RecSys • Score 85
AdaptFuse: Training-Free Sequential Preference Learning via Externalized Bayesian Inference
arXiv:2604.03925v1 Announce Type: new
Abstract: Large language models struggle to accumulate evidence across multiple rounds of user interaction, failing to update their beliefs in a manner consistent with Bayesian inference. Existing solutions require fine-tuning on sensitive user interaction data, limiting their applicability in privacy-conscious settings. We propose AdaptFuse, a training-free framework that externalizes probabilistic computation entirely from the LLM: a symbolic module maintains a Bayesian posterior over a discrete hypothesis set, while a frozen LLM contributes semantic reasoning via multi-sample Dirichlet aggregation. The two signals are combined through entropy-adaptive fusion, which automatically weights each source by its predictive confidence, shifting reliance from the LLM to the symbolic posterior as evidence accumulates. We evaluate across three domains: flight recommendation, hotel recommendation, and web shopping; on Gemma 2 9B, Llama 3 8B, and Qwen 2.5 7B. AdaptFuse consistently outperforms both prompting baselines and fine-tuned Bayesian Teaching models on all tasks, with accuracy improving monotonically over interaction rounds. These results demonstrate that principled inference-time algorithms can substitute for fine-tuning in personalized recommendation, without storing or training on sensitive user data. All the code and materials will be open-sourced.
Fonte: arXiv cs.CL
NLP/LLMs • Score 85
Towards Intelligent Energy Security: A Unified Spatio-Temporal and Graph Learning Framework for Scalable Electricity Theft Detection in Smart Grids
arXiv:2604.03344v1 Announce Type: new
Abstract: Electricity theft and non-technical losses (NTLs) remain critical challenges in modern smart grids, causing significant economic losses and compromising grid reliability. This study introduces the SmartGuard Energy Intelligence System (SGEIS), an integrated artificial intelligence framework for electricity theft detection and intelligent energy monitoring. The proposed system combines supervised machine learning, deep learning-based time-series modeling, Non-Intrusive Load Monitoring (NILM), and graph-based learning to capture both temporal and spatial consumption patterns. A comprehensive data processing pipeline is developed, incorporating feature engineering, multi-scale temporal analysis, and rule-based anomaly labeling. Deep learning models, including Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and Autoencoders, are employed to detect abnormal usage patterns. In parallel, ensemble learning methods such as Random Forest, Gradient Boosting, XGBoost, and LightGBM are utilized for classification. To model grid topology and spatial dependencies, Graph Neural Networks (GNNs) are applied to identify correlated anomalies across interconnected nodes. The NILM module enhances interpretability by disaggregating appliance-level consumption from aggregate signals. Experimental results demonstrate strong performance, with Gradient Boosting achieving a ROC-AUC of 0.894, while graph-based models attain over 96% accuracy in identifying high-risk nodes. The hybrid framework improves detection robustness by integrating temporal, statistical, and spatial intelligence. Overall, SGEIS provides a scalable and practical solution for electricity theft detection, offering high accuracy, improved interpretability, and strong potential for real-world smart grid deployment.
Fonte: arXiv cs.LG
RecSys • Score 85
Regime-Calibrated Demand Priors for Ride-Hailing Fleet Dispatch and Repositioning
arXiv:2604.03883v1 Announce Type: new
Abstract: Effective ride-hailing dispatch requires anticipating demand patterns that vary substantially across time-of-day, day-of-week, season, and special events. We propose a regime-calibrated approach that (i) segments historical trip data into demand regimes, (ii) matches the current operating period to the most similar historical analogues via a similarity ensemble combining Kolmogorov-Smirnov distance, Wasserstein-1 distance, feature distance, variance ratio, event pattern similarity, and temporal proximity, and (iii) uses the resulting calibrated demand prior to drive both an LP-based fleet repositioning policy and batch dispatch with Hungarian matching. In ablation, a distributional-only metric subset achieves the strongest mean-wait reduction, while the full ensemble is retained as a robustness-oriented default that preserves calendar and event context.
Evaluated on 5.2 million NYC TLC trips across 8 diverse scenarios (winter/summer, weekday/weekend/holiday, morning/evening/night) with 5 random seeds each, our method reduces mean rider wait times by 31.1% (bootstrap 95% CI: [26.5, 36.6]; Friedman chi-squared = 80.0, p = 4.25e-18; Cohen's d = 7.5-29.9). P95 wait drops 37.6% and the Gini coefficient of wait times improves from 0.441 to 0.409. The two contributions compose multiplicatively: calibration provides 16.9% reduction relative to the replay baseline; LP repositioning adds a further 15.5%. The approach requires no training, is deterministic and explainable, generalizes to Chicago (23.3% wait reduction using the NYC-built regime library without retraining), and is robust across fleet sizes (32-47% improvement for 0.5x-2.0x fleet scaling). Code is available at https://github.com/IndarKarhana/regime-calibrated-dispatch.
Fonte: arXiv cs.LG
RecSys • Score 85
Supervised Dimensionality Reduction Revisited: Why LDA on Frozen CNN Features Deserves a Second Look
arXiv:2604.03928v1 Announce Type: new
Abstract: Effective ride-hailing dispatch requires anticipating demand patterns that vary substantially across time-of-day, day-of-week, season, and special events. We propose a regime-calibrated approach that (i) segments historical trip data into demand regimes, (ii) matches the current operating period to the most similar historical analogues via a six-metric similarity ensemble (Kolmogorov-Smirnov, Wasserstein-1, feature distance, variance ratio, event pattern, temporal proximity), and (iii) uses the resulting calibrated demand prior to drive both an LP-based fleet repositioning policy and batch dispatch with Hungarian matching. In ablation, a distributional-only subset is strongest on mean wait, while the full ensemble is retained as a robustness-oriented default.
Evaluated on 5.2 million NYC TLC trips across 8 diverse scenarios (winter/summer, weekday/weekend/holiday, morning/evening/night) with 5 random seeds each, our method reduces mean rider wait times by 31.1% (bootstrap 95% CI: [26.5, 36.6]%; Friedman chi-sq = 80.0, p = 4.25e-18; Cohen's d = 7.5-29.9 across scenarios). The improvement extends to the tail: P95 wait drops 37.6% and the Gini coefficient of wait times improves from 0.441 to 0.409 (7.3% relative). The two contributions compose multiplicatively and are independently validated: calibration provides 16.9% reduction; LP repositioning adds a further 15.5%. The approach requires no training, is deterministic and explainable, generalizes to Chicago (23.3% wait reduction via NYC-built regime library), and is robust across fleet sizes (32-47% improvement for 0.5-2x fleet scaling). We provide comprehensive ablation studies, formal statistical tests, and routing-fidelity validation with OSRM.
Fonte: arXiv cs.LG
NLP/LLMs • Score 85
FTimeXer: Frequency-aware Time-series Transformer with Exogenous variables for Robust Carbon Footprint Forecasting
arXiv:2604.02347v1 Announce Type: new
Abstract: Accurate and up-to-date forecasting of the power grid's carbon footprint is crucial for effective product carbon footprint (PCF) accounting and informed decarbonization decisions. However, the carbon intensity of the grid exhibits high non-stationarity, and existing methods often struggle to effectively leverage periodic and oscillatory patterns. Furthermore, these methods tend to perform poorly when confronted with irregular exogenous inputs, such as missing data or misalignment. To tackle these challenges, we propose FTimeXer, a frequency-aware time-series Transformer designed with a robust training scheme that accommodates exogenous factors. FTimeXer features an Fast Fourier Transform (FFT)-driven frequency branch combined with gated time-frequency fusion, allowing it to capture multi-scale periodicity effectively. It also employs stochastic exogenous masking in conjunction with consistency regularization, which helps reduce spurious correlations and enhance stability. Experiments conducted on three real-world datasets show consistent improvements over strong baselines. As a result, these enhancements lead to more reliable forecasts of grid carbon factors, which are essential for effective PCF accounting and informed decision-making regarding decarbonization.
Fonte: arXiv cs.LG
RecSys • Score 85
Principled and Scalable Diversity-Aware Retrieval via Cardinality-Constrained Binary Quadratic Programming
arXiv:2604.02554v1 Announce Type: new
Abstract: Diversity-aware retrieval is essential for Retrieval-Augmented Generation (RAG), yet existing methods lack theoretical guarantees and face scalability issues as the number of retrieved passages $k$ increases. We propose a principled formulation of diversity retrieval as a cardinality-constrained binary quadratic programming (CCBQP), which explicitly balances relevance and semantic diversity through an interpretable trade-off parameter. Inspired by recent advances in combinatorial optimization, we develop a non-convex tight continuous relaxation and a Frank--Wolfe based algorithm with landscape analysis and convergence guarantees. Extensive experiments demonstrate that our method consistently dominates baselines on the relevance-diversity Pareto frontier, while achieving significant speedup.
Fonte: arXiv cs.CL
RecSys • Score 85
Learn then Decide: A Learning Approach for Designing Data Marketplaces
arXiv:2503.10773v2 Announce Type: replace
Abstract: As data marketplaces become increasingly central to the digital economy, it is crucial to design efficient pricing mechanisms that optimize revenue while ensuring fair and adaptive pricing. We introduce the Maximum Auction-to-Posted Price (MAPP) mechanism, a novel two-stage approach that first estimates the bidders' value distribution through auctions and then determines the optimal posted price based on the learned distribution. We establish that MAPP is individually rational and incentive-compatible, ensuring truthful bidding while balancing revenue maximization with minimal price discrimination. On the theoretical side, we establish a statistical viewpoint that recasts revenue optimization as a valuation density estimation problem: we show that revenue regret can be controlled by uniform error in estimating the valuation density. MAPP achieves a regret of $O_p(n^{-1}(\log n)^2)$ when incorporating historical bid data, where $n$ is the number of bids in the current round. For sequential dataset sales over $T$ rounds, we propose an online MAPP mechanism that dynamically adjusts pricing across datasets with varying value distributions. Our approach achieves no-regret learning, with the average cumulative regret converging at a rate of $O_p(T^{-1/2}(\log T)^2)$. We validate the effectiveness of MAPP through simulations and real-world data from the FCC AWS-3 spectrum auction.
Fonte: arXiv stat.ML
RecSys • Score 85
VALOR: Value-Aware Revenue Uplift Modeling with Treatment-Gated Representation for B2B Sales
arXiv:2604.02472v1 Announce Type: new
Abstract: B2B sales organizations must identify "persuadable" accounts within zero-inflated revenue distributions to optimize expensive human resource allocation. Standard uplift frameworks struggle with treatment signal collapse in high-dimensional spaces and a misalignment between regression calibration and the ranking of high-value "whales." We introduce VALOR (Value Aware Learning of Optimized (B2B) Revenue), a unified framework featuring a Treatment-Gated Sparse-Revenue Network that uses bilinear interaction to prevent causal signal collapse. The framework is optimized via a novel Cost-Sensitive Focal-ZILN objective that combines a focal mechanism for distributional robustness with a value-weighted ranking loss that scales penalties based on financial magnitude. To provide interpretability for high-touch sales programs, we further derive Robust ZILN-GBDT, a tree based variant utilizing a custom splitting criterion for uplift heterogeneity. Extensive evaluations confirm VALOR's dominance, achieving a 20% improvement in rankability over state-of-the-art methods on public benchmarks and delivering a validated 2.7x increase in incremental revenue per account in a rigorous 4-month production A/B test.
Fonte: arXiv cs.LG
NLP/LLMs • Score 85
Machine Learning for Network Attacks Classification and Statistical Evaluation of Adversarial Learning Methodologies for Synthetic Data Generation
arXiv:2603.17717v2 Announce Type: replace-cross
Abstract: Supervised detection of network attacks has always been a critical part of network intrusion detection systems (NIDS). Nowadays, in a pivotal time for artificial intelligence (AI), with even more sophisticated attacks that utilize advanced techniques, such as generative artificial intelligence (GenAI) and reinforcement learning, it has become a vital component if we wish to protect our personal data, which are scattered across the web. In this paper, we address two tasks, in the first unified multi-modal NIDS dataset, which incorporates flow-level data, packet payload information and temporal contextual features, from the reprocessed CIC-IDS-2017, CIC-IoT-2023, UNSW-NB15 and CIC-DDoS-2019, with the same feature space. In the first task we use machine learning (ML) algorithms, with stratified cross validation, in order to prevent network attacks, with stability and reliability. In the second task we use adversarial learning algorithms to generate synthetic data, compare them with the real ones and evaluate their fidelity, utility and privacy using the SDV framework, f-divergences, distinguishability and non-parametric statistical tests. The findings provide stable ML models for intrusion detection and generative models with high fidelity and utility, by combining the Synthetic Data Vault framework, the TRTS and TSTR tests, with non-parametric statistical tests and f-divergence measures.
Fonte: arXiv stat.ML
RecSys • Score 85
Preference Guided Iterated Pareto Referent Optimisation for Accessible Route Planning
arXiv:2604.00795v1 Announce Type: new
Abstract: We propose the Preference Guided Iterated Pareto Referent Optimisation (PG-IPRO) for urban route planning for people with different accessibility requirements and preferences. With this algorithm the user can interact with the system by giving feedback on a route, i.e., the user can say which objective should be further minimized, or conversely can be relaxed. This leads to intuitive user interaction, that is especially effective during early iterations compared to information-gain-based interaction. Furthermore, due to PG-IPRO's iterative nature, the full set of alternative, possibly optimal policies (the Pareto front), is never computed, leading to higher computational efficiency and shorter waiting times for users.
Fonte: arXiv cs.AI
RecSys • Score 85
When Career Data Runs Out: Structured Feature Engineering and Signal Limits for Founder Success Prediction
arXiv:2604.00339v1 Announce Type: new
Abstract: Predicting startup success from founder career data is hard. The signal is weak, the labels are rare (9%), and most founders who succeed look almost identical to those who fail. We engineer 28 structured features directly from raw JSON fields -- jobs, education, exits -- and combine them with a deterministic rule layer and XGBoost boosted stumps. Our model achieves Val F0.5 = 0.3030, Precision = 0.3333, Recall = 0.2222 -- a +17.7pp improvement over the zero-shot LLM baseline. We then run a controlled experiment: extract 9 features from the prose field using Claude Haiku, at 67% and 100% dataset coverage. LLM features capture 26.4% of model importance but add zero CV signal (delta = -0.05pp). The reason is structural: anonymised_prose is generated from the same JSON fields we parse directly -- it is a lossy re-encoding, not a richer source. The ceiling (CV ~= 0.25, Val ~= 0.30) reflects the information content of this dataset, not a modeling limitation. In characterizing where the signal runs out and why, this work functions as a benchmark diagnostic -- one that points directly to what a richer dataset would need to include.
Fonte: arXiv cs.LG
RecSys • Score 85
MemRerank: Preference Memory for Personalized Product Reranking
arXiv:2603.29247v1 Announce Type: new
Abstract: LLM-based shopping agents increasingly rely on long purchase histories and multi-turn interactions for personalization, yet naively appending raw history to prompts is often ineffective due to noise, length, and relevance mismatch. We propose MemRerank, a preference memory framework that distills user purchase history into concise, query-independent signals for personalized product reranking. To study this problem, we build an end-to-end benchmark and evaluation framework centered on an LLM-based \textbf{1-in-5} selection task, which measures both memory quality and downstream reranking utility. We further train the memory extractor with reinforcement learning (RL), using downstream reranking performance as supervision. Experiments with two LLM-based rerankers show that MemRerank consistently outperforms no-memory, raw-history, and off-the-shelf memory baselines, yielding up to \textbf{+10.61} absolute points in 1-in-5 accuracy. These results suggest that explicit preference memory is a practical and effective building block for personalization in agentic e-commerce systems.
Fonte: arXiv cs.CL
RecSys • Score 85
Monodense Deep Neural Model for Determining Item Price Elasticity
arXiv:2603.29261v1 Announce Type: new
Abstract: Item Price Elasticity is used to quantify the responsiveness of consumer demand to changes in item prices, enabling businesses to create pricing strategies and optimize revenue management. Sectors such as store retail, e-commerce, and consumer goods rely on elasticity information derived from historical sales and pricing data. This elasticity provides an understanding of purchasing behavior across different items, consumer discount sensitivity, and demand elastic departments. This information is particularly valuable for competitive markets and resource-constrained businesses decision making which aims to maximize profitability and market share. Price elasticity also uncovers historical shifts in consumer responsiveness over time. In this paper, we model item-level price elasticity using large-scale transactional datasets, by proposing a novel elasticity estimation framework which has the capability to work in an absence of treatment control setting. We test this framework by using Machine learning based algorithms listed below, including our newly proposed Monodense deep neural network.
(1) Monodense-DL network -- Hybrid neural network architecture combining embedding, dense, and Monodense layers (2) DML -- Double machine learning setting using regression models (3) LGBM -- Light Gradient Boosting Model
We evaluate our model on multi-category retail data spanning millions of transactions using a back testing framework. Experimental results demonstrate the superiority of our proposed neural network model within the framework compared to other prevalent ML based methods listed above.
Fonte: arXiv cs.LG
NLP/LLMs • Score 85
GISTBench: Evaluating LLM User Understanding via Evidence-Based Interest Verification
arXiv:2603.29112v1 Announce Type: new
Abstract: We introduce GISTBench, a benchmark for evaluating Large Language Models' (LLMs) ability to understand users from their interaction histories in recommendation systems. Unlike traditional RecSys benchmarks that focus on item prediction accuracy, our benchmark evaluates how well LLMs can extract and verify user interests from engagement data. We propose two novel metric families: Interest Groundedness (IG), decomposed into precision and recall components to separately penalize hallucinated interest categories and reward coverage, and Interest Specificity (IS), which assesses the distinctiveness of verified LLM-predicted user profiles. We release a synthetic dataset constructed on real user interactions on a global short-form video platform. Our dataset contains both implicit and explicit engagement signals and rich textual descriptions. We validate our dataset fidelity against user surveys, and evaluate eight open-weight LLMs spanning 7B to 120B parameters. Our findings reveal performance bottlenecks in current LLMs, particularly their limited ability to accurately count and attribute engagement signals across heterogeneous interaction types.
Fonte: arXiv cs.AI
NLP/LLMs • Score 85
Disentangled Graph Prompting for Out-Of-Distribution Detection
arXiv:2603.29644v1 Announce Type: new
Abstract: When testing data and training data come from different distributions, deep neural networks (DNNs) will face significant safety risks in practical applications. Therefore, out-of-distribution (OOD) detection techniques, which can identify OOD samples at test time and alert the system, are urgently needed. Existing graph OOD detection methods usually characterize fine-grained in-distribution (ID) patterns from multiple perspectives, and train end-to-end graph neural networks (GNNs) for prediction. However, due to the unavailability of OOD data during training, the absence of explicit supervision signals could lead to sub-optimal performance of end-to-end encoders. To address this issue, we follow the pre-training+prompting paradigm to utilize pre-trained GNN encoders, and propose Disentangled Graph Prompting (DGP), to capture fine-grained ID patterns with the help of ID graph labels. Specifically, we design two prompt generators that respectively generate class-specific and class-agnostic prompt graphs by modifying the edge weights of an input graph. We also design several effective losses to train the prompt generators and prevent trivial solutions. We conduct extensive experiments on ten datasets to demonstrate the superiority of our proposed DGP, which achieves a relative AUC improvement of 3.63% over the best graph OOD detection baseline. Ablation studies and hyper-parameter experiments further show the effectiveness of DGP. Code is available at https://github.com/BUPT-GAMMA/DGP.
Fonte: arXiv cs.LG
RecSys • Score 85
Let the Agent Steer: Closed-Loop Ranking Optimization via Influence Exchange
arXiv:2603.27765v1 Announce Type: new
Abstract: Recommendation ranking is fundamentally an influence allocation problem: a sorting formula distributes ranking influence among competing factors, and the business outcome depends on finding the optimal "exchange rates" among them. However, offline proxy metrics systematically misjudge how influence reallocation translates to online impact, with asymmetric bias across metrics that a single calibration factor cannot correct.
We present Sortify, the first fully autonomous LLM-driven ranking optimization agent deployed in a large-scale production recommendation system. The agent reframes ranking optimization as continuous influence exchange, closing the full loop from diagnosis to parameter deployment without human intervention. It addresses structural problems through three mechanisms: (1) a dual-channel framework grounded in Savage's Subjective Expected Utility (SEU) that decouples offline-online transfer correction (Belief channel) from constraint penalty adjustment (Preference channel); (2) an LLM meta-controller operating on framework-level parameters rather than low-level search variables; (3) a persistent Memory DB with 7 relational tables for cross-round learning. Its core metric, Influence Share, provides a decomposable measure where all factor contributions sum to exactly 100%.
Sortify has been deployed across two Southeast Asian markets. In Country A, the agent pushed GMV from -3.6% to +9.2% within 7 rounds with peak orders reaching +12.5%. In Country B, a cold-start deployment achieved +4.15% GMV/UU and +3.58% Ads Revenue in a 7-day A/B test, leading to full production rollout.
Fonte: arXiv cs.AI
NLP/LLMs • Score 85
LineMVGNN: Anti-Money Laundering with Line-Graph-Assisted Multi-View Graph Neural Networks
arXiv:2603.23584v1 Announce Type: new
Abstract: Anti-money laundering (AML) systems are important for protecting the global economy. However, conventional rule-based methods rely on domain knowledge, leading to suboptimal accuracy and a lack of scalability. Graph neural networks (GNNs) for digraphs (directed graphs) can be applied to transaction graphs and capture suspicious transactions or accounts. However, most spectral GNNs do not naturally support multi-dimensional edge features, lack interpretability due to edge modifications, and have limited scalability owing to their spectral nature. Conversely, most spatial methods may not capture the money flow well. Therefore, in this work, we propose LineMVGNN (Line-Graph-Assisted Multi-View Graph Neural Network), a novel spatial method that considers payment and receipt transactions. Specifically, the LineMVGNN model extends a lightweight MVGNN module, which performs two-way message passing between nodes in a transaction graph. Additionally, LineMVGNN incorporates a line graph view of the original transaction graph to enhance the propagation of transaction information. We conduct experiments on two real-world account-based transaction datasets: the Ethereum phishing transaction network dataset and a financial payment transaction dataset from one of our industry partners. The results show that our proposed method outperforms state-of-the-art methods, reflecting the effectiveness of money laundering detection with line-graph-assisted multi-view graph learning. We also discuss scalability, adversarial robustness, and regulatory considerations of our proposed method.
Fonte: arXiv cs.LG
RecSys • Score 85
Lightweight Fairness for LLM-Based Recommendations via Kernelized Projection and Gated Adapters
arXiv:2603.23780v1 Announce Type: new
Abstract: Large Language Models (LLMs) have introduced new capabilities to recommender systems, enabling dynamic, context-aware, and conversational recommendations. However, LLM-based recommender systems inherit and may amplify social biases embedded in their pre-training data, especially when demographic cues are present. Existing fairness solutions either require extra parameters fine-tuning, or suffer from optimization instability. We propose a lightweight and scalable bias mitigation method that combines a kernelized Iterative Null-space Projection (INLP) with a gated Mixture-of-Experts (MoE) adapter. Our approach estimates a closed-form projection that removes single or multiple sensitive attributes from LLM representations with no additional trainable parameters. To preserve task utility, we introduce a two-level MoE adapter that selectively restores useful signals without reintroducing bias. Experiments on two public datasets show that our method reduces attribute leakage across multiple protected variables while maintaining competitive recommendation accuracy.
Fonte: arXiv cs.LG
RecSys • Score 85
Entire Space Counterfactual Learning for Reliable Content Recommendations
arXiv:2210.11039v3 Announce Type: replace-cross
Abstract: Post-click conversion rate (CVR) estimation is a fundamental task in developing effective recommender systems, yet it faces challenges from data sparsity and sample selection bias. To handle both challenges, the entire space multitask models are employed to decompose the user behavior track into a sequence of exposure $\rightarrow$ click $\rightarrow$ conversion, constructing surrogate learning tasks for CVR estimation. However, these methods suffer from two significant defects: (1) intrinsic estimation bias (IEB), where the CVR estimates are higher than the actual values; (2) false independence prior (FIP), where the causal relationship between clicks and subsequent conversions is potentially overlooked. To overcome these limitations, we develop a model-agnostic framework, namely Entire Space Counterfactual Multitask Model (ESCM$^2$), which incorporates a counterfactual risk minimizer within the ESMM framework to regularize CVR estimation. Experiments conducted on large-scale industrial recommendation datasets and an online industrial recommendation service demonstrate that ESCM$^2$ effectively mitigates IEB and FIP defects and substantially enhances recommendation performance.
Fonte: arXiv stat.ML
RL • Score 85
Quality Over Clicks: Intrinsic Quality-Driven Iterative Reinforcement Learning for Cold-Start E-Commerce Query Suggestion
arXiv:2603.22922v1 Announce Type: new
Abstract: Existing dialogue systems rely on Query Suggestion (QS) to enhance user engagement. Recent efforts typically employ large language models with Click-Through Rate (CTR) model, yet fail in cold-start scenarios due to their heavy reliance on abundant online click data for effective CTR model training. To bridge this gap, we propose Cold-EQS, an iterative reinforcement learning framework for Cold-Start E-commerce Query Suggestion (EQS). Specifically, we leverage answerability, factuality, and information gain as reward to continuously optimize the quality of suggested queries. To continuously optimize our QS model, we estimate uncertainty for grouped candidate suggested queries to select hard and ambiguous samples from online user queries lacking click signals. In addition, we provide an EQS-Benchmark comprising 16,949 online user queries for offline training and evaluation. Extensive offline and online experiments consistently demonstrate a strong positive correlation between online and offline effectiveness. Both offline and online experimental results demonstrate the superiority of our Cold-EQS, achieving a significant +6.81% improvement in online chatUV.
Fonte: arXiv cs.CL
RecSys • Score 85
Research on Individual Trait Clustering and Development Pathway Adaptation Based on the K-means Algorithm
arXiv:2603.22302v1 Announce Type: new
Abstract: With the development of information technology, the application of artificial intelligence and machine learning in the field of education shows great potential. This study aims to explore how to utilize K-means clustering algorithm to provide accurate career guidance for college students. Existing methods mostly focus on the prediction of career paths, but there are fewer studies on the fitness of students with different combinations of characteristics in specific career directions. In this study, we analyze the data of more than 3000 students on their CET-4 scores, GPA, personality traits and student cadre experiences, and use the K-means clustering algorithm to classify the students into four main groups. The K-means clustering algorithm groups students with similar characteristics into one group by minimizing the intra-cluster squared error, ensuring that the students within the same cluster are highly similar in their characteristics, and that differences between different clusters are maximized. Based on the clustering results, targeted career guidance suggestions are provided for each group. The results of the study show that students with different combinations of characteristics are suitable for different career directions, which provides a scientific basis for personalized career guidance and effectively enhances students' employment success rate. Future research can further improve the precision of clustering and the guidance effect by expanding the sample size, increasing the feature variables and considering external factors.
Fonte: arXiv cs.LG
NLP/LLMs • Score 85
A Multihead Continual Learning Framework for Fine-Grained Fashion Image Retrieval with Contrastive Learning and Exponential Moving Average Distillation
arXiv:2603.20648v1 Announce Type: new
Abstract: Most fine-grained fashion image retrieval (FIR) methods assume a static setting, requiring full retraining when new attributes appear, which is costly and impractical for dynamic scenarios. Although pretrained models support zero-shot inference, their accuracy drops without supervision, and no prior work explores class-incremental learning (CIL) for fine-grained FIR. We propose a multihead continual learning framework for fine-grained fashion image retrieval with contrastive learning and exponential moving average (EMA) distillation (MCL-FIR). MCL-FIR adopts a multi-head design to accommodate evolving classes across increments, reformulates triplet inputs into doublets with InfoNCE for simpler and more effective training, and employs EMA distillation for efficient knowledge transfer. Experiments across four datasets demonstrate that, beyond its scalability, MCL-FIR achieves a strong balance between efficiency and accuracy. It significantly outperforms CIL baselines under similar training cost, and compared with static methods, it delivers comparable performance while using only about 30% of the training cost. The source code is publicly available in https://github.com/Dr-LingXiao/MCL-FIR.
Fonte: arXiv cs.CV
NLP/LLMs • Score 85
FAAR: Efficient Frequency-Aware Multi-Task Fine-Tuning via Automatic Rank Selection
arXiv:2603.20403v1 Announce Type: new
Abstract: Adapting models pre-trained on large-scale datasets is a proven way to reach strong performance quickly for down-stream tasks. However, the growth of state-of-the-art mod-els makes traditional full fine-tuning unsuitable and difficult, especially for multi-task learning (MTL) where cost scales with the number of tasks. As a result, recent studies investigate parameter-efficient fine-tuning (PEFT) using low-rank adaptation to significantly reduce the number of trainable parameters. However, these existing methods use a single, fixed rank, which may not be optimal for differ-ent tasks or positions in the MTL architecture. Moreover, these methods fail to learn spatial information that cap-tures inter-task relationships and helps to improve diverse task predictions. This paper introduces Frequency-Aware and Automatic Rank (FAAR) for efficient MTL fine-tuning. Our method introduces Performance-Driven Rank Shrink-ing (PDRS) to allocate the optimal rank per adapter location and per task. Moreover, by analyzing the image frequency spectrum, FAAR proposes a Task-Spectral Pyramidal Decoder (TS-PD) that injects input-specific context into spatial bias learning to better reflect cross-task relationships. Experiments performed on dense visual task benchmarks show the superiority of our method in terms of both accuracy and efficiency compared to other PEFT methods in MTL. FAAR reduces the number of parameters by up to 9 times compared to traditional MTL fine-tuning whilst improving overall performance. Our code is available.
Fonte: arXiv cs.CV
RecSys • Score 85
A Job I Like or a Job I Can Get: Designing Job Recommender Systems Using Field Experiments
arXiv:2603.21699v1 Announce Type: cross
Abstract: Recommendation systems (RSs) are increasingly used to guide job seekers on online platforms, yet the algorithms currently deployed are typically optimized for predictive objectives such as clicks, applications, or hires, rather than job seekers' welfare. We develop a job-search model with an application stage in which the value of a vacancy depends on two dimensions: the utility it delivers to the worker and the probability that an application succeeds. The model implies that welfare-optimal RSs rank vacancies by an expected-surplus index combining both, and shows why rankings based solely on utility, hiring probabilities, or observed application behavior are generically suboptimal, an instance of the inversion problem between behavior and welfare. We test these predictions and quantify their practical importance through two randomized field experiments conducted with the French public employment service. The first experiment, comparing existing algorithms and their combinations, provides behavioral evidence that both dimensions shape application decisions. Guided by the model and these results, the second experiment extends the comparison to an RS designed to approximate the welfare-optimal ranking. The experiments generate exogenous variation in the vacancies shown to job seekers, allowing us to estimate the model, validate its behavioral predictions, and construct a welfare metric. Algorithms informed by the model-implied optimal ranking substantially outperform existing approaches and perform close to the welfare-optimal benchmark. Our results show that embedding predictive tools within a simple job-search framework and combining it with experimental evidence yields recommendation rules with substantial welfare gains in practice.
Fonte: arXiv stat.ML
RL • Score 85
Stochastic Sequential Decision Making over Expanding Networks with Graph Filtering
arXiv:2603.19501v1 Announce Type: new
Abstract: Graph filters leverage topological information to process networked data with existing methods mainly studying fixed graphs, ignoring that graphs often expand as nodes continually attach with an unknown pattern. The latter requires developing filter-based decision-making paradigms that take evolution and uncertainty into account. Existing approaches rely on either pre-designed filters or online learning, limited to a myopic view considering only past or present information. To account for future impacts, we propose a stochastic sequential decision-making framework for filtering networked data with a policy that adapts filtering to expanding graphs. By representing filter shifts as agents, we model the filter as a multi-agent system and train the policy following multi-agent reinforcement learning. This accounts for long-term rewards and captures expansion dynamics through sequential decision-making. Moreover, we develop a context-aware graph neural network to parameterize the policy, which tunes filter parameters based on information of both the graph and agents. Experiments on synthetic and real datasets from cold-start recommendation to COVID prediction highlight the benefits of using a sequential decision-making perspective over batch and online filtering alternatives.
Fonte: arXiv cs.LG
RecSys • Score 85
MSNet and LS-Net: Scalable Multi-Scale Multi-Representation Networks for Time Series Classification
arXiv:2603.19315v1 Announce Type: new
Abstract: Time series classification (TSC) performance depends not only on architectural design but also on the diversity of input representations. In this work, we propose a scalable multi-scale convolutional framework that systematically integrates structured multi-representation inputs for univariate time series.
We introduce two architectures: MSNet, a hierarchical multi-scale convolutional network optimized for robustness and calibration, and LS-Net, a lightweight variant designed for efficiency-aware deployment. In addition, we adapt LiteMV -- originally developed for multivariate inputs -- to operate on multi-representation univariate signals, enabling cross-representation interaction.
We evaluate all models across 142 benchmark datasets under a unified experimental protocol. Critical Difference analysis confirms statistically significant performance differences among the top models. Results show that LiteMV achieves the highest mean accuracy, MSNet provides superior probabilistic calibration (lowest NLL), and LS-Net offers the best efficiency-accuracy tradeoff. Pareto analysis further demonstrates that multi-representation multi-scale modeling yields a flexible design space that can be tuned for accuracy-oriented, calibration-oriented, or resource-constrained settings.
These findings establish scalable multi-representation multi-scale learning as a principled and practical direction for modern TSC. Reference implementation of MSNet and LS-Net is available at: https://github.com/alagoz/msnet-lsnet-tsc
Fonte: arXiv cs.LG
RecSys • Score 85
DIAL-KG: Schema-Free Incremental Knowledge Graph Construction via Dynamic Schema Induction and Evolution-Intent Assessment
arXiv:2603.20059v1 Announce Type: new
Abstract: Knowledge Graphs (KGs) are foundational to applications such as search, question answering, and recommendation. Conventional knowledge graph construction methods are predominantly static, rely ing on a single-step construction from a fixed corpus with a prede f
ined schema. However, such methods are suboptimal for real-world sce narios where data arrives dynamically, as incorporating new informa tion requires complete and computationally expensive graph reconstruc tions. Furthermore, predefined schemas hinder the flexibility of knowl edge graph construction. To address these limitations, we introduce DIAL KG, a closed-loop framework for incremental KG construction orches trated by a Meta-Knowledge Base (MKB). The framework oper ates in a three-stage cycle: (i) Dual-Track Extraction, which ensures knowledge completeness by defaulting to triple generation and switching to event extraction for complex knowledge; (ii) Governance Adjudica tion, which ensures the fidelity and currency of extracted facts to prevent hallucinations and knowledge staleness; and (iii) Schema Evolution, in which new schemas are induced from validated knowledge to guide subsequent construction cycles, and knowledge from the current round is incrementally applied to the existing KG. Extensive experiments demon strate that our framework achieves state-of-the-art (SOTA) performance in the quality of both the constructed graph and the induced schemas.
Fonte: arXiv cs.AI
RecSys • Score 85
Interplay: Training Independent Simulators for Reference-Free Conversational Recommendation
arXiv:2603.18573v1 Announce Type: new
Abstract: Training conversational recommender systems (CRS) requires extensive dialogue data, which is challenging to collect at scale. To address this, researchers have used simulated user-recommender conversations. Traditional simulation approaches often utilize a single large language model (LLM) that generates entire conversations with prior knowledge of the target items, leading to scripted and artificial dialogues. We propose a reference-free simulation framework that trains two independent LLMs, one as the user and one as the conversational recommender. These models interact in real-time without access to predetermined target items, but preference summaries and target attributes, enabling the recommender to genuinely infer user preferences through dialogue. This approach produces more realistic and diverse conversations that closely mirror authentic human-AI interactions. Our reference-free simulators match or exceed existing methods in quality, while offering a scalable solution for generating high-quality conversational recommendation data without constraining conversations to pre-defined target items. We conduct both quantitative and human evaluations to confirm the effectiveness of our reference-free approach.
Fonte: arXiv cs.AI
NLP/LLMs • Score 85
From Digital Twins to World Models:Opportunities, Challenges, and Applications for Mobile Edge General Intelligence
arXiv:2603.17420v1 Announce Type: new
Abstract: The rapid evolution toward 6G and beyond communication systems is accelerating the convergence of digital twins and world models at the network edge. Traditional digital twins provide high-fidelity representations of physical systems and support monitoring, analysis, and offline optimization. However, in highly dynamic edge environments, they face limitations in autonomy, adaptability, and scalability. This paper presents a systematic survey of the transition from digital twins to world models and discusses its role in enabling edge general intelligence (EGI). First, the paper clarifies the conceptual differences between digital twins and world models and highlights the shift from physics-based, centralized, and system-centric replicas to data-driven, decentralized, and agent-centric internal models. This discussion helps readers gain a clear understanding of how this transition enables more adaptive, autonomous, and resource-efficient intelligence at the network edge. The paper reviews the design principles, architectures, and key components of world models, including perception, latent state representation, dynamics learning, imagination-based planning, and memory. In addition, it examines the integration of world models and digital twins in wireless EGI systems and surveys emerging applications in integrated sensing and communications, semantic communication, air-ground networks, and low-altitude wireless networks. Finally, this survey provides a systematic roadmap and practical insights for designing world-model-driven edge intelligence systems in wireless and edge computing environments. It also outlines key research challenges and future directions toward scalable, reliable, and interoperable world models for edge-native agentic AI.
Fonte: arXiv cs.AI
RecSys • Score 85
Contextual Preference Distribution Learning
arXiv:2603.17139v1 Announce Type: new
Abstract: Decision-making problems often feature uncertainty stemming from heterogeneous and context-dependent human preferences. To address this, we propose a sequential learning-and-optimization pipeline to learn preference distributions and leverage them to solve downstream problems, for example risk-averse formulations. We focus on human choice settings that can be formulated as (integer) linear programs. In such settings, existing inverse optimization and choice modelling methods infer preferences from observed choices but typically produce point estimates or fail to capture contextual shifts, making them unsuitable for risk-averse decision-making. Using a bounded-variance score function gradient estimator, we train a predictive model mapping contextual features to a rich class of parameterizable distributions. This approach yields a maximum likelihood estimate. The model generates scenarios for unseen contexts in the subsequent optimization phase. In a synthetic ridesharing environment, our approach reduces average post-decision surprise by up to 114$\times$ compared to a risk-neutral approach with perfect predictions and up to 25$\times$ compared to leading risk-averse baselines.
Fonte: arXiv cs.LG
NLP/LLMs • Score 85
Graph2Video: Leveraging Video Models to Model Dynamic Graph Evolution
arXiv:2603.13360v1 Announce Type: new
Abstract: Dynamic graphs are common in real-world systems such as social media, recommender systems, and traffic networks. Existing dynamic graph models for link prediction often fall short in capturing the complexity of temporal evolution. They tend to overlook fine-grained variations in temporal interaction order, struggle with dependencies that span long time horizons, and offer limited capability to model pair-specific relational dynamics. To address these challenges, we propose \textbf{Graph2Video}, a video-inspired framework that views the temporal neighborhood of a target link as a sequence of "graph frames". By stacking temporally ordered subgraph frames into a "graph video", Graph2Video leverages the inductive biases of video foundation models to capture both fine-grained local variations and long-range temporal dynamics. It generates a link-level embedding that serves as a lightweight and plug-and-play link-centric memory unit. This embedding integrates seamlessly into existing dynamic graph encoders, effectively addressing the limitations of prior approaches. Extensive experiments on benchmark datasets show that Graph2Video outperforms state-of-the-art baselines on the link prediction task in most cases. The results highlight the potential of borrowing spatio-temporal modeling techniques from computer vision as a promising and effective approach for advancing dynamic graph learning.
Fonte: arXiv cs.CV
RecSys • Score 85
EmDT: Embedding Diffusion Transformer for Tabular Data Generation in Fraud Detection
arXiv:2603.13566v1 Announce Type: new
Abstract: Imbalanced datasets pose a difficulty in fraud detection, as classifiers are often biased toward the majority class and perform poorly on rare fraudulent transactions. Synthetic data generation is therefore commonly used to mitigate this problem. In this work, we propose the Clustered Embedding Diffusion-Transformer (EmDT), a diffusion model designed to generate fraudulent samples. Our key innovation is to leverage UMAP clustering to identify distinct fraudulent patterns, and train a Transformer denoising network with sinusoidal positional embeddings to capture feature relationships throughout the diffusion process. Once the synthetic data has been generated, we employ a standard decision-tree-based classifier (e.g., XGBoost) for classification, as this type of model remains better suited to tabular datasets. Experiments on a credit card fraud detection dataset demonstrate that EmDT significantly improves downstream classification performance compared to existing oversampling and generative methods, while maintaining comparable privacy protection and preserving feature correlations present in the original data.
Fonte: arXiv stat.ML
NLP/LLMs • Score 85
GraphVLM: Benchmarking Vision Language Models for Multimodal Graph Learning
arXiv:2603.13370v1 Announce Type: new
Abstract: Vision-Language Models (VLMs) have demonstrated remarkable capabilities in aligning and understanding multimodal signals, yet their potential to reason over structured data, where multimodal entities are connected through explicit relational graphs, remains largely underexplored. Unlocking this capability is crucial for real-world applications such as social networks, recommendation systems, and scientific discovery, where multimodal information is inherently structured. To bridge this gap, we present GraphVLM, a systematic benchmark designed to evaluate and harness the capabilities of VLMs for multimodal graph learning (MMGL). GraphVLM investigates three complementary paradigms for integrating VLMs with graph reasoning: (1) VLM-as-Encoder, which enriches graph neural networks through multimodal feature fusion; (2) VLM-as-Aligner, which bridges modalities in latent or linguistic space to facilitate LLM-based structured reasoning; and (3) VLM-as-Predictor, which directly employs VLMs as multimodal backbones for graph learning tasks. Extensive experiments across six datasets from diverse domains demonstrate that VLMs enhance multimodal graph learning via all three roles. Among these paradigms, VLM-as-Predictor achieves the most substantial and consistent performance gains, revealing the untapped potential of vision-language models as a new foundation for multimodal graph learning. The benchmark code is publicly available at https://github.com/oamyjin/GraphVLM.
Fonte: arXiv cs.CV
RecSys • Score 88
Federated Personal Knowledge Graph Completion with Lightweight Large Language Models for Personalized Recommendations
arXiv:2603.13264v1 Announce Type: new
Abstract: Personalized recommendation increasingly relies on private user data, motivating approaches that can adapt to individuals without centralizing their information. We present Federated Targeted Recommendations with Evolving Knowledge graphs and Language Models (FedTREK-LM), a framework that unifies lightweight large language models (LLMs), evolving personal knowledge graphs (PKGs), federated learning (FL), and Kahneman-Tversky Optimization to enable scalable, decentralized personalization. By prompting LLMs with structured PKGs, FedTREK-LM performs context-aware reasoning for personalized recommendation tasks such as movie and recipe suggestions. Across three lightweight Qwen3 models (0.6B, 1.7B, 4B), FedTREK-LM consistently and substantially outperforms state-of-the-art KG completion and federated recommendation baselines (HAKE, KBGAT, and FedKGRec), achieving more than a 4x improvement in F1-score on the movie and food benchmarks. Our results further show that real user data is critical for effective personalization, as synthetic data degrades performance by up to 46%. Overall, FedTREK-LM offers a practical paradigm for adaptive, LLM-powered personalization that generalizes across decentralized, evolving user PKGs.
Fonte: arXiv cs.LG
RecSys • Score 85
AgentDrift: Unsafe Recommendation Drift Under Tool Corruption Hidden by Ranking Metrics in LLM Agents
arXiv:2603.12564v1 Announce Type: new
Abstract: Tool-augmented LLM agents increasingly serve as multi-turn advisors in high-stakes domains, yet their evaluation relies on ranking-quality metrics that measure what is recommended but not whether it is safe for the user. We introduce a paired-trajectory protocol that replays real financial dialogues under clean and contaminated tool-output conditions across seven LLMs (7B to frontier) and decomposes divergence into information-channel and memory-channel mechanisms. Across the seven models tested, we consistently observe the evaluation-blindness pattern: recommendation quality is largely preserved under contamination (utility preservation ratio approximately 1.0) while risk-inappropriate products appear in 65-93% of turns, a systematic safety failure poorly reflected by standard NDCG. Safety violations are predominantly information-channel-driven, emerge at the first contaminated turn, and persist without self-correction over 23-step trajectories; no agent across 1,563 contaminated turns explicitly questions tool-data reliability. Even narrative-only corruption (biased headlines, no numerical manipulation) induces significant drift while completely evading consistency monitors. A safety-penalized NDCG variant (sNDCG) reduces preservation ratios to 0.51-0.74, indicating that much of the evaluation gap becomes visible once safety is explicitly measured. These results motivate considering trajectory-level safety monitoring, beyond single-turn quality, for deployed multi-turn agents in high-stakes settings.
Fonte: arXiv cs.CL
NLP/LLMs • Score 85
Lyapunov Stable Graph Neural Flow
arXiv:2603.12557v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) are highly vulnerable to adversarial perturbations in both topology and features, making the learning of robust representations a critical challenge. In this work, we bridge GNNs with control theory to introduce a novel defense framework grounded in integer- and fractional-order Lyapunov stability. Unlike conventional strategies that rely on resource-heavy adversarial training or data purification, our approach fundamentally constrains the underlying feature-update dynamics of the GNN. We propose an adaptive, learnable Lyapunov function paired with a novel projection mechanism that maps the network's state into a stable space, thereby offering theoretically provable stability guarantees. Notably, this mechanism is orthogonal to existing defenses, allowing for seamless integration with techniques like adversarial training to achieve cumulative robustness. Extensive experiments demonstrate that our Lyapunov-stable graph neural flows substantially outperform base neural flows and state-of-the-art baselines across standard benchmarks and various adversarial attack scenarios.
Fonte: arXiv cs.LG
NLP/LLMs • Score 85
Group Resonance Network: Learnable Prototypes and Multi-Subject Resonance for EEG Emotion Recognition
arXiv:2603.11119v1 Announce Type: new
Abstract: Electroencephalography(EEG)-basedemotionrecognitionre- mains challenging in cross-subject settings due to severe inter-subject variability. Existing methods mainly learn subject-invariant features, but often under-exploit stimulus-locked group regularities shared across sub- jects. To address this issue, we propose the Group Resonance Network (GRN), which integrates individual EEG dynamics with offline group resonance modeling. GRN contains three components: an individual en- coder for band-wise EEG features, a set of learnable group prototypes for prototype-induced resonance, and a multi-subject resonance branch that encodes PLV/coherence-based synchrony with a small reference set. A resonance-aware fusion module combines individual and group-level rep- resentations for final classification. Experiments on SEED and DEAP under both subject-dependent and leave-one-subject-out protocols show that GRN consistently outperforms competitive baselines, while abla- tion studies confirm the complementary benefits of prototype learning and multi-subject resonance modeling.
Fonte: arXiv cs.LG
RecSys • Score 85
Robust Post-Training for Generative Recommenders: Why Exponential Reward-Weighted SFT Outperforms RLHF
arXiv:2603.10279v1 Announce Type: new
Abstract: Aligning generative recommender systems to user preferences via post-training is critical for closing the gap between next-item prediction and actual recommendation quality. Existing post-training methods are ill-suited for production-scale systems: RLHF methods reward hack due to noisy user feedback and unreliable reward models, offline RL alternatives require propensity scores that are unavailable, and online interaction is infeasible. We identify exponential reward-weighted SFT with weights $w = \exp(r/\lambda)$ as uniquely suited to this setting, and provide the theoretical and empirical foundations that explain why. By optimizing directly on observed rewards without querying a learned reward model, the method is immune to reward hacking, requires no propensity scores, and is fully offline. We prove the first policy improvement guarantees for this setting under noisy rewards, showing that the gap scales only logarithmically with catalog size and remains informative even for large item catalogs. Crucially, we show that temperature $\lambda$ explicitly and quantifiably controls the robustness-improvement tradeoff, providing practitioners with a single interpretable regularization hyperparameter with theoretical grounding. Experiments on three open-source and one proprietary dataset against four baselines confirm that exponential reward weighting is simple, scalable, and consistently outperforms RLHF-based alternatives.
Fonte: arXiv cs.LG
NLP/LLMs • Score 85
GaLoRA: Parameter-Efficient Graph-Aware LLMs for Node Classification
arXiv:2603.10298v1 Announce Type: new
Abstract: The rapid rise of large language models (LLMs) and their ability to capture semantic relationships has led to their adoption in a wide range of applications. Text-attributed graphs (TAGs) are a notable example where LLMs can be combined with Graph Neural Networks to improve the performance of node classification. In TAGs, each node is associated with textual content and such graphs are commonly seen in various domains such as social networks, citation graphs, recommendation systems, etc. Effectively learning from TAGs would enable better representations of both structural and textual representations of the graph and improve decision-making in relevant domains. We present GaLoRA, a parameter-efficient framework that integrates structural information into LLMs. GaLoRA demonstrates competitive performance on node classification tasks with TAGs, performing on par with state-of-the-art models with just 0.24% of the parameter count required by full LLM fine-tuning. We experiment with three real-world datasets to showcase GaLoRA's effectiveness in combining structural and semantical information on TAGs.
Fonte: arXiv cs.LG
RL • Score 85
Fairness Begins with State: Purifying Latent Preferences for Hierarchical Reinforcement Learning in Interactive Recommendation
arXiv:2603.03820v1 Announce Type: new
Abstract: Interactive recommender systems (IRS) are increasingly optimized with Reinforcement Learning (RL) to capture the sequential nature of user-system dynamics. However, existing fairness-aware methods often suffer from a fundamental oversight: they assume the observed user state is a faithful representation of true preferences. In reality, implicit feedback is contaminated by popularity-driven noise and exposure bias, creating a distorted state that misleads the RL agent. We argue that the persistent conflict between accuracy and fairness is not merely a reward-shaping issue, but a state estimation failure. In this work, we propose \textbf{DSRM-HRL}, a framework that reformulates fairness-aware recommendation as a latent state purification problem followed by decoupled hierarchical decision-making. We introduce a Denoising State Representation Module (DSRM) based on diffusion models to recover the low-entropy latent preference manifold from high-entropy, noisy interaction histories. Built upon this purified state, a Hierarchical Reinforcement Learning (HRL) agent is employed to decouple conflicting objectives: a high-level policy regulates long-term fairness trajectories, while a low-level policy optimizes short-term engagement under these dynamic constraints. Extensive experiments on high-fidelity simulators (KuaiRec, KuaiRand) demonstrate that DSRM-HRL effectively breaks the "rich-get-richer" feedback loop, achieving a superior Pareto frontier between recommendation utility and exposure equity.
Fonte: arXiv cs.LG
Multimodal • Score 90
PinCLIP: Large-scale Foundational Multimodal Representation at Pinterest
arXiv:2603.03544v1 Announce Type: new
Abstract: While multi-modal Visual Language Models (VLMs) have demonstrated significant success across various domains, the integration of VLMs into recommendation and retrieval systems remains a challenge, due to issues like training objective discrepancies and serving efficiency bottlenecks. This paper introduces PinCLIP, a large-scale visual representation learning approach developed to enhance retrieval and ranking models at Pinterest by leveraging VLMs to learn image-text alignment. We propose a novel hybrid Vision Transformer architecture that utilizes a VLM backbone and a hybrid fusion mechanism to capture multi-modality content representation at varying granularities. Beyond standard image-to-text alignment objectives, we introduce a neighbor alignment objective to model the cross-fusion of multi-modal representations within the Pinterest Pin-Board graph. Offline evaluations show that PinCLIP outperforms state-of-the-art baselines, such as Qwen, by 20% in multi-modal retrieval tasks. Online A/B testing demonstrates significant business impact, including substantial engagement gains across all major surfaces in Pinterest. Notably, PinCLIP significantly addresses the "cold-start" problem, enhancing fresh content distribution with a 15% Repin increase in organic content and 8.7% higher click for new Ads.
Fonte: arXiv cs.CV
RecSys • Score 85
Combating data scarcity in recommendation services: Integrating cognitive types of VARK and neural network technologies (LLM)
arXiv:2603.03309v1 Announce Type: new
Abstract: Cold start scenarios present fundamental obstacles to effective recommendation generation, particularly when dealing with users lacking interaction history or items with sparse metadata. This research proposes an innovative hybrid framework that leverages Large Language Models (LLMs) for content semantic analysis and knowledge graph development, integrated with cognitive profiling based on VARK (Visual, Auditory, Reading/Writing, Kinesthetic) learning preferences. The proposed system tackles multiple cold start dimensions: enriching inadequate item descriptions through LLM processing, generating user profiles from minimal data, and dynamically adjusting presentation formats based on cognitive assessment. The framework comprises six integrated components: semantic metadata enhancement, dynamic graph construction, VARK-based profiling, mental state estimation, graph-enhanced retrieval with LLM-powered ranking, and adaptive interface design with iterative learning. Experimental validation on MovieLens-1M dataset demonstrates the system's capacity for personalized recommendation generation despite limited initial information. This work establishes groundwork for cognitively-aware recommendation systems capable of overcoming cold start limitations through semantic comprehension and psychological modeling, offering personalized, explainable recommendations from initial user contact.
Fonte: arXiv cs.CL
NLP/LLMs • Score 85
Bayesian Generative Adversarial Networks via Gaussian Approximation for Tabular Data Synthesis
arXiv:2602.21948v1 Announce Type: cross
Abstract: Generative Adversarial Networks (GAN) have been used in many studies to synthesise mixed tabular data. Conditional tabular GAN (CTGAN) have been the most popular variant but struggle to effectively navigate the risk-utility trade-off. Bayesian GAN have received less attention for tabular data, but have been explored with unstructured data such as images and text. The most used technique employed in Bayesian GAN is Markov Chain Monte Carlo (MCMC), but it is computationally intensive, particularly in terms of weight storage. In this paper, we introduce Gaussian Approximation of CTGAN (GACTGAN), an integration of the Bayesian posterior approximation technique using Stochastic Weight Averaging-Gaussian (SWAG) within the CTGAN generator to synthesise tabular data, reducing computational overhead after the training phase. We demonstrate that GACTGAN yields better synthetic data compared to CTGAN, achieving better preservation of tabular structure and inferential statistics with less privacy risk. These results highlight GACTGAN as a simpler, effective implementation of Bayesian tabular synthesis.
Fonte: arXiv stat.ML
NLP/LLMs • Score 85
Urban Vibrancy Embedding and Application on Traffic Prediction
arXiv:2602.21232v1 Announce Type: cross
Abstract: Urban vibrancy reflects the dynamic human activity within urban spaces and is often measured using mobile data that captures floating population trends. This study proposes a novel approach to derive Urban Vibrancy embeddings from real-time floating population data to enhance traffic prediction models. Specifically, we utilize variational autoencoders (VAE) to compress this data into actionable embeddings, which are then integrated with long short-term memory (LSTM) networks to predict future embeddings. These are subsequently applied in a sequence-to-sequence framework for traffic forecasting. Our contributions are threefold: (1) We use principal component analysis (PCA) to interpret the embeddings, revealing temporal patterns such as weekday versus weekend distinctions and seasonal patterns; (2) We propose a method that combines VAE and LSTM, enabling forecasting dynamic urban knowledge embedding; and (3) Our approach improves accuracy and responsiveness in traffic prediction models, including RNN, DCRNN, GTS, and GMAN. This study demonstrates the potential of Urban Vibrancy embeddings to advance traffic prediction and offer a more nuanced analysis of urban mobility.
Fonte: arXiv cs.AI
RecSys • Score 85
Multi-Behavior Sequential Modeling with Transition-Aware Graph Attention Network for E-Commerce Recommendation
arXiv:2601.14955v1 Announce Type: new
Abstract: User interactions on e-commerce platforms are inherently diverse, involving behaviors such as clicking, favoriting, adding to cart, and purchasing. The transitions between these behaviors offer valuable insights into user-item interactions, serving as a key signal for understanding evolving preferences. Consequently, there is growing interest in leveraging multi-behavior data to better capture user intent. Recent studies have explored sequential modeling of multi-behavior data, many relying on transformer-based architectures with polynomial time complexity. While effective, these approaches often incur high computational costs, limiting their applicability in large-scale industrial systems with long user sequences. To address this challenge, we propose the Transition-Aware Graph Attention Network (TGA), a linear-complexity approach for modeling multi-behavior transitions. Unlike traditional transformers that treat all behavior pairs equally, TGA constructs a structured sparse graph by identifying informative transitions from three perspectives: (a) item-level transitions, (b) category-level transitions, and (c) neighbor-level transitions. Built upon the structured graph, TGA employs a transition-aware graph Attention mechanism that jointly models user-item interactions and behavior transition types, enabling more accurate capture of sequential patterns while maintaining computational efficiency. Experiments show that TGA outperforms all state-of-the-art models while significantly reducing computational cost. Notably, TGA has been deployed in a large-scale industrial production environment, where it leads to impressive improvements in key business metrics.
Fonte: arXiv cs.AI
NLP/LLMs • Score 90
Compass-Embedding v4: Robust Contrastive Learning for Multilingual E-commerce Embeddings
arXiv:2601.11565v1 Announce Type: new
Abstract: As global e-commerce rapidly expands into emerging markets, the lack of high-quality semantic representations for low-resource languages has become a decisive bottleneck for retrieval, recommendation, and search systems. In this work, we present Compass-Embedding v4, a high-efficiency multilingual embedding framework specifically optimized for Southeast Asian (SEA) e-commerce scenarios, where data scarcity, noisy supervision, and strict production constraints jointly challenge representation learning. Compass-Embedding v4 addresses three core challenges. First, large-batch contrastive training under mixed task supervision introduces systematic false negatives that degrade semantic alignment. We propose Class-Aware Masking (CAM), a lightweight modification to the InfoNCE objective that suppresses invalid in-batch negatives and improves semantic discrimination without altering training efficiency. Second, low-resource SEA languages suffer from limited and uneven data coverage. We construct a diversified training corpus through context-grounded synthetic data generation, cross-lingual translation, and structured e-commerce data construction, enabling robust multilingual and domain-specific learning. Third, production deployment requires high-throughput inference while preserving embedding quality. We combine robustness-driven large-batch training with spherical model merging to mitigate catastrophic forgetting, and optimize inference via vLLM and FP8 quantization. Extensive evaluations across multilingual benchmarks and proprietary e-commerce tasks show that Compass-Embedding v4 achieves state-of-the-art performance on major SEA languages, significantly outperforming general-purpose embedding models in domain-specific retrieval and classification, while maintaining competitive performance on high-resource languages.
Fonte: arXiv cs.CL
NLP/LLMs • Score 85
Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization
arXiv:2601.12078v1 Announce Type: new
Abstract: Large Language Models (LLMs) excel at general-purpose tasks, yet adapting their responses to individual users remains challenging. Retrieval augmentation provides a lightweight alternative to fine-tuning by conditioning LLMs on user history records, and existing approaches typically select these records based on semantic relevance. We argue that relevance serves as an unreliable proxy for utility: a record may be semantically similar to a query yet fail to improve generation quality or even degrade it due to redundancy or conflicting information. To bridge this gap, we propose PURPLE, a contextual bandit framework that oPtimizes UseR Profiles for Llm pErsonalization. In contrast to a greedy selection of the most relevant records, PURPLE treats profile construction as a set generation process and utilizes a Plackett-Luce ranking model to capture complex inter-record dependencies. By training with dense feedback provided by the likelihood of the reference response, our method aligns retrieval directly with generation quality. Extensive experiments on nine personalization tasks demonstrate that PURPLE consistently outperforms strong heuristic and retrieval-augmented baselines in both effectiveness and efficiency, establishing a principled and scalable solution for optimizing user profiles.
Fonte: arXiv cs.CL
RecSys • Score 85
PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation
arXiv:2601.09771v1 Announce Type: new
Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together with a structured certificate (JSON) describing the claimed constraint satisfaction. A deterministic verifier recomputes all constraints from the slate and accepts only verifier-checked certificates; if verification fails, a deterministic constrained-greedy repair produces a compliant slate for re-verification, yielding an auditable trace. On MovieLens-100K with governance constraints, PCN-Rec achieves a 98.55% pass rate on feasible users (n = 551, W = 80) versus a one-shot single-LLM baseline without verification/repair, while preserving utility with only a 0.021 absolute drop in NDCG@10 (0.403 vs. 0.424); differences are statistically significant (p < 0.05).
Fonte: arXiv cs.AI
NLP/LLMs • Score 85
Owen-Shapley Policy Optimization (OSPO): A Principled RL Algorithm for Generative Search LLMs
arXiv:2601.08403v1 Announce Type: new
Abstract: Large language models are increasingly trained via reinforcement learning for personalized recommendation tasks, but standard methods like GRPO rely on sparse, sequence-level rewards that create a credit assignment gap, obscuring which tokens drive success. This gap is especially problematic when models must infer latent user intent from under-specified language without ground truth labels, a reasoning pattern rarely seen during pretraining. We introduce Owen-Shapley Policy Optimization (OSPO), a framework that redistributes sequence-level advantages based on tokens' marginal contributions to outcomes. Unlike value-model-based methods requiring additional computation, OSPO employs potential-based reward shaping via Shapley-Owen attributions to assign segment-level credit while preserving the optimal policy, learning directly from task feedback without parametric value models. By forming coalitions of semantically coherent units (phrases describing product attributes or sentences capturing preferences), OSPO identifies which response parts drive performance. Experiments on Amazon ESCI and H&M Fashion datasets show consistent gains over baselines, with notable test-time robustness to out-of-distribution retrievers unseen during training.
Fonte: arXiv cs.AI
RecSys • Score 85
A Causal Information-Flow Framework for Unbiased Learning-to-Rank
arXiv:2601.05590v1 Announce Type: new
Abstract: In web search and recommendation systems, user clicks are widely used to train ranking models. However, click data is heavily biased, i.e., users tend to click higher-ranked items (position bias), choose only what was shown to them (selection bias), and trust top results more (trust bias). Without explicitly modeling these biases, the true relevance of ranked items cannot be correctly learned from clicks. Existing Unbiased Learning-to-Rank (ULTR) methods mainly correct position bias and rely on propensity estimation, but they cannot measure remaining bias, provide risk guarantees, or jointly handle multiple bias sources. To overcome these challenges, this paper introduces a novel causal learning-based ranking framework that extends ULTR by combining Structural Causal Models (SCMs) with information-theoretic tools. SCMs specify how clicks are generated and help identify the true relevance signal from click data, while conditional mutual information, measures how much bias leaks into the
learned relevance estimates. We use this leakage measure to define a rigorous notion of disentanglement and include it as a regularizer during model training to reduce bias. In addition, we incorporate a causal inference estimator, i.e., doubly robust estimator, to ensure more reliable risk estimation. Experiments on standard Learning-to-Rank benchmarks show that our method consistently reduces measured bias leakage and improves ranking performance, especially in realistic scenarios where multiple biases-such as position and trust bias-interact strongly.
Fonte: arXiv cs.AI
NLP/LLMs • Score 85
KP-Agent: Keyword Pruning in Sponsored Search Advertising via LLM-Powered Contextual Bandits
arXiv:2601.05257v1 Announce Type: cross
Abstract: Sponsored search advertising (SSA) requires advertisers to constantly adjust keyword strategies. While bid adjustment and keyword generation are well-studied, keyword pruning-refining keyword sets to enhance campaign performance-remains under-explored. This paper addresses critical inefficiencies in current practices as evidenced by a dataset containing 0.5 million SSA records from a pharmaceutical advertiser on search engine Meituan, China's largest delivery platform. We propose KP-Agent, an LLM agentic system with domain tool set and a memory module. By modeling keyword pruning within a contextual bandit framework, KP-Agent generates code snippets to refine keyword sets through reinforcement learning. Experiments show KP-Agent improves cumulative profit by up to 49.28% over baselines.
Fonte: arXiv cs.AI
RecSys • Score 85
SP-Rank: A Dataset for Ranked Preferences with Secondary Information
arXiv:2601.05253v1 Announce Type: cross
Abstract: We introduce $\mathbf{SP-Rank}$, the first large-scale, publicly available dataset for benchmarking algorithms that leverage both first-order preferences and second-order predictions in ranking tasks. Each datapoint includes a personal vote (first-order signal) and a meta-prediction of how others will vote (second-order signal), allowing richer modeling than traditional datasets that capture only individual preferences. SP-Rank contains over 12,000 human-generated datapoints across three domains -- geography, movies, and paintings, and spans nine elicitation formats with varying subset sizes. This structure enables empirical analysis of preference aggregation when expert identities are unknown but presumed to exist, and individual votes represent noisy estimates of a shared ground-truth ranking. We benchmark SP-Rank by comparing traditional aggregation methods that use only first-order votes against SP-Voting, a second-order method that jointly reasons over both signals to infer ground-truth rankings. While SP-Rank also supports models that rely solely on second-order predictions, our benchmarks emphasize the gains from combining both signals. We evaluate performance across three core tasks: (1) full ground-truth rank recovery, (2) subset-level rank recovery, and (3) probabilistic modeling of voter behavior. Results show that incorporating second-order signals substantially improves accuracy over vote-only methods. Beyond social choice, SP-Rank supports downstream applications in learning-to-rank, extracting expert knowledge from noisy crowds, and training reward models in preference-based fine-tuning pipelines. We release the dataset, code, and baseline evaluations (available at https://github.com/amrit19/SP-Rank-Dataset ) to foster research in human preference modeling, aggregation theory, and human-AI alignment.
Fonte: arXiv cs.AI
RL • Score 95
TeleDoCTR: Domain-Specific and Contextual Troubleshooting for Telecommunications
arXiv:2601.00691v1 Announce Type: cross
Abstract: Ticket troubleshooting refers to the process of analyzing and resolving problems that are reported through a ticketing system. In large organizations offering a wide range of services, this task is highly complex due to the diversity of submitted tickets and the need for specialized domain knowledge. In particular, troubleshooting in telecommunications (telecom) is a very time-consuming task as it requires experts to interpret ticket content, consult documentation, and search historical records to identify appropriate resolutions. This human-intensive approach not only delays issue resolution but also hinders overall operational efficiency. To enhance the effectiveness and efficiency of ticket troubleshooting in telecom, we propose TeleDoCTR, a novel telecom-related, domain-specific, and contextual troubleshooting system tailored for end-to-end ticket resolution in telecom. TeleDoCTR integrates both domain-specific ranking and generative models to automate key steps of the troubleshooting workflow which are: routing tickets to the appropriate expert team responsible for resolving the ticket (classification task), retrieving contextually and semantically similar historical tickets (retrieval task), and generating a detailed fault analysis report outlining the issue, root cause, and potential solutions (generation task). We evaluate TeleDoCTR on a real-world dataset from a telecom infrastructure and demonstrate that it achieves superior performance over existing state-of-the-art methods, significantly enhancing the accuracy and efficiency of the troubleshooting process.
Fonte: arXiv cs.CL
RL • Score 96
Métodos Semânticos Podem Aprimorar Táticas em Esportes Coletivos? Uma Metodologia para Futebol com Aplicações Mais Amplas
Este artigo explora como o raciocínio em espaço semântico, tradicionalmente utilizado em linguística computacional, pode ser estendido à tomada de decisão tática em esportes coletivos. A metodologia proposta modela configurações táticas como estruturas semânticas composicionais, representando cada jogador como um vetor multidimensional que integra atributos técnicos, físicos e psicológicos.
Fonte: arXiv cs.AI
Vision • Score 95
Context-Aware Pesticide Recommendation via Few-Shot Pest Recognition for Precision Agriculture
arXiv:2601.00243v1 Announce Type: new
Abstract: Effective pest management is crucial for enhancing agricultural productivity, especially for crops such as sugarcane and wheat that are highly vulnerable to pest infestations. Traditional pest management methods depend heavily on manual field inspections and the use of chemical pesticides. These approaches are often costly, time-consuming, labor-intensive, and can have a negative impact on the environment. To overcome these challenges, this study presents a lightweight framework for pest detection and pesticide recommendation, designed for low-resource devices such as smartphones and drones, making it suitable for use by small and marginal farmers.
The proposed framework includes two main components. The first is a Pest Detection Module that uses a compact, lightweight convolutional neural network (CNN) combined with prototypical meta-learning to accurately identify pests even when only a few training samples are available. The second is a Pesticide Recommendation Module that incorporates environmental factors like crop type and growth stage to suggest safe and eco-friendly pesticide recommendations. To train and evaluate our framework, a comprehensive pest image dataset was developed by combining multiple publicly available datasets. The final dataset contains samples with different viewing angles, pest sizes, and background conditions to ensure strong generalization.
Experimental results show that the proposed lightweight CNN achieves high accuracy, comparable to state-of-the-art models, while significantly reducing computational complexity. The Decision Support System additionally improves pest management by reducing dependence on traditional chemical pesticides and encouraging sustainable practices, demonstrating its potential for real-time applications in precision agriculture.
Fonte: arXiv cs.CV
NLP/LLMs • Score 95
From Evidence-Based Medicine to Knowledge Graph: Retrieval-Augmented Generation for Sports Rehabilitation and a Domain Benchmark
arXiv:2601.00216v1 Announce Type: new
Abstract: In medicine, large language models (LLMs) increasingly rely on retrieval-augmented generation (RAG) to ground outputs in up-to-date external evidence. However, current RAG approaches focus primarily on performance improvements while overlooking evidence-based medicine (EBM) principles. This study addresses two key gaps: (1) the lack of PICO alignment between queries and retrieved evidence, and (2) the absence of evidence hierarchy considerations during reranking. We present a generalizable strategy for adapting EBM to graph-based RAG, integrating the PICO framework into knowledge graph construction and retrieval, and proposing a Bayesian-inspired reranking algorithm to calibrate ranking scores by evidence grade without introducing predefined weights. We validated this framework in sports rehabilitation, a literature-rich domain currently lacking RAG systems and benchmarks. We released a knowledge graph (357,844 nodes and 371,226 edges) and a reusable benchmark of 1,637 QA pairs. The system achieved 0.830 nugget coverage, 0.819 answer faithfulness, 0.882 semantic similarity, and 0.788 PICOT match accuracy. In a 5-point Likert evaluation, five expert clinicians rated the system 4.66-4.84 across factual accuracy, faithfulness, relevance, safety, and PICO alignment. These findings demonstrate that the proposed EBM adaptation strategy improves retrieval and answer quality and is transferable to other clinical domains. The released resources also help address the scarcity of RAG datasets in sports rehabilitation.
Fonte: arXiv cs.CL
Vision • Score 95
Bandidos Contextuais Aditivos Esparsos: Uma Abordagem Não Paramétrica para Tomada de Decisão Online com Covariáveis de Alta Dimensionalidade
Serviços personalizados são centrais para a economia digital atual, e suas decisões sequenciais são frequentemente modeladas como bandidos contextuais. Aplicações modernas enfrentam dois desafios principais: covariáveis de alta dimensionalidade e a necessidade de modelos não paramétricos para capturar relações complexas entre recompensa e covariáveis. Propomos um algoritmo de bandido contextual baseado em um modelo de recompensa aditiva esparsa que aborda ambos os desafios.
Fonte: arXiv stat.ML
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
FC-MIR: Um Framework de Consciência de Tela Móvel para Recomendação Consciente de Intenção Baseada em Raciocínio Multimodal de Trajetória Comprimida por Frame
Identificar a intenção do usuário a partir de trajetórias de operação da interface móvel é crucial para avançar na compreensão da UI e habilitar agentes de automação de tarefas. Propomos o framework FC-MIR, que utiliza amostragem de keyframes e concatenação adaptativa para reduzir a redundância visual e aumentar a eficiência da inferência, integrando MLLMs de última geração para sumarização de trajetórias e previsão de intenção.
Fonte: arXiv cs.AI
RecSys • Score 96
Gêmeos Digitais Probabilísticos de Usuários: Aprendizado de Representação Latente com Semântica Estatisticamente Validada
Entender a identidade e o comportamento do usuário é central para aplicações como personalização, recomendação e suporte à decisão. Propomos um framework de gêmeo digital probabilístico onde cada usuário é modelado como um estado estocástico latente que gera dados comportamentais observados. Este framework é aplicado a um conjunto de dados de respostas de usuários para capturar aspectos estáveis da identidade do usuário.
Fonte: arXiv cs.LG
RL • Score 96
Seleção de Dados Comportamentais Offline
O comportamento de clonagem é uma abordagem amplamente adotada para aprendizado de políticas offline a partir de demonstrações de especialistas. Este artigo revela a saturação de dados em conjuntos de dados comportamentais offline, onde o desempenho da política rapidamente se estabiliza com uma pequena fração do conjunto. Propomos um método eficaz, Stepwise Dual Ranking (SDR), que extrai um subconjunto compacto e informativo de grandes conjuntos de dados comportamentais offline.
Fonte: arXiv cs.LG
NLP/LLMs • Score 96
MoE-TransMov: Um Modelo Baseado em Transformer para Previsão do Próximo Ponto de Interesse (POI) em Movimentos Familiares e Não Familiares
A previsão precisa do próximo ponto de interesse (POI) nas trajetórias de mobilidade humana é crucial para serviços baseados em localização, permitindo recomendações mais oportunas e personalizadas. Propomos o MoE-TransMov, um modelo baseado em Transformer com arquitetura Mixture-of-Experts (MoE) que captura padrões de mobilidade distintos em diferentes contextos de movimento, melhorando a precisão das previsões.
Fonte: arXiv cs.LG
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
NLP/LLMs • Score 96
Classificação de Hipóteses Inspirada em Solomonoff com LLMs para Previsão sob Incerteza
arXiv:2512.17145v1 Tipo de Anúncio: novo
Resumo: Raciocinar sob incerteza é um desafio fundamental em IA, especialmente para tarefas do mundo real, onde problemas com dados escassos exigem generalização sistemática. Abordagens existentes lutam para equilibrar precisão e simplicidade ao avaliar múltiplas soluções candidatas. Propomos um método inspirado em Solomonoff que pondera hipóteses geradas por LLM com base na simplicidade e no ajuste preditivo. Aplicado a tarefas de benchmark (Mini-ARC), nosso método produz misturas ponderadas por Solomonoff para previsões por célula, resultando em saídas conservadoras e cientes da incerteza, mesmo quando as hipóteses são ruidosas ou parcialmente incorretas. Comparado ao Bayesian Model Averaging (BMA), a pontuação de Solomonoff distribui a probabilidade de forma mais uniforme entre as hipóteses concorrentes, enquanto o BMA concentra peso nas candidatas mais prováveis, mas potencialmente falhas. Em diversas tarefas, isso destaca o valor de priors teóricos da informação algorítmica para um raciocínio multi-hipótese interpretável e confiável sob incerteza.
Fonte: arXiv cs.AI
NLP/LLMs • Score 96
MMRAG-RFT: Ajuste Fino de Reforço em Duas Etapas para Geração Aumentada por Recuperação Multi-modal Explicável
arXiv:2512.17194v1 Tipo de Anúncio: novo
Resumo: A Geração Aumentada por Recuperação Multi-modal (MMRAG) possibilita uma geração altamente credível ao integrar conhecimento externo multi-modal, demonstrando assim um desempenho impressionante em cenários complexos multi-modais. No entanto, os métodos MMRAG existentes falham em esclarecer a lógica de raciocínio por trás da recuperação e geração de respostas, o que limita a explicabilidade dos resultados. Para abordar essa lacuna, propomos introduzir o aprendizado por reforço na geração aumentada por recuperação multi-modal, aprimorando as capacidades de raciocínio de modelos de linguagem grandes multi-modais por meio de uma estrutura de ajuste fino de reforço em duas etapas para alcançar uma geração aumentada por recuperação multi-modal explicável. Especificamente, na primeira etapa, o ajuste fino de reforço baseado em regras é empregado para realizar uma classificação ponto a ponto de granulação grossa de documentos multi-modais, filtrando efetivamente aqueles que são significativamente irrelevantes. Na segunda etapa, o ajuste fino de reforço baseado em raciocínio é utilizado para otimizar conjuntamente a classificação lista a lista de granulação fina e a geração de respostas, orientando modelos de linguagem grandes multi-modais a produzir uma lógica de raciocínio explicável no processo MMRAG. Nosso método alcança resultados de ponta no WebQA e MultimodalQA, dois conjuntos de dados de referência para geração aumentada por recuperação multi-modal, e sua eficácia é validada por meio de experimentos de ablação abrangentes.
Fonte: arXiv cs.AI
RL • Score 95
Método de Direção Alternada de Multiplicadores para Decomposições de Matrizes Não Lineares
Apresentamos um algoritmo baseado no método de direção alternada de multiplicadores (ADMM) para resolver decomposições de matrizes não lineares (NMD). Dada uma matriz de entrada $X \in \mathbb{R}^{m \times n}$ e um rank de fatoração $r \ll \min(m, n)$, NMD busca matrizes $W \in \mathbb{R}^{m \times r}$ e $H \in \mathbb{R}^{r \times n}$ de forma que $X \approx f(WH)$, onde $f$ é uma função não linear elemento a elemento.
Fonte: arXiv stat.ML
RL • Score 95
Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design
arXiv:2512.17659v1 Announce Type: new
Abstract: Designing molecules that must satisfy multiple, often conflicting objectives is a central challenge in molecular discovery. The enormous size of chemical space and the cost of high-fidelity simulations have driven the development of machine learning-guided strategies for accelerating design with limited data. Among these, Bayesian optimization (BO) offers a principled framework for sample-efficient search, while generative models provide a mechanism to propose novel, diverse candidates beyond fixed libraries. However, existing methods that couple the two often rely on continuous latent spaces, which introduces both architectural entanglement and scalability challenges. This work introduces an alternative, modular "generate-then-optimize" framework for de novo multi-objective molecular design/discovery. At each iteration, a generative model is used to construct a large, diverse pool of candidate molecules, after which a novel acquisition function, qPMHI (multi-point Probability of Maximum Hypervolume Improvement), is used to optimally select a batch of candidates most likely to induce the largest Pareto front expansion. The key insight is that qPMHI decomposes additively, enabling exact, scalable batch selection via only simple ranking of probabilities that can be easily estimated with Monte Carlo sampling. We benchmark the framework against state-of-the-art latent-space and discrete molecular optimization methods, demonstrating significant improvements across synthetic benchmarks and application-driven tasks. Specifically, in a case study related to sustainable energy storage, we show that our approach quickly uncovers novel, diverse, and high-performing organic (quinone-based) cathode materials for aqueous redox flow battery applications.
Fonte: arXiv stat.ML
RL • Score 96
O Papel da Ética Islâmica na Prevenção do Abuso de Deepfakes Baseados em Inteligência Artificial (AI)
arXiv:2512.17218v1 Tipo de Anúncio: cruzado
Resumo: O desenvolvimento significativo da tecnologia de deepfake impulsionada por inteligência artificial (AI) gerou preocupações em todo o mundo sobre a alteração de informações falsas, a usurpação de identidades online e a diminuição da confiança pública na autenticidade do conteúdo online. Esses incidentes não apenas levantam questões técnicas, mas também carregam implicações morais complexas, tornando inadequados os métodos convencionais de gestão, impulsionados pela tecnologia e reativos, para abordar as causas subjacentes do problema, incluindo intenção, moralidade e potenciais impactos sociais intangíveis. Com base nessas questões, este estudo visa formular uma estrutura ética islâmica abrangente que possa servir como uma ferramenta preventiva mais completa para mitigar os riscos de uso indevido de deepfakes. O estudo empregou uma Revisão Sistemática da Literatura (SLR) guiada pelo PRISMA, selecionando dez fontes primárias publicadas entre 2018 e 2025 para identificar deficiências éticas, necessidades regulatórias e soluções normativas apropriadas. A análise mostra que a integração dos princípios de (Maqasid al-Shariah), particularmente (hifz al-ird) proteção da honra e (hifz al-nafs) proteção do eu, fornece uma base normativa sólida para regular o uso responsável da tecnologia. Este estudo gera três recomendações estratégicas: mudanças regulatórias que reconheçam o dano intangível e psicológico causado pelo dano à reputação; melhor gestão da tecnologia por meio de escrutínio moral que defenda os valores de justiça (adl), confiança e transparência; e aumento da alfabetização digital pública com base no princípio de (tabayyun) exame e cautela. No geral, este estudo conclui que a aplicação da ética islâmica oferece uma mudança de pensamento de mecanismos punitivos para abordagens preventivas que se concentram na proteção da dignidade humana, na prevenção de danos e no fortalecimento do bem comum na era digital.
Fonte: arXiv cs.AI
NLP/LLMs • Score 96
PILAR: Personalizando Interações de Realidade Aumentada com Explicações Centricas no Humano e Confiáveis Baseadas em LLM para Casos de Uso Diários
arXiv:2512.17172v1 Tipo de Anúncio: cruzado
Resumo: Sistemas de realidade aumentada (AR) impulsionados por inteligência artificial (IA) estão se tornando cada vez mais integrados à vida cotidiana, e com esse crescimento vem uma maior necessidade de explicabilidade nas interações do usuário em tempo real. Métodos tradicionais de inteligência artificial explicável (XAI), que frequentemente dependem de explicações baseadas em características ou exemplos, lutam para fornecer insights dinâmicos, específicos ao contexto, personalizados e centrados no humano para usuários diários de AR. Esses métodos normalmente abordam dimensões de explicabilidade separadas (por exemplo, quando, o que, como) com diferentes técnicas de explicação, resultando em experiências fragmentadas e irreais para interações AR sem costura. Para enfrentar esse desafio, propomos o PILAR, uma nova estrutura que aproveita um modelo de linguagem grande (LLM) pré-treinado para gerar explicações personalizadas e conscientes do contexto, oferecendo uma experiência mais intuitiva e confiável em sistemas AR impulsionados por IA em tempo real. Ao contrário dos métodos tradicionais, que dependem de múltiplas técnicas para diferentes aspectos da explicação, o PILAR emprega uma abordagem unificada baseada em LLM que adapta dinamicamente as explicações às necessidades do usuário, promovendo maior confiança e engajamento. Implementamos o conceito PILAR em uma aplicação AR do mundo real (por exemplo, recomendações de receitas personalizadas), um protótipo de código aberto que integra detecção de objetos em tempo real, recomendação de receitas e explicações personalizadas baseadas em LLM das receitas recomendadas com base nas preferências dietéticas dos usuários. Avaliamos a eficácia do PILAR por meio de um estudo com 16 participantes realizando tarefas de recomendação de receitas baseadas em AR, comparando uma interface de explicação baseada em LLM com uma interface tradicional baseada em templates. Os resultados mostram que a interface baseada em LLM melhora significativamente o desempenho e a experiência do usuário, com os participantes completando as tarefas 40% mais rápido e relatando maior satisfação, facilidade de uso e transparência percebida.
Fonte: arXiv cs.AI
NLP/LLMs • Score 96
Um Referencial de Saúde da Mulher para Modelos de Linguagem de Grande Escala
arXiv:2512.17028v1 Tipo de Anúncio: cruzado. Resumo: À medida que modelos de linguagem de grande escala (LLMs) se tornam fontes primárias de informação em saúde para milhões, sua precisão na saúde da mulher permanece criticamente inexplorada. Apresentamos o Women's Health Benchmark (WHB), o primeiro referencial que avalia o desempenho de LLMs especificamente na saúde da mulher. Nosso referencial compreende 96 modelos rigorosamente validados cobrindo cinco especialidades médicas (obstetrícia e ginecologia, medicina de emergência, cuidados primários, oncologia e neurologia), três tipos de consulta (consulta de paciente, consulta de clínico e consulta de evidência/política) e oito tipos de erro (erros de dosagem/medicação, falta de informações críticas, diretrizes/recomendações de tratamento desatualizadas, conselhos de tratamento incorretos, informações factuais incorretas, diagnóstico diferencial ausente/incorreto, urgência perdida e recomendações inadequadas). Avaliamos 13 LLMs de última geração e revelamos lacunas alarmantes: os modelos atuais apresentam taxas de falha de aproximadamente 60% no referencial de saúde da mulher, com desempenho variando dramaticamente entre especialidades e tipos de erro. Notavelmente, os modelos universalmente têm dificuldades com indicadores de 'urgência perdida', enquanto modelos mais novos como o GPT-5 mostram melhorias significativas em evitar recomendações inadequadas. Nossos achados ressaltam que chatbots de IA ainda não são totalmente capazes de fornecer conselhos confiáveis em saúde da mulher.
Fonte: arXiv cs.AI
RL • Score 96
Conhecimento Inesperado: Auditoria das Recomendações de Busca do Wikipedia e Grokipedia
arXiv:2512.17027v1 Tipo de Anúncio: cruzado. Resumo: Plataformas de conhecimento enciclopédico são portas de entrada essenciais pelas quais os usuários exploram informações online. O recente lançamento do Grokipedia, uma enciclopédia totalmente gerada por IA, introduz uma nova alternativa às plataformas tradicionais e bem estabelecidas como o Wikipedia. Nesse contexto, os mecanismos de busca desempenham um papel importante em guiar os caminhos exploratórios dos usuários, no entanto, seu comportamento em diferentes sistemas enciclopédicos permanece pouco explorado. Neste trabalho, abordamos essa lacuna fornecendo a primeira análise comparativa dos mecanismos de busca no Wikipedia e Grokipedia. Usando quase 10.000 palavras neutras em inglês e suas substrings como consultas, coletamos mais de 70.000 resultados de mecanismos de busca e examinamos seu alinhamento semântico, sobreposição e estrutura tópica. Descobrimos que ambas as plataformas frequentemente geram resultados que estão fraca ou moderadamente relacionados à consulta original e, em muitos casos, apresentam conteúdos inesperados a partir de consultas inócuas. Apesar dessas propriedades compartilhadas, os dois sistemas frequentemente produzem conjuntos de recomendações substancialmente diferentes para a mesma consulta. Através da anotação tópica e da análise de trajetórias, identificamos ainda diferenças sistemáticas em como as categorias de conteúdo são apresentadas e como os resultados dos mecanismos de busca evoluem ao longo de múltiplas etapas de exploração. No geral, nossas descobertas mostram que resultados inesperados de mecanismos de busca são uma característica comum de ambas as plataformas, mesmo que apresentem discrepâncias em termos de distribuição tópica e sugestões de consulta.
Fonte: arXiv cs.AI
NLP/LLMs • Score 96
V-Agent: Um Sistema de Busca de Vídeo Interativo Usando Modelos de Visão-Linguagem
Apresentamos o V-Agent, uma nova plataforma multi-agente projetada para busca avançada de vídeos e conversas interativas entre usuário e sistema. Ao ajustar um modelo de visão-linguagem (VLM) com um pequeno conjunto de dados de preferência de vídeo e aprimorá-lo com um vetor de recuperação de um modelo de recuperação de imagem-texto, superamos as limitações dos sistemas tradicionais de recuperação baseados em texto em cenários multimodais.
Fonte: arXiv cs.AI