141 papers
This paper introduces RED, a robust event-guided motion deblurring network that employs a robustness-oriented perturbation strategy and a modality-specific disentanglement mechanism to effectively reconstruct sharp images from fragmented event data caused by real-world sensor under-reporting.
MLLMRec-R1 is an efficient GRPO-based framework for multimodal sequential recommendation that overcomes the high computational costs of visual token processing and the issue of reward inflation by textualizing visual signals offline and employing a mixed-grained data augmentation strategy to construct high-quality reasoning supervision.
This paper introduces ChatShopBuddy, a conversational shopping agent optimized via Reinforcement Learning using a new benchmark (SmartShopBench), a Hierarchical Reward Modeling framework, and a Dynamic Contrastive Policy Optimization algorithm to effectively balance product correctness, persuasiveness, and operational efficiency in real-world scenarios.
AutoThinkRAG is a complexity-aware framework for image-text interaction that improves document question answering by routing queries based on difficulty and decoupling visual interpretation from logical reasoning to achieve state-of-the-art performance with reduced inference costs.
This paper introduces novel "Text-to-Big SQL" evaluation metrics to address the limitations of existing benchmarks in assessing production-level LLM agents, demonstrating that traditional Text-to-SQL metrics fail to capture critical cost, latency, and efficiency implications that arise when scaling to large datasets.
This paper introduces Aletheia, a novel LLM-powered browser extension that combines Retrieval-Augmented Generation with interactive user features to effectively detect fake news and provide transparent, evidence-based explanations, outperforming existing baselines in both detection accuracy and user engagement.
GaiaFlow is a novel framework that achieves carbon-frugal search by integrating semantic-guided diffusion tuning, retrieval-guided Langevin dynamics, and adaptive efficiency protocols to balance high retrieval accuracy with significantly reduced environmental impact.
The paper proposes R4T, a three-stage framework that leverages reinforcement learning to synthesize objective-aligned training data for a lightweight diffusion retriever, enabling efficient, high-quality set-valued retrieval that optimizes complex properties like diversity and coverage while significantly reducing inference latency compared to RL-based baselines.
The paper introduces OMEGA, a K-generalizable learned top-K search method that leverages a base model trained on K=1 with trajectory-based features and a dynamic refinement procedure to achieve high accuracy and low latency for multi-K vector queries while significantly reducing preprocessing time compared to state-of-the-art methods.
This paper introduces HCT-QA, a comprehensive benchmark comprising thousands of real-world and synthetic human-centric tables with natural language question-answer pairs, designed to evaluate and improve the performance of Large Language Models and Vision Language Models in querying complex tabular data.
This paper proposes a three-step framework to develop sensitivity-aware retrieval-augmented intent clarification systems that balance user utility with the protection of sensitive information in domains like healthcare and legal contexts by defining attack models, designing retrieval-level defenses, and establishing evaluation metrics for the protection-utility trade-off.
This study evaluates a dual-calibration algorithmic nudge and an LLM-based presentation nudge within a personalized diversity framework, finding that while algorithmic nudges effectively increase news consumption diversity and shift long-term reading habits toward balanced domestic and global coverage, LLM-based presentation nudges yield variable results and user-specific topic interest remains the strongest predictor of engagement.
This paper presents an algorithmic audit of TikTok revealing that while the platform technically complies with the Digital Service Act's ban on profiled advertising to minors, it effectively circumvents this protection by delivering highly personalized, often undisclosed influencer marketing content to adolescents, thereby highlighting the urgent need to expand the regulatory definition of "advertisement" to cover such commercial practices.
The paper introduces CBR-to-SQL, a Case-Based Reasoning framework that improves Text-to-SQL generation in healthcare by utilizing abstract case templates and a two-stage retrieval process to achieve higher accuracy, sample efficiency, and robustness compared to standard Retrieval-Augmented Generation methods on the MIMICSQL dataset.
VDCook is a self-evolving, configurable video data operating system that enables researchers and domain teams to automatically generate, update, and manage specialized video training datasets for MLLMs through natural language queries, integrated retrieval-synthesis modules, and automated metadata annotation.
The paper introduces MDER-DR, a novel Retrieval-Augmented Generation framework that combines a Map-Disambiguate-Enrich-Reduce indexing strategy with a Decompose-Resolve retrieval mechanism to significantly improve multi-hop question answering on Knowledge Graphs by preserving contextual nuance and enabling robust reasoning without explicit graph traversal.
This paper examines how various methodological decisions and configuration parameters in YouTube audits significantly impact the accuracy of inferred algorithmic biases, ultimately offering strategies to optimize audit overhead without compromising scientific validity.
This paper introduces Agent-OM, a novel framework leveraging Siamese LLM agents and specialized tools to address ontology matching challenges, demonstrating competitive performance on simple tasks and significant improvements on complex and few-shot scenarios compared to state-of-the-art systems.
This paper introduces a taxonomy for advertising styles in RAG systems, simulates style-based evasion tactics, and demonstrates that while entity recognition models effectively detect generated ads and remain robust to style changes, lightweight models suitable for end-user devices currently lack the necessary resilience.
This paper proposes a novel modality fusion network that leverages domain-specific fine-tuning and a two-stage alignment strategy to effectively unify text and visual signals for improved multimodal retrieval in e-commerce search.