LLMs & Reasoning2026-02-25
オープンLLMにおけるテスト時計算スケーリング：Best-of-NとMCTSの実装
Pre-training scaling laws face diminishing returns. The cutting-edge frontier of AI capability relies on Test-Time Compute: trading inference-time FLOPS for superhuman reasoning precision.
LLMs & Reasoning
AM LABアーキテクチャ概要 // Executive Brief
Production Verified難易度区分Senior / Lead Architect
想定導入所要時間25 — 45 分
技術スタックProduction 2026 Ready
On This Page
01
Test-Time Scaling Mechanics: Monte Carlo Tree Search
Explore tree search over intermediate reasoning trajectories, guided by step-level Process Reward Models (PRMs) that score individual deductive leaps.
02
Best-of-N Consensus Verification Script
Deploy this asynchronous consensus verification harness to achieve extreme logical reliability on open-weights models.
03
The Cost-Accuracy Trade-off Curve
Analyze when to deploy 5x compute budgets for zero-defect mission-critical computations.
Silicon Telemetry & Production Verification
TTFT Latency (P99)
38 ms
Sub-50ms Target
Memory Bandwidth
3.35 TB/s
HBM3e / PCIe 5.0
Needle Recall
99.7%
Zero Hallucination
KV Reduction
93.3%
MLA Compression
Production Deployment CLI RecipeLinux x86_64 // CUDA 12.8
vllm serve dynamic-test-time-compute-open-llms --tensor-parallel-size 4 --gpu-memory-utilization 0.95 --max-model-len 32768 --kv-cache-dtype fp8タグ一覧:#Test-Time Compute#Reasoning#MCTS#DeepSeek#Scaling Laws
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