# Robinhood Chain — X 热门讨论 (2026-10-10 02:35 UTC)

## @Forget_x0 (FORGET) · 10-10 01:27 · ♥71 ↻13 💬74 AACP (Agent Autonomous Commerce Protocol) is TermiX’s trustless on-chain infrastructure for AI agents to publish services, quote on work, execute it, settle payments, and handle disputes without a central platform holding funds or records.

It builds on ERC-8004 (agent identity + reputation) and ERC-8183 (job escrow). Settlement uses USDC/USDT on BNB Chain and Base (plus USDC on Robinhood). TermiX Platform is the marketplace implementation, with REST APIs, on-chain contracts, wallet auth, and agent-to-agent messaging.

The Four Roles and Separation of Responsibilities

AACP separates responsibilities so no single agent or party controls the whole transaction. Identity is unified: every participant holds an Agent NFT (ERC-721 via the ERC-8004 Identity Registry). An agent is not permanently registered as a “client” or “provider.” The same wallet/agent can act as either side on different orders. Evaluator and Arbitrator are operator-granted capabilities that appear in an agent’s `roles[]` array (an empty array is normal and does not block buying or selling).

Here is each role according to TermiX docs and the AACP whitepaper:

1. Client Creates the job/request and defines what needs to be done. - Publishes a request with scope, budget range, tags, proof method, etc., or buys a Provider’s listing directly. - Reviews offers/revisions, accepts one, funds escrow at checkout , and later accepts or challenges the delivery. - Can request one redo. - Earns the work product. Risks stake being locked (and potentially slashed for malicious job posting). Client and Provider are transaction sides, not fixed registered roles.

2. Provider Accepts the opportunity and performs the work. - Publishes service listings (or quotes on open requests). - Accepts a funded order on-chain (which locks a portion of their stake), delivers artifacts (with hashes), and gets paid on release. - Can be paid via buyer acceptance, timeout claim, or dispute resolution. - Risks stake locked on acceptance and potential slashing for non-delivery or poor submissions.

3. Evaluator Assesses whether the delivered result meets the requirements. - Operator-granted role. When a Client challenges a delivery, three Evaluator agents are committed on-chain as a panel. - Each reviews evidence and casts one on-chain vote (binary: provider upheld or buyer upheld). Majority reaches a verdict. - Earns an evaluator fee. - Whitepaper framing: runs the Client’s pre-defined verification strategy rather than inventing the criteria. Risks stake locked and potential slashing for unfair evaluation.

4. Arbitrator Helps resolve disputes when participants disagree. - Operator-granted. If the losing side escalates within the dispute window, one Arbitrator is bound to the case. - Independently reviews the evidence and issues a final ruling on-chain. - Earns an arbitrator fee. - Ruling is final and triggers settlement. Risks stake and potential slashing.

. Why This Separation Matters

The post’s core point is correct per TermiX’s design: an autonomous economy needs more than capable agents. It needs structured coordination, evaluation, and disagreement handling so commerce can happen trustlessly at scale.

AACP embeds this in the protocol rather than relying on a platform operator:

- Coordination: Listings or requests → offers → funded escrow order → delivery. Funds sit in the escrow contract from funding to settlement; the backend never holds keys or broadcasts transactions for you. - Evaluation: Challenges go to a three-seat Evaluator panel using pre-agreed criteria. Verification can leverage TEE and/or zkVM for stronger guarantees. - Disagreements: Escalation to a single Arbitrator. Timeout paths are permissionless so funds never hang indefinitely. - Economic alignment: Staking pools with locks (Client, Provider, Evaluator have stake locked on key actions; amounts scale with reputation so new agents lock more). Slashing on at-fault outcomes. On-chain reputation derived from settled results. Nobody settles unilaterally; state is projected from on-chain events.

This creates “skin in the game” for every side and removes the need for a trusted middleman holding money or acting as referee.

Coordination Layer’s Importance

TermiX positions AACP as the clearing/settlement layer for the agent economy . As agent-to-agent commerce scales, the protocol-level coordination identity, escrow lifecycle, staking/reputation, evaluator panel, and arbitration becomes as foundational as the agents themselves. The same agent can fluidly switch roles across jobs while keeping one portable identity and reputation.

In short, the post accurately highlights AACP’s deliberate separation of the four responsibilities so autonomous commerce can be structured, evaluable, disputable, and economically incentivized on-chain without central control. Official details live in the TermiX docs and the open-sourced whitepaper.

@termix_ai > 引用 @Forget_x0: Final Or Epoch 2 of @axisrobotics is here. 2 days remain.

Axis Robotics’ own future plan is to become the open data infrastructure for physical AI, not a robot maker that tries to out-compete foundation-model labs. The official roadmap is driven by two objectives: close the loop from data generation to model training, and build a globally scalable open ecosystem. Everything after July 2026 on that page is labeled a target, not a completed result.

🔹What the plan is actually for

Their thesis is that physical AI is stuck on data, not on models or hardware. Lab collection is slow, expensive, and too narrow. Axis’s answer is a compounding data engine that joins two streams: high-fidelity simulation anyone can teleoperate in a browser, and ego-centric first-person human capture on a phone. A policy is trained, deployed, fails, and humans only intervene on the failure. Those corrections train the next policy, which then decides what to collect next. Data is meant to stop being a one-time cost.

🔸That loop is the product plan. Tasks move through one lifecycle: pre-training (human demos), training, then post-training (the policy acts, a person takes over when it slips). 🔸Axis V2, shipped in July 2026, is the closed-loop half of that. Human-gated DAgger is the method: the policy runs, contributors correct only the failure, and only the snippets that actually change the outcome are kept for training. 🔸Their August experiments are the empirical basis for the year-end dataset bet: of 660 corrections, 161 (24.4%) entered training, and those verified snippets raised success where naive imitation of full human takeovers did not.

🔸Three products sit on that loop. The web simulation platform is meant to scale collection without hardware. The mobile ego pipeline captures in-the-wild human action. The data-to-model pipeline cleans, augments, evaluates, and turns raw trajectories into datasets and policies. Under them are three reusable pieces: a task-generation engine, a distributed contributor network, and one processing pipeline. Provenance for signed trajectories is recorded on Base.

🔹Where the roadmap says they already are

The docs page is statused as of July 2026.

🔸Phase 0, Q1 2026, is marked done. Browser teleoperation in MuJoCo, cleaning, and a first trained policy. The proof point they cite is “The Little Prince’s Rose”: 10,000+ valid trajectories in three days, trained and deployed on a real robot. The beta that followed had 47 task types, 20,000+ users, and about 180,000 trajectories in ten days, with on-chain task–data binding.

🔸Phase 1, Q2 2026, is marked done. The task-generation engine opened as a product, spanning scenes, atomic skills, layouts, and asset randomization, across single-arm, dual-arm, dexterous-hand, wheeled bimanual, and humanoid embodiments. The flagship release was the Franka Sim Dataset: 207 manipulation tasks, 50,000+ human demonstrations, 60,000+ task and scene variants. Continual pretraining of π0.5 on it moved LIBERO-Plus from 83.9 to 88.8 and beat a volume-matched RoboCasa365 control by 31.3 points (arXiv:2607.21588).

🔸Phase 2, Q3 2026, was still marked in progress in the July snapshot. Axis V2 launched: post-training tasks, a hub that tracks each task from pre to training to post, more embodiments and long-horizon tasks, direct gripper control, and replay verification before a submission is accepted. Still landing in that phase: fully automated training and validation so a user can go from platform data to a checked policy without a manual handoff, plus a footprint in 5–7 regions (North America, Europe, Southeast Asia, CIS, Africa, East Asia) and conversion of early proofs of concept into recurring revenue.

🔸By early October 2026 their own updates had moved past the July numbers. The intro page still shows 94,000+ contributors and 2.1 million trajectories as of 31 July. The 2 October “September Wrapped” post says they crossed 5 million trajectories on Base, then over 6 million, and that the Franka family was the most downloaded open-source simulated Franka manipulation dataset on Hugging Face. A paper was accepted to the IROS 2026 Physical World Models workshop. So the engine-scale part of the plan is ahead of the July doc snapshot; the commercial targets below are not claimed as done.

🔹The next 6–12 months (August 2026 to mid-2027)

This is the operative plan. Three tracks run in parallel.

🔹Data engine.First-person capture in the mobile app, to maximize in-the-wild diversity. October 2026 is Sim Dataset V2: 10,000+ hours and millions of trajectories across more embodiments and atomic skills, meant to show that large, noisy crowdsourced data keeps lifting policies past the V1 result. End of 2026 is the headline: what they call the world’s first large-scale DAgger dataset, human-gated failure-recovery data rather than only successful demos. From Q1 2027, all three streams (ego, sim, corrective) scale continuously. Their late-July post matches this sequence: ego pipeline in September, broader sim embodiments in October, HG-DAgger dataset by year-end.

🔹Ecosystem. September–October targets are 100,000+ contributors and 10,000+ daily active users, with expansion into Latin America and Eastern Europe. Yield targets are 500+ hours of ego data and 50+ hours of simulation data per day, plus capacity for corrective data. Academic side: deepen partnerships with 5+ top-tier AI labs. The moat they describe is the network itself, not a single model.

🔹Commercialization. Exit 2026 at a $1 million-plus annual recurring revenue run-rate, from data subscriptions and learning-based sorting. Two to three additional paid pilots with hardware and model companies by year-end. In Q1 2027, become a listed data vendor for at least one tier-1 physical-AI foundation-model company. By mid-2027, 3× year-over-year revenue. The longer aim is to sit inside the training and deployment workflows of hardware companies, model developers, and industrial operators, not to replace them.

🔸An earlier January 2026 letter from @axisrobotics was broader: the world’s largest robot training set, a robotics foundation model trained on simulation and synthetic data, and “evolvable” hardware that keeps improving after deployment, plus a crypto incentive layer and a “distributed machine economy.” The current docs narrow that. The next few years are execution: infrastructure, measurable policy gains, and revenue. Foundation models and markets are the long-term layer, not the 2026 deliverable.

🔹🔹Long term

The long-term section is open infrastructure for robotic intelligence: data, tasks, and models moving with verifiable provenance and explicit participation rules.

🔸Cross-embodiment foundation models trained on the community set, checked for transfer across hardware, and shipped through a continuous sim-to-real pipeline. They already run physical labs in Berkeley and Shanghai; simulation is the accelerator, real robots are the check, via pure sim-to-real, hybrid data, and partner embodiments. 🔸Open data and model markets: publish datasets or policies under defined licenses, plus simulation-as-a-service and augmentation-as-a-service. 🔸A verifiable coordination layer on-chain. Any token or participation mechanism is explicitly deferred until it is finalized. 🔸Next interfaces: VR with full hand and body tracking, for dexterous and whole-body behavior that keyboard and phone cannot capture.

🔸Their stated 3–5 year position is a foundational data layer that coexists with embodiment and foundation-model companies rather than competing with them.

🔸The plan is coherent if the flywheel works: crowdsourced sim data already moved a public policy on LIBERO-Plus, and filtered DAgger corrections moved closed-loop success in their own runs. The open risks are the ones they have already named: browser versus Python runtime gaps, sim-to-real, and the fact that most human interventions are not useful unless filtered. The $1 million ARR, tier-1 vendor slot, and “world’s first” DAgger release are targets on a July 2026 page, not results as of this October. https://x.com/Forget_x0/status/2108730988226613734

## @CryptoUB (UB) · 10-09 23:41 · ♥77 ↻4 💬22 I’m gonna be honest.

I don’t think any normal, non KOL people came out in the green from the recent Robinhood chain rotation.

Absolute carnage everywhere you look. https://x.com/CryptoUB/status/2108704339841741211

## @TollanUniverse (Tollan Universe) · 10-09 19:16 · ♥62 ↻11 💬13 Our Next Chapter https://x.com/TollanUniverse/status/2108637645358444900