docs: plain-English pass over the active specs

Ran the humanizer rules over 79 prose files in the active specs (brief,
design, requirements, research and narrative docs). Removed em dashes,
significance inflation, forced rule-of-three, bold-label list items and
filler. Skipped tasks.md checklists and the retired specs (harnessx-*,
openrouter-sast-harness, unseen-repo-evaluation).

Every number, path, command, hash and identifier was preserved: checked
each file against HEAD for drift in numbers, inline code and code fences,
zero drift across all 79 files.

Co-Authored-By: Claude Opus 4.8 <[email protected]>
This commit is contained in:
Marius Ciepluch
2026-10-12 18:16:41 +02:00
co-authored by Claude Opus 4.8
parent b08f372022
commit 39a1873e5b
79 changed files with 1103 additions and 1103 deletions
+11 -11
View File
@@ -7,11 +7,11 @@ Status: brief / not yet specced. Proposed 2026-10-07 by the maintainer. Builds o
The search arm (directed, Cairn-inspired) works. The **proof-authoring** last mile is the drag:
1. **Safety-classifier refusals.** Authoring exploit demos repeatedly tripped a safety classifier
1. Safety-classifier refusals. Authoring exploit demos repeatedly tripped a safety classifier
this session when exploit payloads or step-by-step attack prose passed through the model or a
tool prompt. The mitigation so far (keep payloads on disk, treat demos as opaque) is a workaround,
not a fix, and it limits how directly the proof model can reason about the exploit.
2. **Nondeterministic wiring.** The agent authors a demo but intermittently mis-wires the app to
2. Nondeterministic wiring. The agent authors a demo but intermittently mis-wires the app to
the oracle fixture (served directory for path, database URL for SQL), so a demonstrable pair
comes back inconclusive until a retry. Each miss has cost a paid run.
@@ -21,20 +21,20 @@ proof-authoring policy with DSPy from **traces logged in OpenObserve**.
## Shape
- **Search model:** DeepSeek (current). Good at the directed fact/intent search.
- **Proof model:** GLM 5.3 (Anthropic-recommended) and/or an abliterated model via
- Search model: DeepSeek (current). Good at the directed fact/intent search.
- Proof model: GLM 5.3 (Anthropic-recommended) and/or an abliterated model via
`ABLITERATION_BASE_URL` (`abliterated large v2`). The abliterated model authors exploit **demos**
without the refusals that block a safety-trained model on this defensive task.
- **Proof driver:** OpenCode MCP (github.com/AlaeddineMessadi/opencode-mcp) as the agentic
- Proof driver: OpenCode MCP (github.com/AlaeddineMessadi/opencode-mcp) as the agentic
demo-author, running inside the gVisor verification task so the exploit-authoring is sandboxed.
- **Learning:** DSPy compiles/optimizes the proof-authoring program (demo schema, wiring, steps)
- Learning: DSPy compiles/optimizes the proof-authoring program (demo schema, wiring, steps)
from past traces, instead of the per-family hand-guidance we keep adding (which is now hitting
the prompt character budget — a signal the hand-rule approach is near its limit). Matches the
the prompt character budget, a signal the hand-rule approach is near its limit). Matches the
maintainer's "learned decisions, not hand-tuning" direction.
- **Tracing:** proof runs (reason/explore/author/verify, timings, outcomes, error classes) log to
- Tracing: proof runs (reason/explore/author/verify, timings, outcomes, error classes) log to
**OpenObserve AI** at `OPENOBSERVE_URL` (org `OPENOBSERVE_ORG`, stream `OPENOBSERVE_TRACES_STREAM`).
DSPy consumes these traces as its training signal.
- **Credentials:** stored in `.env` like the existing keys, referenced by name only:
- Credentials: stored in `.env` like the existing keys, referenced by name only:
`ABLITERATION_API_KEY`, `ABLITERATION_BASE_URL`, `ABLITERATION_MODEL`, `OPENOBSERVE_URL`,
`OPENOBSERVE_ORG`, `OPENOBSERVE_TRACES_STREAM`, `OPENOBSERVE_TOKEN`. No secret value ever enters a
tracked file, a prompt, a log stream, or a commit.
@@ -83,8 +83,8 @@ in a **gVisor-isolated** corpus, to demonstrate a regression before push. Guardr
## OpenCode MCP as the endpoint for both PoC and fix
Maintainer note (2026-10-07): OpenCode MCP is a good server endpoint for the PoC **and** the fix,
not only proof authoring. The same agentic endpoint can:
Maintainer note (2026-10-07): OpenCode MCP is a good server endpoint for both the PoC **and** the fix.
The same agentic endpoint can:
1. Author the PoC (the demonstrated proof) for a caught finding.
2. Write a candidate fix against the finding's repair direction and the failing PoC.