Applied AI
MSHN
A retrieval-augmented market-anomaly explainer that grounds its answers in SEC filings indexed before a price event.
MSHN explores how language models explain market anomalies using information available before the event. It combines a retrieval pipeline with an evaluation harness that measures factual grounding separately from causal reasoning.
What it does
- Indexes SEC filings in PostgreSQL and pgvector for retrieval-grounded explanations.
- Evaluates explanations across four axes with an LLM-judged harness.
- Caches explanations in PostgreSQL so repeat queries do not require another model call.
Engineering challenges
- Separating factual accuracy from the quality of causal reasoning during evaluation.