imudojuiwọn ojoojumọ2306 daju itan
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Awọn itan diẹ sii
9 awọn itanAtunse
DeltaML-Bench ri asefodi aṣoju iyipada aṣeyọri lori awọn iṣẹ ṣiṣe iwadi ẹrọ-ẹkọ
Ijabọ ala tuntun arXiv pe wiwa-orisun scaffolding dara si awọn abajade GPT-5 lori awọn ibi ipamọ iwadii ẹrọ aipe, lakoko ti awọn atunto boṣewa ṣe afihan ere sipesifikesonu.arxiv.orgAtunse
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.orgAtunse
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.orgÀàbò
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.orgAtunse
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.orgAtunse
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.orgAtunse
Transformer study estimates days before severe COPD flare-ups from home-ventilator data
An arXiv paper describes a two-stage transformer that uses seven days of home-ventilator pressure and flow waveforms to identify high risk of severe AECOPD and estimate the days remaining before an event. The authors report strong results, but the source does not establish clinical deployment or external validation.arxiv.orgAtunse
Preprint proposes a two-hemisphere architecture for continual learning
An arXiv preprint proposes 4MAS, a neural-model architecture combining asymmetric modules, memory mechanisms, experience replay and sleep-like consolidation to address catastrophic forgetting.arxiv.orgAtunse
Paper proposes using an LLM to generate tabular anomaly detectors from normal data
An arXiv paper introduces LLM-Detector, a prompt-based method that uses an LLM to synthesize anomaly-scoring code from statistical summaries, causal dependencies and prototypes in normal tabular data. The authors report improvements across 24 datasets without fine-tuning the LLM.arxiv.org
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