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A recent scroll through social media got me thinking about trust. About half my feed was filled with AI slop that astute users would call out as being machine-generated, often to the embarrassment of the rube who unknowingly spread it and sometimes to the delight of the poster who shared it deliberately because they found it amusing or useful for sowing confusion. Remember the good old days of GPT-3 when we could ask the chatbot anything and it would almost instantly and very confidently spit back a plausible but false answer? That’s when the word “hallucination” as applied to AI answers entered the lexicon. Six years and several versions of a few different chatbots later, I still can’t trust the answers I get, even though Claude or Gemini often do useful work for me. That’s the thing about current AI: Trust but verify. And verify you must. Apply that maxim at scale to maintain the integrity of the scientific record and you get an idea of the challenge facing publishers of peer-reviewed scholarship. In “I Used AI to Fake a Scientific Paper,” freelance writer and doctor of applied mathematics Benjamin Skuse tells the story of the human gatekeepers trying to beat back the rising tide of AI slop by attempting to get his own AI-generated papers past IEEE’s defenses. “He basically hit ‘run experiment’ and ‘write paper.’ And then he didn’t do a damn thing to it.” —Eliza Strickland As features and AI beat editor Eliza Strickland told me, Skuse was surprised at just how effortless it was to generate a paper that was more or less plausible and more or less science. For one of his experiments, “he basically hit ‘run experiment’ and ‘write paper,’” she said. “And then he didn’t do a damn thing to it. He just launched it into the maw of the publishing engine.” Skuse discovered that the combination of IEEE’s human-review processes and research-integrity checker programs are currently keeping pace in what amounts to a cat-and-mouse game against people using free tools. But the more advanced LLMs are no longer making the same dumb mistakes the free versions do. That leaves publishers locked in an arms race between increasingly plausible slop and the systems aimed at detecting it. Scholarly publishers aren’t the only ones trying to hold the line against slop. So is that global depository of human-generated knowledge, Wikipedia. For this month’s Five Questions, Strickland interviewed Ellia Lebleu, a founding member of WikiProject AI Cleanup, a volunteer effort to detect and delete AI-generated material from the platform. Lebleu’s team is confronting the same problem as commercial publishers, but with fewer people and fewer tools. “Wikipedia is known as a repository of human-generated information, and they want to hold on to that,” says Strickland. “Who knows how long they’ll be able to do that.” If we can’t collectively assume that the scientific record is trustworthy or that basic facts about the world are accurate, then our understanding of what is real unspools into uncertainty. Perhaps that delights quantum scientists and the grifters who thrive in an information environment where there is no shared reality. But that uncertainty ultimately undermines trust in the institutions that exist to curate the corpus of human knowledge and to promote the advancement of science and technology for the benefit of humanity. Or maybe in a world where AI is doing the science and writing the papers, it won’t matter whether or not humans trust the outputs. As Strickland put it, “Doesn’t it just feel crazy that we’ll have AI writing for other AIs at some point? We’ll just get left out of the loop.”
It’s disheartening how much power gets generated and then promptly lost as it travels through grid networks. This leaking of electricity happens when it vanishes as heat as well as when it is pilfered by thieves and nonpaying customers. More than half of the countries that track these metrics lost at least 10 percent of their electricity in 2023, according to the World Bank. Losses topped 20 percent for 24 of those nations. Two countries lost more than half of what they generated. With numbers this high, cutting down on losses makes sense. Electricity demand is rising beyond what many grid operators can supply; reducing waste would help meet some of that demand without having to build new power plants. Plus, when the power comes from fossil fuels, any loss means emitting even more greenhouse gases into the atmosphere. And electric losses hit the bottom lines of power providers, which ultimately pass those costs on to everyone else. The trouble is, reducing electricity losses is a hard and expensive process that takes a long time. Typically, the less maintained the grid infrastructure, the more electricity that’s lost. And the more fragile the region’s law enforcement and government, the more prevalent the power theft. Natural disasters and war make things worse. Delhi’s Power Grid Comeback Fixing a power grid requires a systemic approach across many sectors. There’s no one technology that will solve the problem. At the outset, the obstacles to success may feel insurmountable. Equipment across entire grid networks must be updated. Multiple arms of government must agree to reforms and coordinate to ensure power providers are set up to succeed. Regulations must be written or revised, investments made, cultures changed. The city of Delhi did all those things. Over the past 25 years, it cut its electricity losses from about 50 to 5 percent. How the city pulled off that impressive feat is the focus of “The Epic Comeback of Delhi’s Power Grid” by Mini Shaji Thomas, an electrical engineer at the university Jamia Millia Islamia who has lived in Delhi since the 1990s. She gives us a view from the inside—as a resident and a power systems expert. Delhi is a shining example, but some other regions have significantly lowered electricity losses over the last quarter century too. The country of Georgia went from losses of over 16 percent in 2002 to about 8 percent in 2023. In Singapore, losses dropped from 6.6 percent to a nearly nonexistent 0.2 percent over the same time period. Global Electricity Theft Crisis But there are many parts of the world where electricity losses remain a problem or have gotten worse. In Jamaica, where power theft is rampant, losses have hovered between 21 and 28 percent for years. Argentina’s losses nearly doubled between 2015 and 2023, going from an all-time low of about 12 percent to an all-time high of nearly 24 percent. The main problem: Transmission and distribution companies lacked the capital to maintain and upgrade their networks, which left equipment operating under stress. A delay in the installation of smart meters has allowed thieves to more easily siphon power and tamper with meters. Thomas says she hopes her account of Delhi’s grid comeback will serve as a blueprint for others. It’s possible to replicate the sweeping changes Delhi made, she says. But it “requires a concerted effort from all stakeholders, customers, the utility, the government, and their employees.”
If you own an at-home server, a gaming computer, or just a laptop that doesn’t get much love, listen up. You can now put that spare computing power to use and earn some passive income in the process. AI companies are hungry for more compute to run AI inference—the process of using a pre-trained model to respond to queries—and they’re willing to pay you for it. “Imagine Uber or Airbnb, but for AI inference computing tasks,” says Ilman Shazhaev, founder and CEO of Far Labs, based in Abu Dhabi. The AI boom has spurred on construction of massive data centers, often damaging local communities by raising electricity prices, straining local water resources, causing environmental damage and noise, and being just plain ugly. Huge data centers are likely not going anywhere—training new frontier models and running AI models from leading companies will likely still be the purview of these behemoths. But now, several companies are providing AI inference on smaller, mostly open-source models. They are running inference on pre-existing computing power spread throughout homes and small businesses, and compensating owners. “Everyone thinks the only way to do it is data centers. And data centers are extractive for the communities in which they’re built, and they don’t return services or taxes or much of anything to the people there. So why not just turn this whole thing on its head?” says John Federico, founder and CEO of Evolving Edge, in Austin, Texas. “The compute power is out there. If you can orchestrate it, then you’re actually adding value to those communities directly.” The idea isn’t entirely new: From 1999 to 2020, a volunteer-based project called SETI@Home used spare computers to search for signs of extraterrestrial life in radio telescope data, for instance. But now, commercial companies are eager to use the same strategy. Shazhaev’s Far Labs is launching its platform Far AI in the coming weeks, while Federico’s Evolving Edge is currently in open beta. Other companies, like Bless Network, Salad, and Gradient have started to provide similar platforms over the last year. Connecting to the network Federico has been a computer hobbyist since youth, and he has amassed a whole server in his basement to run his projects. “It just hit me one day, there’s all this talk about not having enough compute, and I just thought, well, 92 percent of the country has broadband, and you have people like me who have mini data centers in a closet,” he says. Federico sees the potential hosts as people much like himself who have already invested in home servers, and he aims to make the process of selling spare compute as seamless for them as possible. “Sign up for the program, install an application,” Federico says. “All we want to do is run jobs on your machine when you tell us we’re allowed to. The only thing we do is monitor the resource usage. And of course, you can give us a schedule.” With a large enough network of devices, the platform would have compute available whenever it’s needed. Privacy and security are primary concerns for such hosts. To reassure the users that their local data is secure, and that no malware will be downloaded to their devices, the team open-sourced their scheduling software. “The node software is open source, so anyone can look at it, see what it does. All we want to do is run jobs on your machine when you tell us we’re allowed to,” Federico says. Far Labs’ Shazhaev explains that the company’s software is designed around a principle known as “least privilege”: granting both the host and the user the least access possible to accomplish the task. Inference runs as an isolated workload with authenticated, encrypted communication and explicit limits on the GPU, CPU, memory, storage, and network resources it may use. Customers do not receive arbitrary access to the host machine, and providers can inspect resource use, pause the node, revoke access, and remove the software at any time. The protection also works in the other direction. Workloads are segmented and only the minimum required information is exposed to an individual node. Sensitive enterprise workloads can be restricted to controlled hardware rather than routed through consumer devices. Divide and conquer Massive data centers still have advantages from the user perspective: top of the line GPUs and CPUs, high speed networking, thick cables, and sophisticated cooling. User devices are usually less powerful, more varied, and less reliably connected to one another. “This is quite a difficult issue from the science angle,” Shazhaev says. “You want to do a similar level of tasks that are happening in those high infrastructure data centers, and run them on the user device with limited capacity.” Evolving Edge’s Federico says this is an issue for the largest, state-of-the art AI models. But those are not always needed and are often not even preferred. “There are numerous companies, once they reach a certain scale, suddenly paying for tokens on a state-of-the-art frontier model [that] no longer makes sense for their needs,” he says. “Instead, they are fine-tuning open-source models for specific tasks that they have in their business. These models don’t require anywhere near the resources that some of the state-of-the-art models do. It’s just using the right tool for the job.” Smaller, open-source models can often fit on a single user device. But if that fails, there are tools to split a single inference task over multiple GPUs or CPUs. Evolving Edge is using an open-source tool called Ray to perform this splitting, while Far Labs has developed its own proprietary software that not only splits the workload, but wraps the splitting in a layer of security and reliability-providing software. “One thing we have done is we shared the model,” Shazhaev says. “We take the model, we cut it into many pieces, then these pieces will be distributed through different devices. And we have an orchestrator and a load balancer which manage the task flow, so each device processes a part of the task. Then we combine the answers in the main brain, the orchestrator.” Through a combination of using smaller, more task-specific models, and splitting larger models between disparate devices, the teams claim they can perform inference much cheaper than a traditional data center “because we don’t have capital expenditure,” Shazhaev says. Gaming PCs are a common source of spare computational power in the home. Dizzaract The distributed advantage Not only is it cheaper to run inference this way, it is also more reliable, Shazhaev claims. The companies have access to a distributed network of computing resources, rather than one giant device that can experience outages. Shazhaev compares this to cryptocurrencies, and their resilience through decentralization. “Today, to shut down Bitcoin, you need to nuke the whole planet. Here, we have the same concept,” Shazhaev says. Federico explains that this resiliency would be beneficial not just for AI inference, but for all kinds of applications, including smart cities, environmental sensors, autonomous vehicles, and more. During an Amazon Web Services outage in 2026, for example, smart beds were stuck in their upright positions and their users couldn’t adjust them. Federico says that a distributed network where everything doesn’t need to be routed through a single data center, say, in Ashburn, Va., would make those kinds of outages much less impactful. “We could lose 100 nodes in a network of 250,000 and it wouldn’t matter,” he says. If the network of user devices is substantial enough, every job can be routed to a nearby device, decreasing the latency. Far Labs claims a latency of 100 milliseconds or less on its platform. The lower cost and lower latency of this approach may even enable new use cases, such as in-game AI video generation, which is currently prohibitively slow and expensive. “OpenAI last year had $30 billion in revenue, but they closed the financial year at an $8 billion loss. Why? The official reason is due to the high cost of inference,” Shazhaev says. “And those are mostly text models. For gameplay, you have audio, video, animations: It’s heavy data, and you need real-time responses. So, we’ve been trying to solve this issue.” All of these companies are trying to tap into an untapped resource of local compute, and hoping it’ll benefit the device hosts and users alike. “All these big guys are running around building data centers,” Shazhaev says, “but I believe there is enough compute power that already exists in the world.”
“The lowest-cost place to put AI will be in space, and that will be true within two years, maybe three at the latest,” SpaceX founder Elon Musk told the World Economic Forum in Davos this past January, as his company was preparing to go public. Later that month, SpaceX filed an application with the Federal Communications Commission for an orbital data center constellation of up to 1 million satellites in low Earth orbit, 500 to 2,000 kilometers above Earth. And just three days before the IPO, he discussed some initial design specifications for a new AI-1 satellite data center in a video interview. Musk is prone to hyperbole when it comes to timelines. Full self-driving cars by 2017. First human mission to Mars in 2024. Ten thousand Optimus humanoid robots by the end of 2025. Et cetera. For orbital data centers, which he says will be a cost-effective alternative to terrestrial data centers within three years, the math won’t make sense for several years, if ever. Consider this: There are roughly 14,500 active satellites in orbit. Musk’s Starlink constellation accounts for about two thirds of those. Both the launch cadences and satellite-manufacturing capacity would have to scale up astronomically to deploy a million orbital data center satellites. For context, there have been roughly 7,000 orbital launches in all of human history. To loft 1 million satellites into low Earth orbit on SpaceX’s Starship, which is designed to carry up to 60 satellites per vehicle, would require 16,666 launches exclusively devoted to satellite deployments. Considering that SpaceX launched a record 165 orbital missions in 2025, even at 10 times that cadence, it would take a decade. And how long would it take to build 1 million satellites, given Starlink’s current pace of around 4,000 per year and a generous tenfold increase in capacity? Short of a manufacturing revolution, try 25 years. The reality is that the vision of massive constellations of orbital data centers is nowhere close to being realized. As this month’s cover story, “Why Orbital Data Centers Are So Hard” by Andrew Cavalier of ABI Research, makes clear, the reality is that the vision of massive constellations of orbital data centers is nowhere close to being realized. Dina Genkina, IEEE Spectrum’s computing and hardware editor, put the idea into perspective: “Starcloud (a startup that has applied to the FCC for an 88,000 orbital data center satellite constellation) sent one Nvidia H100 GPU in space so far. Their radiator was too weak to let the chip run at full power.” As Cavalier shows, cooling even a single Nvidia H100 GPU in space is difficult: It draws 700 watts, which will require 1.4 square meters of radiator at 60 °C. A 40-kilowatt rack of servers will need an 80-m² radiator; a 100-megawatt data center will require 2,500 of those radiators. Some astronomers are understandably concerned that a million satellites with giant radiative wings would blot out the stars. So if the economics doesn’t make sense, if the chips are at the mercy of the radiative ravages of space, and if humanity will lose its view of the stars, not to mention increasing the risk of triggering the Kessler syndrome, why are the hyperscalers hyping orbital data centers? Genkina offered the obvious answer: sweet, sweet moolah. “The Elon Musk part of it is honestly genius because he’s got xAI building the data centers, SpaceX sending them to space, and Tesla building solar panels,” Genkina says. “It’s almost like he’s paying himself.” Two Analyst’s Views of SpaceX’s Proposed AI1 Data Center Satellite Michael Pierce, Principal at Technology Strategy Partners Musk’s timelines are notoriously overly ambitious, but I think SpaceX’s orbital data centers might reach cost parity with terrestrial data centers in 5 to 10 years. The Starlink laser-link network already exists as the communication backbone for any SpaceX compute constellation, and that infrastructure is what no new entrant can replicate quickly. The chip-agnostic payload design probably reflects their disclosed difficulty securing AI silicon as much as any modularity philosophy. My view is that the only realistic near-term application is a SpaceX mega-constellation for inference. Training workloads likely cannot tolerate the synchronization and latency constraints of a distributed orbital system. Our report analyzed the market from the integrator’s vantage point, but AI1 is what it looks like when one player has assembled all the necessary advantages simultaneously. The question is whether the terrestrial data center industrial base will degrade or improve on economics. I don’t have insight into SpaceX’s internal costs, as opposed to public pricing, on all their components, so it’s hard to say if they’ll completely dominate or not. Even if they are not cost competitive with terrestrial data centers for another 5 to 10 years, it may simply be faster to get new compute that just happens to be in space. Matt Hasan, AI strategist and independent consultant My initial view is that AI1 does not fundamentally change the rationale for space-based data centers as much as it changes the timeline and scale. The underlying drivers remain the same: escalating AI compute demand, growing power constraints on terrestrial grids, and the desire to colocate energy generation with computation. What AI1 does signal is that the concept is beginning to move from theoretical discussion toward engineering and capital allocation decisions. The announcement adds credibility to the idea that hyperscale computing infrastructure may eventually expand beyond terrestrial constraints rather than simply competing for increasingly scarce grid capacity on Earth. That said, significant economic and technical questions remain. Launch costs, maintenance, hardware replacement cycles, thermal management, latency-sensitive workloads, and overall system economics will ultimately determine whether space-based data centers become a mainstream extension of AI infrastructure or remain a niche capability for specialized applications. The key development is not that these questions have been resolved, but that major industry players now appear willing to invest resources toward answering them.
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Announcing STABL: Societal Transformation and AI Bottlenecks Lab
The Conference on Robot Learning (CoRL) takes place November 9–12, 2026, in Austin, Texas. BAIR faculty, postdocs and students will be presenting research at various sessions, including an Early Career Keynote from Professor Negar Mehr. See below for the full schedule of sessions with BAIR members, including papers co-authored with collaborators at other universities and companies. h2 { border-bottom: 2px solid #003262; padding-bottom: 0.3rem; margin-bottom: 0.2rem; } .corl-day-sub { font-size: 0.8rem; color: #666; margin: 0 0 1em; } .corl-day-sub span + span::before { content: "·"; margin: 0 0.5em; color: #aaa; } .corl-session { display: grid; grid-template-columns: 7.5rem 1fr; gap: 1rem; padding: 1rem 0; border-top: 1px solid #eee; } .corl-session:first-of-type { border-top: 0; } .corl-session.corl-featured { background: #fffbf0; border-left: 3px solid #FDB515; padding-left: 0.8rem; } .corl-time { font-weight: 700; font-size: 0.9rem; line-height: 1.3; } .corl-time small { display: block; font-weight: 400; font-size: 0.7rem; color: #777; text-transform: uppercase; letter-spacing: 0.03em; } .corl-tag { display: inline-block; font-size: 0.7rem; font-weight: 700; text-transform: uppercase; letter-spacing: 0.04em; padding: 0.1rem 0.45rem; border-radius: 3px; background: #eef2f7; color: #003262; } .corl-tag.k-keynote { background: #FDB515; color: #1a1a1a; } .corl-tag.k-oral { background: #003262; color: #fff; } .corl-tag.k-spotlight { background: #3b7ea1; color: #fff; } .corl-tag.k-poster { background: #eef2f7; color: #003262; } .corl-tag.k-lbr { background: #e7f3ec; color: #145a32; } .corl-tag.k-workshop { background: #f1ecf7; color: #4a2a75; } .corl-session h3 { margin: 0.35rem 0 0.15rem; font-size: 1.05rem; } .corl-venue { font-size: 0.8rem; color: #666; margin: 0 0 0.6em; } .corl-chair { font-weight: 700; color: #333; } .corl-paper, .corl-appearance { margin: 0 0 0.9em; } .corl-paper h4, .corl-appearance h4 { font-size: 0.92rem; margin: 0 0 0.2em; line-height: 1.35; font-family: 'Lato', 'Helvetica Neue', Helvetica, sans-serif; font-weight: 700; } .corl-board { display: inline-block; font-size: 0.72rem; color: #555; border: 1px solid #ddd; border-radius: 3px; padding: 0 0.35rem; margin-bottom: 0.25em; } .corl-authors { font-size: 0.82rem; margin: 0 0 0.2em; line-height: 1.45; } .corl-also, .corl-sources, .corl-program, .corl-see { font-size: 0.75rem; color: #666; margin: 0 0 0.15em; } .corl-see { font-family: 'Lato', 'Helvetica Neue', Helvetica, sans-serif; font-style: italic; } .corl-talk { font-style: italic; margin: 0 0 0.2em; } .corl-appearance p { font-size: 0.85rem; margin: 0 0 0.2em; } .corl-organizers { margin: 0; padding-left: 1.1rem; } .corl-organizers li { margin-bottom: 0.5em; font-size: 0.88rem; } .corl-organizers p { margin: 0; font-size: 0.82rem; color: #444; } .corl-about { margin-top: 2.5em; border-top: 2px solid #003262; padding-top: 0.5em; font-size: 0.85rem; } .corl-about-links { font-family: 'Lato', 'Helvetica Neue', Helvetica, sans-serif; display: flex; flex-wrap: wrap; gap: 0.4rem 1.2rem; } @media (max-width: 40em) { .corl-stats { grid-template-columns: repeat(2, 1fr); } .corl-session { grid-template-columns: 1fr; gap: 0.3rem; } .corl-time small { display: inline; margin-left: 0.4em; } .corl-time { font-size: 1rem; } .corl-venue, .corl-also, .corl-sources, .corl-program, .corl-board, .corl-tag, .corl-time small, .corl-day-sub, .corl-stat small, .corl-nav-note, .corl-see { font-size: 0.8rem; } .corl-authors { font-size: 0.9rem; } } @media print { .corl-tools, .corl-status, .corl-nav, .corl-empty { display: none !important; } } November 9–12, 2026Austin, TexasAll times CST · UTC−6 33Main-conference papers 4Late-breaking results (LBR) posters 10Orals & spotlights3 orals · 7 spotlights 4Days of researchNovember 9–12 MonNov 9 TueNov 10 WedNov 11 ThuNov 12 Main conference Nov 9–11 · Workshops Nov 12 Times shown for oral and spotlight papers are session windows. Individual presentation order is not specified. Papers appear in both their talk and poster sessions. Paper inclusion: At least one author is affiliated with UC Berkeley; joint affiliations qualify. Faculty talks, session-chair roles and workshop participation are also included. Late-breaking results: Four late-breaking posters with Berkeley authors are listed so far. This list may grow as more late-breaking posters are confirmed. Find a paper or person Session typeAll sessionsKeynoteOralsSpotlightsPostersLate-breaking resultsWorkshops Reset Showing all sessionsUpdated November 8, 2026 No matching sessions. Try a surname, a shorter paper title, or reset the filters. Monday, November 9 Main conference10 papers + 3 LBR posters 9:00–9:30 AMCST Spotlight · S1 Generalist policies, memory and transfer Hilton Austin MEM: Multi-Scale Embodied Memory for Vision Language Action Models Marcel Torne, Karl Pertsch, Homer Walke, Kyle Vedder, Suraj Nair, Brian Ichter, Allen Z. Ren, Haohuan Wang, Jiaming Tang, Kyle Stachowicz, Karan Dhabalia, Michael Robert Equi, Quan Vuong, Jost Tobias Springenberg, Sergey Levine, Chelsea Finn, Danny Driess Also at Poster Session I · 5:00–6:30 PM · Poster 42 · JW Marriott Paper ↗ Diagnosing Compositional Generalization in Sequential Robot Tasks Yixiao Wang, Cheng-En Wu, Lingfeng Sun, Pengcheng Wang, Xiang Ji, Boyuan Liang, Guojian Zhan, Masayoshi Tomizuka Also at Poster Session I · 5:00–6:30 PM · Poster 115 · JW Marriott Paper ↗ 12:00–1:30 PMCST · LUNCH BREAK Late-breaking results · Posters Agentic Robotics Griffin · Level 2 · JW Marriott Austin Griffin board 119 · LBR #15 Scaling Online Exploration for Robot Agents Runhan Huang, Xiangcheng Zhang, Haoyu Chen, Yitong Li, Yen-Jen Wang, Bohan Lyu, Haotian Ye, Chuye Hong, Yixuan Wang, Chloe Su, Haoyu Xiong, Yangzhen Wu, Enze Xie, Travis Manderson, Stefano Ermon, Pieter Abbeel, Antonio Torralba, Dawn Song, Weirui Ye, Yilun Du Griffin board 120 · LBR #19 Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents Yen-Jen Wang, Haozhe Jiang, Shuying Deng, Haoru Xue, Weirui Ye, Rocky Duan, Nika Haghtalab, S. Shankar Sastry, Pieter Abbeel, Haozhi Qi Also at Agentic Robotics workshop · Thursday, November 12 · 4:15–5:55 PM Griffin board 131 · LBR #54 Flip Anything with Agentic Robotics Self-Learning Shuangyu Xie, Kaiyuan Chen, Ken Goldberg GitHub ↗ Late-breaking results program ↗ 2:00–2:30 PMCST Keynote Early Career Keynote II Hilton Austin Session chair: Ken Goldberg Professor Negar Mehr How Can Robots Learn to Interact and Coordinate with Others? Session details ↗ 5:00–6:30 PMCST Posters Poster Session I JW Marriott Poster 42 MEM: Multi-Scale Embodied Memory for Vision Language Action Models Marcel Torne, Karl Pertsch, Homer Walke, Kyle Vedder, Suraj Nair, Brian Ichter, Allen Z. Ren, Haohuan Wang, Jiaming Tang, Kyle Stachowicz, Karan Dhabalia, Michael Robert Equi, Quan Vuong, Jost Tobias Springenberg, Sergey Levine, Chelsea Finn, Danny Driess Authors listed under Monday, Spotlight S1 Paper ↗ Poster 57 Large Video Planner Enables Generalizable Robot Control Boyuan Chen, Tianyuan Zhang, Haoran Geng, Caiyi Zhang, Peihao Li, Kiwhan Song, William T. Freeman, Jitendra Malik, Pieter Abbeel, Russ Tedrake, Vincent Sitzmann, Yilun Du Paper ↗ Poster 68 How to Peel with a Knife: Aligning Fine-Grained Manipulation with Human Preference Toru Lin, Shuying Deng, Zhao-Heng Yin, Pieter Abbeel, Jitendra Malik Paper ↗ Poster 97 HiRE: Hindsight Reward Editing for Policy Finetuning Haoyi Niu, Zhengtao Han, Yufeng Ji, Zhongyu Li, Koushil Sreenath Paper ↗ Poster 113 Scalable Behavior Cloning with Open Data, Training, and Evaluation Arthur Allshire, Himanshu Gaurav Singh, Ritvik Singh, Adam Rashid, Hongsuk Choi, David McAllister, Justin Yu, Yiyuan Chen, Huang Huang, Pieter Abbeel, Xi Chen, Rocky Duan, Phillip Isola, Jitendra Malik, Fred Shentu, Guanya Shi, Philipp Wu, Angjoo Kanazawa Paper ↗ Poster 115 Diagnosing Compositional Generalization in Sequential Robot Tasks Yixiao Wang, Cheng-En Wu, Lingfeng Sun, Pengcheng Wang, Xiang Ji, Boyuan Liang, Guojian Zhan, Masayoshi Tomizuka Authors listed under Monday, Spotlight S1 Paper ↗ Poster 142 AsyncVLA: An Asynchronous VLA for Fast and Robust Navigation on the Edge Noriaki Hirose, Catherine Glossop, Dhruv Shah, Sergey Levine Paper ↗ Poster 159 GLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation Yousef Liang, Ryan Ardalan, Yarden Goraly, Chenhao Zhu, Jonas Frey, Claire Tomlin, Marco Pavone, S. Shankar Sastry Paper ↗ Poster 202 ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Wenli Xiao, Jia Xie, Tonghe Zhang, Haotian Lin, Letian Fu, Haoru Xue, Jalen Lu, Yi Yang, Cunxi Dai, Zi Wang, Jimmy Wu, Guanzhi Wang, S. Shankar Sastry, Ken Goldberg, Linxi Fan, Yuke Zhu, Guanya Shi Paper ↗ Poster 225 Energy-Based Policy Orchestration Weirui Ye, Yen-Jen Wang, Elvis Hsieh, Zhiyang Dou, Shibo Zhao, Pieter Abbeel, Yilun Du, Leslie Pack Kaelbling, Tomás Lozano-Pérez Paper ↗ Tuesday, November 10 Main conference11 papers + 1 LBR poster 9:00–9:30 AMCST Oral · O4 Reinforcement learning and motion reasoning Hilton Austin Flow Policy Gradients for Robot Control Brent Yi, Hongsuk Choi, Himanshu Gaurav Singh, Xiaoyu Huang, Takara E. Truong, Carmelo Sferrazza, Yi Ma, Rocky Duan, Pieter Abbeel, Guanya Shi, Karen Liu, Angjoo Kanazawa Also at Poster Session II · 5:00–6:30 PM · Poster 63 · JW Marriott Paper ↗ 10:30–11:00 AMCST Oral · O5 Dexterous learning, grasping and assembly Hilton Austin EgoScale: Scaling Dexterous Manipulation with Diverse Egocentric Human Data Ruijie Zheng, Dantong Niu, Yuqi Xie, Jing Wang, Mengda Xu, Yunfan Jiang, Fernando Castañeda, Fengyuan Hu, You Liang Tan, Letian Fu, Trevor Darrell, Furong Huang, Yuke Zhu, Danfei Xu, Linxi Fan Also at Poster Session II · 5:00–6:30 PM · Poster 54 · JW Marriott Paper ↗ 11:30 AM–12:00 PMCST Oral · O7 Humanoid data, control and skills Hilton Austin Session chair: Koushil Sreenath SuperMimic: Scaling Humanoid Whole-Body Control with One Differentiable World Model Qiayuan Liao, Yiyang Shao, Junfeng Long, Kevin Zakka, Roberto Horowitz, Pieter Abbeel, Koushil Sreenath Also at Poster Session II · 5:00–6:30 PM · Poster 217 · JW Marriott Paper ↗ 12:00–1:30 PMCST · LUNCH BREAK Late-breaking results · Posters Real2Sim2Real Griffin · Level 2 · JW Marriott Austin Griffin board 122 · LBR #26 RoboSimStudio: An Interactive Toolkit for Rapid Real2Sim of Robot Manipulation Tasks Kaiyuan Chen, Shuangyu Xie, Uksang Yoo, Xinyue Zhu, Cael Magner, Alvin Zhu, Daehwa Kim, Eric Heiden, Erwin Coumans, Miles Macklin, Spencer Huang, Ken Goldberg Late-breaking results program ↗ 5:00–6:30 PMCST Posters Poster Session II JW Marriott Poster 7 DiscreteRTC: Discrete Diffusion Policies are Natural Asynchronous Executors Pengcheng Wang, Kaiwen Hong, Chensheng Peng, Katherine Rose Driggs-Campbell, Masayoshi Tomizuka, Chenfeng Xu, Chen Tang Paper ↗ Poster 39 Improving Robotic Generalist Policies via Flow Reversal Steering Andy Tang, William Chen, Andrew Wagenmaker, Chelsea Finn, Sergey Levine Paper ↗ Poster 54 EgoScale: Scaling Dexterous Manipulation with Diverse Egocentric Human Data Ruijie Zheng, Dantong Niu, Yuqi Xie, Jing Wang, Mengda Xu, Yunfan Jiang, Fernando Castañeda, Fengyuan Hu, You Liang Tan, Letian Fu, Trevor Darrell, Furong Huang, Yuke Zhu, Danfei Xu, Linxi Fan Authors listed under Tuesday, Oral O5 Paper ↗ Poster 63 Flow Policy Gradients for Robot Control Brent Yi, Hongsuk Choi, Himanshu Gaurav Singh, Xiaoyu Huang, Takara E. Truong, Carmelo Sferrazza, Yi Ma, Rocky Duan, Pieter Abbeel, Guanya Shi, Karen Liu, Angjoo Kanazawa Authors listed under Tuesday, Oral O4 Paper ↗ Poster 77 Why Does Action Chunking Improve Behavioral Cloning Performance in Robotic Control? Filippo Lazzati, Kyle Stachowicz, William Chen, Alberto Maria Metelli, Andrew Wagenmaker, Sergey Levine Paper ↗ Poster 142 FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation Chengbo Yuan, Zicheng Zhang, Mingjie Zhou, Wendi Chen, Yi Wang, Zhuoyang Liu, Dantong Niu, Shuo Wang, Hui Zhang, Wenkang Zhang, Yingdong Hu, Yuanqing Gong, Wanli Xing, Chuan Wen, Cewu Lu, Kaifeng Zhang, Yang Gao Paper ↗ Poster 153 PTLD: Sim-to-Real Privileged Tactile Latent Distillation for Dexterous Manipulation Rosy Chen, Mustafa Mukadam, Michael Kaess, Tingfan Wu, Francois Robert Hogan, Jitendra Malik, Akash Sharma Paper ↗ Poster 184 Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation Zihan Wang, Zhen Wu, Pieter Abbeel, Rocky Duan, Jitendra Malik, Carmelo Sferrazza, Karen Liu, Guanya Shi, Angjoo Kanazawa Paper ↗ Poster 189 VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes Yen-Jen Wang, Jiaman Li, Sirui Chen, Takara E. Truong, Pei Xu, Pieter Abbeel, Rocky Duan, Koushil Sreenath, Angjoo Kanazawa, Carmelo Sferrazza, Guanya Shi, Karen Liu Paper ↗ Poster 195 Social Humanoid: Socially Responsive Human–Humanoid Interaction Vongani Hlavutelo Maluleke, James Ni, Kie Horiuchi, Lea Wilken, Jiahui Lei, Evonne Ng, Roei Herzig, Jitendra Malik, Trevor Darrell, Angjoo Kanazawa Poster 217 SuperMimic: Scaling Humanoid Whole-Body Control with One Differentiable World Model Qiayuan Liao, Yiyang Shao, Junfeng Long, Kevin Zakka, Roberto Horowitz, Pieter Abbeel, Koushil Sreenath Authors listed under Tuesday, Oral O7 Paper ↗ Wednesday, November 11 Main conference12 papers 8:30–9:00 AMCST Spotlight · S6 Optimization, agentic planning and evaluation Hilton Austin Session chair: Negar Mehr Playful Agentic Robot Learning Junyi Zhang, Jiaxin Ge, Hanjun Yoo, Letian Fu, Zihan Yang, Yaowei Liu, Raj Saravanan, Shaofeng Yin, Justin Yu, Dantong Niu, Zirui Wang, Roei Herzig, Ken Goldberg, Yutong Bai, David M. Chan, Ion Stoica, Angjoo Kanazawa, Jiahui Lei, Haiwen Feng, Trevor Darrell Also at Poster Session III · 10:30 AM–12:00 PM · Poster 99 · JW Marriott Paper ↗ GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks Kaiyuan Chen, Shuangyu Xie, Letian Fu, Justin Yu, William Pacini, Sandeep Bajamahal, Hudson Kim, Jaimyn Drake, Daehwa Kim, Haoru Xue, Jonathan Francis, Christian Juette, Peter Schaldenbrand, Muhammet Yunus Seker, Ruwan Wickramarachchi, Uksang Yoo, Guanzhi Wang, Adithyavairavan Murali, Balakumar Sundaralingam, S. Shankar Sastry, Yuke Zhu, Linxi Fan, Spencer Huang, Ken Goldberg Also at Poster Session III · 10:30 AM–12:00 PM · Poster 100 · JW Marriott Paper ↗ 9:00–9:30 AMCST Spotlight · S7 Humanoid locomotion and whole-body control Hilton Austin LadderMan: Learning Humanoid Perceptive Ladder Climbing Siheng Zhao, Yuanhang Zhang, Ziqi Lu, Pieter Abbeel, Rocky Duan, Koushil Sreenath, Yue Wang, Karen Liu, Guanya Shi Also at Poster Session III · 10:30 AM–12:00 PM · Poster 195 · JW Marriott Paper ↗ ResSafe: Learning Safety Filtering with Residual Reinforcement Learning for Humanoids Gechen Qu, Tong Zhang, Bike Zhang, Yen-Jen Wang, Koushil Sreenath, Claire Tomlin, Jason Jangho Choi Also at Poster Session III · 10:30 AM–12:00 PM · Poster 198 · JW Marriott Paper ↗ TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model Anqi Li, Yuxin Chen, Zhaobo Li, Zhuo Cao, Junli Ren, Masayoshi Tomizuka, Dhruv Shah Also at Poster Session III · 10:30 AM–12:00 PM · Poster 200 · JW Marriott Paper ↗ 10:30 AM–12:00 PMCST Posters Poster Session III JW Marriott Poster 51 Do as I Do: Dexterous Manipulation Data from Everyday Human Videos Bhawna Paliwal, Haritheja Etukuru, William Liang, Pieter Abbeel, Nur Muhammad Mahi Shafiullah, Jitendra Malik Paper ↗ Poster 68 WARP-RM: A Warp-Augmented Relative Progress Reward Model for Data Curation Justin Yu, Andrew Goldberg, Kavish Kondap, Karim El-Refai, Ethan Ransing, Qianzhong Chen, Mac Schwager, Fred Shentu, Philipp Wu, Ken Goldberg Paper ↗ Poster 99 Playful Agentic Robot Learning Junyi Zhang, Jiaxin Ge, Hanjun Yoo, Letian Fu, Zihan Yang, Yaowei Liu, Raj Saravanan, Shaofeng Yin, Justin Yu, Dantong Niu, Zirui Wang, Roei Herzig, Ken Goldberg, Yutong Bai, David M. Chan, Ion Stoica, Angjoo Kanazawa, Jiahui Lei, Haiwen Feng, Trevor Darrell Authors listed under Wednesday, Spotlight S6 Paper ↗ Poster 100 GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks Kaiyuan Chen, Shuangyu Xie, Letian Fu, Justin Yu, William Pacini, Sandeep Bajamahal, Hudson Kim, Jaimyn Drake, Daehwa Kim, Haoru Xue, Jonathan Francis, Christian Juette, Peter Schaldenbrand, Muhammet Yunus Seker, Ruwan Wickramarachchi, Uksang Yoo, Guanzhi Wang, Adithyavairavan Murali, Balakumar Sundaralingam, S. Shankar Sastry, Yuke Zhu, Linxi Fan, Spencer Huang, Ken Goldberg Authors listed under Wednesday, Spotlight S6 Paper ↗ Poster 135 T-Rex: Tactile-Reactive Dexterous Manipulation Dantong Niu, Zhuoyang Liu, Zekai Wang, Boning Shao, Zhao-Heng Yin, Anirudh Pai, Yuvan Sharma, Stefano Saravalle, Ruijie Zheng, Jing Wang, Ryan Punamiya, Mengda Xu, Yuqi Xie, Yunfan Jiang, Letian Fu, Konstantinos Kallidromitis, Matteo Gioia, Junyi Zhang, Jiaxin Ge, Haiwen Feng, Fabio Galasso, Wei Zhan, David M. Chan, Yutong Bai, Roei Herzig, Jiahui Lei, Fei-Fei Li, Ken Goldberg, Jitendra Malik, Pieter Abbeel, Yuke Zhu, Danfei Xu, Linxi Fan, Trevor Darrell Paper ↗ Poster 148 Tactile Curiosity Drives Robot Interaction Klemens Iten, Alexander Proshkin, Bhavya Sukhija, Stelian Coros, Andreas Krause, Pieter Abbeel, Carmelo Sferrazza Paper ↗ Poster 159 RPL: Learning Robust Humanoid Perceptive Locomotion on Challenging Terrains Yuanhang Zhang, Younggyo Seo, Juyue Chen, Yifu Yuan, Koushil Sreenath, Pieter Abbeel, Carmelo Sferrazza, Karen Liu, Rocky Duan, Guanya Shi Paper ↗ Poster 175 TEXEDO: Test Time Scaling for Controller-aware Language-conditioned Humanoid Motion Generation Jianuo Cao, Yuxin Chen, Yuzhen Song, Masayoshi Tomizuka, Chenran Li, Thomas Tian Paper ↗ Poster 178 CLIFT: Turning Gemini Robotics On-Device into Humanoid Specialists via Non-Invasive Closed-Loop Iterative Fine-Tuning Yuxin Chen, Hari Srikanth, Nathan Jew, Menglin Wu, Pengcheng Wang, Junli Ren, Masayoshi Tomizuka, Peng Xu, Jinyu Xie, Thomas Tian Paper ↗ Poster 195 LadderMan: Learning Humanoid Perceptive Ladder Climbing Siheng Zhao, Yuanhang Zhang, Ziqi Lu, Pieter Abbeel, Rocky Duan, Koushil Sreenath, Yue Wang, Karen Liu, Guanya Shi Authors listed under Wednesday, Spotlight S7 Paper ↗ Poster 198 ResSafe: Learning Safety Filtering with Residual Reinforcement Learning for Humanoids Gechen Qu, Tong Zhang, Bike Zhang, Yen-Jen Wang, Koushil Sreenath, Claire Tomlin, Jason Jangho Choi Authors listed under Wednesday, Spotlight S7 Paper ↗ Poster 200 TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model Anqi Li, Yuxin Chen, Zhaobo Li, Zhuo Cao, Junli Ren, Masayoshi Tomizuka, Dhruv Shah Authors listed under Wednesday, Spotlight S7 Paper ↗ Thursday, November 12 WorkshopsTalks, papers and organizing roles 8:40–9:50 AMCST Workshop Roadmap to Sample-Efficient Real-World Reinforcement Learning (R2RL) · Keynote block JW Marriott · Room 213 Zhiyuan “Paul” Zhou Sergey Levine’s lab. Speaks during the keynote block. Session details ↗ 9:40–10:15 AMCST Workshop Bimanual Dexterous Tool Use · Spotlights JW Marriott · Rooms 303–304 T-Rex: Tactile-Reactive Dexterous Manipulation Dantong Niu, Zhuoyang Liu, Zekai Wang, Boning Shao, Zhao-Heng Yin, Anirudh Pai, Yuvan Sharma, Stefano Saravalle, Ruijie Zheng, Jing Wang, Ryan Punamiya, Mengda Xu, Yuqi Xie, Yunfan Jiang, Letian Fu, Konstantinos Kallidromitis, Matteo Gioia, Junyi Zhang, Jiaxin Ge, Haiwen Feng, Fabio Galasso, Wei Zhan, David M. Chan, Yutong Bai, Roei Herzig, Jiahui Lei, Fei-Fei Li, Ken Goldberg, Jitendra Malik, Pieter Abbeel, Yuke Zhu, Danfei Xu, Linxi Fan, Trevor Darrell Authors listed under Wednesday, Poster Session III Paper ↗ Do as I Do: Dexterous Manipulation Data from Everyday Human Videos Bhawna Paliwal, Haritheja Etukuru, William Liang, Pieter Abbeel, Nur Muhammad Mahi Shafiullah, Jitendra Malik Authors listed under Wednesday, Poster Session III Paper ↗ Workshop program ↗ 10:15–10:40 AMCST Workshop Bimanual Dexterous Tool Use · Posters & demos JW Marriott · Rooms 303–304 T-Rex: Tactile-Reactive Dexterous Manipulation Dantong Niu, Zhuoyang Liu, Zekai Wang, Boning Shao, Zhao-Heng Yin, Anirudh Pai, Yuvan Sharma, Stefano Saravalle, Ruijie Zheng, Jing Wang, Ryan Punamiya, Mengda Xu, Yuqi Xie, Yunfan Jiang, Letian Fu, Konstantinos Kallidromitis, Matteo Gioia, Junyi Zhang, Jiaxin Ge, Haiwen Feng, Fabio Galasso, Wei Zhan, David M. Chan, Yutong Bai, Roei Herzig, Jiahui Lei, Fei-Fei Li, Ken Goldberg, Jitendra Malik, Pieter Abbeel, Yuke Zhu, Danfei Xu, Linxi Fan, Trevor Darrell Authors listed under Wednesday, Poster Session III Paper ↗ Do as I Do: Dexterous Manipulation Data from Everyday Human Videos Bhawna Paliwal, Haritheja Etukuru, William Liang, Pieter Abbeel, Nur Muhammad Mahi Shafiullah, Jitendra Malik Authors listed under Wednesday, Poster Session III Paper ↗ Workshop program ↗ 10:35–11:05 AMCST Workshop Everything Beneath the Policy · Posters Hilton Austin · Salon E, 4th floor TouchScale: 500 Hours of Human Vision and Touch for Visual-Tactile Learning Dayou Li, Hao Wang, Qianqian Yang, Zihao Zhu, Haoquan Fang, Ziyao Zeng, Yan Han, Zihan Wang, Yan Wang, Baoru Huang, Dilin Wang, Kenji Shimada, Yiyue Luo, Manling Li, Teresa Lv, Mustafa Mukadam, Rakesh Ranjan, Ruohan Zhang, Qi He, Changliu Liu, Xu Chen, Marco Pavone, Bangya Liu, Jiachen Li, Masayoshi Tomizuka, Zhiwen Fan Workhorse: Learning Robust Whole-Body Humanoid Loco-Manipulation from Human Data Songbo Hu, Qiayuan Liao, Yufeng Chi, Kevin Zakka, Sophia Shao, Pieter Abbeel, Koushil Sreenath Paper ↗ Workshop program ↗ 10:40–11:10 AMCST Workshop Bimanual Dexterous Tool Use · Invited talk JW Marriott · Rooms 303–304 Professor Jitendra Malik Talk title has not been announced. Session details ↗ 11:50 AM–12:20 PMCST Workshop R2RL · Panel discussion JW Marriott · Room 213 Zhiyuan “Paul” Zhou Panel with workshop speakers and audience volunteers. Session details ↗ 2:45–3:05 PMCST Workshop Agentic Robotics · Invited talk Room to be announced Professor Ken Goldberg Talk title has not been announced. Session details ↗ Approx. 2:55–3:25 PMCST Workshop Sim-to-Real-to-Field · Invited talks Hilton Austin · Salon E, 4th floor Professor Claire Tomlin Shared invited-talk block with Michael Lutter (Boston Dynamics). Session details ↗ 4:15–5:55 PMCST Workshop Agentic Robotics · Live demos & posters Room to be announced ASPIRE: Agentic /Skills Discovery for Robotics Runyu Lu, Yubo Wu, Ethan Kou, Haotian Lin, Letian Fu, Wenli Xiao, Ajay Mandlekar, Yinzhen Xu, Guanya Shi, Ken Goldberg, Ang Chen, Mosharaf Chowdhury, Jan Kautz, Yuke Zhu, Linxi Fan, Guanzhi Wang Agentic World Action Models Zhuoyang Liu, Dantong Niu, Botong Zhang, Boning Shao, Jing Wang, Ruijie Zheng, Yuvan Sharma, Runyu Lu, Jiaming Liu, Renrui Zhang, Roei Herzig, Yutong Bai, Ken Goldberg, Danfei Xu, Yuke Zhu, Guanzhi Wang, Linxi Fan, Trevor Darrell Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents Yen-Jen Wang, Haozhe Jiang, Shuying Deng, Haoru Xue, Weirui Ye, Rocky Duan, Nika Haghtalab, S. Shankar Sastry, Pieter Abbeel, Haozhi Qi Authors listed under Monday, Late-breaking results, Agentic Robotics Workshop program ↗ Additional involvement Workshop organizers BAIR behind the program Workshop organizing roles. Do Robots Need World Models? Lasse Peters, Negar Mehr’s lab — organizer. Sim-to-Real-to-Field S. Shankar Sastry, Bike Zhang, Siming He, Jonas Frey — organizers. “Oops, I Erred” Sergey Levine — faculty advisor. Zhiyuan “Paul” Zhou and Renhao Wang — organizers. Dexterous Manipulation: Full-Stack Robotics on Learning and Design Jitendra Malik — organizer. Workshop: 2:00–6:00 PM. About this schedule Schedule compiled from the conference program, paper and project pages, and workshop programs. Workshop schedules may change; consult each linked program for updates. Coverage includes 33 main-conference papers, 4 late-breaking results posters and the workshop contributions listed above. The main conference demo track is not covered. Main-conference poster numbers follow an unofficial paper index; check the official program on the day. Hilton stage content will also be streamed to viewing rooms at the Marriott. Paper affiliations reviewed November 6, 2026; late-breaking results updated November 8, 2026. Official CoRL program ↗BAIR faculty ↗Unofficial paper & poster index ↗Back to top ↑ 0; status.textContent = (!search.value && !type.value) ? "Showing all sessions" : visible + " matching session" + (visible === 1 ? "" : "s"); } function reset() { search.value = ""; type.value = ""; filter(); } search.addEventListener("input", filter); type.addEventListener("change", filter); document.getElementById("corl-reset").addEventListener("click", function () { reset(); search.focus(); }); Array.prototype.forEach.call(root.querySelectorAll(".corl-nav a, .corl-see a"), function (a) { a.addEventListener("click", reset); }); window.addEventListener("beforeprint", reset); })();
For our last movie night we watched Princess Mononoke. I remembered loving that movie when I was younger, but watching it as an adult made me once again realize how Miyazaki has a way of expressing diverging sides without picking one. The film shows people, animals and spirits with legitimate and largely incompatible needs, and each faction is further engaged in infighting. A central arc of the story is Lady Eboshi’s Irontown, which gives people who were largely rejected by society work, protection and dignity. She gives shelter and work to those who have leprosy and to former sex workers. The mine and the weapon factory provide them with a livelihood, but they also destroy the forest around them. The forest’s inhabitants, in particular the boars, response choose war against the humans to defend themselves. Eboshi is not portrayed as a villain. She does real good for the people of Irontown, but also is an arms manufacturer who is destroying the forest around it. The film anchors around a character called Ashitaka, who has been condemned to death by an unfortunate encounter with a god that was turned into a demon after being fatally wounded by one of Eboshi’s guns. He doesn’t take sides, but he wants to understand and find the truth. Another character is San (whom they call Mononoke), a girl who was raised by wolves and, despite being human, considers herself part of the forest. This movie’s backdrop is human progress, and it reminded me so much of the challenges we are facing today with AI. AI is useful, empowering and consequential beyond the people who benefit from it. It’s similar to the iron in the story. Irontown gives the outcast purpose and power. There are people today who see themselves in an environment just like that: people who were never let in before and who can now build things that were out of reach. They are wielding AI to their benefit. But there are negative externalities. In Mononoke it’s not just the spirits and animals in the forest who bear the consequences, it’s also other people. Irontown produces guns, and its people are engaged in fights with other humans. There is conflict and power seeking everywhere. Another, unseen character is the power-hungry emperor, who wants the head of the forest spirit, a god, because he believes it will make him immortal. Eboshi is willing to support that cause, not because she thinks it’s right, but because she is at war with the forest’s gods who are preventing her expansion, so they have a common interest. But there really is no need for anyone to kill the forest spirit, in the same way there is no need for any one country to “win at AI”. What drives it is the fear of what happens if someone else wins first. And the emperor is willing to have a god killed for it. The people who help him with that desire, Eboshi included, don’t understand the chaos it will unleash. There it also shows the dangers of seeking overwhelming power without comprehension of how to control its consequences. The pursuit of power itself becomes destructive. In the end, the gods die, and what grows back is not the old forest anymore. Irontown is destroyed. Eboshi wants to rebuild it, and to build a better town this time. Ashitaka stays with the people of Irontown after his curse is lifted by the dying forest spirit. San cannot forgive the humans and stays with the wolves. In the end, nothing is truly resolved, but there is a truce that came at great cost. Ashitaka’s role in the movie is one of a neutral actor. He was wronged both by a spirit turned demon in the forest, who put a curse on him, and, indirectly, by Eboshi, who caused the spirit to turn evil. Yet he harbors no ill will towards either side. He sets out to “see with eyes unclouded by hate,” and in the course of the movie he is seen as an unaligned actor. He helps people on both sides, and stands in their way to prevent further conflict. He doesn’t feel like a pacifist, but he’s the closest to one there is. He’s a truth seeker in a changing world. Because he engages he’s seen as suspicious by both sides, and ends up shot by the people of Irontown when he tries to rescue San. I use AI every day and find it genuinely empowering. But I also recognize the pain it causes some, and the moral problems it poses. It’s genuine progress for humanity, but it comes at significant cost. And many of those costs will be carried by people who aren’t going to benefit from it. So it’s no surprise that AI feels like a threat to many today. Communities currently strongly align themselves with one side and fight against the other. The forst spirit did not have to die and the boars did not have to die in a senseless war. The question is not whether there will be iron, but what kind of Irontown we build with it. I want our conversation about AI to make room for nuance and genuine understanding, for us to look at it and each other with eyes unclouded by hate. We should take advantage of its real benefits, but also recognize the damage it can do, and the choices we still have about what we build and what we’re willing to sacrifice for it. I like to think there is a path for people with differing opinions can find a way to live alongside before losing something we can’t get back.
