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UniMaia: Steering Chess Policies with Language for Human-like Play

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arXiv:2605. 27767v1 Announce Type: new Abstract: Recent advances in large language models have enabled natural language to serve as a flexible interface for controlling complex systems, but often at the cost of large-scale multimodal training or weakened domain-specific inductive biases.

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Authors: Sherman Siu (University of Waterloo), Lesley Istead (University of Waterloo)
Released: 2026-05-29 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because UniMaia: Steering Chess Policies with Language for Human-like Play 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, text, eval, line, rate, control, without. Source: arxiv cs.CL (NLP).
arXiv: 2605.27767

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