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Source-checked AI coverage of product launches, policy shifts, safety research, and industry moves, explained in plain English by a nonprofit education team.
Every story links to the strongest available evidence: original sources when available, otherwise clearly attributed reporting.
What happened, why it matters, and what to watch — without the jargon.
Uma isignali incane, asishicileli lutho kunokupheqa okuphakelayo.
Source-checked AI stories, newest first, for people who need to understand AI without chasing hype.
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.
A new arXiv preprint introduces PU-HNO, a three-stage model for predicting detailed indoor radio maps from lower-fidelity simulations and scene information. The authors report improvements over several baseline approaches, but the supplied record does not provide numerical results or evidence of real-world deployment.
An arXiv preprint introduces Tianmu-TC, a generative forecasting framework trained on Western North Pacific data that its authors say improves tropical-cyclone forecasts across global ocean basins while using less computation.
An arXiv preprint describes an LLM-based system that predicts job execution time and energy use from source code before allocating GPU resources. Its authors report lower energy consumption and queueing time with an unnamed data center, but the record lacks enough detail to independently assess the result.
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