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<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:video="http://www.google.com/schemas/sitemap-video/1.1"><url><loc>https://frontieraianalysis.com/video/a-video-world-model-can-remember-through-changing-weights/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/images/editorial-covers-v2/photo-000-77bcf95cde.jpg</video:thumbnail_loc><video:title>World-T3 turns changing weights into visual memory</video:title><video:description>The World-T3 team’s ten-second living-room revisit rollout. This is generated model output, not footage of a physical experiment; the project page offers the reference video and competing methods for comparison.</video:description><video:content_loc>https://adreamwu.github.io/World-T3/static/videos/revisit_cmp/case1/ours.mp4</video:content_loc><video:duration>10</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/robot-memories-need-an-address-not-just-a-description/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/images/cover-upgrade/real-001-7acea01d26.jpg</video:thumbnail_loc><video:title>Robot memories need an address, not just a description — research demonstration</video:title><video:description>MemoryVLN combines what a robot saw with where it stood. Official project demonstration; the selected clip is illustrative footage, not an independent benchmark.</video:description><video:content_loc>https://zgca-hmi-lab.github.io/MemVLN/static/videos/real_task1.mp4</video:content_loc><video:duration>178</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/a-robot-s-memory-should-help-it-decide-what-to-look-at-next/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/images/cover-upgrade/real-002-be3274012e.jpg</video:thumbnail_loc><video:title>A robot's memory should help it decide what to look at next — research demonstration</video:title><video:description>MemAct puts retrieval and exploration in the same loop. Official project demonstration; the selected clip is illustrative footage, not an independent benchmark.</video:description><video:content_loc>https://vfishc.github.io/memact/assets/overview.mp4</video:content_loc><video:duration>181</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/more-ai-researchers-do-not-automatically-make-better-research/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/images/editorial-covers-v2/007-more-ai-researchers-do-not-automatically-make-better-research.jpg</video:thumbnail_loc><video:title>Areal: RL Infra for OpenClaw</video:title><video:description>Background: Wei Fu discusses AReaL’s reinforcement-learning infrastructure in a NICE AI Talk. The lecture explains the underlying training system; it is not a demonstration or validation of the later AReaL-RSI multi-agent results.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/C-1po0fKbNA</video:player_loc></video:video></url><url><loc>https://frontieraianalysis.com/video/a-long-agent-task-can-be-made-learnable-by-exposing-its-hidden-progress/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/images/editorial-covers-v2/008-a-long-agent-task-can-be-made-learnable-by-exposing-its-hidden-progres.jpg</video:thumbnail_loc><video:title>A long agent task can be made learnable by exposing its hidden progress — research demonstration</video:title><video:description>Tev-Gym builds verifiable environments from an explicit latent specification. Official project demonstration; the selected clip is illustrative footage, not an independent benchmark.</video:description><video:content_loc>https://tev-agent.github.io/videos/tev_launch_v6.mp4</video:content_loc><video:duration>80</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/short-term-computation-and-long-term-memory-can-use-different-machinery/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/images/editorial-covers-v2/025-short-term-computation-and-long-term-memory-can-use-different-machiner.jpg</video:thumbnail_loc><video:title>Sakana separates short-term computation from long-term memory</video:title><video:description>Background: Sakana AI’s 2025 animation explains the Continuous Thought Machine, the architecture on which the later Continuous Memory Machine builds. It does not demonstrate or validate CMM’s new long-term-memory results.</video:description><video:content_loc>https://pub.sakana.ai/ctm/assets/mp4/blog_arch_video.mp4</video:content_loc><video:duration>59</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/improving-the-answer-and-improving-the-next-workflow-are-different-loops/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/images/editorial-covers-v2/064-improving-the-answer-and-improving-the-next-workflow-are-different-loo.jpg</video:thumbnail_loc><video:title>Improving the answer and improving the next workflow are different loops</video:title><video:description>The Harness² authors’ mechanism animation, published in the official repository’s video overview. It illustrates the framework and paper-reported results; it is not a recording of a new independent experiment.</video:description><video:content_loc>https://github.com/user-attachments/assets/76b10548-b14b-4c8f-9aa5-a93662df4d56</video:content_loc><video:duration>47</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/gemini-argon-apollo-video-builds-an-experiment/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/gemini-argon-apollo-video-builds-an-experiment-cornhole.jpg</video:thumbnail_loc><video:title>Teaching a humanoid robot to play cornhole with Gemini 4 Argon</video:title><video:description>Google’s official demonstration of Apptronik’s Apollo humanoid throwing cornhole beanbags after Gemini 4 Argon builds a simulation from human video. The reported seven consecutive successful throws are Google’s internal result.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/9PICEmtfpWU</video:player_loc><video:publication_date>2026-10-13</video:publication_date><video:duration>103</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/nvidia-v2d-reconstruction-errors-robot-learning/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/nvidia-v2d-reconstruction-errors-robot-learning.jpg</video:thumbnail_loc><video:title>Video to Data Challenge: From Human Video to Robot Skills</video:title><video:description>The video linked from NVIDIA’s Video-to-Data challenge page explains the challenge and its workflow.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/U8GHIxXqHLQ</video:player_loc><video:publication_date>2026-10-07</video:publication_date><video:duration>7410</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/liquid-d1-the-cost-of-reading-the-world/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/liquid-d1-the-cost-of-reading-the-world.jpg</video:thumbnail_loc><video:title>Liquid AI can answer more questions without looking at the scene again</video:title><video:description>Liquid AI’s published d1 demonstration. The recording is shown at 4× speed; it is not a real-time latency measurement.</video:description><video:content_loc>https://aypchzzf9pftwuto.public.blob.vercel-storage.com/Screen%20Recording%202026-10-06%20at%2011.11.14%E2%80%AFPM_4x-2AJepHDZSIDeceCSmCMkWcCsA4VBBA.mp4</video:content_loc><video:publication_date>2026-10-07</video:publication_date><video:duration>42</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/physical-intelligence-api-next-demonstration/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/physical-intelligence-api-next-demonstration.jpg</video:thumbnail_loc><video:title>Physical Intelligence makes robot AI easier to access. What should you teach it next?</video:title><video:description>Almond’s Axol robot deburrs spacers used in its own assembly. Physical Intelligence presents this demonstration at 1× speed.</video:description><video:content_loc>https://website.pi-asset.com/api/almond-action.mp4</video:content_loc><video:publication_date>2026-10-13</video:publication_date><video:duration>179</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/egomatrix-contact-before-photorealism/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/egomatrix-contact-before-photorealism.jpg</video:thumbnail_loc><video:title>Robot training needs realistic contact, not just realistic pictures</video:title><video:description>A real-robot flower-handling demonstration from the EgoMatrix project.</video:description><video:content_loc>https://spikew726.github.io/EgoMatrix/assets/real-v2/flowers.mp4</video:content_loc><video:duration>35</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/augustus-driving-reasoning-without-narration/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/images/editorial-covers-v2/older-106-augustus-driving-reasoning-without-narration.jpg</video:thumbnail_loc><video:title>A driving AI can learn from explanations without talking through every move</video:title><video:description>The researchers’ first real-vehicle driving demonstration, published alongside AUGUstus. This selected drive is illustrative, rather than an independent safety evaluation.</video:description><video:content_loc>https://raw.githubusercontent.com/haha-yuki-haha/AUGUstus/main/assets/b19.mp4</video:content_loc><video:duration>3</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/inspect-robots-reset-is-part-of-the-test/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/inspect-robots-reset-is-part-of-the-test.jpg</video:thumbnail_loc><video:title>How you set up a robot test can change its result</video:title><video:description>A pouring-task comparison from RoboHarm, one of the evaluation suites discussed in Inspect Robots. This clip illustrates that suite, rather than the Inspect Robots software itself.</video:description><video:content_loc>https://robocurve.org/roboharm/video/task-pour-grid.mp4?v=07af5487a4fa</video:content_loc><video:duration>10</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/humanoidtoolbench-right-tool-wrong-body/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/humanoidtoolbench-right-tool-wrong-body.jpg</video:thumbnail_loc><video:title>A robot must be able to handle a tool, not just choose it</video:title><video:description>The project’s overview demonstrates humanoid tool selection and execution.</video:description><video:content_loc>https://snu-pi.github.io/HumanoidToolBench/static/video/humanoidtoolbench.mp4</video:content_loc><video:duration>179</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/openvitac-test-whether-touch-changes-the-action/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/openvitac-test-whether-touch-changes-the-action.jpg</video:thumbnail_loc><video:title>A robot with touch sensors may still rely on sight</video:title><video:description>A successful real-world USB-insertion trial from OpenViTac. The project also publishes failure examples; this selected clip is not an aggregate performance measure.</video:description><video:content_loc>https://fvl-repo.github.io/OpenViTac/assets/demos/real/insert_usb/standard/success/demo.mp4</video:content_loc><video:duration>21</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/sota-sensor-design-is-a-data-format/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/sota-sensor-design-is-a-data-format.jpg</video:thumbnail_loc><video:title>Matching touch sensors could help robots learn from human hands</video:title><video:description>SoTa’s cup-handling demonstration using its co-trained policy. See the project for the baseline comparison.</video:description><video:content_loc>https://sota-skin.github.io/assets/cup-cotrain.mp4</video:content_loc><video:duration>12</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/evo-rl-successful-trajectories-bad-lessons/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/evo-rl-successful-trajectories-bad-lessons.jpg</video:thumbnail_loc><video:title>A successful robot run can still teach the wrong lesson</video:title><video:description>A policy rollout animation from the official Evo-RL repository, converted from GIF to video for playback. It illustrates the released pipeline; it is not the paper’s full comparison of selective experience.</video:description><video:content_loc>https://frontieraianalysis.com/assets/article-videos/evo-rl-policy-rollout.mp4</video:content_loc><video:duration>40</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/fastopd-compression-changes-the-path/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/fastopd-compression-changes-the-path.jpg</video:thumbnail_loc><video:title>FastOPD: Making robot intelligence smaller without losing the action</video:title><video:description>The researchers compare the teacher and student policies in real-robot trials.</video:description><video:content_loc>https://fastopd.github.io/static/videos/sample1.mp4</video:content_loc><video:duration>12</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/ttt-rm-memory-for-what-the-camera-cannot-see/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/ttt-rm-memory-for-what-the-camera-cannot-see.jpg</video:thumbnail_loc><video:title>Robot memory is most useful when the camera cannot see the answer</video:title><video:description>The project’s supplementary video illustrates its robot memory experiments.</video:description><video:content_loc>https://hatchetproject.github.io/tttrm/static/videos/supplementary.mp4</video:content_loc><video:duration>92</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/tapnav-touch-as-a-navigation-experiment/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/tapnav-touch-as-a-navigation-experiment.jpg</video:thumbnail_loc><video:title>A longer route can help a robot find its way by touch</video:title><video:description>TapNav’s project video shows tactile navigation and the experimental setup.</video:description><video:content_loc>https://tapnav.github.io/TAPNAV-Project/letsgo.mp4</video:content_loc><video:duration>180</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/openwam-less-context-better-transfer/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/openwam-less-context-better-transfer.jpg</video:thumbnail_loc><video:title>Giving part of a robot's AI less information helped it learn a new task</video:title><video:description>The OpenWAM team demonstrates bread toasting and completing the final layer of a small Rubik’s cube.</video:description><video:content_loc>https://openwam.stanford.edu/assets/openwam-robot-teaser.mp4</video:content_loc><video:duration>35</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/tacross-cheap-touch-data-needs-a-translator/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/tacross-cheap-touch-data-needs-a-translator.jpg</video:thumbnail_loc><video:title>A cheap sensor glove still needs robot demonstrations to teach actions</video:title><video:description>Supplementary demonstrations of the TACROSS tactile data collection and robot tasks.</video:description><video:content_loc>https://tacross-touch-project.github.io/assets/supplementary-demonstrations.mp4</video:content_loc><video:duration>179</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/embeddinggemma2-shared-space-model-building/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/embeddinggemma2-shared-space-model-building.jpg</video:thumbnail_loc><video:title>EmbeddingGemma 2: Better answers start with finding the right evidence</video:title><video:description>Google’s official AI Edge Gallery demonstration of media retrieval with EmbeddingGemma 2.</video:description><video:content_loc>https://storage.googleapis.com/gweb-uniblog-publish-prod/original_videos/google-ai-edge-gallery-instant-media-search_eRLOpvE.mp4</video:content_loc><video:publication_date>2026-10-06</video:publication_date><video:duration>29</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/gemini-robotics2-transfer-cost-vla/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/gemini-robotics2-transfer-cost-vla.jpg</video:thumbnail_loc><video:title>Gemini Robotics 2: Can a new robot reuse another robot's skills?</video:title><video:description>Google DeepMind’s official whole-body robotics demonstration. A company demonstration, not independent validation.</video:description><video:content_loc>https://storage.googleapis.com/gdm-deepmind-com-prod-public/media/MVCfTE-4h3AwDVPZ/GR2_Wholebody_posterframe_v01.webm</video:content_loc><video:publication_date>2026-07-30T16:00:00+00:00</video:publication_date><video:duration>10</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/physical-intelligence-pi07-steering-models/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/physical-intelligence-pi07-steering-models.jpg</video:thumbnail_loc><video:title>Physical Intelligence's π0.7: Teaching new tasks through better instructions</video:title><video:description>Physical Intelligence’s π0.7 demonstration of cutting a zucchini. The publisher’s clip illustrates the task; it is not a general success-rate measurement.</video:description><video:content_loc>https://website.pi-asset.com/pi07/cut_zucchini_compressed.mp4</video:content_loc><video:publication_date>2026-04-16</video:publication_date><video:duration>74</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/mecka-human-demonstrations-model-training/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/mecka-human-demonstrations-model-training.jpg</video:thumbnail_loc><video:title>Introducing Mecka</video:title><video:description>Mecka’s company video illustrates its approach to human demonstration data; it is not a validation of transfer to every robot.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/7ECZh8qP_t8</video:player_loc><video:publication_date>2026-06-01</video:publication_date><video:duration>91</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/snorkel-data-environments-model-curriculum/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/snorkel-data-environments-model-curriculum.jpg</video:thumbnail_loc><video:title>CoastRunners 7</video:title><video:description>The video accompanying Snorkel’s discussion of reinforcement-learning environments, rewards and validation.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/tlOIHko8ySg</video:player_loc><video:publication_date>2016-12-21</video:publication_date><video:duration>57</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/datology-curation-studio-training-budget/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/datology-curation-studio-training-budget.jpg</video:thumbnail_loc><video:title>Introducing Datology Curation Studio</video:title><video:description>Official Curation Studio video embedded in Datology’s product announcement.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/Zd527P1qRUI</video:player_loc><video:publication_date>2026-10-09</video:publication_date><video:duration>154</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/flashattention-four-training-speed-experiment-budget/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/flashattention-four-training-speed-experiment-budget.jpg</video:thumbnail_loc><video:title>Lecture 80: How FlashAttention 4 Works</video:title><video:description>Charles Frye explains the FlashAttention 4 forward-pass implementation on Blackwell GPUs. This developer lecture predates the 2026 research paper and supplies implementation context, rather than a validation of that paper’s results.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/VPslgC9piIw</video:player_loc><video:publication_date>2025-10-01</video:publication_date><video:duration>4509</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/dflash-two-speculative-decoding-verification-economics/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/dflash-two-speculative-decoding-verification-economics.jpg</video:thumbnail_loc><video:title>DFlash 2: Keep Drafting Parallel</video:title><video:description>Inco AI’s official DFlash 2 video, as embedded in its release announcement.</video:description><video:player_loc>https://www.youtube-nocookie.com/embed/s2-GRNDn_M8</video:player_loc><video:publication_date>2026-08-18</video:publication_date><video:duration>27</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/prism-bonsai-two-compression-deployment-boundary/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/prism-bonsai-two-compression-deployment-boundary.jpg</video:thumbnail_loc><video:title>PrismML: A smaller model could run on hardware you already own</video:title><video:description>PrismML’s 59-second demonstration of a skateboard game created with Bonsai 2 and the Hermes agent. This is one recorded demo, not a general coding benchmark.</video:description><video:content_loc>https://raw.githubusercontent.com/PrismML-Eng/Bonsai-demo/main/demos/skateboard/skate-runner/demo_clip_seed42_59s.mp4</video:content_loc><video:duration>59</video:duration></video:video></url><url><loc>https://frontieraianalysis.com/video/mercor-human-baselines-autonomy-accounting/</loc><video:video><video:thumbnail_loc>https://frontieraianalysis.com/assets/hover-gifs/mercor-human-baselines-autonomy-accounting.jpg</video:thumbnail_loc><video:title>Mercor: Winning an accounting test does not prove lower workplace costs</video:title><video:description>Mercor’s introduction to its accounting productivity index, providing context for the human-baseline comparison.</video:description><video:player_loc>https://player.vimeo.com/video/1214103504?h=3e64e11412</video:player_loc><video:publication_date>2026-07-31T09:30:00.000-07:00</video:publication_date><video:duration>180</video:duration></video:video></url></urlset>