Zhihui AI has released GLM-5.3, an open-weights model that builds on the GLM-5.2 base architecture through post-training improvements. The update significantly enhances performance in complex coding and long-horizon tasks, establishing new state-of-the-art results on several public benchmarks.
Zai has released GLM-5.3, an open-weights model that builds on the GLM-5.2 base architecture through extensive post-training. The update delivers significant gains in complex coding and long-horizon tasks, establishing new state-of-the-art results on several public benchmarks.
Zai has introduced GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. It features a hybrid architecture combining sparse and linear attention to reduce long-context serving costs while maintaining precise capabilities.
ZhiPu introduces GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series, featuring a hybrid architecture that combines sparse and linear attention to reduce long-context serving costs while preserving precision.
A comparison of GLM-5.3 and its distilled variant, GLM-5.3 Flash, on the DeepSWE benchmark reveals that distillation preserves core coding capability while significantly reducing rollout costs. The full model achieves a 69.0% pass@1 rate at $3.99 per task, whereas the Flash variant scores 63.4% at just $0.24, representing a 17x price reduction.
Zhipu AI has released the GLM-5.3-Flash model, positioning it as a frontier intelligence solution with low-cost inference capabilities.
Z.ai has formally launched GLM-5.3-Flash, revealing that the previously previewed "Ox Alpha" model is its public identity. The model features 320 billion total parameters with 18 billion active, a 1 million-token context window, and native multimodal capabilities.
Z.ai has released GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series, featuring a mixture-of-experts architecture with 320 billion total parameters and 18 billion active per token. The model supports image and video inputs within a 1,048,576-token context window and is available under an MIT license on Hugging Face.
Zhipu AI has released GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series and the first open-weight release of the glm5_next architecture. The 320B-parameter model is trained on a 30T-token multimodal corpus and features a hybrid sparse and linear attention mechanism to reduce long-context serving costs.
Z.AI confirmed on Wednesday that the Ox Alpha model is a new iteration of its GLM series. The company stated it will release the model's weights later that night, responding to queries by Bloomberg News.
Researchers introduce TelecomGPT-R1, a family of open-source unified telecom reasoning models designed to automate engineering tasks by reasoning over standards, configurations, and logs. The model addresses the limitations of existing general-purpose and specialized LLMs through a structured approach covering protocol, knowledge, modeling, and fault axes.
The authors introduce Fathom-Vaidya, a 30B parameter model that uses synthetic data and rubric-based reinforcement learning to enhance diagnostic and clinical healthcare reasoning. The training framework first applies rule-guided RL to MedBullets-derived questions for diagnosis, then utilizes 5.3k synthetic multi-turn scenarios with multi-dimensional rubrics for interactive clinical tasks.
Zhipu's official AI coding desktop app, ZCode, has been found to silently package and upload users' complete workspaces when logged in. This process includes encrypting the data and sending it directly to Aliyun OSS.
The GLM team has published a blog post on z.ai detailing the architecture and design of their inference infrastructure.
A study evaluates six frontier language models on a two-agent communication task where a questioner identifies a target from N Wikipedia paragraphs using exactly log2(N) yes/no questions. The experiment ran 408 games across document sets of 4 to 1024 paragraphs, revealing significant performance disparities among the tested models.
Charlie O'Neill of Base-10 discusses why the Kimi and GLM models are "almost objectively" superior to Opus 5 in a recent episode featuring Dwarkesh Patel.
InternLM has introduced Intern-S2-397B, a 397-billion parameter multimodal foundation model designed for scientific intelligence and long-horizon agents. The model scales across pre-training, reinforcement-learning task coverage, and interactive agent environments to enhance general reasoning and agentic capabilities.
Researchers demonstrate that directional ablation, a white-box attack removing refusal by projecting out a single direction from weights, remains effective on frontier mixture-of-experts (MoE) models like GLM-5.3-Flash. The study shows that while the attack survives the architecture, its effects are distributed across attention, dense, and routed-expert writers rather than concentrated in one location.
CogEvol introduces a family of models designed to convert course briefs into structured JSON slides or interactive HTML pages in a single pass, replacing multi-turn agent scaffolding. The system utilizes a production-grounded data pipeline and hybrid reward mechanisms to enforce reliability during training.
The authors present WebWorld, an interface that allows a Vision-Language Model (VLM) to interact autonomously with the browser as a deterministic world model for web code. This approach addresses the structural flaw in VLM-driven self-improvement where the model judges its own repairs by introducing a counterparty that cannot be fooled.