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AI is dramatically lowering the cost of reading papers, writing code, and running experiments. But as research output explodes, telling which results can be trusted keeps getting harder. CiteArk connects the claims in scientific papers with their experimental conditions, execution process, result evidence, and verification records, so researchers and agents can verify claims, reuse experimental results, and build follow-up research on them instead of repeating work. We hope these trustworthy research records will grow into a scientific foundation that researchers, labs, and agents can all build on together.

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© 2026 CiteArk · Ligu Corp. The traceable evidence layer for research.

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How Strongly Should Task State Influence an LLM Agent? · CiteArk

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How Strongly Should Task State Influence an LLM Agent?

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Authors:Chenyu Zhang, Wonbin Kweon, Jiawei Han

arXiv 2026 Artificial Intelligence Computation and Language Machine Learning (cs.LG)

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1,501 files · 6.5 MB

CCiteArk Research CompilerCompilation AgentImport verified CAP 1.0 research plan—Current version

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README.md1.7 KB

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