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What happened, why it matters, and what to watch — without the jargon.
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Source-checked AI stories, newest first, for people who need to understand AI without chasing hype.
Researchers at leading Indian institutions have announced two significant developments in cancer research that aim to improve how doctors detect relapse and treat malignant cells.
A four-author arXiv systematic review surveys 55 studies from 2017 to 2023 on machine learning for autism diagnosis and treatment: supervised methods dominate, deep learning is growing, and progress depends on combining genetic, clinical and sensor data. The listing gives no search method or accuracy figures.
A new arXiv preprint proposes H²EDL, a classifier that expresses uncertainty over the label taxonomy, so it can commit to a broad category while declining a specific class. It reports roughly halved calibration error on two image datasets versus cross-entropy baselines; not peer reviewed, no numbers in the abstract.
An arXiv preprint says reshaping the spectral geometry of a vision model's representations during training makes model–brain alignment more symmetric, reporting a 55% relative gain in two-way predictivity. The authors call the demonstration initial, the abstract names no neural data, and the gain is a trade-off.
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shorter rollout and end-to-end training times with LlamaGen and preserved quality, but no numbers are given and it is not peer reviewed.
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.
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.
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.
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.
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.
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.
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.
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