spinchain 0.1.1

Inference-time reasoning optimization using QUBO/Ising formulations

SpinChain

Inference-time reasoning optimization for LLMs using QUBO/Ising formulations. Built as an MCP server for Claude Code.

SpinChain takes multiple diverse reasoning chains, extracts fragments, formulates their selection as a QUBO problem (following QCR-LLM), solves it with simulated annealing, and returns the most stable and coherent fragment subset.

Installation

Prerequisites

• Python 3.11+
• uv (recommended) or pip

Install skills (recommended)

Install SpinChain skills into Claude Code via the plugin marketplace:

/plugin marketplace add ameyakhot/spinchain
/plugin install spinchain-skills@spinchain

This gives you 6 skills: spinchain-optimize, analyze-spinchain, test-spinchain, spinchain-status, spinchain-bench, spinchain-trace.

Clone and install the server

git clone https://github.com/ameyakhot/spinchain.git
cd spinchain
uv sync

To install with dev dependencies (pytest, ruff):

uv sync --extra dev

Register as an MCP server in Claude Code

Add SpinChain to your Claude Code config (~/.claude.json):

{
  "mcpServers": {
    "spinchain": {
      "type": "stdio",
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/spinchain",
        "python",
        "-m",
        "spinchain"
      ],
      "env": {
        "SPINCHAIN_TRACE_DIR": "/path/to/spinchain/traces"
      }
    }
  }
}

Replace /path/to/spinchain with the actual path to your cloned repo.

Restart Claude Code. The optimize_reasoning tool will be available as mcp__spinchain__optimize_reasoning.

How it works

User asks a complex reasoning question
        │
        ▼
Claude generates N diverse reasoning chains
        │
        ▼
Claude calls optimize_reasoning(completions=[...])
        │
        ▼
┌─────────────────────────────────────────────┐
│  SpinChain Pipeline                         │
│                                             │
│  1. Fragment Extraction                     │
│     Split into sentences, embed with        │
│     sentence-transformers, deduplicate      │
│     by cosine similarity                    │
│                                             │
│  2. QUBO Formulation (QCR-LLM)             │
│     Linear: -μ·popularity + α·risk          │
│     Quadratic: -β·(correlation - λ²·sim)    │
│                                             │
│  3. Simulated Annealing (dwave-neal)        │
│     100 reads × 1000 sweeps                 │
│                                             │
│  4. Stability Ranking                       │
│     Select fragments appearing in ≥50%      │
│     of low-energy solutions                 │
└─────────────────────────────────────────────┘
        │
        ▼
Claude receives optimized fragments
and synthesizes final answer

Usage

As an MCP server (primary use)

Once registered, Claude calls SpinChain automatically when using the optimize_reasoning tool. No manual invocation needed.

Standalone example

Run the bundled example with synthetic reasoning chains (no API key needed):

uv run python examples/basic_usage.py

Trace analysis

Every MCP call is logged to SPINCHAIN_TRACE_DIR/spinchain_traces.jsonl. Analyze with:

# Human-readable report
uv run python -m spinchain.analyze

# JSON output
uv run python -m spinchain.analyze --json

# Last 10 calls only
uv run python -m spinchain.analyze --last 10

Run tests

uv run --extra dev python -m pytest tests/ -v

Skills

Install via /plugin marketplace add ameyakhot/spinchain in Claude Code, or use as slash commands when working inside the project directory.

Skill	Description
spinchain-optimize	Core skill — teaches Claude how to generate diverse chains and call optimize_reasoning effectively
analyze-spinchain	Run trace analysis — usage stats, latency breakdown, anomalies
test-spinchain	Run the full test suite (71 tests)
spinchain-status	Health check — config, imports, trace summary
spinchain-bench	Run the example benchmark and inspect pipeline output
spinchain-trace	Inspect raw trace records — recent calls, specific trace IDs

Project structure

spinchain/
├── src/spinchain/
│   ├── server.py                  # MCP server — optimize_reasoning tool
│   ├── tracing.py                 # JSONL trace logger
│   ├── analyze.py                 # Trace analysis CLI
│   ├── formulation/
│   │   ├── fragment_extractor.py  # Sentence splitting, embedding, dedup
│   │   ├── coefficient_builder.py # QCR-LLM linear + quadratic weights
│   │   └── qubo_builder.py        # BQM construction + cardinality constraint
│   └── solvers/
│       ├── base.py                # Abstract solver interface
│       └── simulated_annealing.py # dwave-neal SA solver
├── tests/                         # 71 tests
├── examples/
│   └── basic_usage.py             # Synthetic benchmark
├── .claude/commands/              # Claude Code slash commands
└── traces/                        # Trace logs (created at runtime)

Configuration

Environment variables

Variable	Default	Description
SPINCHAIN_TRACE_DIR	~/.spinchain/traces/	Directory for JSONL trace logs

optimize_reasoning parameters

Parameter	Default	Description
completions	required	List of reasoning chain strings (min 2)
num_reads	100	SA samples per solve
num_sweeps	1000	MC sweeps per sample
similarity_threshold	0.85	Cosine similarity for fragment dedup
selection_threshold	0.25	Fraction of low-energy solutions for ranking
inclusion_threshold	0.50	Min frequency to include a fragment
cardinality_k	None	Target number of fragments to select

QUBO hyperparameters (in code)

Parameter	Default	Description
mu	1.0	Fragment popularity weight
alpha	0.5	Risk/variance penalty
beta	1.0	Pairwise coherence weight
lambda_sim	0.3	Semantic similarity penalty factor
penalty_strength	5.0	Cardinality constraint weight

Solver roadmap

1. Simulated Annealing (current) — dwave-neal, pure Python
2. COBI Ising chip — millisecond solves, hardware accelerated
3. Quanfluence CIM — optical coherent Ising machine
4. IBM Quantum QAOA — gate-based quantum validation

All solvers share the same BaseSolver interface and QUBO formulation.

License

MIT