most citedReinforcement World Model Learning for LLM-based Agents

1 citations · 1 across the 8 of their papers we have counts for

collaborators

8 papers

cs.AI2026

Skim: Speculative Execution for Fast and Efficient Web Agents

Mike Wong, Kevin Hsieh, Suman Nath +1

Skim is a speculative execution framework for web agents that exploits the predictable structure of purpose-built websites. Today's web-agent expense is not intrinsic to the tasks…

cs.IR2026

Thinking Ahead: Prospection-Guided Retrieval of Memory with Language Models

Harshita Chopra, Krishna Kant Chintalapudi, Suman Nath +2

Long-horizon personalization requires dialogue assistants to retrieve user-specific facts from extended interaction histories. In practice, many relevant facts often have low seman…

cs.AI2026

WebXSkill: Skill Learning for Autonomous Web Agents

Zhaoyang Wang, Qianhui Wu, Xuchao Zhang +12

Autonomous web agents powered by large language models (LLMs) remain brittle on long-horizon browser workflows. A key bottleneck is a grounding gap in existing skill formulations:…

cs.CL2026

The Tool Illusion: Rethinking Tool Use in Web Agents

Renze Lou, Baolin Peng, Wenlin Yao +5

As web agents rapidly evolve, an increasing body of work has moved beyond conventional atomic browser interactions and explored tool use as a higher-level action paradigm. Although…

cs.AI2026

ActionEngine: From Reactive to Programmatic Web Agents via State Machine Memory

Hongbin Zhong, Fazle Faisal, Luis França +4

Many web agents operate through a reactive execution loop: they observe the current interface, reason about the next action, execute it, and repeat. This design incurs latency and…

cs.CL2026★ 1 cited

Reinforcement World Model Learning for LLM-based Agents

Xiao Yu, Baolin Peng, Ruize Xu +6

Large language models (LLMs) have achieved strong performance in language-centric tasks. However, in agentic settings, LLMs often struggle to anticipate action consequences and ada…

cs.CL2025

Dyna-Mind: Learning to Simulate from Experience for Better AI Agents

Xiao Yu, Baolin Peng, Michel Galley +6

Reasoning models have recently shown remarkable progress in domains such as math and coding. However, their expert-level abilities in math and coding contrast sharply with their pe…

cs.AI2025

Dyna-Think: Synergizing Reasoning, Acting, and World Model Simulation in AI Agents

Xiao Yu, Baolin Peng, Ruize Xu +5

Recent progress in reasoning with large language models (LLMs), such as DeepSeek-R1, demonstrates impressive capabilities in domains like mathematics and coding, by exhibiting comp…