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New Benchmark Finds Implicit Bias Can Evade Chain-of-Thought Monitoring, With Detection Rates as Low as 5% in Some Settings

A new study uses paired prompts to compare whether models leave detectable evidence in their chains of thought when influenced by explicit instructions versus implicit cues. Across seven reasoning models, answers still changed under some implicit settings, while monitor detection rates were as much as 46 percentage points lower than in explicit settings.

代理工具

AI Scientists Turn to Race Cars and Decks for Prospective Evaluation; Only 10 of GPT-5.2’s 166 Ideas Match Real-World Innovations

The new evaluation seals model-generated ideas before using season-specific technologies and professional tournament decks as answers revealed only later, reducing concerns about data contamination in retrospective benchmarks. The results suggest that models do not lack plausible-sounding ideas; the real bottleneck is filtering, combining, and identifying genuinely useful novelty.

最新模型

ReflectRL Turns Expert Error Trajectories into Training Signals, Improving GRPO Cross-Domain Evaluation by an Average of 19.1 Points

Instead of discarding reasoning trajectories where a strong model produces an incorrect answer, ReflectRL asks the student model to identify localized errors and then gradually removes this hint. The study reports consistent gains across nine benchmarks, although the results come primarily from mathematical reasoning settings, and the listed GitHub project link is currently inaccessible.