AI Coding Live News

Microsoft Research, Microsoft Security and UC Berkeley open-sourced TokenEconomizer on GitHub (Apache 2.0, v0.1.0 released October 26), a plugin for Claude Code, Codex, GitHub Copilot CLI and Pi, with experimental opencode support. It learns from earlier sessions in the same repository: micro-skills remember repo facts, tool pruning drops tool definitions that go unused, and a loop guard nudges an agent that is stuck. The paper reports 18.4–30.9% lower mean cost across five agent and model combinations on 277 SWE-Bench tasks (paper figures).

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On Oct 28 DiffuSpace, whose members come from the HKU NLP group behind Dream 7B, released its first model, Clover-35B-A3B-Preview: a mixture-of-experts diffusion language model initialized from Qwen3.6-35B-A3B, with 35B total and about 3B active parameters, a 128K context window and block-parallel denoising. Vendor-reported results include 73.6 on SWE-bench Verified, 51.7 on Terminal-Bench 2.1 and 83.8 on GPQA Diamond, with 680 tokens/s on a single GB300 in agent workloads. Weights are Apache 2.0 on Hugging Face, ModelScope and GitHub, with SGLang and llama.cpp support.

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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 26 Mirendil unveiled Fern, its first model, pretrained from scratch on its own Mirendil-Base (0.1-0.5T params) with a context window of up to ~10M tokens, about 0.5M lines of code or days of agent traces. Fern 0.1 got only a short round of post-training and targets AI R&D work such as auditing training data, debugging runs from logs and reading agent traces end to end. Vendor-reported: 73.4% on SWE-Bench Verified and 100% single-needle RULER retrieval at 10M, while multi-hop QA drops to 45% at 10M. Access is early-access only; no price or weights yet.

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Published in arXiv:2610.25834, FrontierRefactor establishes the industry's first evaluation benchmark targeting autonomous coding agents on multi-scale, performance-oriented codebase refactoring. Unlike traditional benchmarks like SWE-bench that measure only functional pass rates, FrontierRefactor spans seven programming languages and 60 Amdahl-qualified tasks ranging from kernels to full repositories (10^1 to 10^4+ LOC). Initial evaluations reveal that over 41% of agent patches pass functional equivalence suites yet yield zero verified speedup—with 19.3% triggering severe runtime performance regressions, highlighting the vital need for multi-turn profiling feedback.

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Recursive, founded by Richard Socher, has officially open-sourced Flint (RecursiveSI/flint), a 39.8B Mixture-of-Experts (MoE) coding agent model under Apache 2.0 with day-zero Ollama support (ollama run flint). Activating just 2.5B parameters per token across 47 MoE layers with a 262K context window, Flint hits 125+ tokens/sec on an M4 Pro MacBook Pro. It sets new benchmarks for sub-50B local models, scoring 74.2% on SWE-bench Verified and 89.7% on SWE-bench Pro v2.

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On Oct 21, Recursive, the startup founded by Richard Socher, released its first model, Flint: a 39.8B-total MoE coding model with about 2.5B active parameters per token, with weights on Hugging Face under Apache 2.0. Recursive says it leads sub-50B MoE models on SWE-Bench Pro v2, Terminal-Bench 2.1 and SWE-Bench Verified, runs at 125+ tok/s on an M4 Pro MacBook Pro, and was built from scratch in under four months with its automated research system, the Eureka Machine, contributing to nearly every part of the stack.

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The Ornith AI team officially released Ornith-1.5-122B under the permissive MIT license, a 122-billion parameter mixture-of-experts (MoE) reasoning model optimized to execute on a single GPU node (such as a single DGX). The model is immediately available across vLLM (0.19.1+), SGLang (0.5.9+), Hugging Face, and Ollama (ornith-1.5:122b) with native FP8, GGUF, and MLX quantization. Across independent agentic evaluations in the OpenHands harness, Ornith-1.5-122B registered 84.5 on SWE-bench Verified, 76.9 on SWE-bench Pro (surpassing Qwen3.8-Flash-Next's 43.9 by 33 points), 81.2 on TerminalBench 2.1, and 74.6 on MCP-Atlas.

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Meta AI published the technical report for Muse Glimmer 30B (arXiv:2610.24628), an open-source 30-billion parameter causal language model with an integrated perception encoder designed for on-device autonomous agents. Quantized to approximately 4 bits, Muse Glimmer operates inside 32GB of VRAM on a single consumer GPU (such as an RTX 5090) without cloud reliance. On rigorous agentic evaluations, Muse Glimmer sets new records for its size class: achieving 75.5 on MCPAtlas (surpassing Gemma4-31B at 54.2 and Qwen3.6-27B at 62.5) and 51.2 on SWE-Bench Pro, released fully under the Apache 2.0 license.

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A comprehensive empirical study evaluated native sub-agent concurrency across 2,124 runs on Claude Code, Codex, and Kimi Code (354 tasks, ~$20.8K API cost). On 100 standard SWE-bench Verified tasks, enabling sub-agents caused Claude Code's solve rate to collapse from 83% to 59% (with latency increasing from 17.5 to 27.8 minutes) and Kimi Code from 77% to 59%, while token consumption inflated by 1.41x to 3.31x. The study revealed severe failure modes including budget-exhausting fan-outs, orphaned child processes, and concurrent delivery race conditions.

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Inception co-founder Stefano Ermon officially unveiled Mercury 3, the company's next-generation diffusion-based large language model. Delivering an astonishing 1,068 tokens per second inference speed on NVIDIA GPUs, Mercury 3 scales to 40x the size of Mercury 1 with a native 1M context window. Benchmarked on IFBench, GPQA Diamond, and SWE-Bench Atlas, it matches frontier closed baselines with standard API pricing of $0.20 / $0.70 per 1M input/output tokens (with 50% promotional pricing of $0.10 / $0.35).

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On October 19 Inception released Mercury 3, which it calls the most capable diffusion LLM (dLLM) to date. Per the vendor, it is more than 40x the size of Mercury 1, outputs 1,068 tokens/s, expands context from 260K (Mercury 2.5) to 1M tokens, and beats GPT-6 Luna and Claude Haiku 5.5 (both medium) on GPQA Diamond, IFBench, SWE-Bench Atlas and more. List price is $0.20/M input and $0.70/M output, 50% off at launch. It is live on the Inception API, OpenRouter, Baseten and Vercel AI Gateway with an OpenAI-compatible API. All scores are vendor-reported and not yet independently reproduced.

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NVIDIA published Nemotron-Labs Dory1.0-4B on Hugging Face on Oct 16: a 20-layer shared block runs 1, 2 or 4 times at inference so one checkpoint serves several compute budgets. NVIDIA reports 61.8 on SWE-bench Verified at 4 loops, close to Qwen3.5-9B. BF16/MXFP8/NVFP4/GGUF under OpenMDW-1.1.

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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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