Apr 25, 2026 · Artificial Intelligence

Google & Microsoft Harnesses: Core LLM Post‑Training Methods and 2025‑2026 Trends

These two recent papers—Microsoft’s M⋆, which evolves task‑specific memory harnesses, and Google’s AutoHarness, which automatically generates code‑level constraints—demonstrate reflective code evolution and tree‑search synthesis, achieving state‑of‑the‑art performance across diverse benchmarks and outlining LLM post‑training directions for 2025‑2026.

AutoHarnessHarnessLLM

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Google & Microsoft Harnesses: Core LLM Post‑Training Methods and 2025‑2026 Trends

Tencent Cloud Developer

Jun 27, 2018 · Artificial Intelligence

Search and Optimization Algorithms in Game AI

Game AI relies on a variety of search techniques—ranging from uninformed breadth‑first and depth‑first methods to heuristic‑driven A*, minimax with alpha‑beta pruning, and Monte Carlo Tree Search—as well as optimization approaches such as hill climbing, simulated annealing, genetic and evolution strategies, multi‑objective evolutionary algorithms, and neuroevolutionary methods like NEAT to generate intelligent, balanced, and adaptable game behavior.

A* algorithmMCTSMiniMax

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