Dan Russell writes about distinguishing between true, correct, and valid claims and offers a six-step verification routine for navigating misinformation. He notes that people often confuse fluency with factuality and that each of the three questions—truth, correctness, and validity—fails in distinct ways requiring different checks. His practical steps include pinning down the claim, noticing emotional reactions, tracing evidence upstream, reading laterally, triangulating with independent sources, and calibrating confidence. He also addresses the specific challenge of verifying what AI tells you, noting that fluent, confident outputs break usual credibility cues, and recommends treating citations as claims to be checked rather than evidence to be trusted.
- The distinction between true, correct, and valid is central: a claim can pass one test and fail others (e.g., a real statistic from the wrong year is correct in form but fails on truth).
- Wineburg and McGrew's research found professional fact-checkers outperformed historians and Stanford undergraduates at evaluating websites, largely because they read laterally rather than staying on a single page.
- AI errors cluster in specifics like dates, names, page numbers, and exact figures, while the gist is often right—making them a correctness failure rather than a truth failure.
- The SIFT method (Stop, Investigate the source, Find better coverage, Trace claims to the original) packages lateral reading moves into a teachable framework.
- The distinction between true, correct, and valid is central: a claim can pass one test and fail others (e.g., a real statistic from the wrong year is correct in form but fails on truth).
- Wineburg and McGrew's research found professional fact-checkers outperformed historians and Stanford undergraduates at evaluating websites, largely because they read laterally rather than staying on a single page.
- AI errors cluster in specifics like dates, names, page numbers, and exact figures, while the gist is often right—making them a correctness failure rather than a truth failure.
- The SIFT method (Stop, Investigate the source, Find better coverage, Trace claims to the original) packages lateral reading moves into a teachable framework.
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.
An OWL 2 RL reasoner with Z3-verified inference rules. Written in SLOP, it compiles to efficient C code while using SMT solving to prove properties about the inference logic.
