LLMs often misalign their self-perceived need for tools with true need and utility, but lightweight estimators trained on hidden states can improve tool-calling decisions and task performance across multiple models and tasks.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
RL post-training lifts answer correctness on FHIR-AgentBench from 50% (o4-mini) to 77% with a cheaper Qwen3-8B CodeAct agent.
JTPRO co-optimizes prompts and tool descriptions via reflection to raise overall success rate by 5-20% over baselines on multi-tool benchmarks.
citing papers explorer
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To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling
LLMs often misalign their self-perceived need for tools with true need and utility, but lightweight estimators trained on hidden states can improve tool-calling decisions and task performance across multiple models and tasks.
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Reinforcement Learning for Tool-Calling Agents in Fast Healthcare Interoperability Resources (FHIR)
RL post-training lifts answer correctness on FHIR-AgentBench from 50% (o4-mini) to 77% with a cheaper Qwen3-8B CodeAct agent.
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JTPRO: A Joint Tool-Prompt Reflective Optimization Framework for Language Agents
JTPRO co-optimizes prompts and tool descriptions via reflection to raise overall success rate by 5-20% over baselines on multi-tool benchmarks.