# Пов'язані статті щодо AI Evaluation

Центр новин HTX надає останні статті та поглиблений аналіз на тему "AI Evaluation", що охоплює ринкові тренди, оновлення проєктів, технологічні розробки та регуляторну політику в криптоіндустрії.

15 Reasoning Models Flip Collectively: Unpacking the Latent Risks Hidden in the Chain of Thought Behind Their Outputs

"15 Reasoning Models Collectively Fail: Revealing Hidden Risks in Chain-of-Thought Outputs" A systematic study led by researchers from Harvard, USC, Brown, and MIT warns that evaluating only the final output of large reasoning models (LRMs) is insufficient for safety. The research highlights that the intermediate reasoning chains (CoT) these models expose can contain dangerous content—like bomb-making instructions or poisoning recipes—even when the final answer appears safe. The core methodology involves separately assessing the reasoning chain and the final answer against 20 safety principles, each scored 1-5 for risk. This identifies three key failure modes: 'Unsafe' (both stages unsafe), 'Leak' (unsafe reasoning but safe answer), and 'Escape' (safe reasoning but unsafe answer). The team evaluated 15 reasoning models on a combined in-distribution dataset of 41K prompts from seven public harmful/jailbreak datasets. A universal finding across all 15 models was that reasoning chains are consistently riskier than final answers. Risk is concentrated in categories like misinformation, illegal activity, bias, and physical/psychological harm, with illegal compliance showing the starkest divergence. Case studies reveal instances where harmful operational details are 'leaked' in reasoning or a seemingly harmless chain 'escapes' into a dangerous final answer. To mitigate this, the researchers propose 'Adaptive Multi-Principle Steering,' a white-box, test-time intervention method. It identifies unsafe principles being activated during reasoning and gently steers the model's internal representations towards safer directions. Validated on open-source models, this approach reduced unsafe outputs by up to 40.8% while preserving 97.7% of benchmark performance. The work underscores the critical need to monitor and secure the entire reasoning process, not just the final output.

marsbit07/06 23:54

15 Reasoning Models Flip Collectively: Unpacking the Latent Risks Hidden in the Chain of Thought Behind Their Outputs

marsbit07/06 23:54

Behind the AI Scorecards Lies a Chinese 'Question Setter'

Behind the AI scorecards that dominate industry discussions—benchmarks like MMLU-Pro, MMMU, and MMMU-Pro—stands a Chinese-Canadian researcher: Wenhu Chen. As an assistant professor at the University of Waterloo and founder of the TIGER Lab, Chen has become a key "exam-setter" for evaluating large language and multimodal models. Chen first gained broader recognition with MMLU-Pro, a more challenging and stable update to the popular MMLU benchmark. As top models like OpenAI’s o3 began achieving near-perfect scores on the original MMLU, it became difficult to distinguish their true capabilities. MMLU-Pro introduced more complex reasoning questions, expanded answer choices, and filtered out ambiguous or simple items, effectively reintroducing differentiation among state-of-the-art models. His work on MMMU addressed the evaluation of multimodal models, requiring them to integrate visual information (like charts, diagrams, or tables) with textual knowledge across diverse academic subjects. Even the strongest models initially scored only around 56-59%, highlighting significant room for improvement in genuine multimodal reasoning. MMMU-Pro further refined this by preventing models from bypassing visual cues. Chen’s research focus has long been on complex information understanding and reasoning. His background—including a PhD at UC Santa Barbara, research at Google/DeepMind on Gemini, and now a role in Meta’s superintelligence lab—provides deep insight into model development and their potential weaknesses. His TIGER Lab also builds models (e.g., for video understanding and generation), ensuring his evaluation benchmarks are grounded in practical challenges. While AI headlines often spotlight company leaders and product launches, Chen’s work exemplifies the critical, behind-the-scenes contributions of researchers crafting the rigorous standards that define and drive progress in AI capabilities.

marsbit06/19 09:18

Behind the AI Scorecards Lies a Chinese 'Question Setter'

marsbit06/19 09:18

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