Do LLMs Act on What They Know? From Partner Representations to Cooperative Actions
Across eight LLMs, probes decode Hanabi partner conventions better than the models act on them.
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Across eight LLMs, probes decode Hanabi partner conventions better than the models act on them.
HERMES uses modular Dev-Primitives to beat baseline software-engineering agent harnesses by 12.4% on four benchmarks.
Biomedical reranking raised clinical-note Hit@10 from 46.6% to 60.6%, while Qwen3-8B correctness reached only 48.6%.
A 50,228-pair Agent Error Dataset diagnoses LLM agent failures and improves correction and post-training.
AdviSD trains a small advisor with selective self-distillation, beating advisor-GRPO on BFCL-v3 and EnvScaler.
A rank-8 LoRA lets frozen transformers follow much longer in-context reference chains.

KAIST and Naver AI Lab researchers show LLM reasoning steps like extraction and computation map to distinct activation patterns, strongest in middle layers.
Study quantifies DPO-tuned LLMs undershooting requested emotional intensity, tracing the gap to candidate-pool extremity rather than conditioning format.
A self-distillation safety framework tunes narrow-boundary refusals in Qwen3-8B, raising target-domain refusal to 84.75% while cutting over-refusal from 15.20% to 5.20%.
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