AutoTool uses reinforcement learning with dual-mode rewards to train multimodal LLMs to adaptively choose between tool-assisted and text-centric reasoning, yielding accuracy and efficiency gains on V* and POPE benchmarks.
Advances in Neural Information Processing Systems , volume=
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SWE-Chain provides 155 chained version transitions and 1,660 requirements across 9 Python packages, where frontier agents resolve 44.8% of tasks on average and struggle to preserve functionality across releases.
Proposes nearly balanced TCARDs that minimize the first two generalized word-length pattern components, defines Φ_BCD criterion linked to classical optimality, and constructs designs via coordinate exchange with simulation-calibrated weights for LLM prompt engineering.
citing papers explorer
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Are Tools Always Beneficial? Learning to Invoke Tools Adaptively for Dual-Mode Multimodal LLM Reasoning
AutoTool uses reinforcement learning with dual-mode rewards to train multimodal LLMs to adaptively choose between tool-assisted and text-centric reasoning, yielding accuracy and efficiency gains on V* and POPE benchmarks.
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SWE-Chain: Benchmarking Coding Agents on Chained Release-Level Package Upgrades
SWE-Chain provides 155 chained version transitions and 1,660 requirements across 9 Python packages, where frontier agents resolve 44.8% of tasks on average and struggle to preserve functionality across releases.
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TCARD: Nearly Balanced Two-Level Designs with Treatment Cardinality Constraints with an Application to LLM Prompt Engineering
Proposes nearly balanced TCARDs that minimize the first two generalized word-length pattern components, defines Φ_BCD criterion linked to classical optimality, and constructs designs via coordinate exchange with simulation-calibrated weights for LLM prompt engineering.