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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...
We connect the spurious-reward paradox to a model's reachability and propose random-reward reinforcement learning (RL) as a useful tool for the probing enterprise, addressing a decade-long debate over what probing performance actually reveals about a model. There are two prevailing explanations for the surprising findi...
IntentAlign-MiniLM, the authors' 22M-parameter intent-aligned retriever, outperforms much larger embedding models on held-out intent retrieval and yields the best learned-retriever harmful recall across tested guardrails.
By evaluating paper retrieval, evidence grounding, and answer accuracy separately, LitTraceQA provides a testbed for scientific QA systems that produce verifiable answers rather than unsupported summaries.
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