Conventional datasets are static, not evolving as the robots exposure increases. 🀄 they only train a model, which is input for a robot to work. but what happens as the robot's exposure increases and it approaches new unexplored states? a dataset that evolves parallel to the robot's exposure ? @axisrobotics presents dataset V1. a growable community-driven simulation dataset. if the robot fails in an unexplored state, feedback is sent upstream, on which the Axis dataset layer acts and provides new training data to help the robot navigate out of this state — and get better in future operations. Continual pretraining with the AXIS dataset at AXIS 100% showed to improve performance of the π0.5 model and the success rate of Libero Plus tasks by 37.3 % over a matched RoboCasa365 dataset. Axis's findings suggests that this performance improvement stemed not just from data volume, but from broader data coverage and task diversity. Broader data coverage and task diversity, @axisrobotics provides. 🀄 Read through, the official paper: arxiv.org/pdf/2607.21588 @chris_anm01 | @0xsexybanana | @0xzagen

✱ Bots & Bounty. 🔖⏰ ✱ Real2Sim2Real: more photorealistic scenarios coming. For 6th - 12th September, 2026.

Ritual Quiz e34 had successfully held last week. Co-hosts: JT(@AbrahamJT9) & Pico It focused on ritual LLM & ONNX. 🫆 Weekly quiz questions would now be released periodically on this platform. 📕 For week 34, here's the quiz questions: docs.google.com/document/d/1…

Trust erodes w/o provenance, because: • no one can verify whether a decision followed approved logic or drifted. 🛟 on a good note however, @ritualnet utilizes TEEs for ai computations. computation for ai decisions are run securely in trusted execution environments. 🫆 and post-computation results return with attestations that prove: - a specific measured workload & model ran. - the model, after computing had produced the returned output. beyond TEE attestations, zkML proofs, probabilistic proofs and primitives like vTune further strengthen trust and provenance of an agent's decisions. 🎚️ This makes it far more reasonable to assume the agent’s reasoning wasn’t tampered with before it acts onchain (moving money, etc).

That's cool! How much would ads normally cost?

Pre. vs. Post-training tasks. 🖱️🔖 I recently onboarded a friend onto the axis robotics hub.. curious, was he to know the difference btw these tasks, so I created an analogy. 🗺️ think of these tasks as: [Pre-training] 👨🏼‍🏫 - a student goes to school completely ignorant of a topic, so the teacher lectures on that topic in class. vs. [Post-training] 👨🏼‍🎓 - the next day, the student comes to class and, meeting the teacher along the way, the teacher asks her: ❔ "okay, tell me everything you know about this topic and.. If you're mistaken, I'll correct your misconceptions/errors". 🔖 on the first day (pre-training), the student knows nothing bout the topic, so the teacher teaches her. while after getting exposure to the topic, its terms, definitions and concepts; The student is now asked "show me what you know about this". it is similar to the learning robot. in pre-training tasks, the learner robot gets to know about how to complete a specific task, move around and execute.🦽 then after training with sufficient data, the robot is left to execute autonomously, based on what it now knows. and corrected if mistaken, reinforcing learning. during post-training tasks, the instructor (teleoperator) monitors the robot's execution path, and corrects it's errors.🦼 @0xsexybanana | @plpiaoliang

✱ the bounty hunt is ongoing.⏱️ ✱ @axisrobotics integration of DreamZero. ✱ daily task release schedule adjusted. 👨🏼‍🔧 ✱ seek the unseen badge. 🔖 Unofficial. for 30 Aug - 5 Sep. | @plpiaoliang

Why use "global" finance? | crypto × ai |🗿 crypto is a permissionless, inclusive and accessible-to-all world. and, although the addition of ai could add an element of centralisation/gated access. 🚧 the overall crypto × ai could still serve a global purpose. if employed in the financial sector, it could serve a global world. 🗾 from timely payments to vaults & portfolio management and yield optimisation/rebalancing, autonomous agents can handle it. and it's even better when this autonomous ai finance stuff with agents working within predefined user limits is accessible to all through the crypto/defi world.🗺️ pivot to permissionless autonomous finance steered by agents onchain on @ritualnet & @ritualfnd !

Ritual Quiz EP 32 and 33 focused on: 🗺️ - ritual onnx and llm. - general ritual network questions. ritual onnx allows onchain ml inference from developers' .onnx models connected to the @ritualnet chain. due to connectivity issues, the last session on onnx and llm couldn't properly hold.☔ so, come next tuesday we would treat the questions on onnx and llm. 🌋 let's stay with @ritualnet, read up from the official docs and get prepared: docs.ritualfoundation.org/#s… docs.ritualfoundation.org/#o…

Ritual around the world. 🌍 Do you believe in a time when some global finance is managed by agents moving finances independently? • timely, automated payments, verifiable onchain (@ritualnet). • provenant AI-backed financial decisions.. and more. I believe such a time is coming! ⏳ Art made by @0xmariobruh 👨🏼‍🎨

✱ Upgraded, more accurate simulation. ✱ A binance wallet | @BinanceWallet featured campaign. ✱ Partnership with @Dexmal_AI for trustworthy, intelligent robotics. August 23 - 29, 2026. 🏮

Look, God is good. 💯🙌🏼

Agentic | \ā-ˈjen-tik — 'agentic' in the context of @ritualnet means powered or facilitated by ritual agents. 🀄 Ritual agents are AI-linked contracts capable of triggering execution following offchain inference. There exist: • Sovereign Agents. ♟️ • Persistent Agents. 🧑🏻‍🔧 All on ritual network, read the difference between em through nitter.cf/_Picokel/status/207917…

DAgger or Dagger? 🔪 chill, no violence here. lol DAgger is Dataset Aggregation, a way of improving robotic models after training. It is done after pre-training — when a robotic model already knows a thing about a task, to improve it. 🔖 After pretraining with data (which is valid stuff), a robot can still deviate to out-of-distribution states during execution. At such, it doesn't perform efficiently. 🀄 So to improve, dataset aggregation(DAgger) is applied, where a robotic model or policy is additionally trained on the states where it is weak at. DAgger says: iteratively roll out a learner policy, and let expert demonstrations supply corrective actions on the states the policy fails at. Post-training tasks are used to improve a learner policy here. During training (post-training) here, a robotic model operating autonomously is corrected by an expert teleoperator, when it drifts toward failure. 🏗️🧑🏻‍🔧 This brings new training data to the learner model on the states it never knew or reached from pre-training, boosting the model's performance. Axis robotics uses the HG variant of DAgger. The Human-Gated variant where the human decides when to step in and correct the executing model. For HG-DAgger, the executing model/policy does not autonomously hand over control when it reaches these underexplored states. 〽️ The human watches execution carefully, and takes over in time, in case of error. It is a controlled way to iteratively improve robotic model performance. @axisrobotics | @plpiaoliang

Remade the infographic design today too.🫟

Similarly, the ritual LLM precompile is for AI inference. 🫆 Ritual LLM is a precompile ran at the fixed 0x0802 address. It's duty is for larger ai model inference. 🛰️ Capabilities like reasoning, intent parsing and possibly generative ai features are enabled through this. It enables developers to: - call frontier open-weight models (like GLM-4.7-FP8) directly from smart contracts. The possible capabilities enabled along with this: - evaluating agent decision flows directly from on-chain state updates. 🦍📲 - contracts to request text generation, reasoning & structured output on-chain. - short-running async inference settled in the same transaction via TEE executors. 📉 For lighter, deterministic classical models, the ONNX precompile at 0x0800 is used instead. Pleasesee @ritualnet

Wheeled embodiments are live 🦼👀