activity
20202026
most citedTopicRefine: Joint Topic Prediction and Dialogue Response Generation for Multi-turn End-to-End Dialogue System

7 citations · 41 across the 62 of their papers we have counts for

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16 papers · 1 filter

cs.AI2026

CAP: A Scalable Benchmark for Evaluating Cross-Site Browser Agents with Complex Actions and Perception

Zejun Xu, Taiyi Chen, Jin Li +13

Large language models are increasingly deployed as autonomous agents that interact with the web through browsers. While recent progress has been driven by benchmarks that evaluate…

cs.AI2026

From Knowing to Acting: Benchmarking Self-Awareness Capability of LLM Agents

Yifan Li, Shengbin Yue, Boyu Feng +6

The integration of external tools has transitioned LLM agents from passive responders to autonomous systems. However, current benchmarks prioritize execution success, neglecting se…

cs.AI2026

Multi-Turn Evaluation of Deep Research Agents Under Process-Level Feedback

Rishabh Sabharwal, Hongru Wang, Amos Storkey +1

Existing benchmarks for deep research agents (DRAs) assess only single-shot outputs, ignoring a key question: can DRAs improve their reports when guided by feedback? To investigate…

cs.AI2026

Search-R2: Enhancing Search-Integrated Reasoning via Actor-Refiner Collaboration

Bowei He, Minda Hu, Zenan Xu +7

Search-integrated reasoning enables language agents to transcend static parametric knowledge by actively querying external sources. However, training these agents via reinforcement…

cs.AI20251 cited

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey

Guibin Zhang, Hejia Geng, Xiaohang Yu +22

The emergence of agentic reinforcement learning (Agentic RL) marks a paradigm shift from conventional reinforcement learning applied to large language models (LLM RL), reframing LL…

cs.AI2025

Physics Supernova: AI Agent Matches Elite Gold Medalists at IPhO 2025

Jiahao Qiu, Jingzhe Shi, Xinzhe Juan +6

Physics provides fundamental laws that describe and predict the natural world. AI systems aspiring toward more general, real-world intelligence must therefore demonstrate strong ph…