Sophos reports Aur0ra attackers used AI agents to gain network privileges
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Image: Primary Researchers report that MOSAIC, an architecture for learning from multiple types of input, improved average macro-F1, a classification performance measure, by 7.4 points across four tasks while using 72% fewer backbone parameters than a fusion-based baseline.
Researchers report that TSFLab, a workbench for coding agents conducting time series forecasting research, improved task completion by 20-163% and reduced fresh tokens per completed task by 11-66% across baselines in reproduction and benchmarking experiments.
Researchers report that NablaMimic trains a humanoid motion-tracking policy to a score of 0.82 in 20 minutes, compared with 1.2 h for a leading world-model method.
Researchers report that AMMA, a proposed chip architecture for serving long-context AI models, achieves 15.5X lower attention latency and 6.9X lower energy consumption than NVIDIA's H100 in their evaluations.
Researchers report in a preprint that CoFiBreak, a method for training AI models to test other models' safety defenses, discovered 1.6-2.0x as many distinct successful fine-grained attack strategies as the strongest baseline across frontier victims on HarmBench.
Windows malware called VOLTA-AUTODOXER uses stolen personal data to request AI assessments of victims and rank their assets by cash-out value, Cisco Talos researchers report.