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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 It reports roughly halved calibration error on two image datasets versus cross-entropy baselines; not peer reviewed, no numbers in the abstract.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shorter rollout and end-to-end training times with LlamaGen and preserved quality, but no numbers are given and it is not peer reviewed.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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