Models & Labs

Anthropic Launches Claude Science for Research

MIT Technology Review AIJune 30, 2026high confidence

Why it matters

→Claude Science enhances productivity in scientific research by automating complex tasks.
→It positions Anthropic as a key player in AI for science, challenging established competitors.
→The tool's focus on reproducibility and integration with computing resources addresses critical needs in research.

Anthropic Launches Claude Science for Research — ©MIT Technology Review AI

Anthropic has launched Claude Science, a new AI product designed to support scientific research, particularly in computational biology and drug development. The tool can autonomously perform tasks with high-level instructions and integrates with powerful computing resources to enhance scientific productivity. This move positions Anthropic as a significant player in the AI for science sector, potentially challenging established companies like Google DeepMind. Claude Science is now available to all paid Claude subscribers, marking a strategic expansion of Anthropic's AI offerings.

Read original

More from MIT Technology Review AI

Researchresearch

Anthropic Unveils J-Space for LLM Insights

Anthropic has introduced a novel technique to peer into the inner workings of large language models (LLMs) with their new tool, the Jacobian lens, revealing a hidden area called J-space. This space provides insights into the words and concepts an LLM like Claude Opus 4.6 might consider before generating a response. By monitoring this J-space, Anthropic aims to better understand and control model behavior, offering a glimpse into the decision-making processes of LLMs. While not foolproof, this approach marks a significant step in mechanistic interpretability, potentially enhancing model transparency and reliability.

MIT Technology Review AIJul 9, 2026

More in Models & Labs

Models & Labsmodels

Llama.cpp b9946 Release Enhances Hexagon Optimizations

The latest b9946 release of llama.cpp focuses on optimizing Hexagon operations, particularly unary operations, to improve performance and efficiency. By introducing tiling for wide rows and replacing divisions with fastdiv, the update aims to prevent VTCM overflow and streamline code execution. The release also includes tracing instrumentation and specialized thread functions to enhance code generation. While no new models are introduced, these technical improvements make llama.cpp more robust and efficient for developers working with Hexagon architectures.

llama.cpp ReleasesJul 11, 2026

Models & Labsmodels

Llama.cpp b9948 Release Enhances CUDA Efficiency

The latest b9948 release of llama.cpp focuses on optimizing memory usage in CUDA operations, specifically in the ggml_top_k() and ggml_argsort() functions. By processing data in smaller chunks, the update reduces the need for large temporary buffers, enhancing performance on CUDA-enabled systems. This release also includes minor code improvements like allocating temporary destinations only once and refining the use of ternary operators. While no new model architectures are introduced, these changes make llama.cpp more efficient for developers working with CUDA, particularly in memory-constrained environments.

llama.cpp ReleasesJul 11, 2026

Models & Labsmodels

llama.cpp b9951 Release Enhances ET Backend

The latest b9951 release of llama.cpp marks a significant enhancement in the ET backend, introducing a range of new kernels and performance optimizations. This update includes the addition of various matrix operations and support for FlashAttention, which promises to improve computational efficiency. The release also focuses on vectorization and parallelization, aiming to boost performance across different operations. These changes make the ET backend more robust and capable, potentially benefiting developers working with complex AI models by offering improved speed and functionality.

llama.cpp ReleasesJul 11, 2026