dsh-plugin-recursive-harness · Recursive Harness

A plugin for DeepSeek Harness (dsh) that evolves the agent's harness while the model weights stay frozen, following the two-level recursion of Google Research's Harness² (arXiv:2610.14224):

Per task: Propose → Probe → Compose. A model proposes two candidate harnesses (a general one that sharpens reasoning and tool discipline, and a domain one that adds memory, guardrails and a skill). Each candidate runs in an isolated headless dsh with a copy of the task workspace. Objective checks decide first, behavioral metrics (tool errors, steps, tokens) second, an optional model judge last. Complementary winners are merged and verified again; a worse merge falls back to the best single candidate.
Across tasks: Reflector → Experience Library. What worked and what failed is written as plain Markdown entries that later proposals can draw on, in strict time order.

What gets evolved: four system prompt sections (systemprompt, guardrails, tool_guidance, longTermMemory) and candidate skills (SKILL.md plus small scripts). What never changes: your AGENTS.md, .dsh/, .git/, and anything you list in lockedSurfaces.

Status: experimental. It works end to end on dsh 0.2.0-rc.2, and the evaluation in docs/evaluation-report.md was run with a real model on a 12-task synthetic set. Read the report's limitations before expecting gains on your own tasks.

What you see

Install the plugin. Every new Session now carries the active harness version (four sections once each in the system prompt; /harness inspect shows the version and the mounted skills).
/harness start gen-dev-01 (a built-in task) or /harness start ./my-task.mjs starts an evolution run in the background: reference runs, proposals, probes, comparison, compose. /harness status follows it; /harness cancel stops it and kills the child processes.
When a candidate beats the baseline on the objective checks and by more than the baseline's own variance, it becomes the active version. New Sessions get it; running Sessions are never changed mid-flight. /harness rollback [versionId] goes back.
/harness listExperience lists what the reflector kept.

Requirements

dsh 0.2.x. Tested on dsh 0.2.0-rc.2 (Windows 11, Node.js 24.21) with the web profile; probes use the headless profile that ships with dsh.
Node.js 22.19 or later in the 22 line, or 24 and later (the same as dsh).
A proposer endpoint: any OpenAI-compatible chat completions server (vLLM, SGLang, llama.cpp, Ollama, or a hosted API). Until you configure one, the proposer is a fixed template and no text leaves your machine.
Probes run with the model of the profile you name in probe.templateProfile (default web), or with probe.provider / probe.model if you set them. Each probe is a full agent run; budget accordingly.

Install

dsh plugin --profile web add dsh-plugin-recursive-harness

Through npx: npx @deepseek-ai/dsh plugin --profile web add dsh-plugin-recursive-harness. Restart dsh web. The plugin prints [recursive-harness] ready: … on start.

Update: dsh plugin --profile web update dsh-plugin-recursive-harness, then restart. dsh installs plugins with pnpm, which picks a newly published version only once it is a day old; to update right away, name the version (add dsh-plugin-recursive-harness@0.1.0).

To remove: dsh plugin --profile web remove dsh-plugin-recursive-harness, delete the recursive-harness entry from your profile's cordis.patch.yml if you added one, and delete ~/.dsh/recursive-harness/ if you do not want to keep the harness versions and the experience library.

Settings

Settings go in your profile's cordis.patch.yml (web profile: ~/.dsh/profiles/web/cordis.patch.yml). An entry with id: recursive-harness replaces the plugin's whole config; keys you leave out keep the defaults in cordis.patch.yml.

- id: recursive-harness
  config:
    proposer:
      engine: llm
      endpoint: http://127.0.0.1:8000/v1/chat/completions
      model: my-model
      apiKeyEnv: ""            # env var with the key, if the endpoint needs one
      allowEgress: false       # true before anything goes to a non-loopback endpoint
      allowHttpHosts: []       # non-loopback hosts that may be reached over plain http
      judge: true              # model judge as the third tier; never overrides objective checks
      jsonMode: false          # send response_format json_object (vLLM, OpenAI)
      extraBody: {}            # e.g. { chat_template_kwargs: { enable_thinking: false } }
    probe:
      templateProfile: web     # probes inherit this profile's model rows and credentials
      provider: ""             # override the probe model (a provider id from llm-pi-ai)
      model: ""
      blockedTools: []         # tasks needing these tools are never probed
    lockedSurfaces: []         # extra paths a candidate may never touch
    protectedPatterns: []      # regexes that reject a candidate (e.g. "\\bPROD-DB\\b")
    maxConcurrency: 2          # parallel probe child processes
    maxTokensPerIteration: 120000
    probeTimeoutMs: 600000

Your own tasks

A task file is an ES module that exports a task (or tasks, an array). The plugin writes workspaceFiles into an empty workspace, runs the agent with prompt, and hands the final output to objectiveCheck:

// my-task.mjs
export default {
  taskId: 'orders-report',
  split: 'dev',
  domain: 'general',
  prompt: '讀取 orders.csv，輸出 { total, byRegion } 的 JSON，byRegion 依名稱排序。',
  workspaceFiles: { 'orders.csv': 'region,amount\nEast,10\nWest,5\nEast,2\n' },
  expected: { total: 17, byRegion: { East: 12, West: 5 } },
  objectiveCheck(record, task) {
    const data = JSON.parse(record.finalOutput.match(/\{[\s\S]*\}/)[0]);
    const ok = data.total === task.expected.total;
    return { passed: ok, score: ok ? 1 : 0, details: ok ? [] : [`total 應為 ${task.expected.total}，實得 ${data.total}`] };
  },
};

The proposer never sees expected; the numbers in details are masked before they reach it. Keep a held-out set of tasks the proposer has not seen to tell real improvements from overfitting.

Where things live

$DSH_HOME/recursive-harness/ (by default ~/.dsh/recursive-harness/):

active/ the mounted harness: systemprompt.md, guardrails.md, tool_guidance.md, LongTermMEMORY.md, skills//SKILL.md (+ scripts/). You can edit these by hand; the next Session mounts the edited version.
state/ manifest.json, checkpoints/.json, harnesses// snapshots, sessions/ sidecars, events.jsonl.
experience/ INDEX.md and one folder per entry (EXPERIENCE.md, the harness snapshot it came from).

Each probe runs in /dshp-*/ with its own DSH_HOME (profile, credentials copied from the template profile, the candidate harness) and workspace, deleted when the probe ends. Stale directories left by a killed process are swept at the next start.

What leaves your machine

To the proposer endpoint: the task prompt, the current harness text, summaries of the reference runs (tool names, argument excerpts, error messages, a final-output excerpt with keys and private IPs redacted), and relevant experience entries. Only when proposer.engine is llm.
From the probes: whatever the agent's model receives in a normal dsh Session, through the model rows of the template profile.
Nothing else. There is no telemetry; the plugin never calls GitHub or npm.

Evaluation

See docs/evaluation-report.md for the real-model run (setup, per-task numbers, limitations). The two gates it reports:

G1: with the same probe budget, does single-step Propose → Probe → Compose beat best-of-k on a held-out task set by more than the baseline's own variance?
G2: does the cross-task experience library help on held-out tasks, measured by leave-one-out?

The unit tests (npm test) include a pipeline rehearsal with a mock executor; those numbers are not results and are never written into the report.

Known limits

One evolution run spends 5 to 6 full agent runs plus one or two proposer calls. Budgets (maxTokensPerIteration, maxWallTimeMs, probeTimeoutMs) stop a run; they do not make it cheap.
Probes inherit the template profile's cordis.patch.yml only; model rows passed to dsh with --patch at launch are not seen by the child process.
Windows: child processes are killed with taskkill /T; a probe that is cancelled during a slow model call may take a few seconds to go away.
The runtime stagnation detector (enableRuntimeStagnation) is optional and off by default.

Development

npm test                    # unit tests, including a probe child-process test with a fake dsh binary
npm run eval                # pipeline rehearsal with the mock executor (not a result)
node eval/run-real-eval.mjs --config my-config.json   # real-model evaluation, resumable

Feedback and bug reports: GitHub issues. MIT license.

Install

README

dsh-plugin-recursive-harness · Recursive Harness

What you see

Requirements

Install

Settings

Your own tasks

Where things live

What leaves your machine

Evaluation

Known limits

Development