In verifiable domains such as math and coding, finding one correct solution among many attempts can matter more than the pass rate of each attempt. Post-training can concentrate large language model outputs around a few modes, while increasing sampling temperature has limited effectiveness. We introduce Don't Repeat Yo...
This paper presents an approach to use LLMs in a limited setting to generate a small, human-certifiable ACSL contract from the function's documentation, combined with a deterministic toolchain that supports a restricted ACSL profile and generates a driver that uses the CIVL verifier to check the implementation against...
H. Suh, J. Hückelheim, Stephen F. Siegel· 0 citations
Ontology-driven knowledge graph construction from industrial text remains challenging due to the domain specificity of documents, the scarcity of annotated resources, and the complexity of ontology engineering workflows. This paper presents and investigates the applicability of an ontology learning pipeline that combin...
Abdelhadi Belfadel, Maxence Gagnant, Joseph Kattan et al.· arXiv.org· 0 citations
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A literature-guided descriptor-based filtering framework for reducing composition search spaces and shows that literature-derived embeddings can support intuitive and reproducible filters for narrowing candidate composition spaces while preserving high-performing compositions.
In passage reranking, response ranking and multi-document question answering, LLMs can score several candidate documents or responses together in one prompt, each still receiving its own score. Such scorers are selected on ranking quality, but their scores determine a decision: what a score threshold retains, a reader...
Markus Frohmann, Mahdiyar Ali Akbar Alavi, Elizabeth Lingg et al.· 0 citations
ACache is presented, a cross-request cache reuse mechanism for shared spans, or affixes, at any position: prefix, infix, or suffix, and co-designs it with a modern inference engine, whose attention reads each request's recomputed Anchor KV states alongside one affix cache shared across concurrent requests.
Kai-Huan Liang, An Zhong, Xin Tan et al.· 0 citations
The introduction of WhiteMatter, which allows every layer to draw on past-token representations from any depth, and cyclic iteration, which updates interleaved groups of tokens in turn while processing the tokens within each group in parallel.
We use training-data attribution as an interpretable tool for capability discovery, mapping which regions of the pretraining corpus support social reasoning versus STEM reasoning in OLMo3-7B. Training-data attribution measures how strongly each training document influences a model's predictions on a benchmark, but docu...
Glenn Scott Matlin, Chandreyi Chakraborty, Saehee Eom et al.· 0 citations
Experiments show that Reflective On-policy Self-Distillation (ROSD), which distills transferable reasoning insights rather than complete reference trajectories, consistently outperforms standard OPSD and reinforcement learning baselines, better preserves reasoning diversity, stabilizes training, and mitigates late-stag...
Zi-Qi Zhao, Xin-Yu Ma, Liu Yang et al.· 16 citations
This work revisits classical sentence classification models from a margin enlargement perspective, aggregating token-level features via max pooling and directly estimating sentence scores using a lightweight MLP, and achieves substantial efficiency improvements while maintaining competitive performance with state-of-th...
Speech representations from self-supervised speech models (S3Ms) are known to be sensitive to phonemic contrasts, but their sensitivity to prosodic contrasts has not been directly measured. The ABX discrimination task has been used to measure phonemic contrast in S3M representations via minimal pairs. We introduce pros...
Haitong Sun, Stephen McIntosh, Kwanghee Choi et al.· 0 citations
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.