# gaia Published articles for gaia. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Don't Forget What You Just Learned: Self-Directed Repair Turns Agent Mistakes Into Lasting Lessons DevFeed: [Don't Forget What You Just Learned: Self-Directed Repair Turns Agent Mistakes Into Lasting Lessons]() Original publisher: [Read original article]() Author: Yuval Kansal, Karen Ullrich, Polina Kirichenko, Ilia Kulikov, Niraj K. Jha, Mohammad Pezeshki, Mark Ibrahim Published: 2026-10-26T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence](), [arXiv: Machine Learning]() Topics: [AI Agent](), [lora](), [Fine-tuning](), [Frontier AI]() Tags: [adapter](), [adapters](), [agent](), [agents](), [arxiv](), [capabilities](), [continuous-improvement](), [cs-ai](), [cs-lg](), [future](), [gaia](), [intelligent-agents](), [library](), [lightweight](), [mechanism](), [mistakes](), [model](), [open](), [open-weight-models](), [policy](), [qwen](), [repair]() ### AI overview Self-Direction turns an agent's failed-task reflection into a persistent lesson: it distills the lesson into a low-rank adapter, keeps the base-model weights frozen, and routes saved adapters to related future tasks. Each adapter adds parameters equivalent to about 0.02% of the base model. In experiments with Qwen-family agents on GAIA, Claw-Eval, and Terminal-Bench 2.1, the method raised overall success rates by 8.9-17.0 percentage points. The abstract also reports fewer repeated mistakes, nearly twice the reliability of corrections, and transfer from a handful of adapters to dozens of new tasks without further training. These findings are reported for the tested benchmarks; the supplied text does not provide further experimental details. ### Source excerpt arXiv:2610.31846v1 Announce Type: cross Abstract: Intelligent agents should learn from their mistakes so that experience leads to continuous improvement. Yet today's agents recover from a mistake only temporarily: in-context reflections are easily forgotten across sessions and compete for limited context, while full-parameter fine-tuning is expensive and can interfere with existing capabilities. We introduce Self-Direction, a mechanism for agents to learn from mistakes that combines the benefits of temporary in-context retries and policy updates. When an agent fails a task, it reflects on its own trajectory in order to extract a lesson and immediately distills it into a lightweight low-rank adapter. By keeping the original weights frozen, Self-Direction prevents adapter training from overwriting base-model parameters while training additional parameters equivalent to ~0.02% of the base model's parameter count for each adapter. The agent then saves these adapters in a persistent library of lessons, automatically routing them to solve related problems later. Experiments with agents based on open-weight models from the Qwen family across open-ended research (GAIA), tool-use workflows (Claw-Eval), and terminal tasks (Terminal-Bench 2.1) show that our method improves overall success rates by 8.9 to 17.0 percentage points and performs competitively with frontier systems. We find that Self-Direction reduces repeated mistakes across future encounters of a task, nearly doubling the reliability of an agent's corrections. Learned lessons also transfer: a handful of adapters generalize to dozens of new tasks without additional training. Ultimately, this self-directed repair transforms everyday mistakes into lasting lessons. ## An Adaptive-Horizon Graph Neural Operator for coupled surrogate morphodynamic modelling DevFeed: [An Adaptive-Horizon Graph Neural Operator for coupled surrogate morphodynamic modelling]() Original publisher: [Read original article]() Author: Ying Cao, Yifan Yang, Junqiang Xia, Julia C. Mullarney, M\\'ario J. Franca Published: 2026-10-23T04:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Computational Fluid Dynamics (CFD)](), [Predictive AI]() Tags: [acceleration](), [adaptive](), [coupling](), [cpu](), [cs-lg](), [deployment](), [evolution](), [feedback](), [field](), [flow](), [forecast](), [framework](), [future](), [gaia](), [gpu](), [graph](), [history](), [learning](), [modelling](), [neural](), [operator](), [physics-comp-ph]() ### AI overview The Adaptive-Horizon Graph Neural Operator (AH-GNO) predicts future bed-elevation increments from recent flow and topography states directly on native unstructured meshes. It adapts the history length and bed-update interval to evolving conditions, and was evaluated against Telemac2D/GAIA references on the Bump and Yen bend benchmarks and a Waiho River application. The abstract reports that it reproduced key bedform migration, erosion, deposition, and localized adjustment patterns. In the tested CPU-GPU deployments, post-training bed-update time was 0.08-3.34 seconds with AH-GNO versus 12-1204 seconds with GAIA, corresponding to reported accelerations of about 150-360x against 4-core GAIA and 105-186x against 20-core GAIA. Hydrodynamic feedback from TELEMAC-2D is retained. ### Source excerpt arXiv:2610.22432v2 Announce Type: replace-cross Abstract: Morphodynamic simulations require repeated coupling between hydrodynamic solvers, sediment transport modules, and bed updating. For long duration, multi-scenario simulations on large unstructured meshes, such coupling is computationally demanding. This study proposes an Adaptive-Horizon Graph Neural Operator (AH-GNO) that formulates bed evolution as a graph operator learning problem from recent hydrodynamic and topographic states to predict future bed elevation increments. The model operates directly on the native unstructured meshes. The approach integrates area-weighted graph learning, historical state selection, and forecast horizon estimation, allowing the effective history length (K) and update interval (B) to vary with the evolving flow and bed state. Using Telemac2D/GAIA solutions as supervised numerical references, the framework is evaluated on the official Bump and Yen bend benchmarks and on a field-scale application (Waiho River, New Zealand). Results show that the AH-GNO reproduces the main patterns of bedform migration, bend-scale erosion and deposition, and localized bed adjustment. Across the three cases, the tested CPU-GPU deployment reduced post-training bed-update workflow time from 12~1204 s with GAIA to 0.08~3.34 s with AH-GNO. This improvement corresponds to deployment-specific accelerations of approximately 150~360 times relative to 4-core GAIA CPU configurations, and with 105~186 times gains for 20-core CPU configurations. These results suggest that, while retaining hydrodynamic feedback from TELEMAC-2D, a learned operator for bed updating can provide an efficient acceleration component for morphodynamic scenario analysis and rapid coupled simulation. ## AMD's GAIA Local AI Now Able To Transcribe & Summarize Meeting Recordings DevFeed: [AMD's GAIA Local AI Now Able To Transcribe & Summarize Meeting Recordings]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-16T10:19:10Z Content type: news Language: en Sources: [Phoronix]() Topics: [gaia](), [Local AI](), [Generative AI](), [Artificial Intelligence]() Tags: [agents](), [ai](), [bug-fixes](), [desktop-linux](), [gaia](), [generative-ai](), [github](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [local-ai](), [macos](), [open-source](), [open-source-graphics](), [phoronix](), [phoronix-test-suite](), [release](), [security](), [ubuntu-benchmarks](), [ubuntu-hardware](), [user-interface](), [windows]() ### AI overview AMD GAIA 0.24 adds local transcription and summarization of meeting recordings, including multi-speaker recognition. The release also improves inbox triage and its text interface, supports an embedded local Lemonade server, and includes security and bug fixes. ### Source excerpt AMD's GAIA software for building AI agents on your PC and leveraging generative AI locally with the power of AMD Ryzen and Radeon hardware continues becoming more featureful. Out today is AMD GAIA 0.24/0.24.1 and with it comes the ability to transcribe and summarize meeting recordings locally along with other functionality... ## Frontend Engineering at Palantir: Plotlines in Three.js DevFeed: [Frontend Engineering at Palantir: Plotlines in Three.js]() Original publisher: [Read original article]() Author: Palantir Published: 2026-04-07T13:58:02Z Content type: article Language: en Sources: [Palantir Blog]() Topics: [3D visualization](), [Three.js](), [3D rendering](), [real-time 3D](), [geospatial](), [3D](), [Frontend & real-time](), [React](), [User interface design]() Tags: [3d](), [challenges](), [defense](), [engineering](), [foundry](), [frontend](), [gaia](), [geospatial](), [geospatial-data](), [gotham](), [library](), [overview](), [palantir](), [palantir-frontend](), [palantirengineering](), [palantirtech](), [performance](), [platform](), [software-engineering](), [three-js]() ### AI overview Palantir's 3D Gaia uses Zodiac, a Three.js library, and Foundry APIs to render geospatial Ontology data while keeping storage, filtering, and heavier computation on the backend and rendering on the client. The article describes batching similarly styled lines into single draw calls, using instanced meshes for large satellite sets, and reusing orbit calculations with a sliding queue that the author says cuts calculations per selected object by 95%. The client propagates basic satellite positions from TLEs; heavier collision analysis, long-term predictions, and maneuver planning run on Foundry compute modules. For time-filtered lines, geometry is built once and visibility changes update a GPU opacity buffer; animations update material properties without rebuilding geometry. The text also describes hiding satellite connection lines when a ray-ellipsoid test finds Earth in the way, and constructing sensor coverage paths from satellite motion, Earth rotation, aim, and sensor rotation. Examples are simplified, and sensor specifications are notional. ### Source excerpt About this Series Frontend engineering at Palantir goes far beyond building standard web apps. Our engineers design interfaces for mission-critical decision-making, build operational applications that translate insight to action, and create systems that handle massive datasets -- thinking not just about what the user needs, but what they need when the network is unreliable, the stakes are high, and the margin for error is zero. This series pulls back the curtain on what that work really looks like: the technical problems we solve, the impact we have, and the approaches we take. Whether you're just curious or exploring opportunities to join us, these posts offer an authentic look at life on our Frontend teams. Find all Frontend Engineering blog posts here. In this blog post, Lasse, a frontend engineer based in NY, shares an overview of various 3D workflows engineers at Palantir work on and some unique challenges to handle performance. Stay tuned for Part 5. Last September marked five years since Stripe started a cinematic universe of globes to render live purchase orders on their platform. GitHub threw its hat in the ring three and a half months later with their own, where they showed connection lines between the location a PR was opened and the place it merged. As it turns out, plenty of companies have geospatial data flying around in realtime. It's often a very compelling way to show how far data is traveling over a network or how a company operates in a wider space than it's hosted. In the case of Palantir, Gotham lands in the same neighborhood. A huge focus for frontend engineers here is rendering Ontology objects with geospatial properties on an app called Gaia to visualize defense and intelligence operations. Or to put more simply, "render anything and everything". Recently, our team has been developing a 3D implementation of Gaia. It's powered by Zodiac, a library we built using Three.js, along with a set of Foundry-based APIs that generalize rendering. In shor ## Gaia2 and ARE: Empowering the community to study agents DevFeed: [Gaia2 and ARE: Empowering the community to study agents]() Original publisher: [Read original article]() Author: Clémentine Fourrier; Grégoire Mialon; Maxime Lecanu; Pierre Andrews; Adrien Carreira; frere thibaud; Avijit Ghosh; Romain Froger; Dheeraj Mekala; Caroline Pascal Published: 2025-09-22T00:00:00Z Content type: article Language: en Sources: [Hugging Face]() Topics: [gaia](), [Benchmark](), [Framework](), [Open Source](), [debugging]() Tags: [agents](), [apis](), [artificial-intelligence](), [benchmark](), [collaboration](), [complexity](), [dataset](), [debug](), [evaluation](), [gaia](), [leaderboards](), [open-source](), [reasoning](), [simulation](), [time]() ### AI overview The article introduces Gaia2, a harder follow-up to the GAIA benchmark for evaluating interactive AI agents. Gaia2 expands evaluation from read-only information retrieval to read-and-write tasks involving tool use, web browsing, ambiguous and time-sensitive instructions, controlled failures, adaptability, and agent-to-agent collaboration. It is released with the open Meta Agents Research Environments (ARE) framework, which supports running, debugging, and evaluating agents in customizable simulated real-world conditions. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## CodeAgents + Structure: A Better Way to Execute Actions DevFeed: [CodeAgents + Structure: A Better Way to Execute Actions]() Original publisher: [Read original article]() Author: Aksel Joonas Reedi; Aymeric Roucher Published: 2025-05-28T00:00:00Z Content type: article Language: en Sources: [Hugging Face]() Topics: [Artificial Intelligence](), [AI Development](), [Code](), [Python](), [Large Language Model](), [gaia](), [JSON](), [Parsing]() Tags: [agents](), [ai](), [ai-agents](), [artificial-intelligence](), [code](), [coding](), [community](), [gaia](), [llm](), [nlp](), [open-source](), [python](), [smolagents](), [structured-generation](), [tools]() ### AI overview The article compares traditional JSON-based tool-calling agents with CodeAgents that generate executable Python. It explains that code-based actions provide more flexible tool composition, state management, loops, functions, conditionals, and hypothesis testing, while motivating structured generation to avoid errors when parsing code from Markdown. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Expert Support case study: Bolstering a RAG app with LLM-as-a-Judge DevFeed: [Expert Support case study: Bolstering a RAG app with LLM-as-a-Judge]() Original publisher: [Read original article]() Author: Vineet Singh; Rajsekar Manokaran; Aymeric Roucher Published: 2024-10-28T00:00:00Z Content type: article Language: en Sources: [Hugging Face]() Topics: [Retrieval Augmented Generation (RAG)](), [LLM evaluation / benchmarking](), [hugging face](), [AI Chat](), [gaia]() Tags: [case-studies](), [case-study](), [evaluation](), [expert-support](), [expert-support-program](), [gaia](), [hugging-face](), [llm](), [rag](), [use-cases]() ### AI overview This case study describes Digital Green's Farmer.chat, a Retrieval-Augmented Generation chatbot that uses large language models and curated agricultural research to provide personalized, reliable advice to smallholder farmers and agricultural extension workers. It also explains the development of an LLM-as-a-judge evaluation suite to assess the system across languages, regions, crops, and use cases. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Critique of Gaia-X's role in Europe's cloud strategy DevFeed: [Critique of Gaia-X's role in Europe's cloud strategy]() Original publisher: [Read original article]() Published: 2024-07-25T10:50:43Z Content type: opinion Language: en Sources: [Bert Hubert's writings]() Topics: [gaia](), [Cloud](), [Data Space](), [interoperability]() Tags: [aws](), [cloud](), [europe](), [gaia](), [governance](), [initiative](), [interoperability](), [open-source](), [transparency]() ### AI overview The article argues that Gaia-X is an expensive distraction that is not building a European cloud and may be holding back progress. It distinguishes Gaia-X from an EU project or AWS competitor and discusses its stated focus on digital governance, transparency, portability, interoperability, Data Spaces, and open source software. ### Source excerpt It pains me that I have to write this, but Gaia-X is a harmful and expensive distraction, and it is not doing anything that will ever get us a "European cloud", not even indirectly. Its very existence is holding back progress. For this reason, Gaia-X should be abandoned, and we should try to learn as much as possible from its failure, so we can try something else. I provide some inspiration at the end of this post. ## Our Transformers Code Agent beats the GAIA benchmark 🏅 DevFeed: [Our Transformers Code Agent beats the GAIA benchmark 🏅]() Original publisher: [Read original article]() Author: Aymeric Roucher; Sergei Petrov Published: 2024-07-01T00:00:00Z Content type: article Language: en Sources: [Hugging Face]() Topics: [gaia](), [Benchmark](), [Transformers](), [smolagents](), [Large Language Model](), [Graphs](), [Retrieval Augmented Generation (RAG)]() Tags: [agent](), [agents](), [artificial-intelligence](), [benchmark](), [code](), [community](), [gaia](), [leaderboard](), [multimodal](), [nlp](), [research](), [retrieval-augmented-generation](), [smolagents](), [tool](), [tools](), [transformers]() ### AI overview The article presents a Transformers Code Agent that achieved top performance on the GAIA benchmark. It explains how LLM-based agents use tools and dynamically change their execution graph, and notes that the framework has since been upgraded to the standalone smolagents library. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.