Abhinav Raghunathan

This looks really cool. Using agents to evaluate and optimize DECISIONS, and not just code. Being in a highly regulated industry, I'm noticing the built-in audit trail for how those decisions were arrived at as an important feature. The PyMC Labs team continues to put out great work.

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LLMs are getting commoditized. The pace of core model breakthroughs has clearly slowed. We are no longer seeing step-function jumps from “just a bigger model”. For most real systems today: • architecture matters more than the base LLM, • planning, orchestration, memory, and verification dominate quality, • the model itself is increasingly a replaceable component. In my own code factory, I can swap GPT, Claude, or Gemini with minimal impact. There are differences in latency and edge cases, but they no longer define the system. The next real breakthroughs will not come from GPT-6. They will come from new architectures built around models, not inside them. LLMs are becoming infrastructure. Systems are becoming the product.

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[AI Paper] The "Thinking" model wars are heating up. Meituan just dropped a 560B MoE that beats DeepSeek-R1 on Agentic benchmarks. 🐱⚡ As AI Architects, we are seeing a rapid commoditization of "Reasoning" models. First DeepSeek-R1 democratized RL-driven Chain-of-Thought (CoT). Now, LongCat-Flash-Thinking-2601 (from Meituan) is pushing the boundary for Agentic Reasoning. This isn't just another R1 clone. It’s a massive 560B Parameter Mixture-of-Experts (MoE) designed specifically for Tool Use and Search. The Architectural Innovations: Domain-Parallel MoE Training: Instead of standard dense pre-training, they used a "Split-and-Fuse" strategy—training domain experts in parallel and fusing them. This stabilizes training at the 500B+ scale. DORA (Dynamic Online RL Algorithm): To handle the noise of real-world tool use (e.g., failed API calls, 404s), they extended their RL framework to train across 10,000+ diverse environments, not just math/code verifiers. Heavy Thinking Mode: They introduced a test-time scaling mechanism that expands reasoning width (exploring parallel paths) and depth (re-verifying steps) dynamically based on problem difficulty. The Benchmark: It achieves State-of-the-Art (SOTA) on agentic benchmarks like Agentic Search and Tool-Integrated Reasoning, surpassing DeepSeek-R1 in complex, multi-turn tool interactions. If you are building autonomous agents that need to survive the messy real world (and not just solve math problems), this is the new heavyweight model to watch. 👇 Link to the Technical Report in the comments. ------ Share this with your network if you found this insightful ♻️ Follow me (Bunty Shah) for more AI Architect's insights and tutorials on GenAI and Machine Learning! --- #AIArchitecture #LongCat #MoE #DeepSeekR1 #AgenticAI #ReasoningModels #LLM #Meituan #OpenSource

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