Dimension Reducers builds tools to formalize, stress-test, verify, and structure mathematical knowledge. They offer solutions for LLM training, automated refereeing, and retrieval that understands mathematical structure. Their platform includes tools for refereeing at scale, adversarial testing ("torture testing"), and structured Retrieval Augmented Generation (RAG).
Key products include DiRe-JAX (a dimensionality reduction library), arXiv Math Semantic Search, arXiv Proof Audit Database, Mathematics Torture Chamber, and a Lean 4 Formalization Pipeline. They also publish research and benchmarks in mathematical formalization and OCR, emphasizing semantic accuracy and robustness.
Key products include DiRe-JAX (a dimensionality reduction library), arXiv Math Semantic Search, arXiv Proof Audit Database, Mathematics Torture Chamber, and a Lean 4 Formalization Pipeline. They also publish research and benchmarks in mathematical formalization and OCR, emphasizing semantic accuracy and robustness.
1. **Retrieval-Augmented Generation (RAG):** Ground responses in trusted, retrieved data instead of relying on the model's memory.
2. **Require Citations:** Demand sources for factual claims; retract claims without support.
3. **Tool Calling:** Use LLMs to route requests to verified systems of record (databases, APIs) rather than generating facts directly.
4. **Post-Generation Verification:** Employ a "judge" model to evaluate and score responses for factual accuracy, regenerating or refusing low-scoring outputs. Chain-of-Verification (CoVe) is highlighted.
5. **Bias Toward Quoting:** Prioritize direct quotes over paraphrasing to reduce factual drift.
6. **Calibrate Uncertainty:** Design for safe failure by incorporating confidence scoring, thresholds, and fallback responses.
7. **Continuous Evaluation & Monitoring:** Track hallucination rates and other key metrics to identify and address performance degradation. User feedback loops are critical.
2. **Require Citations:** Demand sources for factual claims; retract claims without support.
3. **Tool Calling:** Use LLMs to route requests to verified systems of record (databases, APIs) rather than generating facts directly.
4. **Post-Generation Verification:** Employ a "judge" model to evaluate and score responses for factual accuracy, regenerating or refusing low-scoring outputs. Chain-of-Verification (CoVe) is highlighted.
5. **Bias Toward Quoting:** Prioritize direct quotes over paraphrasing to reduce factual drift.
6. **Calibrate Uncertainty:** Design for safe failure by incorporating confidence scoring, thresholds, and fallback responses.
7. **Continuous Evaluation & Monitoring:** Track hallucination rates and other key metrics to identify and address performance degradation. User feedback loops are critical.
