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[Submitted on 30 Jun 2026] Title:AhaBench: Do Agents Learn from Prior Experience? A Benchmark for Long-Horizon Continual Learning View a PDF of the paper titled AhaBench: Do Agents Learn from Prior Experience? A Benchmark for Long-Horizon Continual Learning, by Zerui Cheng and 5 other authors View PDF HTML (experimental) Abstract:Modern language agents are expected to operate over long horizons: they ask follow-up questions, reuse worked examples, handle tool feedback, and adapt to delayed consequences. Most evaluations still reset the agent after a prompt or score only the final state of one trajectory. AhaBench asks a more operational question: when a fixed model receives useful experience, does its later behavior improve under a related evaluation condition where the obvious support has been removed, changed, or delayed? The suite contains three components. Aha-Puzzle tests no-hint exploration after solved hidden-state puzzles; Aha-Euler turns Project-Euler-style mathematical ideas into generated taught/held-out tasks with exact validators; and Aha-Vending, an open-source implementation inspired by Vending-Bench, tests whether a simulated vending agent remains profitable while handling delayed feedback and operational incidents. AhaBench reports a three-part scorecard: Initial Score measures starting competence, Post-Experience Score measures the later empirical outcome, and Learning Lift is their difference. This decomposition is the main empirical message: models that use visible support well, models that reach high post-experience scores, and models that improve most during a run are not always the same. On the common eight-model panel, Claude Opus 4.6 leads aggregate Post-Experience Score at 64.3 and aggregate Learning Lift at +25.8, with Gemini 3.1 Pro close behind at 63.4. The component results explain the split: puzzle traces raise supported scores but often fail to become no-hint exploration behavior; Aha-Euler full teaching reaches 78.6-100.0% while answer-only transfer ranges from 0.0 to 73.9%; and Aha-Vending separates profitable incident handling from bankruptcy and no-order failure. We release benchmark tasks, rubrics, validators, simulator code, and interfaces for evaluating new agents. Comments: 37 pages; In submission to TMLR Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL) Cite as: arXiv:2609.05435 [cs.LG] (or arXiv:2609.05435v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.05435 arXiv-issued DOI via DataCite (pending registration) Submission history From: Zerui Cheng [view email] [v1] Tue, 30 Jun 2026 04:04:01 UTC (1,706 KB) Full-text links: Access Paper: View a PDF of the paper titled AhaBench: Do Agents Learn from Prior Experience? A Benchmark for Long-Horizon Continual Learning, by Zerui Cheng and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG new | recent | 2026-09 Change to browse by: cs cs.CL References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)