2 citations · 4 across the 12 of their papers we have counts for
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cs.AI2026
WorldSolver: Can LLM Agents Simulate the Physical Dynamics via Solver Generation?
Siru Jiang, Yongzhe Lyu, Shuo Lu +6
LLM-based agents are increasingly advancing scientific and engineering problem solving, with physics simulation emerging as a challenging yet practical testbed for reproducing comp…
cs.AI2026
AgentStream: How Well Do Self-Evolving LLM Agents Perform Under Streaming Tasks?
Dong Yan, Jian Liang, Dapeng Hu +4
Large language model (LLM) agents can self-evolve by continually improving from their own accumulated experience. However, existing studies predominantly adopt independent evaluati…
cs.AI2025★ 2 cited
Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory
Yexiang Liu, Zekun Li, Zhi Fang +3
Recently, scaling test-time compute on Large Language Models (LLM) has garnered wide attention. However, there has been limited investigation of how various reasoning prompting str…