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Topic: "optimization"
Chinese Models Launch - MiniMax-M1, Hailuo 2 "Kangaroo", Moonshot Kimi-Dev-72B
minimax-m1 hailuo-02 kimi-dev-72b deepseek-r1 ale-agent minimax-ai moonshot-ai deepseek bytedance anthropic langchain columbia-university sakana-ai openai microsoft multi-agent-systems attention-mechanisms coding optimization prompt-injection model-performance video-generation model-training task-automation jerryjliu0 hwchase17 omarsar0 gallabytes lateinteraction karpathy
MiniMax AI launched MiniMax-M1, a 456 billion parameter open weights LLM with a 1 million token input and 80k token output using efficient "lightning attention" and a GRPO variant called CISPO. MiniMax AI also announced Hailuo 02 (0616), a video model similar to ByteDance's Seedance. Moonshot AI released Kimi-Dev-72B, a coding model outperforming DeepSeek R1 on SWEBench Verified. Discussions on multi-agent system design from Anthropic and LangChain highlighted improvements in task completion and challenges like prompt injection attacks, as demonstrated by Karpathy and Columbia University research. Sakana AI introduced ALE-Agent, a coding agent that ranked 21st in the AtCoder Heuristic Competition solving NP-hard optimization problems. There is unverified news about an acquisition involving OpenAI, Microsoft, and Windsurf.
Gemini's AlphaEvolve agent uses Gemini 2.0 to find new Math and cuts Gemini cost 1% — without RL
gemini gpt-4.1 gpt-4o-mini o3 o4-mini google-deepmind openai algorithm-discovery coding-agents matrix-multiplication optimization reinforcement-learning model-weights training-efficiency safety-evaluations instruction-following coding-tasks model-releases _philschmid scott_swingle alex_dimakis henry jason_wei kevinweil michpokrass scaling01 gdb
Deepmind's AlphaEvolve, a 2025 update to AlphaTensor and FunSearch, is a Gemini-powered coding agent for algorithm discovery that designs faster matrix multiplication algorithms, solves open math problems, and improves data center and AI training efficiency. It achieves a 23% faster kernel speedup in Gemini training and surpasses state-of-the-art on 20% of applied problems, including improvements on the Minimum Overlap Problem and Kissing number problem. Unlike Deep-RL, it optimizes code pieces rather than model weights. Meanwhile, OpenAI released GPT-4.1 in ChatGPT, specializing in coding and instruction following, with a faster alternative GPT-4.1 mini replacing GPT-4o mini for all users. OpenAI also launched the Safety Evaluations Hub and the OpenAI to Z Challenge using o3/o4 mini and GPT-4.1 models to discover archaeological sites. "Maybe midtrain + good search is all you need for AI for scientific innovation" - Jason Wei.
not much happened today
prime gpt-4o qwen-32b olmo openai qwen cerebras-systems langchain vercel swaggo gin echo reasoning chain-of-thought math coding optimization performance image-processing software-development agent-frameworks version-control security robotics hardware-optimization medical-ai financial-ai architecture akhaliq jason-wei vikhyatk awnihannun arohan tom-doerr hendrikbgr jerryjliu0 adcock-brett shuchaobi stasbekman reach-vb virattt andrew-n-carr
Olmo 2 released a detailed tech report showcasing full pre, mid, and post-training details for a frontier fully open model. PRIME, an open-source reasoning solution, achieved 26.7% pass@1, surpassing GPT-4o in benchmarks. Performance improvements include Qwen 32B (4-bit) generating at >40 tokens/sec on an M4 Max and libvips being 25x faster than Pillow for image resizing. New tools like Swaggo/swag for Swagger 2.0 documentation, Jujutsu (jj) Git-compatible VCS, and Portspoof security tool were introduced. Robotics advances include a weapon detection system with a meters-wide field of view and faster frame rates. Hardware benchmarks compared H100 and MI300x accelerators. Applications span medical error detection using PRIME and a financial AI agent integrating LangChainAI and Vercel AI SDK. Architectural insights suggest the need for breakthroughs similar to SSMs or RNNs.
OpenAI beats Anthropic to releasing Speculative Decoding
claude-3-sonnet mrt5 openai anthropic nvidia microsoft boston-dynamics meta-ai-fair runway elevenlabs etched osmo physical-intelligence langchain speculative-decoding prompt-lookup cpu-inference multimodality retrieval-augmented-generation neural-networks optimization ai-safety governance model-architecture inference-economics content-generation adcock_brett vikhyatk dair_ai rasbt bindureddy teortaxestex svpino c_valenzuelab davidsholz
Prompt lookup and Speculative Decoding techniques are gaining traction with implementations from Cursor, Fireworks, and teased features from Anthropic. OpenAI has introduced faster response times and file edits with these methods, offering about 50% efficiency improvements. The community is actively exploring AI engineering use cases with these advancements. Recent updates highlight progress from companies like NVIDIA, OpenAI, Anthropic, Microsoft, Boston Dynamics, and Meta. Key technical insights include CPU inference capabilities, multimodal retrieval-augmented generation (RAG), and neural network fundamentals. New AI products include fully AI-generated games and advanced content generation tools. Challenges in AI research labs such as bureaucracy and resource allocation were also discussed, alongside AI safety and governance concerns.
Gemini Nano: 50-90% of Gemini Pro, <100ms inference, on device, in Chrome Canary
gemini-nano gemini-pro claude-3.5-sonnet gpt-4o deepseek-coder-v2 glm-0520 nemotron-4-340b gpt-4-turbo-0409 google gemini huggingface anthropic deepseek zhipu-ai tsinghua nvidia model-quantization prompt-api optimization model-weights benchmarking code-generation math synthetic-data automatic-differentiation retrieval-augmented-generation mitigating-memorization tree-search inference-time-algorithms adcock_brett dair_ai lmsysorg
The latest Chrome Canary now includes a feature flag for Gemini Nano, offering a prompt API and on-device optimization guide, with models Nano 1 and 2 at 1.8B and 3.25B parameters respectively, showing decent performance relative to Gemini Pro. The base and instruct-tuned model weights have been extracted and posted to HuggingFace. In AI model releases, Anthropic launched Claude 3.5 Sonnet, which outperforms GPT-4o on some benchmarks, is twice as fast as Opus, and is free to try. DeepSeek-Coder-V2 achieves 90.2% on HumanEval and 75.7% on MATH, surpassing GPT-4-Turbo-0409, with models up to 236B parameters and 128K context length. GLM-0520 from Zhipu AI/Tsinghua ranks highly in coding and overall benchmarks. NVIDIA announced Nemotron-4 340B, an open model family for synthetic data generation. Research highlights include TextGrad, a framework for automatic differentiation on textual feedback; PlanRAG, an iterative plan-then-RAG decision-making technique; a paper on goldfish loss to mitigate memorization in LLMs; and a tree search algorithm for language model agents.