MTP3 was checked alongside the baseline on B300 DEP8: AIME25: 0.9500 → 0.9667 GPQA: 0.9268 → 0.9242 OCRBench: 0.896 → 0.895 Two validation rounds completed with zero request errors. These are results from the tested build, not a claim that final-release validation is complete. 4/6

In this paper, a 7B language model trained with reinforcement learning learns to orchestrate larger frontier models like GPT-5, Claude Sonnet 4, and Gemini 2.5 Pro. It does so by writing natural-language subtasks, assigning each to one of the workers, and specifying which previous outputs that worker sees in context. The resulting system outperforms every individual frontier model on benchmarks including GPQA Diamond, LiveCodeBench, and AIME25, while averaging about three model calls per question—fewer than the multi-agent pipelines and self-reflection loops it beats. The work provides evidence that prompt engineering and pipeline design, currently done by hand in commercial AI products, can be learned end-to-end through reward signals alone. Read with an AI tutor: PDF:

🧬 For agents to evolve themselves safely, the protocol itself needs a redesign — this work fills the pieces missing from MCP and A2A with a self-evolution protocol. Title: Autogenesis: A Self-Evolving Agent Protocol URL: 🧬 Overview The Autogenesis Protocol (AGP) lets LLM agents dynamically improve their own prompts, tools, and policies during execution. Its central idea is to decouple "what evolves" from "how evolution occurs." ❓ Challenges Solved Existing protocols (Anthropic's MCP, Google's A2A) standardize connectivity and invocation but lack the primitives evolution needs. ・Resources stay tightly coupled to agent code ・No version control or rollback for evolutionary steps ・Heuristic edits, with no standardized operators forming a rigorous control loop 💡 Methodology & Approach AGP has two layers. ・RSPL: models prompts, agents, tools, environments, and memory as "registered resources" with explicit state, lifecycle, and versioned interfaces ・SEPL: formalizes evolution as typed, composable operators, routing every change through RSPL so it's versioned and reversible On top, the AGS system runs sub-agents concurrently on an Agent Bus and, when traces signal failures, self-improves via a Reflect → Select → Improve → Evaluate → Commit loop. 🎯 Use Cases It fits building agents that need long-horizon planning and diverse tool use. Because improvements keep an auditable lineage and can be rolled back, you can run self-evolution safely while avoiding brittle glue code. 📊 Results ・Science & math: weaker models gain most. gpt-4o improves +100% on AIME25, gpt-4.1 +71.38% on AIME24, while saturated strong models see small gains (ceiling effects) ・GAIA: Test 79.07% → 89.04% (+12.61%), with +33.34% on the hardest Level 3 ・Code generation: C++ pass rate 79 → 99, time-limit errors 9 → 0, runtime efficiency +46.4%. Jointly evolving prompts and outputs worked best #AIAgents# #SelfEvolution#

Aimé Leon Dore is seeking new investors According to @TweedMiss, the LVMH-backed brand has hired JP Morgan bankers to help sell a controlling stake and fund its next stage of growth