectures, our approach combines architecturally diverse small transformer-based large language models (sLLMs) - BERT, RoBERTa, DistilBERT, DeBERTa, and ELECTRA, each fully fine-tuned for emotion classification. To preserve error diversity, we minimize parameter convergence while taking advantage of the unique biases of each model. A dual-weighted voting mechanism integrates both global credibility (validation F1 score) and local confidence (instance-level probability) to dynamically weight model contributions. Experiments on the DAIR-AI dataset demonstrate that our credibility-confidence ensemble achieves a macro F1 score of 93.5 percent, surpassing state-of-the-art benchmarks and significantly outperforming large-scale LLMs, including Falcon, Mistral, Qwen, and Phi, even after task-specific Low-Rank Adaptation (LoRA). With only 595M parameters in total, our small LLMs ensemble proves more parameter-efficient and robust than models up to 7B parameters, establishing that carefully designed ensembles of small, fine-tuned models can outperform much larger LLMs in specialized natural language processing (NLP) tasks such as emotion detection.
Fonte: arXiv cs.CL
RL • Score 96
Aprimorando a Classificação de Espécies de Árvores: Insights do YOLOv8 e IA Explicável Aplicados a Projeções de Nuvem de Pontos TLS
Classificar espécies de árvores é uma área de pesquisa central em sensoriamento remoto florestal há décadas. Novos sensores e abordagens de classificação, como TLS e deep learning, alcançam precisão de ponta, mas seus processos de decisão permanecem obscuros. Propomos um método inovador que liga explicações do Finer-CAM a segmentos de projeções TLS, avaliando sistematicamente quais características impulsionam a discriminação de espécies.
Fonte: arXiv cs.AI
Vision • Score 95
DESSERT: Diffusion-based Event-driven Single-frame Synthesis via Residual Training
arXiv:2512.17323v1 Announce Type: new
Abstract: Video frame prediction extrapolates future frames from previous frames, but suffers from prediction errors in dynamic scenes due to the lack of information about the next frame. Event cameras address this limitation by capturing per-pixel brightness changes asynchronously with high temporal resolution. Prior research on event-based video frame prediction has leveraged motion information from event data, often by predicting event-based optical flow and reconstructing frames via pixel warping. However, such approaches introduce holes and blurring when pixel displacement is inaccurate. To overcome this limitation, we propose DESSERT, a diffusion-based event-driven single-frame synthesis framework via residual training. Leveraging a pre-trained Stable Diffusion model, our method is trained on inter-frame residuals to ensure temporal consistency. The training pipeline consists of two stages: (1) an Event-to-Residual Alignment Variational Autoencoder (ER-VAE) that aligns the event frame between anchor and target frames with the corresponding residual, and (2) a diffusion model that denoises the residual latent conditioned on event data. Furthermore, we introduce Diverse-Length Temporal (DLT) augmentation, which improves robustness by training on frame segments of varying temporal lengths. Experimental results demonstrate that our method outperforms existing event-based reconstruction, image-based video frame prediction, event-based video frame prediction, and one-sided event-based video frame interpolation methods, producing sharper and more temporally consistent frame synthesis.
Fonte: arXiv cs.CV
RL • Score 95
Safeguarded Stochastic Polyak Step Sizes for Non-smooth Optimization: Robust Performance Without Small (Sub)Gradients
arXiv:2512.02342v2 Announce Type: replace-cross
Abstract: The stochastic Polyak step size (SPS) has proven to be a promising choice for stochastic gradient descent (SGD), delivering competitive performance relative to state-of-the-art methods on smooth convex and non-convex optimization problems, including deep neural network training. However, extensions of this approach to non-smooth settings remain in their early stages, often relying on interpolation assumptions or requiring knowledge of the optimal solution. In this work, we propose a novel SPS variant, Safeguarded SPS (SPS$_{safe}$), for the stochastic subgradient method, and provide rigorous convergence guarantees for non-smooth convex optimization with no need for strong assumptions. We further incorporate momentum into the update rule, yielding equally tight theoretical results. On non-smooth convex benchmarks, our experiments are consistent with the theoretical predictions on how the safeguard affects the convergence neighborhood. On deep neural networks the proposed step size achieves competitive performance to existing adaptive baselines and exhibits stable behavior across a wide range of problem settings. Moreover, in these experiments, the gradient norms under our step size do not collapse to (near) zero, indicating robustness to vanishing gradients.
Fonte: arXiv stat.ML
NLP/LLMs • Score 96
Adversarial VR: Um Testbed Open-Source para Avaliação da Robustez Adversarial na Detecção e Mitigação de Ciberdoença em VR
Métodos automatizados de detecção de ciberdoença baseados em deep learning (DL) podem melhorar o conforto e a interação do usuário. No entanto, esses sistemas são suscetíveis a ataques adversariais, que podem degradar o desempenho do modelo e interromper a experiência imersiva. Este artigo apresenta o Adversarial-VR, um testbed em tempo real para avaliar estratégias de detecção e mitigação de ciberdoença sob condições adversariais.
Fonte: arXiv cs.AI
Vision • Score 95
AnyCXR: Segmentação da Anatomia Humana em Radiografias de Tórax em Qualquer Posição de Aquisição Usando Dados Sintéticos Randomizados de Domínio em Múltiplas Etapas com Anotações Imperfeitas e Aprendizado de Regularização de Anotação Conjunta Condicional
A segmentação anatômica robusta de radiografias de tórax (CXRs) continua desafiadora devido à escassez de anotações abrangentes e à variabilidade substancial das condições de aquisição no mundo real. Propomos o AnyCXR, um framework unificado que permite a segmentação multi-orgânica generalizável em ângulos de projeção arbitrários de CXR usando apenas supervisão sintética.
Fonte: arXiv cs.CV
Vision • Score 95
Interpretable Similarity of Synthetic Image Utility
arXiv:2512.17080v1 Announce Type: new
Abstract: Synthetic medical image data can unlock the potential of deep learning (DL)-based clinical decision support (CDS) systems through the creation of large scale, privacy-preserving, training sets. Despite the significant progress in this field, there is still a largely unanswered research question: "How can we quantitatively assess the similarity of a synthetically generated set of images with a set of real images in a given application domain?". Today, answers to this question are mainly provided via user evaluation studies, inception-based measures, and the classification performance achieved on synthetic images. This paper proposes a novel measure to assess the similarity between synthetically generated and real sets of images, in terms of their utility for the development of DL-based CDS systems. Inspired by generalized neural additive models, and unlike inception-based measures, the proposed measure is interpretable (Interpretable Utility Similarity, IUS), explaining why a synthetic dataset could be more useful than another one in the context of a CDS system based on clinically relevant image features. The experimental results on publicly available datasets from various color medical imaging modalities including endoscopic, dermoscopic and fundus imaging, indicate that selecting synthetic images of high utility similarity using IUS can result in relative improvements of up to 54.6% in terms of classification performance. The generality of IUS for synthetic data assessment is demonstrated also for greyscale X-ray and ultrasound imaging modalities. IUS implementation is available at https://github.com/innoisys/ius
Fonte: arXiv cs.CV
RL • Score 96
Riscos de Segurança de Veículos Agentes: Uma Análise Sistemática de Ameaças Cognitivas e Intercamadas
A Inteligência Artificial Agente está sendo cada vez mais explorada em veículos manuais e autônomos, resultando na noção de Veículos Agentes (AgVs). Este artigo investiga ameaças de segurança em AgVs, incluindo riscos no estilo OWASP e ciberataques de outras camadas que afetam a camada agente. Um novo framework é proposto para analisar esses riscos em plataformas de veículos atuais e emergentes.
Fonte: arXiv cs.AI
NLP/LLMs • Score 96
Luzes, Câmera, Consistência: Um Pipeline Multietapas para Histórias em Vídeo com IA de Personagens Estáveis
Gerar histórias em vídeo longas e coesas com personagens consistentes é um desafio significativo para a IA atual de texto-para-vídeo. Apresentamos um método que aborda a geração de vídeo de maneira semelhante a um cineasta, utilizando um pipeline que envolve a criação de um roteiro detalhado e a geração de visuais consistentes para cada personagem.
Fonte: arXiv cs.AI
RL • Score 95
A Certified Unlearning Approach without Access to Source Data
arXiv:2506.06486v3 Announce Type: replace-cross
Abstract: With the growing adoption of data privacy regulations, the ability to erase private or copyrighted information from trained models has become a crucial requirement. Traditional unlearning methods often assume access to the complete training dataset, which is unrealistic in scenarios where the source data is no longer available. To address this challenge, we propose a certified unlearning framework that enables effective data removal \final{without access to the original training data samples}. Our approach utilizes a surrogate dataset that approximates the statistical properties of the source data, allowing for controlled noise scaling based on the statistical distance between the two. \updated{While our theoretical guarantees assume knowledge of the exact statistical distance, practical implementations typically approximate this distance, resulting in potentially weaker but still meaningful privacy guarantees.} This ensures strong guarantees on the model's behavior post-unlearning while maintaining its overall utility. We establish theoretical bounds, introduce practical noise calibration techniques, and validate our method through extensive experiments on both synthetic and real-world datasets. The results demonstrate the effectiveness and reliability of our approach in privacy-sensitive settings.
Fonte: arXiv stat.ML
RL • Score 95
Fairness via Independence: A (Conditional) Distance Covariance Framework
arXiv:2412.00720v2 Announce Type: replace-cross
Abstract: We explore fairness from a statistical perspective by selectively utilizing either conditional distance covariance or distance covariance statistics as measures to assess the independence between predictions and sensitive attributes. We boost fairness with independence by adding a distance covariance-based penalty to the model's training. Additionally, we present the matrix form of empirical (conditional) distance covariance for parallel calculations to enhance computational efficiency. Theoretically, we provide a proof for the convergence between empirical and population (conditional) distance covariance, establishing necessary guarantees for batch computations. Through experiments conducted on a range of real-world datasets, we have demonstrated that our method effectively bridges the fairness gap in machine learning. Our code is available at \url{https://github.com/liuhaixias1/Fair_dc/}.
Fonte: arXiv stat.ML
RL • Score 91
Refined Analysis of Federated Averaging and Federated Richardson-Romberg
arXiv:2412.01389v2 Announce Type: replace
Abstract: In this paper, we present a novel analysis of \FedAvg with constant step size, relying on the Markov property of the underlying process. We demonstrate that the global iterates of the algorithm converge to a stationary distribution and analyze its resulting bias and variance relative to the problem's solution. We provide a first-order bias expansion in both homogeneous and heterogeneous settings. Interestingly, this bias decomposes into two distinct components: one that depends solely on stochastic gradient noise and another on client heterogeneity. Finally, we introduce a new algorithm based on the Richardson-Romberg extrapolation technique to mitigate this bias.
Fonte: arXiv stat.ML
Theory/Optimization • Score 92
Unifying Distributionally Robust Optimization via Optimal Transport Theory
arXiv:2308.05414v2 Announce Type: replace-cross
Abstract: In recent years, two prominent paradigms have shaped distributionally robust optimization (DRO), modeling distributional ambiguity through $\phi$-divergences and Wasserstein distances, respectively. While the former focuses on ambiguity in likelihood ratios, the latter emphasizes ambiguity in outcomes and uses a transportation cost function to capture geometric structure in the outcome space. This paper proposes a unified framework that bridges these approaches by leveraging optimal transport (OT) with conditional moment constraints. Our formulation enables adversarial distributions to jointly perturb likelihood ratios and outcomes, yielding a generalized OT coupling between the nominal and perturbed distributions. We further establish key duality results and develop tractable reformulations that highlight the practical power of our unified approach.
Fonte: arXiv stat.ML
Privacy/Security/Fairness • Score 92
Mitigating Forgetting in Low Rank Adaptation
arXiv:2512.17720v1 Announce Type: cross
Abstract: Parameter-efficient fine-tuning methods, such as Low-Rank Adaptation (LoRA), enable fast specialization of large pre-trained models to different downstream applications. However, this process often leads to catastrophic forgetting of the model's prior domain knowledge. We address this issue with LaLoRA, a weight-space regularization technique that applies a Laplace approximation to Low-Rank Adaptation. Our approach estimates the model's confidence in each parameter and constrains updates in high-curvature directions, preserving prior knowledge while enabling efficient target-domain learning. By applying the Laplace approximation only to the LoRA weights, the method remains lightweight. We evaluate LaLoRA by fine-tuning a Llama model for mathematical reasoning and demonstrate an improved learning-forgetting trade-off, which can be directly controlled via the method's regularization strength. We further explore different loss landscape curvature approximations for estimating parameter confidence, analyze the effect of the data used for the Laplace approximation, and study robustness across hyperparameters.
Fonte: arXiv stat.ML