Interactive demo · Adversarial benchmarks
Multi-channel retrieval that recovers evidence dense cosine buries — causal, hyperbolic, and relational signals in one 1024d tensor.
Calibrated hard pack: dense Miss@10 ≈ 93%. Multi-channel Stage-1 expansion + Stage-2 fusion
recovers the buried gold documents. Numbers from committed artifacts under
demos/finance_demo/results/hard_eval/.
| Config | Recovered@10 | Full R@10 | Visual |
|---|
Pick a real adversarial query. Left = dense ranking (gold buried). Right = multi-channel ranking from the measured scorecard (gold recovered into top-10).
Dense rank of gold: — · Multi recovered@10: —
Cohesive distractors share vocabulary with the symptom. Gold root-cause / sub-clause / predicate docs sit at ranks 12–200 until Stage-1 structure brings them into the pool and Stage-2 topological bonuses overcome the cosine gap.
| Channel | Failure mode it fixes |
|---|---|
| Causal DAG | Symptom neighborhoods ≠ root cause |
| Hyperbolic taxonomy | Parent/sibling policies outrank deep child clauses |
| Relational predicates | Fuzzy match beats exact amount / callback / timezone rules |
git clone https://github.com/insightitsGit/VectorPrism.git
cd VectorPrism
pip install -e ".[all]"
vectorprism pilot-check
# Scorecards (Docker recommended on Windows for sentence-transformers)
docker compose run --rm vectorprism python demos/finance_demo/run_multichannel_recovery.py --skip-train
docker compose run --rm vectorprism python demos/finance_demo/run_post_validation.py
docker compose run --rm vectorprism python demos/finance_demo/run_robustness_validation.py
Artifact index: RESULTS_INDEX.md · Technical report: TECHNICAL_REPORT.md