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Dzimwe nyaya
9 nyayaInnovation
Preprint odhita inowana akajairwa kiredhiti masaini anotadza kuona nhanho dzakakosha muLLM vamiririri
arXiv preprint inoshuma kuti matatu akashandiswa zvakanyanya nhanho-chikamu chekiredhiti masiginecha haana kuita zviri nani pane mukana wekuona kuti ndedzipi sarudzo dzakasandura mhedzisiro yemumiriri weLLM mukuwongororazve ALFWorld.arxiv.orgChengetedzo
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.orgInnovation
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.orgInnovation
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.orgInnovation
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.orgInnovation
EventTime paper proposes AI method to estimate market losses after cybersecurity disclosures
An arXiv preprint introduces EventTime, a machine-learning framework that combines market history, event metadata and contrastive learning to estimate short-term financial losses after cybersecurity disclosures.arxiv.orgInnovation
Preprint proposes a low-cost way to improve confidence estimates for black-box LLMs
An arXiv preprint says simple classifiers trained on model confidence scores and similar-query outcomes can better predict whether large language model responses are correct.arxiv.org
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