The Archive

Anthropic donates additional $20M to Public First Action, totaling $40M commitment to AI policy advocacy.

OpenAI launches ChatGPT for Small Businesses program offering training and automation tools via ChatGPT Work.

There are new 3.6 and 3.5 models today, but Google is already training Gemini 4.

A federal judge has signed off on Anthropic's $1.5 billion class action settlement with authors who accused the company of training its AI models on copyrighted books, as reported earlier by Reuters. In an order on Monday, Judge Araceli Martínez-Olguín writes that the settlement will provide "meaningful relief," offering authors around $3,000 for each book allegedly pirated by Anthropic. The $1.5 billion settlement is the "largest known copyright recovery in history," according to the law firm representing the plaintiffs. A group of authors - Andrea Bartz, Charles Graeber, and Kirk Wallace Jo...

RL framework for predicting lean blowout in gas turbine combustors via optimal reactor clustering.

Network-agnostic GNN initialization layer to improve spatial generalization in traffic assignment models.

Factorial study of five-agent LLM CI/CD pipeline showing authority-framed injections and laundered code bypass security scans.

Auditable fraud detection pipeline combining gradient boosting, graph features, TreeSHAP, and bounded LLM investigation agent.

BioSecBench-Surveillance: 100-task verifiable benchmark for AI agents inferring pathogen genomic analysis pipelines from raw data.

PathAgentBench: benchmark for vision-language agents on gigapixel whole-slide pathology images evaluating multi-scale evidence-seeking.

Robust financial statement fraud detection framework using LLMs on structured+textual data with temporal generalization evaluation.

Controlled study of prompt design (format, instruction count, context length) effects on instruction adherence and hallucination across five LLMs.

Multi-relational GCNs for user modeling in personalized systems; applies existing GNN techniques to recommendation tasks.

Inference-time steering mitigates cross-lingual factual inconsistency in LLMs through contextual intervention strategies.

Neural network surrogates for thermodynamic property prediction in supercritical combustion; domain-specific physics simulation.

DBMol leverages AlphaFold-3 and Boltz-2 structure prediction models for de novo small molecule drug design.

MeetingToM benchmark evaluates multimodal LLMs on theory-of-mind reasoning in multi-party meeting scenarios.

MASHT combines random convolutional features with pretrained tabular foundation models for time series classification.

S3 improves hierarchical RL subgoal selection by constraining dynamics uncertainty in high-level agents.

Preregistered replication study on label agreement and monotonicity in NLI datasets; methodological validation.

Investigates cost-quality tradeoffs in RL post-training for neural machine translation with reasoning verification.

AdaFlash improves speculative decoding via adaptive diffusion drafters with on-policy distillation for LLM inference acceleration.

RLAES framework uses RL with rubric-based rewards to jointly optimize essay scoring and feedback generation, introducing RFE for measurable feedback evaluation.

Report on drone computing infrastructure vision addressing software-hardware capability gaps for large-scale logistics, disaster response, and infrastructure inspection.

Methods for assessing student team performance in tabletop crisis-response exercises using recorded actions and communication data.

Treasury Secretary Scott Bessent said the U.S. could sanction Chinese open AI models over alleged IP theft, expanding the Trump administration's campaign to slow China's AI advances.

MIRA-Ev: multilingual clinical NLP benchmark with span-level evidence detection and argumentation graphs on Spanish MIR exam cases.

Offline RL method using adaptive regularization and conservative query selection for preference-based policy improvement without environment interaction.

ATLAS: diffusion-based sampler for generating Boltzmann-distributed amorphous material structures, addressing rare-event sampling in molecular systems.

Large deviations theory applied to derive free energy functionals for dense associative memory systems with polynomial interactions.