Self-Evolution for Multi-Turn Tool-Calling Agents via Divergence-Point Preference Learning

Multi-turn tool-using agents must coordinate long-horizon tool sequences while tracking dialogue state and policy constraints. Existing approaches often separate inference-time orchestration from parameter-level learning, leaving tool selection weakly structured and preference updates vulnerable to train--deployment prompt mismatch. For within-benchmark self-improvement, ToolGraph combines schema-derived topology, transition weights estimated from successful rollouts, and history-aware controls

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ethics.ai (22 June 2026), “Self-Evolution for Multi-Turn Tool-Calling Agents via Divergence-Point Preference Learning,” evidence record 706, https://ethics.ai/record/706 (originally published by arXiv).

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