Archive
新模型、開放權重、多模態、語音、影像與模型架構。
最新模型
Agnes AI has relabeled the 33B weights on Hugging Face as Preview, explicitly stating that they are not the API model tested by Artificial Analysis. The two differ in context length, parameter disclosure, and benchmark results, so deployers can no longer use the leaderboard score of 36 to endorse the open checkpoint.
推論系統
The new release brings disaggregated inference, vLLM multiprocess routing, and multi-worker replica management into a single open-source serving layer. It also allows models to be downloaded in advance, launched from an existing cache, and resumed after interrupted downloads, although the release notes provide no throughput or latency comparisons.
代理工具
The public preview launched on September 11 uses OpenAPI 3.x extensions to let agents access existing backends through `tools/list` and `tools/call`, without requiring a separate MCP server. The initial release supports tools only, with no streaming, long-running calls, or binary responses, so authentication and protocol compatibility still need to be reviewed before production migration.
最新模型
InclusionAI’s 124B multimodal MoE now offers FP8, FP4, and INT4 weights as well as a working SGLang recipe, bringing image, video, and tool calling capabilities into a single model. The latest independent test by Artificial Analysis produced a score of 25, but it used a different evaluation version from the model card’s claimed score of 42, so deployers should not treat the two as being on the same scale.
AI 安全
The September 12 nightly of Gemini CLI 0.61.0 tracks command arguments derived from external content and requires renewed user approval after build configuration changes. A separate set of patches prevents sandboxes from mounting home directories, credentials, and `.env` files, but a stable release is not yet available.
最新模型
SenseNova-U1.5 uses a Mixture-of-Transformers (MoT) architecture that does not rely on a separate vision encoder or VAE, enabling a single model to handle understanding, reasoning, generation, and editing. The technical report added on September 11 details its training methods and evaluations. Existing weights and inference code are available, but the SFT, RL, and multi-expert distillation code has not yet been fully released.
最新模型
OpenAI is making its life sciences–specific model available to eligible institutions worldwide through ChatGPT, Codex, and the API, with standard pricing taking effect on October 5. The simultaneously released Codex plugin divides entity normalization, evidence retrieval, and multi-tool orchestration into 50 skills, although model access remains subject to use-case and governance review.
AI 基礎設施
Habitat, the online storage service underpinning ChatGPT, Codex, and the API, now handles 95% of production traffic in Rust, with aggregate throughput exceeding 70 million requests per second. The gains are based on OpenAI’s internal measurements, while details about database-layer and multi-tenant optimizations will be disclosed in a follow-up article.
代理工具
The new `claude plugin eval` command compares performance with a plugin enabled and disabled, producing JSON and HTML evaluation reports. Initial testing suggests that automatically generated criteria are better at verifying instruction following than proving that a plugin genuinely improves task quality.
推論系統
RunningHub has published a multi-GPU inference recipe for MiniMax H3 combining SGLang, step distillation, SageAttention2, Cache-DiT, and `torch.compile`. It reduces the reported latency for a five-second video from 348.8 seconds to 28.7 seconds, but the critical internal acceleration weights have not been released, and the public LoRA cannot directly reproduce that result.
最新模型
TinyDiT trains a 210M-parameter DiT from scratch on a single RTX PRO 6000 in 400,000 steps over 3.5 days, while releasing its code, weights, and stage-by-stage measurements. The results show that flow-matching loss is a poor proxy for image-generation progress, whereas registers, timestep shifting, and compiled training provide more actionable signals.
最新模型
T1 records and replays MoE expert selections for every token, preventing numerical differences between rollout and training from sending them through different subnetworks. Its open weights support self-hosting, but benchmark results and competitor comparisons still come mainly from the team’s own evaluations.