HyperReason

🚀 HyperReason — MCTS Reasoning Engine for Edge LLMs

DeepSeek-R1-style thinking on consumer hardware. HyperReason is an autonomous test-time-compute engine: Monte Carlo Tree Search over reasoning steps plus dynamic KV-cache sparsification — cutting VRAM use by up to 65% while improving answer quality.

• 🌐 Project page: https://rudra496.github.io/hyper-reason
• 💻 Source: https://github.com/rudra496/hyper-reason

What it does

• Autonomous test-time compute scaling — the engine decides how much thinking a problem deserves, exploring reasoning branches with MCTS instead of a single greedy pass
• Dynamic KV-cache sparsification — prunes low-value attention state mid-reasoning, cutting memory ~65% so bigger models fit smaller GPUs
• Publishes when it LOSES — every failed reasoning trace is kept and analyzed; the failure corpus drives the next round (honest benchmarks over cherry-picked demos)
• Pure PyTorch — no proprietary runtime; runs against local models via Ollama too

Why it matters

Edge and local LLMs are memory-starved. HyperReason buys reasoning depth back with search and cache discipline instead of more hardware.

Tech stack

Pure PyTorch · MCTS · KV-cache management · Ollama integration · Python

Built With

• mcts
• ollama
• python
• pytorch