LLM benchmark labels are frozen at release and silently propagated into downstream benchmarks, errors and all. We introduce an Item Response Theory-based indicator that surfaces likely mislabels at 95% precision in the top 200 examples across seven preference and multiple-choice benchmarks using responses from 114 models, outperforming a supervised classifier. We trace these errors to mechanical labeling heuristics, upstream annotation mistakes inherited unchanged from source datasets, and fundamentally ambiguous items without a defensible single label. The same model fit reveals that reward models specialize in stylistic preference rather than factual knowledge, and identifies one frontier reward model that agrees with detected mislabels at 78% accuracy versus 38% for its peers, consistent with benchmark contamination or benchmark-specific over-optimization.
NINJA (short for Needle-in-haystack jailbreak attack), a method that jailbreaks aligned LMs by appending benign, model-generated content to harmful user goals to reveal fundamental vulnerabilities in modern LMs.
R. Shah, C. Wu, Shashwat Saxena et al.· arXiv.org· 4 citations
SimulRAG, a simulator-based RAG framework with a generalized retrieval interface that translates between text and simulator parameters/outputs, is proposed, which improves informativeness and factuality over the strongest adapted RAG baselines, while UE+SBA enhances claim-level efficiency and quality.
Haozhou Xu, D. Wu, M. Chinazzi et al.· arXiv.org· 3 citations
This paper identifies two different routes through which models can acquire geometrically separable features: they can learn them from complementary co-occurrence signals in general language data, including text-number co-occurrence and cross-number interaction, or from multi-token addition problems.
Interactions are introduced as a fine-grained tool to analyze prompt sensitivity of LLMs and it is discovered that subtle changes to prompts can trigger severe instability in interactions, even when the outputs of the LLM remain the same.
Ruiyang Qin, Qingzhuo Wang, Tianhao Wang et al.· 2 citations· ⚡1
A benchmark built on the Speech Accessibility Project (SAP) dataset is introduced that tests whether diagnosis labels, clinician-derived speech ratings, and progressively richer clinical descriptions improve transcription accuracy for dysarthric speech, finding that current models do not meaningfully use this context.
P. Moure, Niclas Pokel, Bilal Bounajma et al.· arXiv.org· 2 citations
A pipeline that integrates a large language model to generate intermediate implicit premises based on the explicit premise and claim, a neuro-symbolic reasoner based on a SAT solver to determine entailment, and a neuro-symbolic reasoner based on a SAT solver to determine entailment is proposed.
Xuyao Feng, Anthony Hunter· arXiv.org· 2 citations
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MIT News · Artificial Intelligence· news.mit.eduAug 31, 2026
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
