最新模型

新模型、開放權重、多模態、語音、影像與模型架構。

AI 研究與訓練

VIGOR Dynamically Allocates RLVR Rollouts Based on Reward Variance, Cutting Sampling for Mathematical Reasoning Training by Up to 2.3×

A new study replaces the fixed number of reasoning trajectories generated for every problem with a strategy that concentrates additional compute on samples exhibiting greater reward disagreement. The authors report higher rollout efficiency on mathematics and coding tasks, but have not released their code, while actual GPU time and scalability to larger models remain to be verified.

最新模型

AMD Opens the Full Instella-MoE Training Pipeline, With the 16B Model Activating Only 2.8B Parameters per Token

AMD has released six Instella-MoE checkpoints spanning pretraining, mid-training, long-context extension, SFT, DPO, and reinforcement learning, along with the training frameworks, data recipes, and inference code. The model uses Gated MLA and FarSkip-Collective to reduce attention and cross-GPU communication costs, but a formal technical report and independent evaluations are still pending.

最新模型

Photon-1 Learns to Operate Computers from Unlabeled Screen Videos, Replacing Action Labels with Latent-State Prediction

Induction Labs has introduced Photon-1, a 106B-A5B sparse MoE model that learns during pretraining solely by watching screen videos, without requiring mouse or keyboard action labels. The model first predicts the next latent screen state, then generates the actions needed to reach it, though current evidence of its performance comes primarily from the team’s internal evaluations.

代理工具

Cursor Router Assigns Coding Models Per Request, While Cost Mode Requires Retaining Grok 4.5 as a Routing Option

Cursor has transformed Auto mode into a Router that classifies each request and selects a model, allowing enterprises to switch among Intelligence, Balance, and Cost strategies. The company claims online testing shows savings of 60%, but hidden-by-default model selections and mandatory routing options for certain modes make auditability a key deployment concern.

最新模型

UK–US Joint Evaluation: Kimi K3 Completes a Simulated Enterprise Intrusion, but None of 41 Exploit Attempts Achieves Arbitrary Code Execution

Preliminary testing by the UK AISI and the US CAISI shows that Kimi K3 scored 32% on ExploitBench, outperforming GLM-5.2 but trailing the tested US frontier models by a wide margin. The model also completed a 32-step simulated enterprise attack once, but the limited evaluation conditions and sample size do not support direct conclusions about real-world attack success rates.

AI 安全

AgentForger Uses URL Parameters to Forge ChatGPT Workspace Agents and Inherit Users’ Existing Connector Permissions

Zenity has disclosed a patched CSRF-style attack chain: merely opening a crafted link while logged in could cause ChatGPT Agent Builder to automatically create, schedule, and run an attacker-controlled agent. The vulnerability did not bypass OAuth; instead, it inherited existing authorizations, highlighting a new trust gap among agent creation workflows, execution identities, and tool permissions.

AI evaluation

EvolvingIntent Turns Static Benchmarks Into Conversations With Changing Requirements, Exposing Gaps in Agents’ Ability to Track Revised Instructions

Microsoft Research introduced EvolvingIntent, which transforms existing math, SQL, search, and programming tasks into multi-turn conversations in which requirements are progressively disclosed, revised, or redirected. Experiments show that models’ performance on single-turn benchmarks does not transfer reliably to scenarios where requirements continually change. The data-construction and evaluation code has been released publicly.

硬體

VibeVoice-ASR-BitNet Shrinks Speech Recognition to 1.58 GB With Heterogeneous Quantization, Enabling Real-Time Transcription on Three CPU Threads

Microsoft Research has released VibeVoice-ASR-BitNet, applying INT8 and ternary-weight quantization to the speech encoder and language model, respectively, so multilingual ASR can run without a GPU. Official tests show the model is 1.6× to 2.3× faster than Whisper.cpp at a similar size, though it currently supports only offline batch transcription.