works on

From the 1 of 58 linked papers with an AI index.

activity
20242026
most citedRM-R1: Reward Modeling as Reasoning

2 citations · 4 across the 20 of their papers we have counts for

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15 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.AI20261 cited

Position: Agent Should Invoke External Tools ONLY When Epistemically Necessary

Hongru Wang, Cheng Qian, Manling Li +6

As large language models evolve into tool-augmented agents, a central question remains unresolved: when is external tool use actually justified? Existing agent frameworks typically…

cs.AI20261 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.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…