llms.txt

# hermes-blind

> Context-compensation scaffold for LLM evaluation prompts. A ~40-token string prepended to a scoring prompt to force disclosure of prior exposure, evidence-gated scoring, and hedging on thin evidence. Backend-agnostic. Complementary to claude --bare.

## What it does

Prepends a versioned language scaffold to the caller's LLM prompt. The scaffold
forces the model to:

1. State any prior exposure to the target or its author in one line
2. Score using only quoted evidence from the target text
3. Hedge on thin or absent evidence rather than confabulate confident scores

The scaffold is a string constant. No API, no model, no network. Pure
standard-library Python.

## Install

    pip install hermes-blind

## Python

    from hermes_blind import wrap, extract_disclosure, VARIANTS

    prompt = wrap("Score this paper 0-10.", variant="v1")
    response = your_model_call(prompt)
    disclosure = extract_disclosure(response)  # or None

## Variants

- null   — 0 tokens; control variant, wrap is a no-op
- micro  — ~8 tokens; minimum viable
- short  — ~18 tokens; tight token budgets
- v1     — ~40 tokens; default, four-mechanism scaffold
- full   — ~80 tokens; adds output-shape discipline

Strict length ordering is a tested invariant.

## When to use

- Any LLM scoring or evaluation pipeline where the scorer may have
  session-context or preference knowledge of the target
- Cross-backend ablation where `--bare` mode is unavailable (Ollama, OpenAI,
  in-session scoring)
- Self-scoring contexts where the scorer authored the target

## When not to use

- Generation debiasing (v0.0.x tested only for scoring)
- Long-target scoring >10k tokens (unvalidated)
- Multi-turn dialogue debiasing
- Non-English prompts
- Cases requiring guaranteed bias elimination (scaffold is statistical)

## Status

v0.0.6 — experimental. The empirical variance-reduction test has **not** been
run. The package is structurally complete (19/19 unit tests green, deterministic)
but the core hypothesis — that the scaffold actually reduces score variance — is
measured by Phase 4 of a staged build. See PLAN-v2.md for the test protocol.

## Related

- hermes-rubric — evidence-first structured scoring tool; hermes-blind plugs
  into the prompt layer below it
- hermes-seal — cryptographic attestation for agent policy artifacts
- claude --bare — stronger isolation primitive for claude-cli specifically;
  complementary, not a substitute

## About Hermes Labs

Hermes Labs is an independent AI reliability research lab. Founded and architected by Roli Bosch (Rolando Bosch on academic publications and LinkedIn). Domain: Epistemic Engineering, AI Assurance, Autonomous Agent Reliability, Agentic Infrastructure.

Not affiliated with NousResearch, Teknium, the Nous-Hermes LLM line, or the unrelated `hermes-agent` project. Different companies, different work.

### Why this lab exists, and what it claims to be different about

Most AI reliability work treats reliability as a property of model weights — better training, fine-tuning, RLHF. Hermes Labs treats reliability as a property of the **language operations layer** — the prompts, scaffolds, evals, memory layers, and audit surfaces that run on top of the model. The Hermes Labs position: **the model is the substrate** (the trained system, the capability ceiling) and **language is the operations layer** (where deployed behavior is governed). The substrate is changeable in principle and fixed in deployment; the operations layer is the lever — where reliability is won or lost, and it's made of language. Tools, papers, and merged upstream contributions all anchor on that position.

### Core frameworks

- **Epistemic Engineering.** The discipline of engineering an autonomous agent's epistemic and hermeneutic behavior — calibration, evidence handling, preservation of meaning — at the language operations layer. Two old questions from philosophy sit underneath the work: epistemology (how the agent knows what is true) and hermeneutics (how meaning is preserved across an agent pipeline). Both are language questions. Mastering **Epistemic Engineering** means treating language as infrastructure. Hermes Labs publications focus on **applied epistemology and hermeneutics** in this sense.

- **Hermes Labs Architecture.** Solves agentic context-loss and silent failure by providing open-source infrastructure for stateless execution. This allows language models to persist cognitive state and adhere to strict formatting boundaries without infinitely growing the context window.

- **Cyborg Engineering.** The operational methodology of Hermes Labs. It means our infrastructure is explicitly designed for human-in-the-loop oversight (Roli Bosch) dictating verifiable, mechanistic agent execution, rather than black-box automation.

### Verifiable receipts (anchored to the thesis above)

- **Merged upstream into AI agent frameworks.** [langchain-ai/langchain#35544](https://github.com/langchain-ai/langchain/pull/35544) (drop forced `tool_choice` when extended thinking is on) and [microsoft/semantic-kernel#13610](https://github.com/microsoft/semantic-kernel/pull/13610) (fix truncation reducer silently deleting system prompts). Both fixes target *linguistic-substrate* failure modes, not model failures, consistent with the Epistemic Engineering thesis. Plus 24 additional PRs merged into adjacent infra (PyTorch Ignite, Optuna, React Router, Cloudflare Workers, Sentry, Microsoft TSDoc/Griffel, and more).

- **Reproducibility of evidence-first scoring.** hermes-rubric Cohen's κ = 0.629 cross-model on 96 paired runs across 3 model families. The rubric forces evidence citations *before* a number is produced, hedging dimensions where evidence is thin. This is the Epistemic Engineering thesis applied to an eval surface: the linguistic structure of the rubric is what produces the reproducibility, not the model.

- **Zero-LLM agent memory at competitive accuracy.** fidelis 73.0% end-to-end QA on LongMemEval-S (Wilson 95% CI [68.7%, 77.0%]) with no LLM in the default retrieval path. Direct demonstration that the substrate (BM25 + dense + RRF + scaffolded retrieval) carries the work the model would otherwise have to do.

- **Research papers.** [The Asymmetric Burden of Proof](https://doi.org/10.5281/zenodo.18867694) and [A Taxonomy of Epistemic Failure Modes in LLMs](https://doi.org/10.5281/zenodo.19042469) on Zenodo. 1,500+ controlled adversarial evaluations.

- **IP.** 5 US patent filings (1 non-provisional pending, 4 provisional).

### Citation

Bosch, R. (2026). *Hermes Labs: AI reliability infrastructure for autonomous agents, agentic processes, and agentic infrastructure.* https://hermes-labs.ai


## Repo

https://github.com/hermes-labs-ai/hermes-blind