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Google DeepMind says it is expanding its games research through a partnership with Fenris Creations, the studio behind the EVE universe. The program will test agents’ memory, continual learning, long-horizon planning and multi-agent behavior, beginning with an offline EVE Online environment.
The Nearby Glasses project uses Bluetooth data to flag some smart-glasses devices on Android and iOS. Its own documentation says the system can produce false positives, miss devices and should not be used to confront people.
Google Threat Intelligence Group says adversaries are using AI to accelerate vulnerability research, evade detection, automate malware actions and target the software supply chain around AI systems.
An arXiv preprint reports that language models may reveal sensitive data placed in context, even after refusing direct extraction requests. In controlled experiments, the authors say adaptive attacks recovered short secrets from ordinary outputs and extracted full Social Security numbers from a production-style agent.
A new arXiv paper introduces Thinkingbox, a sandbox and benchmark for testing AI agents on multi-step business tasks. The strongest tested model achieved a 65.36% pass@1 score but only a 25.25% pass^20 score, highlighting the gap between occasional success and dependable execution.
An arXiv preprint evaluating 3 LLMs across 11 compression methods reports that aggregate accuracy and perplexity can conceal uneven knowledge loss, overconfidence on newly lost information, and offsetting shifts in subgroup bias.
An arXiv preprint reports that task-irrelevant text consistently biases multimodal language models in visual judgments, and describes the effect as a measurable affine shift in decision margins.
An arXiv preprint identifies growing imbalance among neural-network weight singular values as a possible cause of lost adaptability, then proposes periodically clipping those values in continual and reinforcement learning.
A new arXiv preprint introduces a diagnostic for deciding when region-specific combinations of regression models can outperform a fixed blend. In tests described by the authors, a small labeled target-domain probe predicted gains and helped reject one deployment that produced more than 30 times the static loss.
An arXiv preprint reports evidence that Llama 3.1 8B internally tracks first differences in specially designed numerical sequences. The study offers a proposed mechanism for this behavior, but its scope and robustness remain unclear from the supplied abstract.
A preprint accepted at MLHC 2026 describes PAFIR, a reinforcement-learning framework that selects changing, person-specific fall-risk signals from repeated multimodal health measurements. The paper reports better pattern capture than baselines, but the supplied record gives no effect sizes or evidence of reduced falls.
An arXiv preprint describes DCGCNet, a model that combines ECG reconstruction and atrial-fibrillation classification, reporting AUC above 0.98 across seven cross-dataset settings and resilience to several noise types.
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