--- title: "LLM-as-a-Verifier: Architecture, Costs & Pilot" canonical: https://wavect.io/blog/llm-as-a-verifier/ language: en description: "Learn how LLM-as-a-Verifier scores agent trajectories, differs from LLM-as-a-Judge, what it costs, and how to run a production pilot." image: "https://wavect.io/img/blog/headers/header_llm-as-a-verifier.png" --- [**Back**](/blog/overview/) [![Kevin Riedl](/img/team/kevin.webp)](/team/kevin-riedl/) [Kevin Riedl](/team/kevin-riedl/) https://linkedin.com/in/wsdt 12 min read · 21 Aug 2026 Last reviewed August 21, 2026 [**Next**](/blog/openviking-agent-memory-review/) # LLM-as-a-Verifier Explained: Architecture, Costs, and Production Fit TL;DR LLM-as-a-Verifier is an open-source framework that scores agent outputs with the probability distribution over ordered score tokens instead of asking a model for one discrete grade. It can compare candidates, select a best-of-N result, and track progress across a trajectory. The paper reports gains on coding, robotics, and medical-agent benchmarks, but those results are project-run and do not prove reliability for your workflow. Production use also requires a verifier backend that exposes token log probabilities, multiple model calls, task-specific criteria, calibrated human labels, deterministic checks, and a safe fallback. Pilot it only where several plausible candidate trajectories exist and a better selection rate can repay added latency and inference cost. Measure accepted-task rate, false acceptance, p95 latency, cost per accepted task, and human-review minutes against a fixed baseline. **LLM-as-a-Verifier is a probabilistic verification framework for ranking agent outputs and tracking task progress.** Instead of asking a model to emit one coarse grade, it reads the probability distribution over ordered score tokens, averages repeated evaluations across explicit criteria, and returns a fine-grained signal. The open-source [LLM-as-a-Verifier repository](https://github.com/llm-as-a-verifier/llm-as-a-verifier) exposes three main workflows: compare two candidates, select the best of several candidates, and score an agent trajectory over time. The business case is narrower than the headline. This is useful when your system can generate several plausible attempts and choosing a better one is valuable enough to pay for extra inference. It does not replace unit tests, schema validation, policy checks, domain experts, or production monitoring. This guide owns that implementation decision. Our separate [LLM evaluation ROI guide](/blog/llm-evaluation-cost-roi-production/) owns the broader question of whether an eval harness is worth building. ## What is LLM-as-a-Verifier? LLM-as-a-Verifier treats verification as a continuous scoring problem. For each candidate trajectory, the verifier is prompted with a task, a criterion, and the observed work. It assigns probabilities to an ordered set of score tokens. The framework converts those probabilities into an expected score, repeats the evaluation, and averages across criteria. The accompanying [LLM-as-a-Verifier research paper](https://arxiv.org/abs/2607.05391) defines three scaling axes: 1. **Score granularity:** more ordered score tokens provide more separation than a binary pass or fail. 2. **Repeated evaluation:** multiple evaluations reduce variance from one noisy model response. 3. **Criteria decomposition:** separate checks for correctness, completeness, evidence, or safety reduce the burden on one vague prompt. This produces a learned confidence signal, not mathematical proof. The verifier can still misunderstand a task, reward a persuasive failure, or miss evidence outside its context. ## LLM-as-a-Verifier vs LLM-as-a-Judge | Dimension | LLM-as-a-Judge | LLM-as-a-Verifier | | --- | --- | --- | | Typical output | One label, integer score, or preference | Expected score from a distribution over ordered score tokens | | Primary use | Grade one answer or compare two answers | Rank several trajectories, track progress, or provide a dense reward | | Uncertainty | Usually hidden behind the emitted grade | Partly retained through token probabilities and repeated scoring | | Scaling controls | Prompt, model, rubric, repetitions | Granularity, repetitions, criteria, candidates, and pivots | | Hard requirement | A model that can return a verdict | A verifier backend that exposes score-token log probabilities | The distinction matters for search and architecture. “LLM-as-a-Judge” describes a broad evaluation pattern. “LLM-as-a-Verifier” here refers to this specific logprob-based framework and its selection algorithm. Neither term means deterministic verification. ## How does the verifier select the best agent result? The library offers `compare`, `select`, and `track`. A production selection flow looks like this: 1. Generate several candidate answers or complete agent trajectories. 2. Remove candidates that fail deterministic gates such as tests, schemas, permissions, or policy rules. 3. Score surviving candidates against narrow, observable criteria. 4. Use the Probabilistic Pivot Tournament to compare candidates without a full round robin. 5. Return the highest-ranked candidate only when its score and hard checks satisfy a defined release threshold. A full pairwise tournament needs quadratic comparisons. The paper's pivot tournament first scores a ring of adjacent candidates, chooses a small pivot set, then compares the remaining candidates with those pivots. Its stated verification budget falls from O(N²) to O(Nk), where k is the pivot count. Alternating candidate order is designed to reduce position bias. ## What do the published benchmarks actually show? The official [project results and benchmark tables](https://llm-as-a-verifier.com/) report the following outcomes. Read them as evidence that the method deserves evaluation, not as a forecast for an unrelated product. | Benchmark | Base pass@1 or comparison | Reported verifier result | What it supports | | --- | --- | --- | --- | | Terminal-Bench V2 | 83.1% | 86.5% | Better selection among coding-agent trajectories in the reported setup | | SWE-Bench Verified | 76.1% | 78.2% | A smaller reported gain on repository issue resolution | | MedAgentBench | 70.2% | 73.3% | Potential value outside coding, with domain-specific risk still present | | RoboRewardBench | 70.8% discrete LLM judge | 87.4% | Stronger preference accuracy than the listed discrete judge baseline | The authors also report an 88.0% best-of-five result on Terminal-Bench 2.1 self-verification versus 78.7% pass@1 and a 96.6% oracle. That gap is commercially important: the candidate pool contained more correct answers than the verifier selected. Verification improved selection, but it did not recover every available success. We did not reproduce these experiments. Candidate models, verifier model, prompts, criteria, benchmark harness, available logprobs, and cost all affect the outcome. A production decision needs your own frozen tasks and human labels. ## Where can LLM-as-a-Verifier create business value? - **Coding agents:** rank several patches after tests, static analysis, and security checks remove obvious failures. - **Research agents:** prefer the trajectory that addresses the question, uses the supplied evidence, and declares uncertainty. - **Operations agents:** score progress and stop or resample a stalled attempt before it consumes the full budget. - **Multimodal workflows:** compare trajectories that include images or video when the chosen verifier supports those inputs. - **Reinforcement learning:** use the fine-grained score as a dense reward during a controlled training workflow. The strongest commercial target is a repeated, high-value task with multiple viable attempts and expensive human review. A one-shot low-value chatbot response rarely justifies best-of-N generation plus repeated verification. ## What are the production limits and risks? ### Logprobs constrain model choice The method needs probability data for the score tokens. Some hosted APIs do not expose the required token-level log probabilities, so the model generating candidates may not be able to serve as the verifier. Check the exact API response, model version, region, and provider contract before designing around it. ### More candidates multiply latency and cost Best-of-N first pays to generate N attempts, then pays for repeated comparisons across criteria. Parallel calls can reduce wall-clock time but not total consumption. Include cached and uncached input, reasoning output, retries, rate limits, and failed candidates in the cost model. Our [AI agent cost-per-action model](/blog/ai-agent-cost-per-action-2026/) explains why cost per accepted result is more useful than cost per token. ### A learned verifier is not a security boundary Candidate content can contain persuasive claims, prompt injection, fabricated test output, or hidden side effects. Keep tool permissions, sandboxing, authorization, executable tests, policy engines, and human approval outside the verifier. The model should judge observed evidence, not an agent's narration that it succeeded. ### Criteria can encode the wrong target A precise score for the wrong rubric is still wrong. Derive criteria from acceptance tests and real failure reports. Maintain counterexamples where verbose but incorrect work should lose. Recheck agreement against human labels whenever the verifier model, prompt, score scale, or task distribution changes. ## How should a team integrate it? ``` task -> generate N candidates -> deterministic and security gates -> verifier ranking -> confidence and budget policy -> human review or execution -> production outcome logged to the eval set ``` The separate [TurboAgent repository](https://github.com/llm-as-a-verifier/TurboAgent) demonstrates a proxy pattern for coding clients: concurrent candidate generation, optional context refinement, verification, and final selection. Treat that as a reference implementation, not a production control plane. Pin versions, isolate credentials, redact sensitive traces, set concurrency and spend limits, and define what happens when the verifier is unavailable. For a business system, place deterministic gates before and after learned scoring. Before scoring, reject malformed or unauthorized work. After selection, run the winning candidate through the same tests and policy checks again because ranking does not make it safe to execute. ## A two-week LLM verifier pilot 1. **Choose one workflow.** Start with a repeated agent task where human reviewers can identify a correct or acceptable result. 2. **Freeze 50 to 100 cases.** Include ordinary tasks, costly failures, adversarial instructions, and ambiguous cases. 3. **Record the baseline.** Measure pass@1, accepted-task rate, false acceptance, p95 latency, model cost, retries, and review minutes. 4. **Add hard gates.** Implement every cheap deterministic assertion before adding model judgment. 5. **Test best-of-three first.** Use narrow criteria and repeated A/B order swaps. Avoid a large candidate pool until the small one shows value. 6. **Calibrate the verifier.** Compare rankings with blinded human decisions and inspect disagreements, not just the aggregate score. 7. **Set a release rule.** Adopt only if accepted-task improvement exceeds the added inference, latency, operational, and review cost without raising false acceptance. ## Build internally or use an AI engineering partner? | Situation | Recommended path | | --- | --- | | You have a stable agent harness, labeled cases, and model-platform expertise | Run a small internal proof with the open-source package | | You lack a trustworthy baseline or acceptance criteria | Fix the evaluation design before integrating a verifier | | The workflow touches customer data, money, regulated decisions, or privileged tools | Fund security, governance, and human-review design as part of the implementation | | You need a production pilot, provider selection, cost model, and handover | Use an implementation partner with measurable delivery gates | Wavect's [AI enablement service](/services/ai-enablement/) covers agent architecture, eval design, model routing, security, observability, and team handover. The [Twinsoft AI case study](/case-studies/twinsoft-ai/) shows the surrounding production work that turns a model capability into an operated system. If you are comparing delivery models, use our [AI enablement versus generic AI consultancy guide](/compare/ai-enablement-vs-generic-ai-consultancy/). ## LLM-as-a-Verifier FAQ ### Is LLM-as-a-Verifier the same as LLM-as-a-Judge? No. LLM-as-a-Judge is the broad pattern of using a model to grade outputs. This framework derives fine-grained expected scores from score-token log probabilities, then scales granularity, repetitions, criteria, and candidate selection. ### Can the same LLM generate and verify its own answer? The project reports successful self-verification experiments, but same-model evaluation can preserve shared blind spots. Validate self-verification against independent human labels and deterministic checks before relying on it. ### Does LLM-as-a-Verifier guarantee correctness? No. It produces a learned score and ranking. It cannot replace executable tests, formal verification, authorization, policy controls, domain review, or human approval for high-impact actions. ### What should an LLM verifier pilot measure? Measure accepted-task rate, false acceptance, pass@1 versus best-of-N, p95 latency, total model cost per accepted task, retry rate, verifier-human agreement, and human-review minutes. ### When is LLM-as-a-Verifier not worth it? Skip it when one cheap deterministic check can decide correctness, when the task is low value, when latency must stay minimal, or when generating multiple candidates costs more than the selection improvement is worth. ## Primary sources and verification date The four cited primary sources above were reviewed on 21 August 2026. The article distinguishes project-reported benchmark results from Wavect's recommendations. We did not reproduce the benchmarks or test a live provider integration. ## Final thoughts LLM-as-a-Verifier turns model uncertainty into a more useful ranking signal by reading score-token probabilities, repeating evaluations, decomposing criteria, and comparing candidate trajectories efficiently. That makes it a credible test-time scaling tool for agent systems that can afford several attempts. It is not a correctness oracle. Production value depends on a representative dataset, observable criteria, logprob-capable infrastructure, deterministic gates, human calibration, and a budget policy. Start with best-of-three on one valuable workflow. Adopt it only when the improvement in accepted tasks survives false-positive, latency, cost, and operational checks. ## You may also like.. [**When Is an LLM Eval Worth Building?** Calculate the wider cost and ROI of deterministic checks, model judges, and human calibration.](/blog/llm-evaluation-cost-roi-production/) [**AI Agent Cost per Action** Model the full cost of successful work, including retries, verification, and human correction.](/blog/ai-agent-cost-per-action-2026/) Agent engineering ## Continue through this cluster Coding agents, MCP, context systems, evaluation and the controls required for dependable automation. [Start with the cornerstone**Graph Engineering for AI Agents: When Does a Knowledge Graph Pay Off?**](/blog/graph-engineering-ai-agents/) - [OpenViking Review 2026: Is Filesystem Memory Production-Ready?](/blog/openviking-agent-memory-review/) - [TrueForge Review: Is the Open-Source Agent Harness Production-Ready?](/blog/trueforge-agent-harness-review/) - [Agent-Readable Websites: llms.txt, Markdown Mirrors and What Breaks](/blog/agent-readable-website-llms-txt-markdown-mirrors/) - [Localized URLs Break hreflang: Keep One English Slug](/blog/english-slugs-vs-localized-urls-hreflang/) - [Can an AI Agent Use Your Product, or Only Read About It?](/blog/can-an-ai-agent-use-your-product/) Inbox, without the noise ## Follow the work that matters to you Get a short email when we publish something new. Follow the whole blog or only the problems you care about. [**Back**](/blog/overview/) [![Kevin Riedl](/img/team/kevin.webp)](/team/kevin-riedl/) [Kevin Riedl](/team/kevin-riedl/) https://linkedin.com/in/wsdt 12 min read · 21 Aug 2026 Last reviewed August 21, 2026 [**Next**](/blog/openviking-agent-memory-review/) New posts by email × × Get new posts by email A short email when we publish. Free, no tracking. ## Structured Data ```json { "@context": "https://schema.org", "@graph": [ { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ], "employee": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } } ], "founder": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } } ], "legalRepresentative": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } } ], "name": "Wavect GmbH", "subjectOf": { "@id": "https://wavect.io/verified-claims.json#dataset", "@type": "Dataset", "creator": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] }, "description": "A machine-readable registry of quantitative and qualitative claims published by Wavect, with review dates, localized page appearances and public third-party citations where available.", "inLanguage": "en", "isAccessibleForFree": true, "license": "https://creativecommons.org/licenses/by/4.0/", "name": "Wavect verified publication claims", "url": "https://wavect.io/verified-claims.json" }, "url": "https://wavect.io/" }, { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "sameAs": [ "https://www.wikidata.org/wiki/Q139796365", "https://www.linkedin.com/in/wsdt", "https://github.com/wsdt" ], "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "sameAs": [ "https://www.wikidata.org/wiki/Q139796367", "https://www.linkedin.com/in/jocr77/", "https://github.com/jo-chris" ], "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/#website", "@type": "WebSite", "inLanguage": [ "en", "de", "es", "zh" ], "name": "Wavect", "potentialAction": { "@type": "SearchAction", "query-input": "required name=search_term_string", "target": { "@type": "EntryPoint", "urlTemplate": "https://wavect.io/search/?q={search_term_string}" } }, "publisher": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] }, "url": "https://wavect.io/" }, { "@id": "https://wavect.io/blog/llm-as-a-verifier/#webpage", "@type": "WebPage", "dateModified": "2026-08-21", "inLanguage": "en", "isPartOf": { "@id": "https://wavect.io/#website", "@type": "WebSite" }, "lastReviewed": "2026-08-21", "url": "https://wavect.io/blog/llm-as-a-verifier/" } ] } ``` ```json { "@context": "https://schema.org", "@type": "BlogPosting", "abstract": "LLM-as-a-Verifier is an open-source framework that scores agent outputs with the probability distribution over ordered score tokens instead of asking a model for one discrete grade. It can compare candidates, select a best-of-N result, and track progress across a trajectory. The paper reports gains on coding, robotics, and medical-agent benchmarks, but those results are project-run and do not prove reliability for your workflow. Production use also requires a verifier backend that exposes token log probabilities, multiple model calls, task-specific criteria, calibrated human labels, deterministic checks, and a safe fallback. Pilot it only where several plausible candidate trajectories exist and a better selection rate can repay added latency and inference cost. Measure accepted-task rate, false acceptance, p95 latency, cost per accepted task, and human-review minutes against a fixed baseline.", "articleBody": " Blog overview/AI and agents/Agent engineering LLM-as-a-Verifier Explained: Architecture, Costs, and Production Fit TL;DR LLM-as-a-Verifier is an open-source framework that scores agent outputs with the probability distribution over ordered score tokens instead of asking a model for one discrete grade. It can compare candidates, select a best-of-N result, and track progress across a trajectory. The paper reports gains on coding, robotics, and medical-agent benchmarks, but those results are project-run and do not prove reliability for your workflow. Production use also requires a verifier backend that exposes token log probabilities, multiple model calls, task-specific criteria, calibrated human labels, deterministic checks, and a safe fallback. Pilot it only where several plausible candidate trajectories exist and a better selection rate can repay added latency and inference cost. Measure accepted-task rate, false acceptance, p95 latency, cost per accepted task, and human-review minutes against a fixed baseline. LLM-as-a-Verifier is a probabilistic verification framework for ranking agent outputs and tracking task progress. Instead of asking a model to emit one coarse grade, it reads the probability distribution over ordered score tokens, averages repeated evaluations across explicit criteria, and returns a fine-grained signal. The open-source LLM-as-a-Verifier repository exposes three main workflows: compare two candidates, select the best of several candidates, and score an agent trajectory over time. The business case is narrower than the headline. This is useful when your system can generate several plausible attempts and choosing a better one is valuable enough to pay for extra inference. It does not replace unit tests, schema validation, policy checks, domain experts, or production monitoring. This guide owns that implementation decision. Our separate LLM evaluation ROI guide owns the broader question of whether an eval harness is worth building. What is LLM-as-a-Verifier? LLM-as-a-Verifier treats verification as a continuous scoring problem. For each candidate trajectory, the verifier is prompted with a task, a criterion, and the observed work. It assigns probabilities to an ordered set of score tokens. The framework converts those probabilities into an expected score, repeats the evaluation, and averages across criteria. The accompanying LLM-as-a-Verifier research paper defines three scaling axes: Score granularity: more ordered score tokens provide more separation than a binary pass or fail. Repeated evaluation: multiple evaluations reduce variance from one noisy model response. Criteria decomposition: separate checks for correctness, completeness, evidence, or safety reduce the burden on one vague prompt. This produces a learned confidence signal, not mathematical proof. The verifier can still misunderstand a task, reward a persuasive failure, or miss evidence outside its context. LLM-as-a-Verifier vs LLM-as-a-Judge DimensionLLM-as-a-JudgeLLM-as-a-Verifier Typical outputOne label, integer score, or preferenceExpected score from a distribution over ordered score tokens Primary useGrade one answer or compare two answersRank several trajectories, track progress, or provide a dense reward UncertaintyUsually hidden behind the emitted gradePartly retained through token probabilities and repeated scoring Scaling controlsPrompt, model, rubric, repetitionsGranularity, repetitions, criteria, candidates, and pivots Hard requirementA model that can return a verdictA verifier backend that exposes score-token log probabilities The distinction matters for search and architecture. “LLM-as-a-Judge” describes a broad evaluation pattern. “LLM-as-a-Verifier” here refers to this specific logprob-based framework and its selection algorithm. Neither term means deterministic verification. How does the verifier select the best agent result? The library offers compare, select, and track. A production selection flow looks like this: Generate several candidate answers or complete agent trajectories. Remove candidates that fail deterministic gates such as tests, schemas, permissions, or policy rules. Score surviving candidates against narrow, observable criteria. Use the Probabilistic Pivot Tournament to compare candidates without a full round robin. Return the highest-ranked candidate only when its score and hard checks satisfy a defined release threshold. A full pairwise tournament needs quadratic comparisons. The paper's pivot tournament first scores a ring of adjacent candidates, chooses a small pivot set, then compares the remaining candidates with those pivots. Its stated verification budget falls from O(N²) to O(Nk), where k is the pivot count. Alternating candidate order is designed to reduce position bias. What do the published benchmarks actually show? The official project results and benchmark tables report the following outcomes. 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This framework derives fine-grained expected scores from score-token log probabilities, then scales granularity, repetitions, criteria, and candidate selection." }, "name": "Is LLM-as-a-Verifier the same as LLM-as-a-Judge?" }, { "@type": "Question", "acceptedAnswer": { "@type": "Answer", "text": "The project reports successful self-verification experiments, but same-model evaluation can preserve shared blind spots. Validate self-verification against independent human labels and deterministic checks before relying on it." }, "name": "Can the same LLM generate and verify its own answer?" }, { "@type": "Question", "acceptedAnswer": { "@type": "Answer", "text": "No. It produces a learned score and ranking. It cannot replace executable tests, formal verification, authorization, policy controls, domain review, or human approval for high-impact actions." }, "name": "Does LLM-as-a-Verifier guarantee correctness?" }, { "@type": "Question", "acceptedAnswer": { "@type": "Answer", "text": "Measure accepted-task rate, false acceptance, pass@1 versus best-of-N, p95 latency, total model cost per accepted task, retry rate, verifier-human agreement, and human-review minutes." }, "name": "What should an LLM verifier pilot measure?" }, { "@type": "Question", "acceptedAnswer": { "@type": "Answer", "text": "Skip it when one cheap deterministic check can decide correctness, when the task is low value, when latency must stay minimal, or when generating multiple candidates costs more than the selection improvement is worth." }, "name": "When is LLM-as-a-Verifier not worth it?" } ] } ```