dsh-rigorquant

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_{Edgeworth box — hand-drawn in TikZ, no AI-generated imagery}

Unattended-within-a-session, long-running empirical/computational mathematics research for DeepSeek Harness — economics, finance, portfolio construction/optimization, simulation, computational econ/finance.

RigorQuant is an agent preset + bundled skills that turns one DSH session into a context-isolated multi-agent research lab:

• Parallel explorers propose candidate methods (subagent_explorer, blank context).
• An OffGridThinker (subagent_offgrid) works off the grid when a route must be isolated: raw model intelligence plus compute tools (sympy, numpy, mpmath, Lean checkers) — no web, no literature, no other agents' results.
• A ground-truth track re-derives the analytic closed forms, invariants, and bounds for simplified cases — twice, by different means (two independent subagent_double_checker calls).
• An adversary eliminates routes by counterexample only.
• A four-part check battery (closed-form equality, exact invariants, analytic bounds, statistical hardening) runs BEFORE numerical implementation.
• A meta-validator (rq_check.py) refuses a PASS whose evidence is missing: empty stage outputs, an empty derivations/, a registry with no audit-referenced passed route, or deliverables that do not compile. Its evidence checks read the audit record, not study.json — a study may not vouch for itself.
• Fixed-seed + LLN conventions for stochastic work.
• A jacobian MCP escalation lane (opt-in; Lean as a manual external lane) settles proof-critical claims before implementation.
• PASS → auto-implement and proceed; BLOCKED → 3 rounds of the same gap → strongest derivation + exact gap; BUDGET → 5 rounds → checkpoint + report.

The operating pattern adapts Shanmu Jin's Crouzeix-conjecture run (prompt, Lean audit) and Terence Tao's blueprint/equational-theories projects to numerical work. Full design record: docs/architecture.md.

"Unattended", precisely: the framework runs unattended within one live session. Crossing a session boundary disarms the goal; one human turn ("continue") re-arms it. It does not continue autonomously across restarts.

The research team — and how it works

Eight roles around one hub, each a separate tool with its own powers and limits. The Orchestrator is the only role that sees every report; the separation is enforced by the composition, so the producer never checks its own work — an idea dies only on a concrete counterexample, never on style or vibes.

Orchestrator · root persona — fans out the work, synthesizes, and writes the state. Bound by four rules: producer ≠ checker, counterexample-only elimination, seeds always recorded, no handwaved load-bearing claims.

Explorer · subagent_explorer — blank-context and divergent. Proposes lemmas, equations, constructions, and candidate methods with exact statements. Status reports are rejected.

OffGridThinker · subagent_offgrid — the off-grid lane. Raw model intelligence plus the pinned compute lane (sympy, numpy, mpmath, cvxpy, hypothesis, jax; Lean checkers when provisioned) — and nothing else: no web, no skills, no delegation, no other agents' results. Its own agent, not an Explorer variant: isolation is the identity.

DoubleChecker · subagent_double_checker — blind (no web, no skills, no delegation, no drafts). Re-derives the load-bearing claims from first principles, twice by different means.

Adversary · subagent_adversary — runs the check group and hunts counterexamples. Ends in a verdict: PASS or NEEDS-EDITS.

Literature · subagent_lit_line · _adversary — a walled citation-graph sweep, then an independent adversary re-retrieves each claim and certifies it's real and current.

Validator · rq_check.py + schemas — refuses a PASS with missing evidence. Reads the audit record, never the study's own claims — a study cannot vouch for itself.

Document adversary · subagent_document_adversary — an independent agent that audits each finished deliverable for self-completeness (the thing 90% of AI-generated writing drops): every jargon term, symbol, and abbreviation the document uses must be defined in the artifact itself or the audience spec's symbol registry. Returns VERDICT: PASS / VERDICT: NEEDS-EDITS; a NEEDS-EDITS is a blocking gap the validator refuses a PASS without.

The team, live — the activity view

The plugin ships a live activity panel (the rq-activity host half, the shell.overlay floater in the browser half): while a RigorQuant session runs, a pill appears vertically centered on the main window's right edge (it follows the conversation column, so the workspace rail and right-docked panels stay clear), expanding into a panel that shows, for the current session's lab only (never other sessions, and only while the current session is a RigorQuant one), the five-move stage the run is on, a hub-and-spoke role map (the Orchestrator at the hub, every role it can delegate to as a spoke), a working/idle roster with their docs/figs/ portraits, each role's last action, and a newest-first activity feed. It is pure observation — it reads the events the core already publishes and serves a JSON snapshot + portraits over /plugins/dsh-rigorquant/..., and it changes no tool, route, or model. Colors are --dsw-alias tokens, so it follows the shell's own light/dark theme.

The picture above is the reader-safe rendering of the same design (the live panel is only visible in a running web session) — adapted from the live activity panel of dsh-agent-teams — the picture in its README — showing RigorQuant's own eight roles at a fan-out moment. The panel SVG is generated from docs/figs/agent-team-activity.js.

Attribution. The activity-panel design is adapted from dsh-agent-teams by NanmiCoder (程序员阿江 / Relakkes) — Copyright (c) 2026, MIT License. The role portraits are this repo's own docs/figs/ assets. The header banner is likewise reworked from the upstream hero graphic.

The loop, in five moves. Each round is fan-out → ground truth → adversary → synthesize.

1. Promise — record the original question verbatim, split it into sub-problems with crisp criteria, pick hand-checkable simplified cases, and pin seeds, tolerances and the schema/validator digests.
2. Fan out — blank-context explorers and literature lines run in parallel; most are never told the favored approach.
3. Ground-truth it — the blind DoubleChecker re-derives the load-bearing claims without seeing anyone's draft; two independent derivations for anything the study rests on.
4. Attack it — the adversary runs the four-gate battery, then hunts counterexamples; divergent tracks are lined up as an adjudication docket.
5. Certify & ship — the validator checks nothing is missing; the paper and slides are assembled from validated records, never written fresh.

The check battery, run before any numerical implementation: A closed-form equality · B exact invariants · C analytic bounds · D statistical hardening (fixed seed + LLN shrinking ≈ C/√N).

With receipts: in one hard run, 21 errors were caught by a specific mechanism and none by luck (11 of them the orchestrator's own); only 35% of 81 literature claims survived independent verification; and the honesty gate is itself tested — a forged study must fail.

Install

Requires DSH ≥ 0.1.5-alpha.2. The preset uses native child agentOptions.reasoningEffort (0.1.2-alpha.1), the prefix/suffix persona split (0.1.3-alpha.2 — a row whose config fails rejects the whole preset mount), the final-assistant-message delivery contract (report was removed in 0.1.2-rc.1), and the present deliverables tool (0.1.5).

One line, everything — the preset, the compute lane, and the plugin (role model router + its Settings card):

npx dsh-rigorquant
# npx dsh-rigorquant --profile tui     # a profile other than web

From a clone — the same install, from your own working tree. A checkout installs itself into the profile, so re-run this after editing dsh/ to refresh the plugin:

git clone https://github.com/linxichen/dsh-rigorquant
cd dsh-rigorquant
./install.sh
# ./install.sh --skill-only     # only the skills, for any preset, no plugin
# ./install.sh --uninstall      # removes everything, plugin included

Plugin only — the same everything, via the ecosystem's bundle path. The package declares a dsh.bundle manifest whose rows include a boot-sync half (rq-preset-sync): on the profile's next start it lands the agent preset into $DSH_HOME/.agent-presets/rigorquant and the compute lane into $DSH_HOME/share/rigorquant/, so dsh plugin add alone yields a working distribution (docs/architecture.md Decision 22):

dsh --version                 # must be >= 0.1.5-alpha.2
dsh plugin --profile web add dsh-rigorquant

The sync is idempotent (a byte-identical tree is left untouched; derived state like .venv is never copied or pruned) and keeps same-version local edits to the installed preset — an upgrade replaces shipped files, exactly like re-running ./install.sh. There is no uninstall hook in DSH's plugin CLI, so removal stays explicit (./install.sh --uninstall); if you remove only the plugin, the synced preset keeps working standalone and simply routes nothing.

Start a new DSH session and pick the RigorQuant preset. Then:

rigorquant: derive and validate a method for [problem], simplified cases first, before any numerical implementation.

Compute lane (one-time)

The pinned uv lane lives at $DSH_HOME/share/rigorquant/env, placed there by install.sh or by the plugin's boot-sync row — whichever ran last owns the anchor, and both land identical bytes (see env/README.md). The venv itself is never installed: it is derived state, provisioned lazily inside the anchor by the first uv run --frozen --project <env_lane> (subsequent runs are instant; --frozen honors the committed lockfile). The jacobian escalation lane ships disabled and pinned (jacobian@0.12.0): enable the mcp-jacobian row, and the framework asks for approval before any one-time provisioning (npx -y jacobian@0.12.0 upgrade, or the Lean toolchain via the skill's scripts/provision-lean.sh). See mcp/jacobian.md.

Role-routed models (rq-model-router)

The bundled plugin gives each RigorQuant role a model + reasoning-effort policy, with one fallback per role. The DoubleChecker and adversary tool rows use DSH 0.1.2's native agentOptions for their shipped primary (deepseek-v4-pro @ high); the router only overlays explicit Settings choices and fallback retries. Configure overrides in Settings → Plugins → RigorQuant model routing: the last saved selection persists (settings user layer). Shipped defaults:

On a terminal primary failure (no adapter / HTTP 4xx, including the official quota response 1308 / “Usage limit reached”) the role degrades to its fallback for one forced retry, and recovers on the next success or after 10 minutes. Untagged agents (other presets, workflow workers, forks) are never touched. The router needs DSH ≥ 0.1.5-alpha.2 (its persona-section constant follows the 0.1.3-alpha.2 deployment:persona-prefix rename). Design record: docs/architecture.md Decision 16.

Repository layout

package.json                dsh.bundle manifest (dsh plugin add support)
cordis.patch.yml            bundle patch: skills layer + rq-model-router +
                            rq-activity + rq-preset-sync rows
dsh/                        host halves (rq-model-router router, rq-activity
                            monitor, rq-preset-sync boot-sync) and the web
                            client bundle (settings card + activity floater)
agent-presets/rigorquant/   preset composition + persona + bundled skills
  skills/rigorquant/        SKILL.md + references/ + scripts/ + schemas/
  .../scripts/rq_check.py   the meta-validator (single canonical copy)
  .../schemas/              study.json + registry.json JSON Schemas, which the
                            validator loads — so schema and checker cannot drift
env/                        pinned uv compute lane (sympy/cvxpy/hypothesis/…)
mcp/jacobian.md             escalation lane wiring
docs/architecture.md        grilled decision record + sources
docs/figs/agent-team-activity.svg  reader-safe activity-view picture
docs/figs/agent-team-activity.js   its generator (freshness-pinned in tests)
docs/figs/agent-team-hero.svg       team-graph banner, reworked from the
                             dsh-agent-teams hero graphic (see credit below)
tests/                      the validator's test suite (see Testing below)
studies/                    one study folder per task (Mode B; a checkout's own
                            live studies — not shipped in the npm bundle)

Testing

The validator has a test suite, and its centrepiece is a forged study — empty derivations, empty stage outputs, a one-line adversary report, and a paper whose body reads "This paper says nothing." It must FAIL. A framework whose honesty gate is not itself tested is a framework that certifies whatever it is handed.

uv sync --frozen --project env
uv run --frozen --project env python -m pytest tests/ -q

Pre-commit coverage gate (validator ≥95%)

The checked-in hook at .githooks/pre-commit runs the same full suite under coverage and refuses a commit when the shipped validator (rq_check.py) drops below 95% line coverage. ./install.sh enables it in a git checkout; in an existing checkout, enable it explicitly:

git config core.hooksPath .githooks

The validator is a subprocess, so coverage is deliberately explicit rather than plugin magic: RQ_COVERAGE=1 makes tests/conftest.py::run_check invoke coverage run --parallel; the hook combines those child data files and applies coverage report --fail-under=95. CI runs the identical gate. Bypass one commit only with Git's standard git commit --no-verify.

tests/test_repo_consistency.py covers the other half: one validator, one schema, documented commands that resolve, and layout blocks that match the filesystem. That is the defect class this repository actually produces.

What a green validator means: nothing declared is missing, and the deliverables build. It does not mean the mathematics is right — that stays with the check battery, the independent ground-truth track, and the adversary.

Studies

A study is one self-contained rigorquant task with an identical folder structure everywhere: durable deliverables at the study root (study.json, STUDY.md, registry.json, journal.md, derivations/, audits/, artifacts/) are meant to be committed; all scratch lives in a gitignored interim/. Two modes, implied by location:

• One study per repo — study.json at the repo root.
• Multiple studies per repo — studies/<slug>/study.json; the roster is studies/*/study.json.

Intake detects an existing study and continues it silently; a new study asks one question (mode + slug) and never asks again. See docs/architecture.md §12.

Publishing

This repo is a community DSH plugin distribution (bundle + preset + skill form): it declares a dsh.bundle manifest in package.json, is tagged dsh-plugin, and is discoverable by the ecosystem's topic-based indexes — see dsh-find-plugins and the awesome-deepseek-harness list for the conventions.

MIT License.