dsh-verifier
适用于 DeepSeek Harness 的 LLM-as-a-Verifier 插件：基于 logprob 的细粒度评分、成对枢轴锦标赛（best-of-N）以及自动回合结束质量门控。
插件会安装到这里；不确定时保持 web。
npx -y @deepseek-ai/dsh plugin --profile web add github:DevRico003/dsh-verifier#ad0021b62f4ee1358fbc23a4738b42a78094a6d3Verifier 以 dsh-verifier 发布，当前版本为 0.1.0。Plugin Hub 会校验它的 manifest，并保存精确安装来源，便于复现安装结果。
点击下方 Badge 复制 Markdown，粘贴到 README 即可。
验证 package.json 声明的 GitHub 仓库，即可管理这个公开页面。认领后，Hub 会优先安排当前版本的安全扫描，并在通过后公开展示结果。
认领这个 Plugin →An LLM-as-a-verifier plugin for DeepSeek Harness (dsh). It adds a quality gate that runs at the end of every agent turn, plus three tools the agent can call to check its own work.
The scoring method is a port of llm-as-a-verifier by Kwok et al. (project site llm-as-a-verifier.com, paper arXiv 2607.05391, MIT). That repo selects the best of N agent trajectories. This plugin takes the same math and wires it into a running harness.
The method comes from the llm-as-a-verifier authors. Their framework (source: llm-as-a-verifier.com): probability over the logits instead of a sampled token, a fine-grained scoring token, repetition, and decomposition into simpler criteria, aggregated as R(x, tau) = 1/(C K) sum over criteria, repeats and scale values of p(v | x, c, tau) phi(v).
LLM-as-a-Verifier framework: uncertainty, granularity, repetition, decomposition
What that buys with the same model this plugin runs on (chart from the llm-as-a-verifier README; Terminal-Bench 2.1, mini-swe-agent, DeepSeek V4 Flash as generator and verifier, costs at OpenRouter prices of 2026-08-17): best-of-3 lifts DeepSeek V4 Flash from 78.7% to 86.5%, best-of-5 to 88.0%, at roughly a quarter of the cost per task of GPT-5.6 Sol in Codex.
The gate in this plugin is the cheaper cousin of that best-of-N selection: one trajectory, scored once, repaired once. verifier_select is the best-of-N itself.
The gate. When an agent is about to end a turn, the plugin serializes the turn (the task, the assistant messages, every tool call and its observed output) and asks a verifier model to score it per criterion on a 20-letter scale. The score is not the sampled letter. It is the expectation over the logprob distribution of the score token, so the verdict is continuous in [0, 1]. If the mean falls below gate.threshold, the verifier's findings go back to the agent as a plugin message and the harness runs another step. The agent sees text like this:
[dsh-verifier] Automatic verification of your last turn scored 0.28 / 1.00 (pass threshold 0.60). Round 1 of 1.
Per-criterion rewards: Empirical verification & error signals=0.13, Specification adherence=0.21, Code quality & root cause=0.50.
Verifier findings, Empirical verification & error signals (0.13):
The agent launched three background subagents ... There is zero observed verification in the trajectory: no npm test run, no curl of the service endpoints, no screenshot ...
That example is from a real run. The agent answered "Fair criticism, let me check what's actually on disk now instead of trusting narration" and went back to work. Continuations are capped per turn (gate.maxRounds, default 1). Turns that end with a question to the user are never forced on.
The tools.
| Tool | What it does |
|---|---|
verifier_assess(task, answer) | Scores one result per criterion and returns the findings. By default it also hands the verifier the observed trajectory of the current turn, so the verdict rests on tool output rather than on the agent's summary. |
verifier_select(task, candidates[]) | Best-of-N. Pairwise comparisons with slot swapping, then a probabilistic pivot tournament picks the winner in N + k(N-k) comparisons instead of N². |
verifier_compare(task, a, b) | One directed pairwise reward. |
The backend. openai-compatible calls any chat-completions endpoint with logprobs and top_logprobs (vLLM, SGLang, the DeepSeek API). That is what makes the fine-grained score possible. harness routes through the harness LLM seam instead, which carries no logprobs, so scores fall back to the literal letter. Use the direct endpoint when you can.
| Reference concept | File |
|---|---|
| 20-letter scale, pairwise orientation (A = best) and progress orientation (T = best) | src/core/scale.ts |
Reward as logprob expectation, last-tag match, fused >A tokens, whitespace skip, literal-letter fallback | src/core/scoring.ts |
Criteria × repeats with slot swap on odd repeats; compare, select, assess | src/core/verifier.ts |
| Pivot tournament: ring pass, top-k pivots, pivot round, Bradley-Terry argmax | src/core/tournament.ts |
Prompts with the invariant part first and the criterion last (prefix-cache friendly); criteria sets general, coding, terminal | src/core/prompts.ts |
| Trajectory serialization from the harness session log | src/trajectory.ts |
Things the reference repo does not have: the turn gate (src/gate.ts), the tools (src/tools.ts), hot-reloadable settings, and the handling of unscored verdicts described below.
git clone https://github.com/DevRico003/dsh-verifier
dsh plugin --profile web add /path/to/dsh-verifier
dsh plugin --profile headless add /path/to/dsh-verifier
dsh plugin --profile desktop add /path/to/dsh-verifier # DSH Desktop
lib/ is committed, so no build step is needed to install. Restart dsh web or the desktop app afterwards.
Defaults live in cordis.patch.yml. The shipped backend.baseURL is the placeholder http://YOUR_SPARK_HOST:8000/v1; until you set a real endpoint the plugin loads but every verifier call fails with a message naming this setting (the gate then closes turns unverified and logs a warning, verifier_assess returns scoredCriteria: 0 with the message in findings). Set at least backend.baseURL and backend.model. Everything is a hot-reloadable verifier: section in $DSH_HOME/settings.yaml; no restart.
verifier:
backend:
kind: openai-compatible # or: harness
baseURL: http://YOUR_SPARK_HOST:8000/v1
model: deepseek-v4-flash-0731
apiKeyEnv: SPARK_API_KEY # env var or credential reference; empty = no Authorization header
reasoningEffort: none # keep the verifier cheap; none|low|high|max on vLLM
maxTokens: 4096
temperature: 1.0 # the reference default; keeps the logprob distribution informative
topLogprobs: 20
concurrency: 4
retriesOnFallback: 1
gate:
enabled: true
threshold: 0.6
maxRounds: 1
evaluations: 1 # repeats per criterion
criteriaMode: auto # coding when the turn used tools, else general
criteria: general
skipWhenAskingUser: true
skipSubagents: true # child agents are not gated; their parent turn is
minSteps: 1
feedbackMaxChars: 2500
timeoutMs: 300000
select:
evaluations: 2 # for the tools; 2 or more cancels slot bias
pivots: 1
seed: 0
criteria: general
trajectory:
maxStepChars: 2000
maxTotalChars: 60000
tools: true
verbose: false
gate.enabled: false keeps the tools and drops the gate. enabled: false turns the plugin off.
One gate pass is criteria × evaluations verifier calls, three by default, fanned out with backend.concurrency. On a vLLM server with prefix caching a short turn takes 6 to 10 seconds of wall clock, because the criterion sits at the prompt tail and the rest of the prompt is cached. verifier_select with three candidates is 5 pairs × 3 criteria × 2 repeats, about 30 calls.
A verifier reply sometimes carries no parseable score tag. The plugin retries such a call (backend.retriesOnFallback, default 1). What still has no verdict is excluded from every mean and reported as scored: false, never counted as a neutral 0.5. A gate pass with zero scored criteria closes the turn unverified and logs it instead of steering the agent on noise.
skills/graph-verified-coding/SKILL.md is a dsh skill (also usable by Claude Code or Codex from ~/.agents/skills) that tells the agent how to use the verifier inside a graph-engineered coding process. Seven steps, each with a done-condition: contract (acceptance criteria with observable artifacts), cut false edges (independent nodes in parallel, dependent ones in sequence), work node (keep the proving output), gate (tests, browser loop for rendered output, verifier_assess when evidence is ambiguous), join (verifier_select over competing candidates), cycle with a stop (repair and re-gate, bounded rounds), report with evidence. A reference block lists the tools and their cost so the agent places gates where they pay: before merges and before the final answer. Link it into a skill root dsh scans:
ln -s /path/to/dsh-verifier/skills/graph-verified-coding ~/.dsh/skills/graph-verified-coding
Typechecking needs a DeepSeek Harness checkout next to this repo (the link: devDependencies point at ../deepseek-harness). The published @deepseek-ai/dsh-* packages on npm are an older generation and do not typecheck against the current harness.
pnpm install
pnpm run build # tsc -> lib/
pnpm test # node:test unit tests
kind: harness degrades to a 20-level letter judge.select call. There is no on-disk cache across calls.MIT. The scoring method and prompt texts are ported from llm-as-a-verifier (llm-as-a-verifier.com, MIT); see LICENSE. Thank you to the authors for publishing the method, the prompts and the benchmarks.