Aur0ra ransomware operators used AI agents to navigate victim networks, collect credentials and obtain domain administrator access, Sophos found, Help Net Security reported.
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.