Bob is the Sponge (✱,✱)

How should VLAs learn from naturally suboptimal robot demonstrations? Introducing AXIS-Bench: What Matters in Quality-Aware VLA Pretraining, a benchmark and empirical study of how demonstration quality should be defined, labeled, and used during pretraining. Across three VLAs and 24 downstream tasks, we systematically compare quality-aware learning choices and find that no single recipe works for every model. A collaboration with researchers from @GeorgiaTech @CarnegieMellon @UCBerkeley @NTUsg @NUSingapore @RiceUniversity @UCRiverside @TAMU. Details & Companion Dataset Release ⬇️

Statement on the $AXIS Sell-off of 28–29 October Over the past two days, a series of addresses sold a large volume of $AXIS on-chain. We want to set out clearly what these tokens were and what we are doing about it. 1. No team or investor tokens were sold. All Core Team and investor allocations, together with every other locked allocation, 835,000,000 $AXIS (83.5% of total supply), remain in the vesting contract, which is jointly administered with Binance: 0xAFE41cfd4C21f1D896678e32C5AD456ea41835e6. Anyone can verify the balance on BaseScan. Not a single token has left it, and none unlocks before the 12-month cliff. 2. Where the sold tokens came from. The tokens sold came from the initial circulating supply set out in our published tokenomics, specifically part of the Ecosystem allocation. Many ecosystem recipients are not crypto-native, so their allocation was delivered through a single OTC counterparty, under a written agreement that includes lock-up terms. 3. What we believe happened. The pattern of the sales, which went through exchange deposit routes and hundreds of fresh addresses within hours of delivery, strongly suggests that the OTC counterparty sold these tokens on the open market. If so, this is inconsistent with the agreement. 4. What we are doing. - We have suspended all further dealings with this counterparty. - We are reviewing the agreement with legal counsel and will pursue every remedy available under it. - Going forward, any remaining Ecosystem tokens will be distributed only through on-chain vesting. The team‘s tokens remain locked, and our commitment to building Axis is unchanged.

Axis is scaling into a model company. We built the data infrastructure for one reason: to produce general robotic intelligence. Our approach is centered on skill: we turn reinforcement learning experts into reusable, conditioned skills, and those skills feed a general model. Data is the input. The model is the product, and it keeps improving on its own outputs. As it grows, the ceiling keeps rising toward general robotic intelligence. We're just getting started.

Day 1 of $AXIS on @Base, and it's trading across the board. Now on: → @binance Alpha → @coinbase Spot → @okx Perps & @wallet Boost → @Official_Upbit Spot → @bitget Spot, Perps & Launchpool → @MEXC Spot → @kucoincom Spot (12:00 UTC) → @HTX_Global Spot → @Gate Perps → @bitvavocom Spot → @Bybit_Official Perps → @Aster_DEX Perps → @Uniswap

$AXIS will be live on @okx perpetuals. The Physical AI data engine is heading to a global stage.

The final snapshot is locked in. Season 1 wraps up today. A huge thank you to every contributor who's been building with us. Season 2 kicks off on Oct 23, so there won't be any tasks from Oct 18 through Oct 22. The last batch of Points, including Challenger Points, drops tomorrow. Look back at what we've built over the last 7 months on @Base: over 7M trajectories for the Physical AI data engine, and our open-source Franka dataset, the most downloaded simulated Franka manipulation collection on Hugging Face with 160K+ downloads. None of it exists without you. Now it's time to harvest. Take a break, and buckle in.

Introducing $AXIS, the native token of Axis Robotics. The bottleneck for robotics is data. LLMs inherited the internet; robots have no corpus of physical experience. Axis is the @base-native Compounding Data Engine building it, and $AXIS powers this self-improving engine for Physical AI. 🧵 Token allocation, release schedule, airdrop details, and utility below

Announcing our commercial partnership with @lotuscars @GeelyAutoGlobal Lotus Cars builds iconic high-performance sports cars and hypercars. Through our Axis Suite, we deliver an end-to-end Sim2Real automation solution for production-line parts sorting. We build a simulation environment of the real line, scale the sorting policy through task generation and simulation-driven training, and deploy it onto the physical line. One simulation-trained policy handles 500 to 800 distinct automotive parts, replacing per-part manual engineering, with fully automated sorting and no manual intermediate step. This marks Axis Suite's move into real automotive manufacturing, validating our end-to-end loop from scenario design to deployment.