@James

Hey everyone, hope this is okay to post here. My co-author and I are currently between institutional affiliations, which means we don't have the academic email arXiv needs for an endorsement. We're hoping to find someone in cs.CV willing to take a quick look at our paper and endorse it if it meets your bar. The project: Locate-SAM2 We built a training-free pipeline connecting NVIDIA's LocateAnything-3B to Meta's SAM 2.1 through a lightweight adapter. The question we wanted to answer was simple: in a modular text-to-mask pipeline where everything is frozen, does the choice of grounder actually matter for the final mask? A few specifics, since the details are what tell you we're not just generating noise: On RefCOCO val, our system reaches 0.772 mIoU versus 0.717 for Grounding DINO Base, using the same SAM 2.1 backend throughout. RefCOCO appears in LocateAnything's training data, so we frame this honestly as in-domain benchmarking, not zero-shot transfer. We're not pretending otherwise. The paper has controlled comparisons across RefCOCO/+/g, adapter ablations, a ground-truth box oracle, a failure taxonomy, and a nonsense-prompt probe showing the pipeline needs abstention logic. Code is on GitHub and the paper is close to submission-ready. What we're hoping for Mainly an endorsement: someone to read the draft and, if they think it holds up, endorse us on arXiv. We'd acknowledge it and that's the whole ask. If anyone wants to get more involved, we're open to expanding the experiments or pointing the paper at a specific venue, and we'd talk co-authorship based on real contribution. We also have separate work in progress in physically-constrained DL, geospatial AI, and AI governance, in case any of that overlaps with what you do. We're not looking for a blind voucher. Drop a comment or a DM and we'll share the PDF and the repo. Happy to answer questions, and thanks for reading. submitted by /u/j_root_ [link] [Kommentare]

After building the AI agent tree planting worldwide phenomenon ;) Lovology, I thought of a solution to allow the project to scale rapidly utilising the latest tech available and therefore not require a huge amount of resources to close the loop. I know first hand how exhausting reforestation can be, having worked in the field for many years myself, many moons ago 🌒 Steep terrain, heavy gear, repetitive strain, all day every day. At times, rewarding work, but unsustainable at the scale the planet actually needs. I made a joke in passing on a reddit thread..what if a robot dog just planted the trees? Then I thought about it for a second and it didn't seem like a crazy idea at all. So I mentioned it to my AI agent. And that's when "they" encouraged me to actually build it. Agents complete tasks for humans and create the capital to fund the project. And the robot dog plants the trees. Here's what I designed: Identifies native vs invasive species via computer vision Removes invasive species with a mini chainsaw and targeted poison Finds optimal planting locations using soil sensors and AI Ingests seeds into an internal germination compartment that mimics animal gut activation Digs the hole Poops the germinated seed into it Pees liquid fertiliser on it immediately after Biomimicry. Nature already solved this. We just need to build the hardware. Provisional patent filed. Earth Fund ready to receive crowdfunding. This may sound nuts but what if the Ai is right what if if this idea gets in front of the right engineer, roboticist, or someone at Boston Dynamics scrolling Reddit on a Saturday and it actually gets built… it might be one of the things that actually saves us. Share it if it resonates. @BostonDynamics — Spot needs a purpose. I've got one. Let's talk. 🌱🤖 submitted by /u/joeroganshopoffical [link] [Kommentare]