Regulatory-aligned LLM integration in pilot university hospitals can enhance health care education and decision-making across standardized taxonomy, evidence-based personalized clinical care algorithms, computational tasks, and nondiscrimination policies, under structured interdisciplinary expert oversight.
Elena Sblendorio, Vincenzo Dentamaro, M. De Maria et al.· JMIR Medical Informatics· 0 citations
Mate choice copying, the use of social information from others' mating decisions, is a taxonomically widespread form of social learning that can shape sexual selection. Two meta-analyses concluded that social information has a moderate positive effect on mate choice, but both rest on literature searches now at least se...
Eduardo S. A. Santos, Aleksandra Milenović, Aneta Arct et al.· Biology Open· 0 citations
This work presents, to their knowledge, the first simultaneous sign-to-sign (S2S) translation system, with two wait-k regimes: test-time wait-k inference applied directly to a full-sentence model, and a trained wait-k model via stochastic multi-path supervision.
Ze-Tian Wu, Bo-Wen Xie, Stefan Lee et al.· 0 citations
S PRING uses SMT solver as a training-time verifier of intermediate reasoning steps to provide process-level supervision and introduces the notion of a novel reasoning step, namely, a step that is logically valid, consistent with the evolving reasoning state, and not already implied by previously accepted non-contradic...
Muhammad Asif Ali, Wen-Qing Wang, Huan Wang et al.· 0 citations
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Experiments on multiple LVLMs show that the Balanced Fitting method consistently outperforms prior PTQ approaches under both weight-only and weight-activation quantization, while lower reconstruction loss does not reliably translate into better downstream performance.
Min-Chan Kang, Kyeonghye Park, Seungyeon Sa et al.· 0 citations
The results invert the pre-registered prediction that most attention heads behave like Kohn-nearsighted insulators, pointing instead to critical, glassy or heavy-tailed regimes; the inversion is specific to the<= 350M scale tested, while the rank-face no-gain result held to 7-8B.
Retrieval-augmented generation (RAG) lets language models answer questions more accurately by consulting relevant documents. Many valuable collections, such as medical records, cannot be pooled because of privacy rules. Federated RAG leaves each collection with its owner, or node, which scores candidate answers from it...
Only on MathNet-Retrieve could an inversion, benchmark score up and real retention down, to one edit of a training file be pined, and three trained models are released.
A. Habibullah, M. Alshiekh, Yazan Alshoibi et al.· 0 citations
Serving long documents to a Large Language Model (LLM) repeatedly is expensive: computations grow with context length, and the memory footprint of the key-value (KV) cache balloons. Compressed KV (CKV) representations aim to mimic the cache of a document and are typically computed once and for all, ahead of inference t...
Sonia Laguna, João Monteiro, Marco Cuturi et al.· 1 citation
When pricing agents meet repeatedly on a platform, the platform decides who faces whom. We ask whether that choice moves the prices the agents learn, and whether a rise comes with learned punishment. In a pre-registered randomised experiment in the Bertrand duopoly of Calvano et al., each agent's price is set by a tabu...
Paul-Peter Arslan, Yubin Kim, Xiao Xiao· 0 citations
Memorization has been proposed as a mechanism to explain how language models fit the tail of their training distributions, but its training dynamics are not understood well. In this work, we take a fine-grained look at memorization by decomposing the loss trajectory of memorized sequences over training and model parame...
Florian Eichin, Philipp Mondorf, Andrei Mircea et al.· 0 citations
Quality-Gated Length Advantage Shaping (QGLAS), which first computes advantages from quality rewards alone, then adds bounded bonuses only to shorter positive-advantage responses, leaving all other advantages unchanged, consistently achieves a stronger quality--length trade-off than representative baselines.
Zi-Jun Weng, Zhong-An Bi, Xuan-Ang Gao et al.· 0 citations
