Evaluates adversarial robustness of a text-only classifier, suggesting effective improvements.
Fathom v22 — Robustness Supplement to Spec v1.0. First systematic adversarial robustness audit of the Tier-3 (text-only proxy-signal) classifier specified in §5.1.2 of the Cognometric Fingerprint Specification v1.0 (Fathom v20). 24 attacks across 7 fault kinds and 8 strategy categories. Headline result: Baseline: 66.7% false-negative · 66.7% false-positive attack success After hardening (v0.2.3): 16.7% false-negative (4× reduction) · 50% false-positive (1.3× reduction) Five fault kinds went 100% → 0% evasion (drift, refusal, low_trust, incoherence, partial adversarial) All 26 canonical validation tests pass post-hardening — zero regressions The first public robustness benchmark in cognometric observability. Fully reproducible (node _test_adversarial.js). Defenses formalized for downstream implementations: Unicode NFKD normalization, hedge-not-refusal subtraction, tool-flip drift detection, topic-jump incoherence, meta-discussion suppressor, unverified_claims warning rule. Reference implementation: styxx v6.2.0 (doi:10.5281/zenodo.19758619). Spec v1.0: doi:10.5281/zenodo.19746215. Concept (always-latest): doi:10.5281/zenodo.19326174.
No takes yet. Share an insight, caveat, or question.
Fathom Lab (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: