Towards Robust Tool Use in Agents via Experience-Driven Adaptive Guidance

Language: English (default) / 中文

Paper: arXiv:2608.03403 Code: https://github.com/WangCan1178/ExpG

Overview

This folder archives ExpG, a tool-use enhancement built on Agentscope ReMe. ExpG mines, distills, and reuses experience from historical tool calls to provide capability boundaries and best-practice guidance, which helps agents:

• Select and invoke tools more robustly under dynamic or noisy environments;
• Let smaller models with guidance outperform larger, memoryless baselines;
• Improve consistently across tool selection, tool calling, and response generation.

How ReMe is used: Start the Tool Memory service; historical tool calls are written and evaluated via add_tool_call_result, distilled into tool-level guidance via summary_tool_memory, then retrieved and injected into later reasoning via retrieve_tool_memory. ReMe provides the vector store and service APIs; the acquisition / distillation / reuse strategy is implemented by ExpG. Full implementation and experiments are in WangCan1178/ExpG.

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ExpG Mechanism

ExpG treats tool invocations as learnable experience and runs a three-stage pipeline:

1. Experience Acquisition

   • Analyze invocation quality from historical trajectories (success/failure, cost, latency, etc.);
   • Build structured experience units per tool, recording context, parameter patterns, and outcomes.

2. Experience Distillation

   • Filter noisy or unhelpful experiences and keep representative patterns;
   • Aggregate by equivalence classes to cover common and rare failure modes;
   • Summarize with an LLM into generalizable textual guidance.

3. Experience Reuse

   • Retrieve relevant experience / guidance for future tasks;
   • Inject guidance into tool selection, argument generation, and response synthesis;
   • Improve stability under dynamic environments and imperfect feedback.

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Main Results

Performance comparison (%) across MetaTool, API-Bank, and BFCL-V3. Bold indicates the best results within each model.

Model	Method	MetaTool Pass@1	MetaTool Avg@3	MetaTool Pass@3	API-Bank Pass@1	API-Bank Avg@3	API-Bank Pass@3	BFCL-V3 Pass@1	BFCL-V3 Avg@3	BFCL-V3 Pass@3	Total Pass@1	Total Avg@3	Total Pass@3
GPT-5 nano	No Method	72.62	72.76	78.49	82.96	83.46	86.97	53.80	53.00	60.95	70.82	70.62	76.63
GPT-5 nano	Few-shot	74.12	75.11	82.32	83.71	83.96	87.22	56.18	55.24	61.39	72.36	72.65	79.28
GPT-5 nano	DRAFT	73.94	73.04	78.97	84.21	83.46	87.22	57.27	57.27	62.26	72.52	71.58	77.23
GPT-5 nano	Mem0	74.96	76.13	82.92	84.96	85.21	87.22	60.95	61.61	65.08	73.98	74.67	80.35
GPT-5 nano	ExpG	81.67	82.07	84.60	86.72	86.55	87.22	64.43	63.99	66.38	79.32	79.22	81.69
DeepSeek-V3	No Method	83.10	82.94	84.66	84.71	84.38	85.46	58.79	59.65	65.94	78.92	78.66	81.37
DeepSeek-V3	Few-shot	82.74	83.90	86.28	85.21	84.63	86.22	60.52	60.30	67.90	79.08	79.45	82.92
DeepSeek-V3	DRAFT	80.23	80.79	82.44	84.96	85.63	86.47	62.26	61.61	68.55	77.70	77.80	80.54
DeepSeek-V3	Mem0	83.88	84.56	86.40	85.46	85.55	86.47	65.08	65.15	68.33	80.70	80.91	83.12
DeepSeek-V3	ExpG	85.26	85.38	86.52	87.72	87.39	87.97	69.41	69.92	72.02	82.76	82.61	84.11
Qwen3-8B	No Method	76.51	76.97	77.71	83.96	83.88	84.21	58.79	58.28	60.30	74.46	74.41	75.56
Qwen3-8B	Few-shot	79.93	79.83	82.92	83.71	82.62	84.96	60.09	59.29	61.39	76.91	76.27	79.32
Qwen3-8B	DRAFT	78.19	77.33	77.89	85.71	84.96	85.46	60.74	60.30	62.91	76.20	75.18	76.35
Qwen3-8B	Mem0	75.07	75.47	82.38	86.22	86.05	86.47	63.34	64.93	66.16	74.69	74.98	80.07
Qwen3-8B	ExpG	83.52	84.88	85.08	86.47	87.89	87.97	67.46	66.96	68.33	81.06	81.82	82.48
Qwen3-32B	No Method	80.05	79.43	80.17	84.71	84.88	85.21	65.15	65.08	66.16	78.05	77.55	78.41
Qwen3-32B	ExpG	84.68	85.02	86.28	86.97	87.30	87.72	70.72	71.01	73.32	82.48	82.56	84.14
Qwen3-235B	No Method	78.25	79.23	80.29	85.46	85.46	85.71	71.37	71.15	73.54	78.13	78.49	79.91
Qwen3-235B	ExpG	86.34	86.70	86.94	87.47	86.97	88.22	79.61	78.52	80.04	85.29	84.98	85.69

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Reference Code

Path	Role
tool_memory.py	HTTP client for official ReMe Tool Memory APIs (add_tool_call_result / summary_tool_memory / retrieve_tool_memory)
parse_tool_call_result_prompt.yaml	Prompt for multi-aspect evaluation of each tool call
summary_tool_memory_prompt.yaml	Prompt for summarizing tool call history into guidance
tool_memory_flows.yaml	Tool Memory flow / op config excerpt

These are reference snippets. For the full runnable codebase, see WangCan1178/ExpG.

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Citation

@misc{wang2026expg,
  title         = {Towards Robust Tool Use in Agents via Experience-Driven Adaptive Guidance},
  author        = {Can Wang and Haoran Chen and Li Yu and Ding Hao and Bohai Zhao and Zhaoyang Liu and Zhiying Tu},
  year          = {2026},
  eprint        = {2608.03403},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AI},
  url           = {https://arxiv.org/abs/2608.03403},
  howpublished  = {\url{https://github.com/WangCan1178/ExpG}}
}