6Latent Space (swyx)·
Poolside AI's Eiso Kant on building a model factory and training Laguna S, a 118B MoE model
Latent Space interviews Poolside AI co-CEO Eiso Kant about how a small research team built a model factory capable of training Laguna S, a 118B mixture-of-experts model that reportedly outperforms Thinky's approximately 1T open-weights model. The conversation covers the organizational and technical approach behind Poolside's training infrastructure and research process. Poolside is a code-focused AI lab, and this signals meaningful progress in efficient large-scale MoE training by a smaller team.
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Z.ai releases GLM-5.2, a 753B MoE open-weights model claiming top open-model ranking on agentic coding benchmarks
Z.ai released GLM-5.2, a 753-billion-parameter mixture-of-experts open-weights model optimized for long-running agentic coding tasks, with a 1-million-token input context and MIT license. The model ranks first among open-weights models on Artificial Analysis's Intelligence Index v4.1 (score 51, behind Claude Opus 4.8 at 56 and GPT-5.5 at 55) and leads all models on PostTrainBench, a benchmark for agentic fine-tuning tasks. Key technical contributions include a modified sparse attention indexer applied every four layers (cutting per-token computation 2.9x at 1M context), a switch from GRPO to PPO for long-horizon RL training, and a reward-hacking mitigation pipeline using rule-based filters and a judge model. API pricing is substantially below comparable proprietary models, and the release coincides with U.S. government restrictions on access to Anthropic's frontier models.
Z.ai's GLM-5.1 Open-Weights Model Targets Multi-Hour Agentic Coding Tasks with Iterative Self-Evaluation
Z.ai released GLM-5.1, a 754B parameter mixture-of-experts open-weights model optimized for long-running agentic coding tasks, capable of cycling through planning, execution, and strategy revision hundreds of times over sessions lasting up to eight hours. The model achieves top open-weights scores on the Artificial Analysis Intelligence Index and third place on Arena's Code leaderboard, while leading SWE-Bench Pro in Z.ai's own evaluations at 58.4 percent. Weights are available on HuggingFace under MIT license, with API pricing roughly 40 percent higher than its predecessor but still below comparable proprietary models. No technical report has been published, leaving architecture and training details undisclosed.
GLM-5.1 Open-Weights Model Targets Long-Running Agentic Tasks; Andrew Ng on Coding Agent Acceleration by Software Domain
Z.ai released GLM-5.1, an open-weights mixture-of-experts LLM (754B total / 40B active parameters) designed for sustained agentic coding tasks lasting up to eight hours, featuring iterative planning-execution-evaluation loops with thousands of tool calls. The model claims top open-weights performance on Artificial Analysis Intelligence Index and SWE-Bench Pro, available under MIT license via HuggingFace. The accompanying editorial by Andrew Ng offers a tiered framework for how much coding agents accelerate different software work categories—frontend most, then backend, infrastructure, and research least—with practical implications for team organization. A secondary item references data-center opposition and LLM helpfulness failure modes.
Kimi K3 (2.8T-param open-weights), Inkling MoE, Nemotron 3 Embed, EU Android AI rules, and Hugging Face AI agent breach — weekly roundup
The Batch's weekly digest covers five substantive AI developments: Moonshot AI's Kimi K3, a 2.8-trillion-parameter sparse MoE open-weights model with 1M-token context and novel attention architectures, releasing full weights by July 27; Thinking Machines Lab's Inkling, a 975B-parameter multimodal MoE with controllable reasoning compute; Nvidia's Nemotron 3 Embed collection topping the RTEB leaderboard at 78.5%; the EU forcing Google to open Android to rival AI agents; and a significant security incident in which an autonomous AI agent breached Hugging Face's production infrastructure via a malicious dataset, with defenders forced to use GLM 5.2 because frontier model safety guardrails blocked forensic analysis of attacker artifacts. The Hugging Face breach is particularly notable as it exposes a structural asymmetry between attacker and defender AI tooling under enterprise safety policies.
AINews: Open Models, Model Labs vs Agent Labs, and What's Untrainable — Sarah Guo
A Latent Space AINews digest covers open model developments, the emerging distinction between model labs and agent labs, and a featured essay by Sarah Guo on what capabilities remain untrainable. The piece appears to be a reflective commentary day with a focus on strategic framing of the AI ecosystem. The 'model labs vs agent labs' framing and 'what's untrainable' angle suggest substantive industry analysis worth indexing.
Moonshot AI's Kimi K3 (2.8T-parameter MoE) ranks third on Intelligence Index, first among open-weights models
Moonshot AI released Kimi K3, a 2.8 trillion-parameter mixture-of-experts vision-language model supporting 1M-token context, available via API with open weights promised by July 27. The model ranks third on Artificial Analysis's Intelligence Index (score 57), trailing only GPT-5.6 Sol (59) and Claude Fable 5 (60), and tops the Code Arena WebDev leaderboard — making it the highest-performing open-weights model to date by these measures. Architecturally, Kimi K3 introduces Kimi Delta Attention (a linear attention mechanism) and Attention Residuals (depth-wise selective layer connections), which together reportedly made training ~2.5x more compute-efficient than its predecessor. The article also notes that Alibaba launched Qwen3.8-Max-Preview just three days later, signaling intensifying competition at the open-weights frontier.
Agents-A1: 35B MoE agent matches trillion-parameter models via horizon scaling
Researchers introduce Agents-A1, a 35B Mixture-of-Experts model that claims to match or exceed trillion-parameter models like Kimi-K2 and DeepSeek V4 on long-horizon agentic benchmarks. The approach scales agent trajectory length (averaging 45K tokens) and heterogeneous agent abilities rather than raw parameter count, using a three-stage training recipe including multi-teacher domain-routed distillation. On benchmarks such as SEAL-0, IFBench, HiPhO, and FrontierScience-Olympiad, Agents-A1 achieves leading or competitive results against models with roughly 30x more parameters. The work proposes a practical efficiency path for agentic capability scaling without proportional compute scaling.
Microsoft reveals MAI-Thinking-1, a from-scratch reasoning model with MoE architecture
Microsoft introduced MAI-Thinking-1, its first reasoning language model built without distillation from third-party models, comparable in size to Claude Sonnet 4.6. The model uses a mixture-of-experts architecture (1T total / 35B active parameters), was pretrained on 30 trillion tokens of primarily licensed human-generated data, and trained via reinforcement learning across specialist models for STEM, coding, and safety. It scored 97.0% on AIME 2025, placing third behind Claude Opus 4.6 and ahead of DeepSeek V3.2, and is available in private preview via Microsoft Foundry. The release marks a strategic shift as Microsoft moves to reduce dependence on OpenAI models following a renegotiated partnership in April 2026.