AI Coding Live News

On Oct 27 Ant Ling (inclusionAI) open-sourced Ling-3.1-flash-1019, a further post-trained upgrade of the early-October Ling-3.1-flash: 560B total parameters, 25B active per token, a 1M-token context and an MIT license. Ant says it scores 44 on the Artificial Analysis Intelligence Index v4.3.2 (up from 41), with clear gains on agent tasks such as AA-Briefcase and AutomationBench-AA. It also released Ling-3.1-flash-base, an open foundation checkpoint for further training.

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Shanghai AI Laboratory has moved Atria Dawn from Preview to a full release, with open weights (plus an FP8 build) on Hugging Face under internlm/Atria-Dawn and on ModelScope, MIT licensed. Built on the 744B-parameter MoE GLM-5.2, it targets long-horizon research and engineering agent work; context doubles from the Preview's 256K to 512K and input is text only. The vendor table shows it leading the listed models on AutomationBench, CyberGym and SkillsBench, while still trailing Claude Opus 5 on coding tests such as SWE-bench Pro and Terminal-Bench 2.1 (all vendor-reported).

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On Oct 13 Liquid AI released LFM2.5-24B-A2B, the largest and final LFM2.5 model: 24B total parameters, ~2.45B active per token, 131K context, and always-on reasoning. By Liquid's own numbers it leads or matches Qwen3.6-35B-A3B on 5 of 8 tool-use/agent benchmarks (AutomationBench 27.69 vs 8.72) while trailing on SWE-Bench Verified, GPQA and other knowledge/coding tests. Open weights are on Hugging Face with GGUF, MLX, FP8 and ONNX builds; API pricing is $0.10 / $1.00 per million tokens and it is also on OpenRouter.

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DeepMind announced frontier Gemini 4 Argon for long-horizon coding, enterprise knowledge work, and cyber defense—first via Fairwind trusted defenders. Official: DeepSWE v1.1 77.9%, AutomationBench #1 at 51.3%, LVBench 91.7%, CWE-bench v1 tied 68%; intro $2/$10 per 1M tokens; 1M output limit.

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Addressing the limitation where synthetic training environments only generate tasks within isolated individual domains, researchers introduced CompoWorld. By scaling task spaces across 448 typed services exposing 10,130 tools and generating cross-service workflows via dependency graphs, paired with Completion-Focused Rubric Rewards in RL, a Qwen3.6-35B-A3B agent trained on just 3K SFT trajectories and 1K RL tasks gains +9.17 points across eight benchmarks and surpasses Claude Opus 4.6 on AutomationBench.

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