README.md

metadata

license: apache-2.0
language:
  - en
tags:
  - agents
  - benchmark
configs:
  - config_name: tasks
    default: true
    data_files:
      - split: test
        path: preview/tasks.jsonl

ReScopeBench

Towards Robust Long-Lived Agents

Can agents keep experience useful when their environment changes?

Homepage | Code | Quick Start | Main Results

18 stateful environments  ·  352 tasks  ·  26 drift events

Overview

ReScopeBench evaluates the lifecycle of agent experience: acquiring local knowledge, reusing it for new goals, and selectively revising it when the environment changes. Its task streams share structural and rule knowledge, while unannounced drift events make only part of that knowledge obsolete.

• Structural experience: how resources are located, connected, and accessed.
• Rule experience: the local conditions and consequences that govern actions.
• Evolving tasks: streams of 16, 24, or 32 tasks with persistent business state and one, two, or three drift events.

This dataset contains the executable benchmark cases. The GitHub repository provides the agent harness, model API adapters, metric computation, and report generation.

Dataset Contents

• cases/ contains environment implementations, initial states, task streams, drift definitions, and evaluator assets. Each environment has one driver.py and one formal-binding.json.
• preview/tasks.jsonl exposes the 352 public task prompts for browsing. The case_path field identifies the corresponding environment directory.
• cases-manifest.json records the file inventory and SHA-256 checksums used by the installer.

The preview is a view of the task streams, not an independent-question evaluation set. Evaluating a row in isolation does not reproduce the benchmark.

Run the Benchmark

Follow the installation guide to set up Python 3.10, Docker, and the evaluation repository. From its root, download the matching cases:

python scripts/download_cases.py

The installer selects the immutable dataset revision pinned in the code repository's config/cases.json and verifies the downloaded files. Use that matched code/data pair rather than combining arbitrary revisions.

For every model and environment, run both complete settings:

• w exp: retain naturally produced memory, skills, and permitted history.
• w/o exp: clear explicit experience between tasks.

The two streams start from the same initial environment and then maintain their own business state. Clearing experience does not reset that state. See the quick start for model configuration, batched tool calls, and execution commands.

Reference knowledge and grading assets belong to the evaluator, not to learner inputs. These cases use the ReScopeBench harness; they are not native Harbor task bundles. Preserve applicable third-party notices and licenses.

Evaluate Results

The metric pipeline reports task performance, structural and rule experience coverage, immediate updating, and recovery timeliness, with CSV and HTML outputs. Experience scores measure coverage of selected reference knowledge and accept equivalent expressions; task graders also allow other valid strategies.

The main results compare 20 models across all 18 environments under both settings.

Acknowledgements

This research was financially supported by the Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), under Grant No. GML-KF-26-06.