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arXiv:2607. 17136v1 Announce Type: cross Abstract: Agentic computer-use RL is reported in single runs, and those numbers mislead.
The paper examines safety routers—systems that route user requests to different language models—and finds that their performance degrades significantly when evaluated under distribution shift. In standard benchmarks, routers appear effective because the best single model is chosen from the same evaluation data, but when the data distribution changes, the routing advantage diminishes or disappears. The study quantifies this bias across multiple safety corpora, showing that routers offer little benefit under realistic shift conditions and that recognition‑based defenses can be undermined by attackers who know the model being used.
arXiv:2608. 15980v1 Announce Type: cross Abstract: Preference benchmarks are built by hiring annotators, and the identity of those annotators is treated as an implementation detail.
The paper proposes a framework for studying how alignment difficulty scales with model size, treating alignment as a family of measurable scaling relations for different risk categories. It introduces operational definitions of burden, distinguishes observed, audited, and true alignment, and proves that the largest exponent among corrected risks determines long‑run behavior. The authors preregistered and applied the protocol to public data, finding scaling exponents for attack success, truthfulness, dispositions, sycophancy, and a backdoor, and released interactive browser games to illustrate the laws.
arXiv:2610. 41588v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) treats every reported comparison as correct, yet preference data contain reversed, tied or otherwise noisy comparisons.
arXiv:2605. 22148v3 Announce Type: replace Abstract: A large language model (LLM) agent that writes and edits its own skill library must also decide which skills to keep, from one noisy scalar per skill.