Language-subspace interventions aim to improve multilingual reasoning by suppressing language-related components in mid-layer activations. Using MATH and MGSM across four open-weight LLMs, we ask three questions: do estimated language subspaces capture translation-associated variation? Does projecting them out remove l...
Shaanil Punglia, Dadhichi Telwadkar, V. Rathore et al.· 0 citations
Byte level language models avoid tokenizer dependence and provide universal coverage, but processing every byte at full resolution is expensive. We introduce DeepMem, a causal multiscale architecture that separates semantic compression from exact byte preservation. A learned invertible subband transform constructs a hi...
Fairness evaluation of clinical prediction models for binary outcomes often involves fairness metrics that compare the area under the receiver operating characteristic curve (AUC, or c-statistic) across subgroups. However, the AUC has been shown to be a non-collapsible metric, meaning that its subgroup specific and ove...
Jo\~ao Matos, Ben Van Calster, Richard D. Riley et al.· 0 citations
The study provides a reproducible benchmark for comparing agent architectures, language models, and action-validation methods for supervisory fault management in chemical processes.
Javal Vyas, Mehmet Mercangöz· 0 citations
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Reliable streamflow prediction across time and in ungauged basins requires representations that capture hydrologically relevant basin characteristics. Existing methods rely on either fixed handcrafted attributes or general-purpose Earth observation embeddings not tailored to basin structure. We introduce \textbf{Basin2...
Muhammad Talha, Xiaofeng Liu, Yuan Yang et al.· 0 citations
Robot foundation models have shown remarkable performance in robot control, yet adapting them to a new task still relies heavily on teleoperated demonstrations. Egocentric human videos offer a scalable substitute, but overlaying rendered robots on human videos or retargeting human poses into robot actions leaves signif...
Seung-Yong Lee, Suhyeok Jang, Minyoung Kim et al.· 0 citations
Scaling vision-language foundation models has significantly advanced generalist robot manipulation, but the increased model scale introduces computational latency that degrades real-time physical reactivity. Existing Vision-Language-Action (VLA) architectures execute high-level semantic reasoning and action ex- ecution...
Sombit Dey, Luc Van Gool, Danda Pani Paudel· 0 citations
This work investigates why irrelevant historical audio changes answers even when the queried event remains represented, and shows that source-role information can influence a decision without exclusively controlling which historical values affect it.
Hong-Jin Song, Weiqiao Shan, Run-Wu Shi et al.· 0 citations
LLM inference decode executes fine-grained collectives whose latency directly limits per-user interactivity, yet scale-up fabrics are still built from electronic packet switches designed for training. We observe that, for a given model deployment and parallelization, the communication structure of decode is fixed, and...
M. Thürmer, Nicolas Scotto di Perto, Eloise Stein et al.· 0 citations
Predicting affinity between small molecules and their protein targets remains one of the central problems in drug discovery. Due to the substantial cost of wet-lab experiments, machine learning models of drug-target affinity are usually trained on relatively small datasets, ranging from tens to hundreds of molecules. G...
This work proposes RFWorld, a Rollout-Free World model for end-to-end trajectory evaluation, which constructs a shared scene memory and shapes it into a future-readable representation via lightweight training-time temporal readout with semantic supervision, keeping training overhead low.
Multimodal discrete diffusion language models (dLLMs) generate responses through iterative mask prediction and offer a promising alternative to autoregressive large vision-language models. However, they still suffer from hallucination, producing outputs that are plausible under language priors but insufficiently ground...
Junzhe Chen, Tian Qin, Eugenie Shi et al.· 0 citations
