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代理工具

DisCo Distills 1,000 Machine Learning Repositories Into 5,353 Verifiable Agent Skills

New research turns source code, documentation, and tests into operational knowledge that can be loaded on demand, delivering gains across four research benchmarks with the same underlying model and agent framework. Skill-building costs are accounted for separately, however, and the results remain the authors’ own evaluation, so they should not be treated as evidence that skills can replace model training at low cost.

最新模型

LLaDA-Image Unifies Image Generation and Editing in a Single 6B Diffusion Model, With Turbo Cutting Sampling to 4 Steps

Ant Group’s inclusionAI has released LLaDA-Image weights and Diffusers inference code. A single model handles text-to-image generation, reference-image editing, and Chinese and English text rendering. The distilled version uses Twin-DMD to reduce the recommended sampling process from 50 steps to 4, but training code and independent performance validation are still unavailable.

推論系統

Magnitude Selects Local Models Based on Memory Bandwidth and Capacity, Automatically Configuring Quantization and Speculative Decoding for Coding Agents

The new open-source inference server Magnitude benchmarks hardware before recommending a complete combination of model, quantization, context length, and estimated speed. It automates the memory and concurrency settings most often misconfigured for local agents, though performance and model quality currently still depend largely on the project’s catalog and testing.

最新模型

Puffin-World Brings Gravity, Depth, and Images Into a Single World Model, Directly Generating Multi-View RGB-D and Point Clouds

Puffin-World uses a nine-channel Omni-Camera condition to unify absolute physical orientation with relative camera motion, while handling understanding, generation, and 3D reconstruction within a single architecture. The code, weights, and Puffin-16M dataset have been released, but its physical state modeling is currently focused mainly on gravity and latitude in static scenes.

最新模型

NeoMME Encodes Text and Images with a Single Bidirectional Transformer, Compressing Visual Document Indexes to 6 kB per Page

Hcompany has open-sourced the 260M- and 800M-parameter NeoMME models, which process text tokens and raw image patches with the same encoder instead of attaching a vision tower or causal decoder. Its retrieval variants produce both dense and late-interaction representations, although the current efficiency and accuracy figures come primarily from evaluations conducted by the authors.