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最新模型

GLM-5.3 Releases 744B MoE Weights, but Million-Token Deployment Still Requires Data-Center-Class Resources

Two weeks after launching the API, Z.ai has released the GLM-5.3 weights. The model retains GLM-5.2’s base architecture, with capability gains driven primarily by post-training for agentic coding and cybersecurity. The FP8 files still total about 756GB, and the custom license and vendor-led evaluations mean that performance, safety, and legal validation remain necessary before adoption.
AI 安全

Fake Professional Dashboards Make LLM Agents More Willing to Bet, Raising the Action-Commitment Rate Across 12 Models to 36.8%

A new study finds that pairing inherently unpredictable questions with authoritative-looking data dashboards makes LLM agents more likely to make directional decisions—even when every figure is fabricated. Fine-tuning on 540 synthetic examples can temporarily repair the action gate, but enforcing a rigid output format causes the improvement to disappear.
機器人與具身 AI

Isaac 0.5 Unifies Video Understanding and Robot Control, but Its Key Future-Perception Training Objective Remains Closed

Perceptron AI has released the weights for the 36-billion-parameter Isaac 0.5 model, along with a LeRobot integration, enabling a single sparse backbone to output language, spatial coordinates, and continuous or discrete actions. The company claims that general video can dramatically reduce the need for teleoperation data, but the figure is extrapolated from offline action loss, and the core self-supervised objective remains proprietary.
最新模型

Qwen3.8-Flash-Next Uses Block-Sparse Attention and n-gram Parameter Scaling to Preview a New Architecture for Qwen4

Alibaba has released the weights for Qwen3.8-Flash-Next, which uses Qwen Sparse Attention, Gated Residual, and 51 billion n-gram embedding parameters to reduce the computational cost of long contexts. The model natively supports 262,144 tokens, but its performance figures come primarily from official evaluations, and it is not licensed under the commonly used Apache 2.0 license.
AI 安全

D2C-Routing Separates AI Text Detection into Content and Expression Sources, Achieving 0.8603 TPR in Four-Class Classification at a Low False-Positive Rate

A Beihang University team no longer asks only whether text was generated by AI, but separately identifies the sources of its content development and surface expression. The system outperforms a rerun of RACE on the same data split in the reconstructed MixD2C test set, though cross-dataset generalization and full reproducibility remain limited.
AI 安全

LoopHarness Preserves Risk State Across Iterations, Reducing Overall ASR Across Five Agent Attack Classes to 0.1%

New research finds that guardrails reset after each trajectory cannot detect attack evidence distributed across multiple iterations, while simply decaying risk scores over time can be bypassed by waiting. Across 1,000 attacker-controlled episodes, LoopHarness preserved a 96.9% clean-task completion rate, though the results still require independent reproduction and validation in real-world systems.