RUC-AIBOX/AEWM-Action-Judge-Bench

AEWM Action Judge Benchmark

Can a model distinguish productive agent decisions from unproductive ones before execution? The AEWM Action Judge Benchmark evaluates this capability on 3,000 annotated decisions across Search, Terminal, and Software Engineering (SWE). Given a task, its observed interaction history, and a proposed reasoning-action pair, a model predicts whether the decision is Critical, Exploratory, or Noisy. The benchmark tests decision-level judgment in long-horizon tasks, rather than final-answer accuracy or tool-response prediction.

Benchmark Results

Paper results: macro-F1 (%). Overall scores pool all 3,000 decisions.

AEWM achieves 70.5% overall macro-F1, outperforming the strongest compared baseline, DeepSeek-V4-Pro (59.9%), by 10.6 percentage points. Its macro-F1 scores are 60.9% on Search, 72.1% on Terminal, and 77.8% on SWE.

About AEWM and EditAct

Long-horizon agents can suffer from task-state contamination: unsupported assumptions become accepted facts, outdated plans persist, and partial progress is mistaken for completion. The Agent-Editing World Model (AEWM) models how an agent's reasoning and actions shape task progress, instead of simulating environment responses.

AEWM combines two capabilities:

• Action Judge (AJ) assesses a proposed reasoning-action pair in the context of the observed history, identifying decisions that warrant intervention.
• State Revision (SR) rewrites noisy reasoning and actions from the same history to steer the agent toward productive next steps.

EditAct integrates these capabilities into inference: the agent proposes a decision, AEWM judges and selectively edits it, and the selected action is executed with real tools. Actual environment feedback is then added to the history. A unified AEWM is learned through cross-domain mid-training followed by supervised fine-tuning.

This benchmark evaluates Action Judge, not State Revision or end-to-end task completion. The model weights and inference and evaluation code are released separately.

Dataset Overview

Domain	Source benchmarks	Critical	Exploratory	Noisy	Total
Search	BrowseComp	300	245	455	1,000
Terminal	Terminal-Bench 2.0	300	245	455	1,000
SWE	Doc2Repo (500) + NL2Repo (500)	300	245	455	1,000
Overall		900	735	1,365	3,000

• Critical: directly advances the task toward completion.
• Exploratory: gathers relevant information or tests a plausible approach without yet making decisive progress.
• Noisy: is redundant, irrelevant, or misleading in the current task context.

Examples are selected from benchmark trajectories using outcome-based eligibility checks, annotation consistency, quality filtering, and diversity sampling. Each selected decision has three agreeing annotations. Its 3,000 examples are individual decisions, not 3,000 distinct tasks.

Data Format and Evaluation

All examples are in action_judge_benchmark_3000.jsonl, with one JSON object per line:

Field	Description
sample_id	Unique decision identifier.
domain	search, terminal, or swe.
source_benchmark	Benchmark from which the decision was drawn.
messages	Complete system and user prompts, including the pre-action history and candidate decision.
gold_action_type	Reference label: critical, exploratory, or noisy.
balanced_core	Membership in an optional 2,205-example class-balanced subset.

Send only messages to the evaluated model. The candidate action's observation and subsequent trajectory are withheld; gold labels and metadata are used only for scoring.

Report accuracy and three-class macro-F1, both per domain and over the full 3,000-example test set. The optional balanced-core subset is not the main benchmark. Prompts can contain long histories; do not silently truncate them. See the evaluation guide for the runner and scoring protocol.