MMLU-Pro
mmlu-pro-2baddf83·4 events·first seen 1mo agoAliases: MMLU-Pro
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Resolution Diagnostics for Paired LLM Evaluation: Many Leaderboard Rankings Statistically Unresolved
This paper frames pairwise LLM evaluation as a hypothesis-testing problem and introduces a resolution ratio q = N/N* to diagnose whether leaderboard comparisons are statistically powered. Applying this to two public leaderboards, the authors find that 11/40 Open LLM Leaderboard v1 pairwise comparisons and 4-6/9 MMLU-Pro top-10 adjacent-rank pairs fail to meet conventional (alpha=0.05, power=0.8) resolution targets. A key finding is that the widely-used unpaired Cohen-h shortcut underestimates required sample size by approximately a factor of two in close-comparison regimes, a flaw silently inherited by three major statistical calculators. The unresolved-pair pattern persists under multiplicity correction and sequential testing.
Training-Free Looped Transformers: Inference-Time Recurrence via ODE-Motivated Layer Reapplication
The paper introduces a method to retrofit recurrence onto frozen pretrained transformer checkpoints at inference time by looping a contiguous mid-stack block of layers without any fine-tuning or architectural changes. Naive block reapplication degrades performance, so the authors motivate their approach by treating pre-norm transformer blocks as forward Euler ODE steps and replacing one large update with smaller damped sub-steps. Evaluated across seven model families including dense, sparse MoE, and MLA+MoE architectures, the method yields consistent benchmark improvements (e.g., +2.64 pp on MMLU-Pro for Qwen3-4B-Instruct) at no training cost.
Activation Capping Technique Stabilizes LLM Assistant Personas Against Drift and Jailbreaks
Researchers from MATS, Oxford, and Anthropic introduced the 'assistant axis,' a vector derived from LLM layer outputs that quantifies how closely a model adheres to its trained assistant persona. They developed 'activation capping,' an inference-time method that corrects deviations from this axis when similarity falls below a threshold. Testing on Gemma 2 27B, Qwen3 32B, and Llama 3.3 70B showed harmful response rates to jailbreak prompts dropped by roughly half (e.g., 83% to 41% for Qwen3 32B) without degrading benchmark performance. The technique targets character-based jailbreaks that bypass system prompts by manipulating a model's internal representational state.
Data Points: Thinking Machines Interaction Model, ERNIE 5.1, Co-Mathematician, RL Conductor, and More
This edition of The Batch covers five notable AI developments: Thinking Machines' research preview of an 'interaction model' with a 200ms micro-turn multimodal architecture; Baidu's ERNIE 5.1, a compressed derivative of ERNIE 5.0 using only 6% of typical pre-training compute; Google DeepMind's Co-Mathematician collaborative workbench reaching 48% on FrontierMath Tier 4; a 7B RL Conductor model that orchestrates multi-agent workflows via reinforcement learning; and Google's Magic Pointer cursor system powered by Gemini. Secondary items include GitHub Copilot pricing restructuring ahead of usage-based billing.