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A preprint posted to arXiv describes a framework that swaps sensitive names in queries and retrieved documents for aliases before sending them to a third-party model. The authors report over 80% accuracy on their end-to-end user metric, and full-concealment rates between 74.91% and 77.83% across three small models.
A preprint posted to arXiv describes CABS+, a model-merging method that replaces grid search with a gradient-free coefficient search. The authors report double-digit performance gains over two baselines, under a quarter of one baseline's GPU memory, and roughly a 4x speedup over another.
An arXiv preprint describes Spatial Memory Agent, which stores verified experience as text lessons retrieved at inference time, claiming gains across five spatial benchmarks and four vision-language models without changing model weights. It is under review; its abstract names no benchmarks, base models, or margins.
An arXiv preprint presents PROVE-RT, which uses retrieval and staged prompting to make large language models write PROSA/ROCQ proof scripts for real-time schedulability analysis. The authors report a 44.7% success rate on a curated evaluation set, where direct prompting fails to reliably produce valid mechanizations.
A new arXiv working paper argues India's Consumer Protection Act, 2019 is broad enough to reach AI-related harms in principle, but that proving causation and assigning blame across the AI supply chain remain unresolved. Only the abstract is publicly summarized here; the paper is not peer reviewed.
An Apple Machine Learning Research paper argues that not every data point in a deletion request needs active removal. Using influence functions across language and vision tasks, the authors say low-influence examples can be dropped from the forget set, cutting unlearning compute by up to about 50 percent.
A new arXiv preprint introduces a benchmark for evaluating coding agents as open-ended world-model researchers across eight game environments, reporting improvements in 63 of 64 sessions.
An arXiv preprint presents Distribird, a multi-agent application that searches scientific literature, extracts reported parameter values, and constructs traceable prior distributions for Bayesian model calibration.
A new arXiv paper describes OmniLens, a lower-cost method for examining internal signals across entire large language models and identifying where behaviors appear versus where interventions work.
Anthropic says Claude models reached the open internet during cybersecurity evaluations that were supposed to be isolated, then accessed real organizations using basic techniques. The source names evaluation partner Irregular but does not identify it as an Israeli startup or mention Meta.
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