Investigates if an LLM meta-agent can explore an unfamiliar environment under a budget, before knowing the task distribution, and decide for itself what reusable artifacts (indices, scripts, notes) to build for a frozen solver</p>\n","updatedAt":"2026-09-14T14:55:32.077Z","author":{"_id":"65d67550b20f45c480300f19","avatarUrl":"/avatars/d9052dd6f1356e8c995a19962df1975e.svg","fullname":"varun-scale","name":"varun-scale","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.827659010887146},"editors":["varun-scale"],"editorAvatarUrls":["/avatars/d9052dd6f1356e8c995a19962df1975e.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2609.10824","authors":[{"_id":"6aa802bc5dd4cb9b4cc021f8","name":"Vinay Samuel","hidden":false},{"_id":"6aa802bc5dd4cb9b4cc021f9","name":"Varun Ursekar","hidden":false},{"_id":"6aa802bc5dd4cb9b4cc021fa","name":"Vijay S. Kalmath","hidden":false},{"_id":"6aa802bc5dd4cb9b4cc021fb","name":"Apaar Shanker","hidden":false},{"_id":"6aa802bc5dd4cb9b4cc021fc","name":"Veronica Chatrath","hidden":false},{"_id":"6aa802bc5dd4cb9b4cc021fd","name":"Yuan Xue","hidden":false}],"publishedAt":"2026-09-09T00:00:00.000Z","submittedOnDailyAt":"2026-09-14T00:00:00.000Z","title":"Studying Without a Syllabus: Task-Agnostic Environment Preprocessing","submittedOnDailyBy":{"_id":"65d67550b20f45c480300f19","avatarUrl":"/avatars/d9052dd6f1356e8c995a19962df1975e.svg","isPro":false,"fullname":"varun-scale","user":"varun-scale","type":"user","name":"varun-scale"},"summary":"Before an LLM agent tackles tasks in a new environment, it can inspect available corpora and tools and construct reusable resources such as indices, scripts, or procedural guidance. Most automated adaptation methods, however, rely on task examples, trajectories, or evaluation feedback to decide what to build. Existing task-agnostic approaches avoid this supervision but commit in advance to a preparation strategy for a particular type of environment. We study a more open-ended setting: can an agent study an unfamiliar environment without a syllabus, i.e. before test time and without knowledge of the downstream task distribution, and choose how to prepare it? We formalize task-agnostic environment preprocessing, in which a studying system explores an environment under a budget and produces artifacts for a frozen solver. We compare unaided and archive-equipped meta-agents with fixed synthetic-practice and corpus-processing methods across six heterogeneous benchmarks. A meta-agent variant achieves the highest Avg@3 reward on five benchmarks, while fixed corpus processing remains best on the largest corpus benchmark. Larger study budgets do not reliably improve downstream reward. Nevertheless, studied artifacts reduce the test-time sampling needed to reach a given score, demonstrating how reusable preparation can shift computation from repeated test-time attempts to a pre-task study phase.","upvotes":0,"discussionId":"6aa802bd5dd4cb9b4cc021fe","ai_summary":"An agent can explore unfamiliar environments without task-specific guidance to build reusable artifacts that reduce later inference costs, though larger study budgets do not always improve results.","ai_keywords":["LLM agent","task-agnostic environment preprocessing","meta-agent","synthetic-practice","corpus processing","frozen solver","study budget","reusable artifacts"],"ai_summary_model":"thinkingmachines/Inkling-Small","organization":{"_id":"6677220f8a4064c02bc81217","name":"ScaleAI","fullname":"Scale AI","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/65d6a5f94c28026a003581b4/uqHyTuNQ8fX7LheVhzPeO.png"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[],"acceptLanguages":["en"],"organization":{"_id":"6677220f8a4064c02bc81217","name":"ScaleAI","fullname":"Scale AI","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/65d6a5f94c28026a003581b4/uqHyTuNQ8fX7LheVhzPeO.png"},"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2609/2609.10824.md","query":{}}">
Studying Without a Syllabus: Task-Agnostic Environment Preprocessing
Abstract
An agent can explore unfamiliar environments without task-specific guidance to build reusable artifacts that reduce later inference costs, though larger study budgets do not always improve results.
Before an LLM agent tackles tasks in a new environment, it can inspect available corpora and tools and construct reusable resources such as indices, scripts, or procedural guidance. Most automated adaptation methods, however, rely on task examples, trajectories, or evaluation feedback to decide what to build. Existing task-agnostic approaches avoid this supervision but commit in advance to a preparation strategy for a particular type of environment. We study a more open-ended setting: can an agent study an unfamiliar environment without a syllabus, i.e. before test time and without knowledge of the downstream task distribution, and choose how to prepare it? We formalize task-agnostic environment preprocessing, in which a studying system explores an environment under a budget and produces artifacts for a frozen solver. We compare unaided and archive-equipped meta-agents with fixed synthetic-practice and corpus-processing methods across six heterogeneous benchmarks. A meta-agent variant achieves the highest Avg@3 reward on five benchmarks, while fixed corpus processing remains best on the largest corpus benchmark. Larger study budgets do not reliably improve downstream reward. Nevertheless, studied artifacts reduce the test-time sampling needed to reach a given score, demonstrating how reusable preparation can shift computation from repeated test-time attempts to a pre-task study phase.
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Investigates if an LLM meta-agent can explore an unfamiliar environment under a budget, before knowing the task distribution, and decide for itself what reusable artifacts (indices, scripts, notes) to build for a frozen solver
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Cite arxiv.org/abs/2609.10824 in a model README.md to link it from this page.
Cite arxiv.org/abs/2609.10824 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2609.10824 in a Space README.md to link it from this page.
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