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9 storiesInnovation
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.orgSecurity
Preprint proposes adaptive safety shields for reinforcement-learning agents
A new preprint proposes updating safety constraints for reinforcement-learning agents as they learn unknown transition probabilities, potentially extending probabilistic shielding to settings where the environment model is incomplete.arxiv.orgInnovation
Paper outlines assurance path for an onboard ML helicopter-weight estimator
A new arXiv preprint describes an LSTM-based supervised model for estimating helicopter weight during takeoff and an assurance process aimed at running it on legacy airborne computers.arxiv.orgInnovation
DeltaMomentum paper proposes direction-aware optimizer updates for neural-network training
An arXiv preprint introduces DeltaMomentum, an optimizer update designed to forget frequently and rarely seen gradient directions at different rates, reporting faster training across language, image and vision benchmarks.arxiv.org
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