News without the noise
Baser et al. test whether knowledge edits and unlearning remain effective when an adversary changes how an input is divided into tokens, while preserving the underlying text. Across five language models, six datasets and six modification techniques, alternative tokenizations frequently restored the model’s pre-modification response.
Wang et al. introduce OracleLadder, a diagnostic tool that provides increasing levels of oracle help to large language models on multi-step math problems. By giving models a roadmap of sub-goals and their answers, they isolate failures into five categories. The dominant failure mode across all tested models is a composition gap: models can solve each intermediate step alone but cannot compose them into the correct final answer.
todaySchuhmann and colleagues introduce VoiceNet, a benchmark for recognizing fine-grained emotion and performance characteristics in permissively licensed, real-world speech. Their VoiceCLAP models substantially outperform existing audio-text baselines, although the evaluation measures representations and retrieval rather than conversational understanding.
todayMukherjee and Sitaram test whether an incorrect LLM judgment reflects missing knowledge or a failure to express information already represented inside the model. Across 64 open-weight evaluators and 14 datasets, they find a substantial internal-output gap on pairwise tasks with misleading surface cues, but little benefit from internal read-outs when those cues are absent.
todayThe authors developed a behavioral test suite to distinguish whether language models rely on shallow patterns or true generalization. Tracking models across pre-training checkpoints, they found that models frequently and abruptly alternate between these modes, often within tens of billions of tokens. This mode-hopping is not an artifact of noise or metric selection and cannot be smoothed by checkpoint averaging.
todayZhou et al. test whether relationships learned from persona prompts predict new responses and carry across models and prompts. They find persona effects are predictable within a model but not portable: borrowed gains underperform target refitting in most transfers, and rewording consistently breaks reuse.
todayThe authors introduce TCSAlgBench, a benchmark and reusable pipeline for natural-language proof discovery in theoretical computer science. It consists of 398 theorem-level challenges from 138 papers from STOC and COLT 2026. In their evaluations, the best-performing system (GPT-5.6 Sol max with discussion) achieved 23.6% verifier-accepted coverage after 10 rounds, while an agentic planning workflow reached 25.4%.
todayEvery paper read and written up by the research desk from the daily arXiv scan · threads are maintained lines of inquiry with running syntheses