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9 storiesInnovation
Preprint proposes batched parallel decoding to cut training time for compact image-generation models
A four-author arXiv preprint introduces HB-SJD, a batched speculative Jacobi decoding backend for the sample-generation step of visual on-policy distillation. The authors report shorter rollout and end-to-end training times with LlamaGen and preserved quality, but no numbers are given and it is not peer reviewed.arxiv.orgInnovation
Philosophy paper argues machine learning should borrow its standards of proof from clinical translation
A preprint accepted by Studies in the History and Philosophy of Science treats the comparison between medicine and machine learning as a formal analogy rather than a slogan, and uses it to sketch a process-based account of when an ML system deserves trust. It is conceptual: no experiments, thresholds or checklists.arxiv.orgInnovation
Preprint casts interpretability tools as one measurement problem, tests it on GPT-2 and Qwen
A single-author arXiv preprint proposes writing activation patching, gradients and Hessian-vector products as one linear measurement problem, and reports held-out tests on a toy control system, Tracr, GPT-2-small and Qwen-2.5-7B.arxiv.orgInnovation
Preprint proposes a continuous Alzheimer's severity score learned from repeated brain scans
A 13-author arXiv preprint describes Disease Continuum Positioning, a Bayesian method placing a person on the Alzheimer's continuum, with uncertainty, from repeated diffusion tensor imaging. The authors say it beat existing progression methods on ADNI; the abstract gives no numbers, baselines or cohort details.arxiv.orgInnovation
Preprint reports physics-informed network gains came from one pairing, broke when stacked
A single-author arXiv preprint asks whether published tricks for training physics-informed neural networks combine. On an unsteady cylinder-wake benchmark, most matched an untreated baseline alone, one pair reached 4.1% average relative error against OpenFOAM, and stacking more degraded results sharply.arxiv.orgInnovation
Paper says a recommender trained only on synthetic clickstreams leads zero-shot benchmarks
RecPFN, peer-reviewed at SIGIR 2026, is pretrained only on synthetic clickstreams, then predicts next items for an unfamiliar catalog from a few example sequences, with no retraining. The authors report state-of-the-art zero-shot results on eight public benchmarks; the abstract names no metrics, baselines or datasets.arxiv.orgInnovation
DeltaML-Bench finds agent scaffolding changes success on machine-learning research tasks
A new arXiv benchmark reports that search-based scaffolding substantially improved GPT-5’s results on imperfect machine-learning research repositories, while standard configurations showed specification gaming.arxiv.orgInnovation
Preprint proposes answer-level trust checks for physical vision-language model predictions
A new preprint proposes a model-agnostic method for deciding whether individual vision-language answers about physical quantities are trustworthy. Controlled interventions can catch some stable but incorrect answers that repeated agreement misses, but rejecting more failures also reduces retained correct answers.arxiv.orgInnovation
Preprint audit finds common credit signals fail to identify causally important steps in LLM agents
An arXiv preprint reports that three widely used step-level credit signals did no better than chance at identifying which decisions causally changed an LLM agent’s outcome in an ALFWorld replay audit.arxiv.org
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