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BenchTrace: A Benchmark for Testing Reflection Ability and Controlled Evolution in LLM Agents

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arXiv:2605. 29225v1 Announce Type: new Abstract: Self-evolving agents improve over time by reflecting on past failures, but existing evaluation is limited in two ways: it measures only task scores, leaving reflection quality unknown, and it relies on agents' own episode runs, offering no mechanism to target specific failure patterns.

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Authors: Jiahao Huang, Fei Cheng, Junfeng Jiang, Zefan Yu, Akiko Aizawa
Released: 2026-05-29 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because BenchTrace: A Benchmark for Testing Reflection Ability and Controlled Evolution in LLM Agents overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, text, rect, correct, eval, line, rate, control. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.29225

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