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Ornith introduced Ornith-1.5, a family of 397B-parameter mixture-of-experts (MoE), 35B MoE, and 9B dense models trained through a self-improvement loop. The system generates progressively harder tasks, creates task-specific scaffolds containing instructions, tools, decomposition, and orchestration, then produces reinforcement-learning rollouts. Rewards optimize task generation, scaffold construction, and solutions jointly using validity, frontier difficulty, novelty, solution quality, and resistance to reward hacking. The target task success rate is 0.2, favoring problems difficult enough to expose capability gaps while still yielding successful training trajectories. Ornith reports that its 397B model scored 86.1 on Terminal-Bench 2.1 and 56.0 on DeepSWE, compared with 85.0 and 59.0 for Claude Opus 4.8. It scored 86.0 on SWE-bench Verified, 92.8 on GPQA Diamond, and 80.0 on MCP-Atlas. The 35B MoE model activates 3B parameters per token and scored 68.5 on Terminal-Bench 2.1 using Claude Code and 79.0 on SWE-bench Verified. The 9B model scored 47.0 and 70.6 on those benchmarks, respectively; Ornith says its quantized Mobile version can run on iPhone and Android devices. Ornith reports that its benchmark results are averaged over five independent runs.

Google Scholar is a freely accessible web search engine that indexes the full text or metadata of scholarly literature across various formats. It provides a platform for researchers to find academic papers, theses, books, and conference proceedings.

LittleLearner is a hosted 5B model designed for interactive use in a browser, allowing users to study knowledge acquisition in language models. It utilizes an 88B-token corpus filtered to the U.S. elementary-school curriculum, enabling controlled training and comparison with unfiltered models.

AI Model Atlas visualizes populations of machine learning models as interconnected 3D graphs. This tool facilitates the exploration and analysis of model relationships and performance.

DeepSpec is an AI tool designed for formal verification of software systems. It utilizes the DSpark framework to enhance the reliability and security of software through formal methods.

Anthropic's Trust Center focuses on AI safety and research, emphasizing transparency and secure practices in AI development. The initiative aims to support the safe transition to transformative AI technologies.

Attention Residuals (AttnRes) serves as a drop-in replacement for standard residual connections in Transformers, allowing each layer to selectively aggregate earlier representations. It includes two variants: Full AttnRes, where each layer attends over all previous outputs, and Block AttnRes, which groups layers into blocks to reduce memory usage from O(Ld) to O(Nd).

The LLM Architecture Gallery compiles architecture figures and fact sheets from significant LLM comparisons. Users can enlarge figures and navigate to corresponding sections using model titles.