Fathom v18 / styxx v3.9.0: The Trust Layer for LLMs — One-Decorator Hallucination Prevention

Headline: styxx v3.9.0 ships @trust — the one-decorator, one-line hallucination prevention layer for any LLM call. from styxx import trust followed by @trust on any LLM-calling function is the entire API. Every output is cognometrically verified (via styxx.guardrail.check(), AUC 0.9012 on HaluEval-QA — see v17) before it reaches the caller. Risk above threshold is intercepted via fallback / retry / raise / annotate. Design principles: Zero config out of the box; ships with HaluEval-calibrated LR weights. Shape-preserving: handles OpenAI (choices[0].message.content), Anthropic (content[0].text), LangChain, dict, str — automatically. Prompt auto-detection from kwargs (prompt, question, query, messages) or positional args. Sync and async both supported (coroutine auto-detected). Four halt policies: fallback (safe text), retry (up to N), raise (TrustViolation), annotate (TrustResult). No residual-stream access required. No API keys. No external services. Tests: 31 new tests in tests/test_trust.py. Full suite 562 pass, 1 skip, 0 fail. Installation: pip install styxx==3.9.0 The bet: this is the product that tries to change the LLM space forever. TLS for LLM cognition. Nothing crosses unseen. Repository: github.com/fathom-lab/styxx (tag v3.9.0). Package: pypi.org/project/styxx/3.9.0. Predecessor: v17 (10.5281/zenodo.19701750) introduced the four-signal guardrail at AUC 0.9012. v18 packages it behind a single decorator so every LLM developer can install and use it in one line. License: CC-BY-4.0 (this deposit, paper, demo) / MIT (styxx code).