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Evan: Welcome to Daily Paper Cast.

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Ashley: Today's paper is from the Hugging Face daily paper list of September 29, 2026, and it has received 41 upvotes.

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Evan: The paper is titled 'Self-Evolving Coding Agents: From Digital Programs to Physical-World Intelligence.'

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Ashley: The first two authors are Hongcheng Gao and Jingjing Zhou, and the corresponding author is Xiao He from hexafuture.ai.

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Evan: Alright, Ashley, let's dive into the introduction.

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What's the background and objective of this paper?

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Ashley: Evan.

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The authors start by discussing Vision-language-action and world-action models, which map observations and instructions directly to robot actions.

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The direct link between observations and actions in these models can cause failures when there are minor layout or viewpoint changes.

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Due to this, instructions also tend to generalize poorly.

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Evan: So the models struggle with adapting to new environments?

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Ashley: Exactly.

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The problem lies in how task requirements, conditions, progress, and failure recovery are implicitly encoded in action sequences.

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This makes them difficult to inspect or revise.

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Previous models, like the QwenGR00T policy, show high success rates when scenes determine the task, but they lose efficiency if viewpoint changes or object layout is perturbed.

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Evan: That sounds like a significant limitation for real-world applications.

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Ashley: Certainly.

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The authors propose a new paradigm called Physical Coding, which represents task state and execution as code.

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This is inspired by digital coding agents, like large language models that call tools, verify results, and revise based on feedback.

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By using this approach, the authors aim to bring more generalization and long-horizon execution capabilities to physical-world interactions.

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Evan: Interesting.

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How exactly does Physical Coding work?

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Ashley: Physical Coding includes two main components: Code as World and Code as Policy.

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Code as World represents task-relevant objects, relations, constraints, observations, and progress predicates.

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Code as Policy organizes planning, action execution, verification, and recovery procedures.

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Evan: So, it's like creating a comprehensive blueprint for task execution in the physical environment.

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Ashley: Precisely.

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The authors build HexaAnything, an agent that integrates perception, planning, and control tools using this coding paradigm.

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HexaAnything can make in-the-loop decisions from external feedback and call tools, including vision-language-action and world-action models.

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Verified traces of its actions become data and memory, enabling evolution from tools and Harness to model weights, architectures, and ultimately hardware and task designs.

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Evan: Sounds promising.

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And what are the key contributions of this paper?

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Ashley: The paper makes several noteworthy contributions.

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First, it formulates Coding Agents for the Physical World and introduces Physical Coding as an executable interface between physical state, action, evidence, and revision.

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Second, it develops HexaAnything to integrate language models, state observation, verifiers, and action tools into a unified physical execution loop.

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Third, it provides evidence for Harness, data, model, and tool improvement on long-horizon manipulation tasks.

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Last, it shows on the PhyBench simulated laboratory benchmark that the same Harness autonomously completes scientific experiments, demonstrating a path toward broader self-evolution.

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Evan: That's an impressive range of contributions.

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It sounds like this method could significantly impact how robots interact with and adapt to their environment.

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Ashley: Indeed, it does.

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And that's the end of the Introduction section.

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Evan: Alright, Ashley, let's delve into the methods discussed in the paper.

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How do the authors implement this Physical Coding paradigm?

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Ashley: Sure, Evan.

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The authors present HexaAnything, a physical coding agent that integrates state observation, task planning, action tools, verification, recovery, and feedback collection into a unified execution loop.

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This allows the agent to interact with both simulated and physical environments recursively.

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Evan: What exactly does this unified execution loop involve?

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Ashley: The execution loop is organized around two main representations: Code as World and Code as Policy.

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These two components work together to bridge the gap between digital coding and physical execution.

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Code as World records the task-relevant state such as objects, relations, constraints, observations, and progress predicates.

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Code as Policy organizes the planning, tool calls, action execution, verification, and recovery processes.

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Evan: It sounds like these components are laying the foundation for task execution in the physical world.

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Ashley: Exactly.

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Let's break down how they work.

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Code as World creates an executable account of the physical world, representing the state of objects, relationships, constraints, and observations.

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It stores information from images, depth sensors, proprioception, and tool outputs.

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The idea is that these entries can be updated incrementally as the agent re-observes the scene, making the representation dynamic and adaptable.

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Evan: So it’s proactive in adjusting its own understanding of the environment?

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Ashley: Yes, it is.

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For instance, if a robot observes an object in a new location or a change in its state, Code as World updates this information rather than overwriting it, ensuring that failures or changes are visible for inspection.

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Evan: And what about Code as Policy?

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How does it function within this framework?

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Ashley: Code as Policy represents how the agent acts within the world.

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It comprises plans, tool calls, actions, verification steps, and recovery procedures.

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The policy consists of nodes that observe, act, verify, branch, loop, and recover based on the world state described.

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Each node is typed, meaning that proposed workflows can be statically checked before they are executed.

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Evan: Typed nodes sound like they add a layer of robustness to the execution process.

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Ashley: Indeed.

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This structure allows the agent to make decisions dynamically, like interrupting actions, retrying, re-observing, or stopping altogether based on the evaluation outcomes.

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The agent evaluates predicates from Code as World to decide the next steps, ensuring actions align with observed evidence.

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Evan: How does HexaAnything handle verification and recovery?

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Ashley: Verification is integral to the HexaAnything framework.

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The system verifies predicates of the world program, such as checking if objects are inside a container or if conditions are met.

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It uses independent verification mechanisms to avoid self-confirming biases, ensuring that the feedback loop is grounded in observable facts and not just model predictions.

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Evan: Interesting.

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And how does recovery work?

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Ashley: Recovery involves handling failed actions, such as missed grasps or stalled tasks.

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If an error or ambiguity is detected, the agent can initiate retries, re-observations, or apply local recovery edits.

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The recovery procedures are designed to ensure safe and productive error handling, allowing the system to adapt and proceed despite setbacks.

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Evan: This must make HexaAnything quite adaptable in dynamic environments.

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Ashley: Definitely.

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It allows the agent to accumulate data and learning signals through verified execution traces, updating its memory and improving the model incrementally.

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This architecture is crucial for enabling the agent to self-evolve.

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Evan: What about the practical setup of HexaAnything?

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How is it implemented physically?

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Ashley: HexaAnything is implemented on a dual-arm AgileX PiPER-X robot setup.

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This includes two 6-DoF arms with parallel-jaw grippers, RGB-D cameras, and a high-rate proprioception system.

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Tools, perception, and planning models run on off-board GPUs, with the foundation model accessed via an API.

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Human interventions, such as task specifications or tool operations, share the same programming interfaces as the agent, ensuring consistency in protocol.

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Evan: That's a comprehensive setup.

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How do HexaAnything's tools evolve over time?

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Ashley: Tools evolve through the agent’s ability to revise and improve them.

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For instance, in the RoboDojo benchmark, HexaAnything revises tools like grasping and placing based on feedback from failed trials, making incremental improvements tested in subsequent rounds.

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This process ensures that tools become progressively more efficient and reliable.

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Evan: And what about its performance in different benchmarks?

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Ashley: On RoboCasa365, HexaAnything improves success rates impressively, especially in Composite-Unseen tasks.

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It also demonstrates significant improvements across various tasks in the RoboDojo benchmark by revising tools and workflows iteratively.

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Furthermore, HexaAnything autonomously completes scientific experiments in the simulated PhyBench laboratory, demonstrating its ability to adapt across different domains.

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Evan: How does HexaAnything's recursive model–Harness–environment loop contribute to its evolution?

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Ashley: This loop facilitates continuous self-improvement.

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The agent collects execution evidence, updates artifacts based on diagnostics, and verifies these updates through independent evaluation.

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Successful improvements are admitted and stored as reusable records, whereas unsuccessful modifications are rejected and used as counterexamples for further refinement.

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This ensures that HexaAnything evolves in a controlled and traceable manner.

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Evan: Well, Ashley, that covers the methods they used in this study.

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We now have a solid understanding of how HexaAnything functions and evolves.

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It's fascinating to see how comprehensive and adaptive this approach is.

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Ashley: Indeed.

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That's the end of the Method section.

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Evan: Ashley, let's move on to the experiments and results.

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How did the authors test the effectiveness of HexaAnything?

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Ashley: The authors conducted a series of experiments to evaluate HexaAnything on multiple benchmarks.

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They started with RoboCasa365, which measures task success across different splits: Atomic-Seen, Composite-Seen, and Composite-Unseen.

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They used XR-1, a state-of-the-art Vision-Language-Action model, as the action tool called within HexaAnything.

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Evan: Interesting.

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What kind of improvements did they observe?

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Ashley: HexaAnything showed substantial improvements across the board.

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For the Composite-Unseen split, it raised success rates from 34.3% to 38.3%.

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For Composite-Seen, it improved from 54.8% to 61.5%, and overall, success rates increased from 56.6% to 61.1%.

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These gains were achieved by integrating HexaAnything's state observation, planning, and verification capabilities with the VLA.

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Evan: That’s impressive.

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Did they provide any specific case studies within these experiments?

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Ashley: Yes, they provided detailed case studies.

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For instance, in the LoadKebabSandwich task, the native XR-1 sometimes closed the oven door after placing only one ingredient, leaving the other ingredient outside.

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HexaAnything, however, verified the presence of both ingredients before closing the door, resulting in higher success rates.

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Evan: So, verification and recovery processes really make a difference here.

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Ashley: Exactly.

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The authors also noted that extending the action budget for XR-1 didn't improve its native performance, indicating that HexaAnything's execution loop itself was responsible for the observed gains.

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Evan: What about the tool and model improvements?

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Did HexaAnything undergo any evolution based on these experiments?

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Ashley: Indeed, it did.

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HexaAnything underwent tool evolution in the RoboDojo benchmark.

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Specifically, they tested three tasks: Fold cloth, Pour vase, and Press by number.

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After iterative tool revisions guided by agent diagnostics, success rates significantly improved, reaching up to 100% for some tasks.

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For example, in Fold cloth, successive revisions added features like pinch mode for thin layers and synchronized dual-arm transport, improving from 0% to 80% success rates.

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Evan: It sounds like HexaAnything adapts and evolves well within these benchmarks.

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Ashley: Moreover, they trained a new model, HexaModel v0.1, using data returned from the Harness.

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This new model showed better performance across all splits in RoboCasa365 compared to its base model.

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For instance, success rates on Composite-Unseen improved from 37.3% to 39.5%.

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Evan: That’s an incremental yet substantial improvement.

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Did they test it in any other settings apart from these benchmarks?

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Ashley: They also evaluated it in PhyBench, a simulated laboratory designed for scientific experiments.

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Here, HexaAnything autonomously conducted tasks like estimating spring constants and gravitational acceleration, achieving mean relative errors below 5%.

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This showcased its ability to design, execute, and analyze scientific experiments effectively.

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Evan: Can you give an example of one of these experiments?

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Ashley: Sure.

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In the Hooke’s law experiment, HexaAnything placed weights on a spring-supported tray and read measurements from a visual ruler.

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It then fit these measurements to estimate the spring constant.

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The system achieved a relative error of just 1.4% against the reference value, executing the entire process autonomously from planning to analysis.

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Evan: That's very precise.

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What about its real-world performance?

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Ashley: HexaAnything was deployed on an AgileX PiPER-X dual-arm robot for real-world tasks.

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In these deployments, HexaAnything successfully performed tasks like unscrewing bottle caps and playing tic-tac-toe.

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Remarkably, it completed these tasks significantly faster than previous models, taking advantage of its modular and iterative improvement capabilities.

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Evan: So HexaAnything not only performs well in simulation but also demonstrates strong real-world applicability.

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Ashley: Exactly.

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The combination of simulated and real-world testing provides robust validation for HexaAnything's capabilities.

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It effectively uses the recursive model–Harness–environment loop to evolve and improve continually.

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Evan: That's a solid overview of the experiments and results.

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It's fascinating to see how HexaAnything evolves and adapts to both simulated and real-world environments.

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Ashley: Indeed.

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That concludes the Experiment section.

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Evan: Ashley, let's explore how this work fits into the broader landscape of research.

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What related work do the authors discuss in their paper?

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Ashley: The authors organize the related work into several key areas that lay the groundwork for their approach.

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They start by discussing action models, specifically Vision-Language-Action, or VLA, and World-Action Models, or WAM.

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Evan: What are these models and why are they important?

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Ashley: VLA models map visual observations and language instructions to robot actions.

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These models often generalize poorly due to minor changes in scene layout or viewpoint, as we mentioned earlier.

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The authors reference models like QwenGR00T, noting that their performance drops significantly with slight perturbations in object layout, highlighting a structural limitation.

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Evan: So, they pointed out the shortcomings of existing models to set the stage for their new approach.

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Ashley: Precisely.

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The authors argue that instead of another action primitive, the missing capability is an interface that makes task state, execution, and feedback explicit.

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They draw inspiration from digital coding agents, like large language models that execute code and revise from feedback.

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Evan: Interesting.

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What does the paper say about Code as Policy and Code as World?

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Ashley: Code as Policy emerged from earlier work where language models wrote Python programs to control robots.

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These programs were compositional, executable, and verifiable but showed dependence on human-designed abstractions.

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This dependence was termed 'designer scaffolding.'

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Evan: I see.

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So the authors are building on that foundation but pushing it further by integrating these concepts with physical execution.

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Ashley: Exactly.

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They extend this idea by coupling Code as Policy with Code as World.

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The latter came from attempts to make vision-language models better at answering semantic questions about images.

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In embodied settings, state representations like scene graphs and spatial constraints were explored, but often for single queries or episodes.

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HexaAnything makes these state representations persistent across tasks, so an incomplete execution can be analyzed and improved upon in future attempts.

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Evan: Got it.

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Ashley: Furthermore, the concept of coding agents is also expanded.

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Software agents have shown that their capabilities depend largely on the tools exposed and the sandbox in which they operate.

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Notably, the Harness in HexaAnything extends these software programming techniques to physical robots, establishing verifiable and revisable program artifacts.

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Evan: So, by creating a physical equivalent of a software coding environment, they aim for a more robust and adaptable system.

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Ashley: Correct.

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This approach ensures that any learned capabilities, tool improvements, or workflow optimizations persist and can be built upon.

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They call this the recursive model–Harness–environment loop, where interaction generates evidence, which updates the digital system, and the updated model determines the next round of interaction.

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Evan: And how do they position their work relative to earlier efforts involving self-evolving agents?

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Ashley: The authors review prior work on adaptive components like training data generation, reward evolution, and policy adaptation.

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They argue that while these components adapt individually, integrated adaptation across all components—such as environment, tasks, data, tools, and models—remains an open challenge.

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This paper aims to address this gap.

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Evan: So their contribution is not just about improving a specific component but about creating a framework where multiple components co-evolve.

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Ashley: Yes, that's a key distinction.

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Existing adaptive systems often leave many components fixed, like the programming language or the tool semantics. 'HexaAnything,' they claim, incorporates continual interaction across all critical components, aiming for a holistic evolution where changes in one area naturally influence and improve other areas.

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Evan: That’s quite an ambitious goal.

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Ashley: It certainly is.

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The aim is to achieve a self-evolving system with separate attribution and evidence for each component.

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They emphasize the need for independent verifiers, provenance tracking, and rollback capabilities to ensure that improvements are genuine and not just adaptations to specific scenarios.

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Evan: That's insightful.

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Anything else they discuss in the related work?

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Ashley: They also talk about continuous self-improvement of hardware and computational substrates, referring to prior works like CompilerGym and KernelBench, which optimize execution substrates from feedback.

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They envision a future where even the hardware can evolve along with the software and models.

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Evan: So the entire system, from hardware to high-level tasks, is adaptable.

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Ashley: Exactly.

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They foresee a trajectory where improvements are systematically validated, ensuring scalability and robustness across various physical and computational environments.

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And that wraps up the Related Work section.

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Evan: Ashley, let's wrap up by summarizing the key contributions and takeaways from this paper.

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What should our listeners remember most about HexaAnything and Physical Coding?

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Ashley: Certainly, Evan.

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The paper’s key contributions are both innovative and extensive.

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First, the authors introduce the concept of Physical Coding, combining Code as World and Code as Policy to create an executable interface that bridges the gap between digital coding and physical-world execution.

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Evan: One key idea is that tasks and execution procedures are represented as code, enabling more precise control and inspection.

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Ashley: Exactly.

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This allows physical agents to update their world state dynamically and adapt to new observations in real-time.

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Secondly, they developed HexaAnything, a robust agent that integrates perception, planning, and control.

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This agent can make in-the-loop decisions based on external feedback and evolves over time through a recursive model–Harness–environment loop.

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Evan: And they demonstrated impressive results across various benchmarks, right?

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Ashley: Yes, HexaAnything showed significant improvements on RoboCasa365, particularly in Composite-Unseen tasks, and seamlessly transitioned to real-world tasks on an AgileX PiPER-X dual-arm robot.

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Furthermore, it excelled in the PhyBench simulated laboratory benchmark, autonomously completing scientific experiments with high precision.

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Evan: So, in summary, this paper pushes the boundaries of what self-evolving coding agents can achieve, showing potential for broad applications in robotics and beyond.

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Ashley: Correct.

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The combination of dynamic task representation, real-time adaptability, and iterative self-improvement sets HexaAnything apart as a forward-thinking approach to autonomous systems.

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Evan: Well, that brings us to the end of today’s episode.

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We hope you found this discussion on HexaAnything and Physical Coding insightful.

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Ashley: Thank you for tuning in.

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If you enjoyed this episode, don't forget to subscribe and leave us a review.

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Evan: We’ll be back with more cutting-edge research from the Hugging Face daily paper list.

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Until next time, stay curious!

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Ashley: Goodbye everyone!

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Have a great day.