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813 papers
A supervised ensembling framework that trains a classifier over heterogeneous UQ-based scorer outputs on a small, domain-specific dataset of labeled LLM responses, then applies it to out-of-sample hallucination classification without retrieval, tools, or reference documents is studied.
Parason is introduced, which reveals and learns both forms of parallelism in LLM reasoning, and identifies Trial Parallelism as the majority of parallelizable reasoning computation, and it becomes increasingly dominant on hard problems.
FPGAgent is the first task-specification-to-executable HLS generation framework experimentally validated on a well-established benchmark, and the value of end-to-end validation is demonstrated.
A multilevel conceptual pathway in wearable reflectance PPG is supported, in which mechanical conditions at the sensor-skin interface are associated with changes in PPG signal characteristics, derived features, and, in a smaller body of studies, downstream physiological estimation.
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This paper argues that a small win does not show that routing did anything, the authors' or anyone else's, and argues that a small win does not show that routing did anything, theirs or anyone else's.
"List as many words as you can that start with M." The verbal fluency (VF) task is simple, yet even a typical university student only manages to produce about 15 words within 1 min, and there is substantial variability around this mean. The present study examined how linguistic and domain-general abilities contributed to VF performance in a large sample of healthy adult native speakers of Dutch (N = 571). We assessed the effects of linguistic knowledge, processing speed, short-term/working memory, and fluid intelligence on performance in the VF task. To examine whether linguistic and domain-general abilities contribute differently across VF task types, we included semantic trials (category-based generation: animals, food) and phonemic trials (letter-based generation: words beginning with S or M). We assessed the total number of correct words produced and the time to first response. Mixed-effects modeling showed that linguistic knowledge predicted the total number of correct responses in both semantic and phonemic VF. Short-term/working memory and processing speed were also significant predictors, but with smaller estimated effect sizes. Time to first response showed little effect of linguistic skills. We discuss how linguistic knowledge shapes the structure of the mental lexicon such that it affects both meaning-driven and form-driven access to lexical items. In addition, we provide updated norms for VF performance in Dutch and practical suggestions for using the task. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
The results suggest that LLM data mixing should be treated not only as a prediction problem, but also as an experimental-design problem in which the proxy mixtures themselves can be chosen to improve statistical efficiency.
Agent harnesses record a failed tool call and its error message in the transcript and ask the model to continue, on the assumption that the error is corrective information, and it is found the gain is negative for every instruction-tuned model tested.
This work introduces a general steering technique called Semantic Overlays: small learned adapters applied at chosen prefill positions to a frozen model's residual stream that defends against the broad class of prompt injections that add instructions in untrusted context.
This work introduces Mixture of Channel Experts (MoCE), a structured sparse channel-mixing layer, inspired by MoE, that replaces pointwise (1x1) channel-reduction projections and matches or exceeds dense baselines and prior channel-selection methods while reducing MACs by 16.7% and end-to-end latency.
This work presents MARS (Multi-Agent Relay of Specialized LLMs), a prompt-only framework in which each agent is a topic specialist---dynamic programming, graphs, strings, geometry, and so on---grounded by retrieval-augmented generation over an algorithm-theory corpus.
A reproducible reliability audit of the developer-accessible on-device foundation model is presented, framed as an oversight question: can a user or a resource-constrained developer tell when the model is wrong?
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