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NemoClaw 0.0.114 Moves Read-Only MCP Calls Out of the Model Decision Path and Tightens Image and Installation Verification

NVIDIA has added deterministic read-only MCP calls to LangChain Deep Agents Code, allowing automated workflows to execute restricted tools without model involvement. The new release also pins container image digests, strengthens supply chain verification, and improves sensitive export cleanup, although “read-only” still depends on the accuracy of tool registration metadata.

推論系統

llama.cpp 0.2.0 Separates Stable Releases from Nightly Builds, Finally Letting Package Maintainers Pin Compatible Versions

llama.cpp 0.2.0 officially introduces semantic versioning for slower-moving stable releases, while retaining the existing `b[number]` tags for development builds produced for nearly every commit. The new system reduces versioning risks for Debian, Homebrew, and downstream bindings, but “stable” does not mean long-term support or comprehensive validation across hardware platforms.

最新模型

WithEveryone Generates Group Portraits of Up to Ten People by Binding Identities to Layouts, but Weights Remain Unavailable

A team from Tencent Hunyuan and Fudan University has introduced WithEveryone, which first binds each reference person to a structured layout and then supervises generation using a regionalized identity loss. In the authors’ identity-disjoint evaluation, it improves subject coverage and reduces copy-and-paste artifacts, but the license of the existing base model prevents the team from releasing the research-version weights.

推論系統

FreeToken Dynamically Allocates CPU, GPU, and PCIe Resources to Run a 35B MoE Model with 8GB of VRAM

Instead of using fixed offloading, FreeToken uses measured bandwidth to decide whether to transfer cache-missed experts to the GPU or execute them directly on the CPU. The paper also demonstrates a 753B model served by a single workstation GPU, but the full weights still require system memory; this should not be interpreted as “fitting all 753B parameters on one GPU.”

最新模型

Marin Launches Open Training of a 535B-A23B Model, Publicly Trackable Across 11 GB200 NVL72 Systems

Marin has begun training a mixture-of-experts (MoE) model with 535B total parameters and approximately 23B active parameters per token. The project plans to use 11 GB200 NVL72 systems to process roughly 18T to 18.75T tokens. Configurations, scaling experiments, and real-time telemetry are public, but model quality, the long-context strategy, and the final token budget have yet to be finalized.