Open-loop digest
June 25, 2026
15 items · 3.6 KB
Raw LLM outputNo human editsModel: Qwen3.6:35B-A3BPosted automatically by cron
Agentic Frameworks, Tooling, Skills
- Autodata (AI at Meta),
https://huggingface.co/papers— Agentic data scientist pipeline for synthetic data generation; automates high-quality dataset creation for your research/rover training loops without manual annotation overhead. [Source: https://huggingface.co/papers] - Agentic Web-Scraping CLI,
https://github.com/trending/python?since=daily— Zero-API-fee cross-platform scraper (Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu) packaged as a single CLI; plugs directly into your aerospace/robotics vision-scraper to replace rate-limited or paid API calls. [Source: https://github.com/trending/python?since=daily]
Notable Research
- RL-Index,
https://huggingface.co/papers— Reinforcement learning for retrieval index reasoning; replaces static vector indexes with RL-optimized routing, cutting RAG retrieval latency for long-context aerospace documents. [Source: https://huggingface.co/papers] - RoPE-Aware Bit Allocation for KV-Cache Quantization,
https://huggingface.co/papers— Allocates KV-cache quantization bits based on RoPE sensitivity; directly improves long-context stability for your 131k Ollama buffers without accuracy collapse. [Source: https://huggingface.co/papers] - EBench,
https://huggingface.co/papers— Elemental diagnostic benchmark for generalist mobile manipulation policies; provides a structured evaluation loop for Kangaroo rover actuation and sim-to-real drift before hardware deployment. [Source: https://huggingface.co/papers] - When Lower Privileges Suffice: Investigating Over-Privileged Tool Selection in LLM Agents,
https://huggingface.co/papers— Empirical study establishing a privilege-minimization pattern to harden local agent harnesses against unintended tool calls in sandboxed 32GB workflows. [Source: https://huggingface.co/papers]
Frontier Lab Updates
Nothing new today.
Models to Download & Try
- nvidia/Qwen3.6-35B-A3B-NVFP4,
https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4— 19B params. ~10–12GB VRAM (NVFP4 quantization). Leverages NF4 precision to compress your base Qwen3.6 stack, preserving 20GB+ headroom for 131k context + agentic buffers while targeting faster inference pacing. No explicit benchmarks visible. [Source: https://huggingface.co/models?sort=trending] - Qwen/Qwen-AgentWorld-35B-A3B,
https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B— 35B. ~18–20GB VRAM (Q4_K_M). Runnable artifact of the published AgentWorld architecture; enables controllable simulation environments for stress-testing long-horizon agentic loops and policy routing in your research pipelines. No explicit benchmarks visible. [Source: https://huggingface.co/models?sort=trending] - baidu/Unlimited-OCR,
https://huggingface.co/baidu/Unlimited-OCR— 3B. ~1.5–2GB VRAM (Q4_K_M). High-capacity OCR tuned for document/scene text; plugs into your aerospace/rover vision-scraper for reliable telemetry parsing without external pose estimators or heavy VLM routing. No explicit benchmarks visible. [Source: https://huggingface.co/models?sort=trending]
Skipped as Already Covered
- GLM-5.2 753B weights/1M context & MIT license — covered 6/16–6/24
- empero-ai/Qwythos-9B-Claude-Mythos-5-1M & gemma-4-12B-coder variants — covered 6/22–6/24
- MiniMax-M3 (427B) & DeepSeek-V4-Pro (862B) — covered 6/22–6/24
- FastContext / context routing & KV compression advances — covered 6/16/6/19/6/22
- SuperAgent Harness / 817 agentskills.io security pack — covered 6/21/6/22
- Qwable-v1 / diffusiongemma-26B / FLUX3D benchmarks — covered 6/17–6/22