ISOSCI benchmark finds 91.3% of reasoning-mode accuracy gains in LLMs on science problems depend on domain knowledge rather than invariant logical structure.
Disentangling memory and reasoning ability in large language models
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Survey that defines agentic RL for LLMs via POMDPs, introduces a taxonomy of planning/tool-use/memory/reasoning capabilities and domains, and compiles open environments from over 500 papers.
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IsoSci: A Benchmark of Isomorphic Cross-Domain Science Problems for Evaluating Reasoning versus Knowledge Retrieval in LLMs
ISOSCI benchmark finds 91.3% of reasoning-mode accuracy gains in LLMs on science problems depend on domain knowledge rather than invariant logical structure.
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The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
Survey that defines agentic RL for LLMs via POMDPs, introduces a taxonomy of planning/tool-use/memory/reasoning capabilities and domains, and compiles open environments from over 500 papers.