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This work constructs TypewriterCorpus, a 54B-token historical corpus with extensive temporal filtering, proposes lexically grounded instruction tuning that constrains all responses to vocabulary from historical source documents, and introduces History-Event, a benchmark of 2,344 events for evaluating both competence an...
This work proposes $D^2-Monitor, in which a lightweight probe runs on every sample, producing a base prediction and a hesitation estimate that decides whether to escalate the sample to a more expressive probe, and exhibits the best trade-off between effectiveness and efficiency relative to 8 baselines.
Whether unlearning runs exhibit exploitable structure in weight space is investigated, and it is observed that models from different runs still lie in a shared evaluation-performance basin, suggesting that stronger models may be recovered through an unlearning-tailored soup strategy, reducing the need for repeated tuni...
Using controlled corruption, this work decouple these two roles and finds a reversal: making a noisy token easier to predict reduces its damage as a target but increases it as context.
Autoresearch agents are reshaping the research ecosystem, but they can also let flawed claims enter the literature at scale. Human advisors catch such issues in drafts through careful, traceable feedback, yet advisor-style assessment requires extensive manual effort and does not scale. To shift automated paper assessme...
Research Attention Prediction (RAP), a rolling benchmark covering 278 AI/ML fields and 1,390 episodes, is introduced, a rolling benchmark covering 278 AI/ML fields and 1,390 episodes for large language models to track shifts in research attention.
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