{"data":{"event":{"id":"b47e608a-f24a-4d4b-979e-6494833ff926","slug":"toward-self-adaptive-physical-ai-can-llm-agents-manage-long-horizon-phys-c39a931452","title":"Toward Self-Adaptive Physical AI: Can LLM Agents Manage Long-Horizon Physical Tasks?","short_summary":"arXiv:2609.13436v1 Announce Type: new \nAbstract: Large Language Model (LLM) agents offer a promising path toward autonomously managing long-term physical tasks without human intervention. However, physical tasks require agents to continuously observe the environment, make consequential actions, and remain effective as the environment changes. Existing approaches either require substantial data and retraining, or primarily focus on agents operating in the virtual world. In this work, we explore the feasibility of building a self-adaptive physical AI agent that manages long-term physical tasks i","full_description":"arXiv:2609.13436v1 Announce Type: new \nAbstract: Large Language Model (LLM) agents offer a promising path toward autonomously managing long-term physical tasks without human intervention. However, physical tasks require agents to continuously observe the environment, make consequential actions, and remain effective as the environment changes. Existing approaches either require substantial data and retraining, or primarily focus on agents operating in the virtual world. In this work, we explore the feasibility of building a self-adaptive physical AI agent that manages long-term physical tasks in a zero-shot manner and adapts to environmental changes without human intervention. We design a multi-agent framework that integrates planning, tool calling, observation, and verification, and evaluate it on agricultural tasks against reinforcement learning (RL) agents under different weather patterns. Our results show that zero-shot LLM agents can achieve comparable management outcomes to RL agents under the same weather pattern and adapt more effectively than RL when evaluated under a shifted environment, highlighting a promising path toward self-adaptive physical AI agents.\n\nUpdate (2026-09-08): The Work Now Within Reach. Explore how more capable, affordable AI can expand the work people and businesses can accomplish—and make growth more economical.\n\nUpdate (2026-09-17): Show HN: Share your AI Setup, Learn from others. I kept seeing engineers share what they were building with AI; however, I was always more curious about how they worked. Which agents did they use? What skills and tools had stuck or been thrown out the window? How did they manage longer-running tasks?   So I built this with the hope we could have a dedicated space to share and be open about our setups.\nComments URL: https://news.ycombinator.com/item?id=49740105\nPoints: 10\n# Comments: 2\n\nUpdate (2026-09-17): Show HN: AutoBot – live voice control for long-running AI work. I wanted to manage long horizon agentic workstreams via voice, then put my phone down, and have a harness manage completion - extending into full computer use.\nI was trying to build a personal Jarvis, so I benchmarked AutoBot to see how close I could get:\n- OSWorld: 32.41% (moved Sol Max from 4th to 1st, beating Opus 5)\n- AssistantBench: 50.70%\nHermes and OpenClaw, but without needing a weekend and VMs. And with the ability to manage deep personalization AND keep strict privacy rules, storing data on encrypted disk.\nAutoBot is a passion project that grew out of trying to make this all work for myself.\nI’m sharing it here because I suspect other here have the same frustration. And I’m curious what else everyone is doing for this.\nIt’s an MIT-licensed harness that lives within a project.\nNative voice lets me discuss tasks, check progress, and steer work; a local ledger tracks unfinished outputs and the evidence needed to call them done. Memory drives more autonomy over time, and “defrags and locks in learning nightly” while a heartbeat system persists execution.\nI’m not selling anything. If you’re \n\nUpdate (2026-09-17): Show HN: Craigslist for agent skills, curated by a human. hey, i'm nick (@skeptrune on X). i have been using ai for a lot of things i'm not an expert in and thought it would be nice to try and buy paid skills for helping it accomplish those tasks more efficiently.\nfor example, redlining contracts, creating ai generated videos, different website designs, and more.\ncurious to see if this resonates with folks here. i figure a more engineering'y audience that's ai coding-agent forward would have similar problems to what inspired me to make this\nComments URL: https://news.ycombinator.com/item?id=49743459\nPoints: 13\n# Comments: 11\n\nUpdate (2026-09-23): Show HN: I built a post-mortem debugger for native Windows x64/x86 crashes. Hello HN!\nI've spent years debugging Windows crashes with tools that were either friendly but limited (e.g. Visual Studio) or powerful but archaic (e.g. WinDbg). I developed patterns and methods for understanding what was going on, and decided to build it into a much more effective debugging tool called ForensicDbg.\nI built a modern interface to minimize the friction when debugging. All of the data shown to you is analyzed, interpreted, and presented to you clearly, so you can focus on what matters. Everything is interlinked so you can quickly and intuitivly navigate through the process space.\nForensicDbg comes with an MCP server which allows for agenic debugging. The work done to interpret and interlink your data also benefits AI tools. It removes the risk of hallucinations while building a stable foundation for them to work from without spending tokens.\nIf you want to try it out you can sign up and get a free beta license here: https://www.forensicdbg.com/beta\nComments URL: https://news.ycombinator.com/item?id=49821086\nPoints: 9\n# Comments: 0","ledger_type":"benefit","primary_domain_id":"99fd8a40-a540-41d3-af98-57c1709ee422","event_status":"provisional","event_date":"2026-09-15T00:00:00.000Z","discovery_date":"2026-09-15T00:00:00.000Z","first_published_date":"2026-09-15T00:00:00.000Z","last_reviewed_date":"2026-09-23T00:00:00.000Z","geographic_scope":"International","affected_population":null,"base_impact_tier":1,"base_score":"1.00","attribution_multiplier":"0.1000","evidence_multiplier":"0.5000","realization_multiplier":"0.2000","durability_multiplier":"0.5000","current_event_score":"0.005000","confidence_level":"low","score_explanation":"Ingest update: independent source raised evidence; added a follow-up source. 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However, physical tasks require agents to continuously observe the environment, make consequential actions, and remain effective as the environment changes. Existing approaches either require substantial data and retraining, or primarily focus on agents operating in the virtual world. 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Which agents did they use? What skills and tools had stuck or been thrown out the window? How did they manage longer-running tasks?   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I developed patterns and methods for understanding what was going on, and decided to build it into a much more effective debugging tool called ForensicDbg.\nI built a modern interface to minimize the friction when debugging. All of the data shown to you is analyzed, interpreted, and presented to you clearly, so you can focus on what matt","content_hash":null,"source_reliability_class":"medium","is_primary_source":false,"is_independent":true,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-23T21:03:01.196Z"}],"claims":[{"id":"d58f843c-910c-4c67-9c36-02cde2f8bc9a","event_id":"b47e608a-f24a-4d4b-979e-6494833ff926","claim_text":"Toward Self-Adaptive Physical AI: Can LLM Agents Manage Long-Horizon Physical Tasks?","claim_type":"outcome","claim_status":"supported","confidence_score":null,"created_at":"2026-09-15T04:00:55.067Z","updated_at":"2026-09-15T04:00:55.067Z"}],"revisions":[{"id":"f3cd68d5-e1e2-43e9-a5a1-be76e40dfdfd","event_id":"b47e608a-f24a-4d4b-979e-6494833ff926","model_id":null,"previous_score":"0.000000","new_score":"0.001000","previous_factors":{},"new_factors":{"evidence":0.1,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"change_reason":"Auto-published from news ingest.","trigger_type":"news_ingest","trigger_source_ids":null,"reviewer_id":null,"review_status":"published","created_at":"2026-09-15T04:00:55.075Z"},{"id":"31810b0d-085e-4ef4-a7b2-29da77f2a176","event_id":"b47e608a-f24a-4d4b-979e-6494833ff926","model_id":null,"previous_score":"0.001000","new_score":"0.002000","previous_factors":{"tier":1,"evidence":0.1,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"new_factors":{"tier":1,"models":["535014f7-c941-4d39-aa9b-8f9c1d3d07b7"],"evidence":0.2,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"change_reason":"Ingest update: independent source raised evidence; added a follow-up source.","trigger_type":"ingest_update","trigger_source_ids":["9462142f-923e-4892-831c-c327cd94ecae"],"reviewer_id":null,"review_status":"published","created_at":"2026-09-17T03:01:02.109Z"},{"id":"1f6066e7-87f4-4769-b999-1a6c871056aa","event_id":"b47e608a-f24a-4d4b-979e-6494833ff926","model_id":null,"previous_score":"0.002000","new_score":"0.003000","previous_factors":{"tier":1,"evidence":0.2,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"new_factors":{"tier":1,"models":["535014f7-c941-4d39-aa9b-8f9c1d3d07b7"],"evidence":0.3,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"change_reason":"Ingest update: independent source raised evidence; added a follow-up source.","trigger_type":"ingest_update","trigger_source_ids":["a58007a4-e6bf-4c2f-981b-abed679d6939"],"reviewer_id":null,"review_status":"published","created_at":"2026-09-17T15:01:01.948Z"},{"id":"711c8114-1bce-4ab2-aafc-df18eb6615cf","event_id":"b47e608a-f24a-4d4b-979e-6494833ff926","model_id":null,"previous_score":"0.003000","new_score":"0.004000","previous_factors":{"tier":1,"evidence":0.3,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"new_factors":{"tier":1,"models":["535014f7-c941-4d39-aa9b-8f9c1d3d07b7"],"evidence":0.4,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"change_reason":"Ingest update: independent source raised evidence; added a follow-up source.","trigger_type":"ingest_update","trigger_source_ids":["127b8692-7c27-44d1-aa0f-7ad62ab96aa7"],"reviewer_id":null,"review_status":"published","created_at":"2026-09-17T18:00:51.814Z"},{"id":"34c41916-9254-44a7-8054-6a6332f1158f","event_id":"b47e608a-f24a-4d4b-979e-6494833ff926","model_id":null,"previous_score":"0.004000","new_score":"0.005000","previous_factors":{"tier":1,"evidence":0.4,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"new_factors":{"tier":1,"models":["535014f7-c941-4d39-aa9b-8f9c1d3d07b7"],"evidence":0.5,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"change_reason":"Ingest update: independent source raised evidence; added a follow-up source.","trigger_type":"ingest_update","trigger_source_ids":["7a2da7a7-34f5-4f25-bbbd-44cdc323a871"],"reviewer_id":null,"review_status":"published","created_at":"2026-09-17T21:01:01.355Z"},{"id":"45859089-41c1-4bcc-8554-4ce6c7240bf3","event_id":"b47e608a-f24a-4d4b-979e-6494833ff926","model_id":null,"previous_score":"0.005000","new_score":"0.005000","previous_factors":{"tier":1,"evidence":0.5,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"new_factors":{"tier":1,"models":["535014f7-c941-4d39-aa9b-8f9c1d3d07b7"],"evidence":0.5,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"change_reason":"Ingest update: added a follow-up source.","trigger_type":"ingest_update","trigger_source_ids":["da8a94d5-512e-4d44-921e-ec534f91c3dc"],"reviewer_id":null,"review_status":"published","created_at":"2026-09-23T21:03:01.243Z"}],"secondary":[]},"methodology_version":"0.1"}