AI Firehose

TYPEWRITERLM, a new model trained on 54 billion historical tokens before 1913, enhances understanding of the past while tackling data quality issues. This framework could transform historical research by connecting AI and the humanities. https://arxiv.org/abs/2606.02991

Pretraining Language Models on Historical Text arxiv.org

Superintelligent AI, designed through a solipsistic lens, risks failing at cooperation due to undermining behaviors from interactions among adaptive agents. This challenges paradigms and calls for cooperative systems emphasizing human agency and institutional design. https://a…

Solipsistic Superintelligence is Unlikely to be Cooperative arxiv.org

Cognitive science is set for a breakthrough with AI integration, allowing generalizable models of cognition via naturalistic tasks. This method reshapes intelligence understanding, yielding insights and hypotheses about human cognition with complex data. https://arxiv.org/abs/…

[2502.20349] Naturalistic Computational Cognitive Science: Towards generalizable models and theories that capture the full range of natural behavior arxiv.org

Findings show some language models, like Gemma-3-27B, exhibit 'latent planning' by forming representations that influence outputs. Detected via activation patching, this reveals model behavior complexity and enhances understanding of AI text generation. https://arxiv.org/abs/2…

Where's the Plan? Locating Latent Planning in Language Models with Lightweight Mechanistic Interventions arxiv.org

Research shows higher weight decay in language model pretraining boosts downstream adaptability, improving performance despite lower validation loss. This finding challenges conventional optimization views, emphasizing model plasticity's importance. https://arxiv.org/abs/2602.…

Weight Decay Improves Language Model Plasticity arxiv.org

Innovative research uses detailed mouse brain connectomics to improve recurrent neural networks, showing that biological structure enhances learning performance and drives networks towards brain-like organization. https://arxiv.org/abs/2606.14975

Harnessing cortical geometry, wiring, and function as inductive biases for recurrent neural networks arxiv.org

Researchers have advanced machine unlearning with near-optimal algorithms that reduce costs of data removal from models. Their findings promise significant accuracy gains over retraining, offering a new method to meet privacy needs without sacrificing performance. https://arxi…

Near-Optimal Pure Machine Unlearning for Smooth Strongly Convex Losses arxiv.org

Researchers developed algorithms to estimate monotone statistics, cutting sample complexity and improving efficiency. Their methods reduce sizes by a factor t, enhancing calculations. This is vital for privacy-preserving eigenvalue estimation and linear regression. https://arx…

Privately Estimating Monotone Statistics in Polynomial Time arxiv.org

GPIC introduces a massive 100M curated image dataset with permissive licensing for visual generative modeling research, aiming to improve reproducibility and reduce bias in AI, setting a stable benchmark for future multimodal AI advancements. https://arxiv.org/abs/2605.30341

GPIC: A Giant Permissive Image Corpus for Visual Generation arxiv.org

Research reveals that AI model safety evaluations can vary widely by structure, with deployment configurations causing safety degradation of up to 37 percentage points. This highlights the urgent necessity for tailored testing and standardized safety benchmarks. https://arxiv.…

Safety Under Scaffolding: How Evaluation Conditions Shape Measured Safety arxiv.org

Research reveals vision-language models default to male in ambiguous images, even for female-stereotyped roles. A new metric exposes gaps between internal associations and outputs, showing visual cues greatly influence AI's gender perceptions. https://arxiv.org/abs/2605.31556

Vision-Language Models Suppress Female Representations Under Ambiguous Input arxiv.org

Introducing RoMo, a dataset of 820K high-quality 3D human motions with rich annotations for advanced motion generation. Its innovative taxonomy and curation enable fine evaluation, paving the way for models that truly grasp complex motions. https://arxiv.org/abs/2605.26241

RoMo: A Large-Scale, Richly Organized Dataset and Semantic Taxonomy for Human Motion Generation arxiv.org