ccr-review 0.1.0

Cross-Context Review: Model-agnostic LLM bias elimination through session isolation

CCR: Cross-Context Review

Eliminate LLM blind spots. Automatically.

LLMs miss 64.5% of errors when reviewing their own output (Tsui, 2025). CCR fixes this by running multiple completely isolated review sessions — no shared context, no anchoring bias, no sycophancy.

One command. Three independent reviewers. 30 seconds. ~$0.02.

pip install ccr-review
ccr review mycode.py

The Problem

When you ask an LLM to "review what you just wrote," it's like asking someone to proofread their own essay — they see what they meant to write, not what they actually wrote.

This is not a model limitation. It's a structural bias that persists across GPT, Claude, Gemini, and every future model. As long as the reviewer shares context with the producer, blind spots are inevitable.

The Solution

CCR (Cross-Context Review) breaks this cycle through session isolation:

Your Code ──→ [Reviewer 1] ──→
         ──→ [Reviewer 2] ──→  [Director] ──→ Consolidated Report
         ──→ [Reviewer 3] ──→

Each reviewer is a separate API call.
No shared memory. No anchoring. No sycophancy.

Key research findings (360 reviews, 30 artifacts, 150 ground-truth errors):

• CCR outperforms same-context review: F1 28.6% vs 24.6% (p=0.008)
• Critical errors show the largest gap: 40% vs 29% detection rate
• Repeating reviews in the same context doesn't help (SR2 ≈ SR, p=0.11)
• Session isolation is the key mechanism, not repetition

Quick Start

# Install
pip install ccr-review

# Set your API key (pick one)
export ANTHROPIC_API_KEY=sk-ant-...
export OPENAI_API_KEY=sk-...

# Review code
ccr review mycode.py

# Review with more reviewers
ccr review mycode.py --reviewers 5

# Verify a research paper
ccr verify paper.tex

# Use a different model
ccr review app.js --provider openai --model gpt-4o

# See all models and pricing
ccr models

Python API

from ccr import CCRReviewer

reviewer = CCRReviewer(
    provider="anthropic",
    model="claude-haiku-4-5-20251001",  # ~$0.02/review
    num_reviewers=3,
)

result = reviewer.review_file("mycode.py")

print(result.summary())

# Consensus findings = agreed by 2+ independent reviewers
for finding in result.consensus_findings:
    print(f"[{finding.severity.value}] {finding.description}")

How It Works

CCR implements the protocol from Song (2026):

Step	What Happens	Why
Extract	Only the artifact is taken	Removes conversation history = removes bias source
Review	N independent API calls review it	Each call has zero prior context
Integrate	Director merges all reviews	Consensus filtering reduces false positives

5-Axis Verification Framework

Every review systematically covers:

Axis	What It Checks
FACT	Factual accuracy — numbers, names, technical claims
CONS	Internal consistency — contradictions, terminology mismatches
CTXT	Contextual fitness — works in intended environment?
RCVR	Receiver perspective — could readers misunderstand?
MISS	Completeness — anything important missing?

Consensus Filtering

Not all findings are equal. When 2+ independent reviewers flag the same issue without seeing each other's reviews, it's almost certainly a real problem. These consensus findings are marked with ★.

Model-Agnostic

CCR works with any LLM. The bias it eliminates is structural, not model-specific:

Model	Per Review	Monthly (10/day)
GPT-4o mini	~$0.01	~$3
Gemini 2.5 Flash	~$0.01	~$3
Claude Haiku 4.5	~$0.05	~$15
GPT-4o	~$0.11	~$33
Claude Sonnet 4.6	~$0.19	~$57

Run ccr models for current pricing.

Research

CCR is based on peer-reviewed research with 660+ experimental sessions:

• CCR: Cross-Context Review: Eliminating Anchoring Bias in LLM Self-Review — The core method. Session isolation > repetition.
• D-CCR: More Rounds, More Noise: Why Multi-Turn Review Fails — Proves that repeating reviews hurts (FP +62%). One independent round is optimal.
• HCCA: Hierarchical Cross-Context Aggregation — Multi-agent architecture with intentional information restriction.

Roadmap

• Core CCR protocol (independent reviewers + director)
• CLI (ccr review, ccr verify, ccr models)
• Anthropic & OpenAI backends
• Google Gemini backend
• HCCA mode (hierarchical multi-agent verification)
• GitHub Action (auto-review on PR)
• CCR Benchmark dataset on HuggingFace
• VS Code extension

License

MIT

Citation

@article{song2026ccr,
  title={Cross-Context Review: Eliminating Anchoring Bias in LLM-Based
         Self-Review Through Context Isolation},
  author={Song, Tae-Eun},
  journal={arXiv preprint arXiv:2603.12123},
  year={2026}
}

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