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arXiv:2606.10279v2 Announce Type: replace Abstract: Supervised fine-tuning with synthetic rationales is widely assumed to improve clinical prediction by teaching models not only what to predict but why. We test this assumption on five-year Alzheimer's…
arXiv:2610.30116v1 Announce Type: cross Abstract: Through a tangle of real-world interactions, from synthetic training pipelines to data shared on the open web, frontier LLMs absorb what their predecessors and competitors create. How well does…
arXiv:2610.03098v2 Announce Type: replace Abstract: Codon optimization, the process of selecting synonymous codons to improve mRNA translation efficiency and protein expression, is central to therapeutic protein production and mRNA vaccines, yet it remains a…
arXiv:2511.16080v3 Announce Type: replace-cross Abstract: Modern data processing workflows frequently encounter ragged data: multidimensional collections whose inner lengths vary and are revealed only at runtime. Ragged data is prevalent in domains throughout science and…
arXiv:2610.28773v1 Announce Type: new Abstract: Non-uniform allocation can improve post-training pruning of large language models, yet quality can still degrade sharply at high sparsity. To control degradation as sparsity increases, we introduce LADDER (Layer-Wise…
arXiv:2609.38645v3 Announce Type: replace Abstract: Models are usually aligned based on their observed outputs, using demonstrations, preference data, or reward signals. These objectives reward responses that look aligned. More capable models may learn to…
arXiv:2610.28771v1 Announce Type: new Abstract: Objects move, cameras pan, and weather drifts with the wind: much of what changes in sequential data is motion. Motion changes where things are, not what they are, and…
arXiv:2604.09175v2 Announce Type: replace Abstract: We develop a theory of generalization and scaling for Mixture-of-Experts (MoE) Transformers that cleanly separates emph{active} per-input capacity from routing combinatorics. By conditioning on fixed routing patterns and union-bounding…
arXiv:2610.28770v1 Announce Type: new Abstract: RLVR improves language-model reasoning, but commonly relies on independent stochastic rollouts for exploration, which can repeatedly sample similar failed strategies. Prior work has seen success with approaches that guide…
arXiv:2510.16322v2 Announce Type: replace Abstract: Deep learning has led researchers to rethink the relationship between memorization and generalization. In many settings, memorization does not hurt generalization due to implicit regularization and may help by…
arXiv:2610.28699v1 Announce Type: new Abstract: Reinforcement learning (RL) enables adaptive and autonomous decision-making in embedded, edge, and heterogeneous computing systems. Field-programmable gate arrays (FPGAs) and integrated circuits (ICs) offer low latency, energy efficiency, and…
arXiv:2610.28703v1 Announce Type: new Abstract: Large-scale topology optimization (TO) remains computationally demanding, especially for stress-constrained problems. To address this challenge, we propose the simultaneous neural topology optimization (SNTO) framework where we represent state, adjoint,…