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On-policy self-distillation (OPSD) trains a large language model using supervision from a teacher instantiated from the same model as the student. The teacher is conditioned on privileged information (PI), such as verified solutions. The student, which does not observe PI, learns from the teacher's next-token distribut...
GraphMind, a model that jointly processes micro- and macro-level structures for novelty prediction and rationale generation, is proposed that significantly outperforms baseline LLMs in both novelty score prediction and rationale generation.
This work connects papers in a graph whose edges are typed by these four facets, derived from facet items and citation signals, and distil it into a generative retriever whose identifiers are the papers'own facet text.
This work proposes ASRD (Anchor Supervised Revocable Decoding), a training-free framework that operates within the embedding space and outperforms recent remasking baselines, achieving accuracy improvements of up to 6.4\% while accelerating inference throughput by up to 7.2$\times$.
Assessing scientific novelty is inherently complex, requiring evaluation of both a paper's internal structure and its contribution within the broader research landscape. Existing large language model (LLM) approaches often rely on surface-level similarity or citation retrieval, overlooking the integration between a pap...
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