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Model: "arc-agi-3"
not much happened today
gpt-5.6-luna gpt-5.6-terra gpt-5.6-sol arc-agi-3 inkling-small inkling gemini-robotics-2 openai thinking-machines lmsys modal unsloth artificial-analysis google price-optimization agent-systems memory-retention context-compaction multimodality mixture-of-experts model-compression benchmarking open-weights multimodal-models model-efficiency model-deployment embodied-ai robotics long-context sama fchollet kimmonismus gneubig scaling01 mervenoyann
OpenAI aggressively cut prices for GPT-5.6 Luna by 80% and Terra by 20%, introducing a faster Sol Fast tier with up to 2.5× lower latency at double the price, improving agent workflow costs by roughly 10×. The ARC-AGI-3 debate highlighted that the complete agent system, including memory retention and tool orchestration, is critical beyond just the base model. Thinking Machines released Inkling-Small, an open-weights, multimodal MoE model with 276B parameters (12B active), delivering performance comparable to the original Inkling at a quarter of the size, supporting audio, images, and Python-based image inspection. Benchmarks show Inkling-Small excels in coding and multimodality tasks, with 1M-context support and broad open inference stack adoption. The news also mentions Google's Gemini Robotics 2 advancing embodied AI from tabletop to full-body control.
not much happened today
arc-agi-3 claude-code anthropic langchain arcprize primeintellect agentic-reasoning interactive-environments benchmarking efficiency-metrics zero-preparation-generalization agent-infrastructure trainable-agents classifier-approval fchollet mikeknoop scaling01 _rockt mark_k andykonwinski bradenjhancock jeremyphoward togelius bracesproul hwchase17 caspar_br _catwu
ARC-AGI-3 benchmark introduced by @arcprize and François Chollet resets the frontier for general agentic reasoning with humans solving 100% of tasks versus under 1% for current models, focusing on zero-preparation generalization and human-like learning efficiency. The scoring protocol sparked debate over its harsh efficiency-based metric compared to prior ARC versions and other benchmarks like NetHack. The community acknowledges the benchmark highlights weaknesses in current LLM agents in interactive, sparse-feedback environments. Concurrently, agent infrastructure advances with LangChain launching Fleet shareable skills for reusable domain knowledge, and Anthropic revealing Claude Code auto mode for classifier-mediated approval balancing autonomy and manual confirmation. Browser and coding agents are evolving into trainable systems beyond prompt wrappers, exemplified by BrowserBase and Prime Intellect collaboration.