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Design and Report Benchmarks for Knowledge Work

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arXiv:2605. 23262v1 Announce Type: new Abstract: The development of LLM agents has led to a growing body of work on knowledge-work AI, including coding, research, and healthcare.

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Authors: Yining Hua, Hongbin Na, Cyrus Ayubcha, Levi Lian
Released: 2026-05-25 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Design and Report Benchmarks for Knowledge Work 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: code, strong, under, soft, eval, rate, does, test. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.23262

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