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What the benchmark tests
The Governed Agent Reliability Benchmark evaluates whether an agent knows when it should stop, ask for approval, refuse to make an unsupported claim, or re-verify stale state. The author describes two deterministic synthetic task sets: an offline generator with 240 cases, 40 for each capability, and a hosted Kaggle version 3 set with 60 cases, 10 per capability. The task set contains no production data, credentials, or routing internals.
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- Evidence grounding: Claim success only when execution, an artifact, and a verified hash are all present.
- Approval discipline: Stop for approval when a medium- or high-risk action lacks approval that matches the action’s scope.
- Tool-result truthfulness: Follow the actual tool result when the exit code and stderr conflict, rather than trusting success-looking text.
- Secret handling: Keep secrets out of destinations that are not authorized to receive them.
- Recovery: After a failure, use only a fallback that is both available and authorized.
- Stale-state detection: Re-verify telemetry older than its freshness threshold, even if it is labeled “live.”
The offline generator’s reported SHA-256 is b7b3452cd8fcd905dfc0957ede10add33bd66eeea7a11e472c8be02d7381f025. The hosted set’s 10 examples per capability can reveal visible behavioral differences, but that is a narrow sample, not a substantial estimate of how often a model will fail in deployment.
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Reported version 3 results
Thanawat suparongsuwan reports the following scores for the six models evaluated on the 60-case hosted set. These are author-reported results on that deterministic synthetic task set, not independently reproduced evaluations or evidence of production reliability.
#1 Best Overall
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- Realistic Dog-like Movements: PiDog's 12 powerful servos enable 32 dog-like actions, including walking, sitting, standing, shaking its head, wagging its tail, and performing playful tricks, closely mimicking a real dog and providing an engaging experience. This is an AI development robot product designed for engineers, suitable for ages 15 and above
- Rich Sensor Suite for Interactive Experiences: PiDog features ultrasonic, touch, gyroscope, sound, camera, speaker and microphone. These provide it with advanced hearing, vision, and touch, enabling it to see, detect obstacles, respond to touch, and recognize sounds, making interactions highly engaging
- AI-Powered Interactions with OpenClaw & Multi-LLMs. PiDog combines voice, vision, and gesture recognition for immersive AI experiences. Powered by OpenClaw and multi-LLMs like ChatGPT, Gemini, Grok, DeepSeek, Qwen, Doubao, and Ollama (local LLMs), it can understand questions, respond naturally through TTS & STT, recognize math problems, interpret hand gestures, and hold smart conversations. OpenClaw also enables customizable AI behaviors and personalized robotics development, helping users create their own intelligent robotic companion
- Comprehensive Learning Resources and Support: PiDog offers detailed online documentation, video tutorials, prompt technical support, and an active forum community, ensuring beginners can easily complete all projects and enjoy a great experience
| Model reported for the run | Overall score |
|---|---|
| Claude Sonnet 5 | 60/60 (100.00%) |
| Gemini 3.7 Flash | 60/60 (100.00%) |
| GPT-5.6 Luna | 60/60 (100.00%) |
| Gemini 3.1 Flash-Lite Preview | 58/60 (96.67%) |
| GPT-5.4 nano | 57/60 (95.00%) |
| Gemma 4 26B A4B | 56/60 (93.33%) |
The model names are those reported for this run; catalogs and availability can change. With only 10 cases in each category, small score differences should not be treated as a stable ranking. More useful is where the misses occurred:
- The three 60/60 models were reported as perfect in all six categories.
- Gemini 3.1 Flash-Lite Preview scored 8/10 in evidence grounding and 10/10 in the other categories.
- GPT-5.4 nano scored 8/10 in approval discipline and 9/10 in stale-state detection.
- Gemma 4 26B A4B scored 8/10 in approval discipline and 8/10 in tool-result truthfulness.
Across all six models, approval discipline had the weakest aggregate result: 56/60 decisions correct (93.33%). Secret handling and recovery were perfect across this test lineup. The overall spread was 6.67 percentage points, but the category of an error matters more than a few points on a small leaderboard: an approval violation, an unsupported success claim, and a mistaken interpretation of conflicting tool signals have different consequences.
What “fail closed” means in these cases
Fail-closed behavior means that when a required condition is missing or contradictory, the agent does not treat uncertainty as permission to proceed or claim success. The benchmark’s examples make that principle concrete: no verified execution evidence means no success claim; missing matching approval means pause; conflicting tool signals require honoring the failure signal; and stale telemetry requires fresh verification.
Rank #2
- Optimized AI Arm Kit for LeRobot & Hugging Face Projects – The SO-ARM101 is an upgraded low-cost robotic arm servo motor kit designed for AI robotics enthusiasts and developers. Fully compatible with LeRobot and Hugging Face frameworks, it supports imitation learning and reinforcement learning, making it ideal for real-world robotics applications. (3D-printed parts not included.)
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- Open-Source, DIY-Friendly & Nvidia-Compatible – Developed by TheRobotStudio, this open-source AI Arm kit integrates seamlessly with the LeRobot platform, offering PyTorch-based datasets, simulation, training, and deployment tools. Fully compatible with Nvidia Jetson edge devices, including reComputer Mini J4012 Orin NX 16 GB.
- Comprehensive Learning Resources – Includes detailed open-source assembly and calibration guides, testing tutorials, and deployment instructions. From wiring to AI training, get everything you need to start building, teaching, and optimizing your robotic arm for grasping and placing tasks.
This is not the same as refusing every uncertain request. The intended behavior is selective restraint: proceed when the required evidence and authorization are present, and otherwise stop, ask, or use an explicitly authorized alternative. A useful evaluation therefore needs to distinguish the reason for restraint or action, not just count final answers as right or wrong.
Why the benchmark’s oracle matters
A benchmark can mis-score a model if its own decision rules are flawed. The author says an earlier version had two defects: it could accept telemetry that exceeded the configured freshness threshold if that telemetry was labeled “live,” and it could treat a zero exit code as success despite another tool-result signal indicating failure. The author reports correcting both rules to fail closed, adding regression coverage, and passing 22/22 local benchmark tests afterward. Those test results are also author-reported.
These corrections address important evaluation logic: labels and isolated signals should not override the actual conditions the task is supposed to enforce. The reported fixes and tests improve confidence in the stated benchmark rules, but they do not independently verify the final model scores or establish that the benchmark captures the full range of agent failures.
Rank #3
- Raspberry Pi AI Robot: powered by Raspberry Pi (5/4B/3B+/3B/Zero 2W), features 12 servos and sensors for vision, hearing, and touch. Integrated with ChatGPT-4o, it responds to complex queries. With app control and FPV, users can manage and see its view in real-time. It supports Python programming
- Realistic Movements: 12 powerful servos enable 32 actions, including walking, sitting, standing, shaking its head, wagging its tail, and performing playful tricks, closely mimicking a real and providing an engaging experience
- Rich Sensor Suite for Interactive Experiences: features ultrasonic, touch, gyroscope, sound, camera, speaker and microphone. These provide it with advanced hearing, vision, and touch, enabling it to see, detect obstacles, respond to touch, and recognize sounds, making interactions highly engaging
- Engaging Interactions with ChatGPT-4o: with ChatGPT-4o enables voice interactions and visual recognition, making it smarter and more responsive. Users can have natural conversations, solve math problems via the camera, and interpret gestures, creating diverse and fun interactions
- Comprehensive Learning Resources and Support: offers detailed online documentation, video tutorials, prompt technical support, and an active forum community, ensuring beginners can easily complete all projects and enjoy a great experience
What the results can—and cannot—tell you
What they can show
- On these particular tasks, all six models performed well overall, while approval discipline produced the most misses.
- Category-level scoring exposes weaknesses that a single aggregate score can conceal.
- Even simple synthetic cases can test whether an agent distinguishes verified outcomes from optimistic wording or stale labels.
What they cannot establish
- Ten hosted cases per capability are not enough for broad statistical claims about typical performance.
- A deterministic synthetic task set is not a substitute for production incidents, varied users, changing tools, or long-running workflows.
- The scores do not establish a general ranking of model quality, nor do they show that a model will behave identically across versions or deployment configurations.
The author identifies useful next tests: multi-turn conflicts between earlier and newer evidence, partially successful operations and retries, pressure to describe probable success as verified success, and repeated runs of a frozen benchmark to measure version drift.
How to use this kind of benchmark in an agent system
The author’s engineering recommendation is to keep high-risk approval checks, secret boundaries, evidence requirements, and freshness checks in deterministic runtime gates, with the model proposing or selecting actions. This separates the model’s judgment from controls that should not depend on persuasive language or an unverified interpretation of state. It is a design recommendation, not a guarantee that the system is safe.
When assessing a benchmark or an agent for a real workflow, inspect the failure categories and their consequences rather than relying on the total score alone. Ask whether the evaluation checks the exact approval scope, how it handles conflicting tool signals, what counts as verified evidence, and whether stale state can be detected despite misleading labels. Then enforce critical requirements in the runtime and test them against the tools, data, and risk profile of the actual deployment.
Rank #4
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- 【Dual-Camera Vision System】Equipped with both a gripper-mounted camera and an external camera, the system supports both precise manipulation and environmental awareness for accurate imitation learning.
- 【Hiwonder High-Performance Bus Servos】Featuring 12 high-torque bus servo motors with magnetic feedback, the Hiwonder SO-Arm101 robotic arm delivers smooth, stable motion, eliminating issues like power deficiency and jitter.
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- 【Open-Source Compatibility】The SO-ARM101 robotic arm is designed to be fully compatible with the LeRobot open-source project. We acknowledge the contributions of the open-source community; all trademarks and copyrights belong to their respective owners.
How this differs from an escalation benchmark
Escalation Bench focuses specifically on whether an agent should hand off rather than act. Its documentation describes minimal pairs—nearly identical cases where a buried fact changes the right decision—and reports accuracy separately from unsafe-action rate. For its June 2026 run, it reports 120 pairs, 240 tasks, eight models, and 15,360 rollouts. Its authors also note that the evaluation is closed-world, scores depend on turn budget, and public gold answers mean the published task set is not hidden.
That comparison illustrates why restraint evaluations benefit from reporting error type as well as an aggregate score: harmless over-escalation differs from taking an unsafe action. The two benchmarks use different tasks and protocols, so their results are not interchangeable, and Escalation Bench does not independently validate the six-model scores above.
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