Paper Overview
Field: NLP Authors: Vinay Samuel, Varun Ursekar, Vijay S. Kalmath arXiv: 2509.05820
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. However, most automated adaptation methods 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.
This paper studies 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? The authors formalize task-agnostic environment preprocessing, in which a studying system explores an environment under a budget and produces artifacts for a frozen solver.
Key Findings
- The paper compares unaided and dossier-equipped meta-agents against fixed synthetic-exercise and corpus-processing methods on six heterogeneous benchmarks.
- Meta-agent variants achieved the highest Avg@3 reward on five of six benchmarks; fixed corpus processing remained best on the benchmark with the largest corpus.
- Larger study budgets did not reliably improve downstream reward.
- Nonetheless, learned artifacts reduced the test-time sampling needed to reach a given score, demonstrating how reusable preparation shifts computation from repeated test attempts to a pre-task learning phase.