hypermcts 2.0.0

HyperReason v2 — an honest Adaptive-Entropy MCTS test-time-compute engine.



v1.x of this project shipped an engine that never called a model and an invented

benchmark table. v2 is a full honest rebuild: it genuinely drives a real LLM

(Z.AI GLM by default; Ollama / HuggingFace supported), runs Adaptive-Entropy

MCTS over model-generated candidates, picks the answer by self-consistency, and

projects KV-cache savings from real per-node token counts.



Quickstart:

    pip install git+https://github.com/rudra496/hyper-reason

    export ANTHROPIC_BASE_URL=https://api.z.ai/api/anthropic

    export ANTHROPIC_API_KEY=...

    python -c "from hyper_reason import wrap_model, GLMBackend; \

print(wrap_model(GLMBackend()).reason('What is 17 + 25?')['boxed_answer'])"



Real benchmark (GSM8K-mini, N=20, GLM-4.6):

    greedy (T=0)            : 95.0%

    self-consistency (K=4)  : 90.0%

    AE-MCTS (sims=6,k=2,d3) : 85.0%



We publish the run where AE-MCTS UNDERPERFORMS greedy: GLM-4.6 is already near

the ceiling on easy GSM8K, so modest-budget search does not pay off there. This

is the opposite of a SOTA claim. Re-run and scale it:

    python eval/gsm8k_mini.py --n 100 --sims 16 --k 4



Entropy is labeled "sample_diversity_entropy (no logprobs)". KV savings are a

projected simulator (no real GPU). Backends RAISE when offline — they never

fabricate a response. Raw per-problem traces: eval/runs/*.jsonl.



Full docs: https://github.com/rudra496/hyper-reason  (README.md)

Live demo: https://rudra496.github.io/hyper-reason

License: MIT. Author: Rudra Sarker.