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Tree Search

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May 27, 2026 · Artificial Intelligence

Testing Four Non‑Vector RAG Approaches: BM25, GraphRAG, Tree Search, and Agentic Search

The article evaluates four non‑vector Retrieval‑Augmented Generation methods—BM25 lexical search, GraphRAG graph traversal, Tree‑Search document navigation, and an Agentic search loop—using a small JSON‑based corpus, showing each method’s strengths, weaknesses, and when to combine them for production‑grade retrieval.

Agentic SearchBM25GraphRAG

0 likes · 12 min read

Testing Four Non‑Vector RAG Approaches: BM25, GraphRAG, Tree Search, and Agentic Search

Data Party THU

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.

AgentAutoHarnessHarness

0 likes · 10 min read

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

0 likes · 20 min read