activity
20232026
most citedTowards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond

2 citations · 3 across the 24 of their papers we have counts for

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Showing 2025 · cs.AIShow all

7 papers · 2 filters

cs.AI2025

Unlocking the Power of Multi-Agent LLM for Reasoning: From Lazy Agents to Deliberation

Zhiwei Zhang, Xiaomin Li, Yudi Lin +8

Large Language Models (LLMs) trained with reinforcement learning and verifiable rewards have achieved strong results on complex reasoning tasks. Recent work extends this paradigm t…

cs.AI2025

A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applications

Minhua Lin, Zongyu Wu, Zhichao Xu +6

The advent of large language models (LLMs) has transformed information access and reasoning through open-ended natural language interaction. However, LLMs remain limited by static…

cs.AI2025

Seeing but Not Believing: Probing the Disconnect Between Visual Attention and Answer Correctness in VLMs

Zhining Liu, Ziyi Chen, Hui Liu +9

Vision-Language Models (VLMs) achieve strong results on multimodal tasks such as visual question answering, yet they can still fail even when the correct visual evidence is present…

cs.AI2025

TRAJECT-Bench:A Trajectory-Aware Benchmark for Evaluating Agentic Tool Use

Pengfei He, Zhenwei Dai, Bing He +10

Large language model (LLM)-based agents increasingly rely on tool use to complete real-world tasks. While existing works evaluate the LLMs' tool use capability, they largely focus…

cs.AI2025

Adaptive Test-Time Reasoning via Reward-Guided Dual-Phase Search

Yingqian Cui, Zhenwei Dai, Pengfei He +8

Large Language Models (LLMs) have achieved significant advances in reasoning tasks. A key approach is tree-based search with verifiers, which expand candidate reasoning paths and u…

cs.AI2025

AgentTTS: Large Language Model Agent for Test-time Compute-optimal Scaling Strategy in Complex Tasks

Fali Wang, Hui Liu, Zhenwei Dai +8

Test-time scaling (TTS) enhances the performance of large language models (LLMs) by allocating additional compute resources during inference. However, existing research primarily i…

cs.AI2025

How Far are LLMs from Real Search? A Comprehensive Study on Efficiency, Completeness, and Inherent Capabilities

Minhua Lin, Hui Liu, Xianfeng Tang +7

Search plays a fundamental role in problem-solving across various domains, with most real-world decision-making problems being solvable through systematic search. Drawing inspirati…