---
title: "Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.13034v1"
permalink: "https://feed.astralor.com/items/itm_e48ef37c99836611e72edebb"
published: "2026-07-14T17:59:31.000Z"
score: 79
channel: "ai"
tags: ["ACRR","E3","GPT-4o","MSE-Bench","Complexity-aware execution"]
---

# Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution

## AI commentary

> This paper hits the core pain point of current LLM agent deployment—runaway costs. The E3 framework is simple yet effective, with convincing data from both simulated and real environments. It is a must-read for researchers and engineers developing automated code editing and task planning tools. However, I'm curious whether E3 can remain robust on more complex, non-deterministic tasks; I look forward to future extensions.

## At a glance

Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires, often following a maximum-context-first strategy that turns a one-line edit into a small code-base audit. This paper argues the missing capability is task-aware execution-scope estimation: judging a task's difficulty, the information it truly needs, and the shortest reliable path before committing budget. The authors formalize minimum-sufficient execution and the Agent Cognitive Redundancy Ratio (ACRR), and propose E3 (Estimate, Execute, Expand): the agent estimates an initial operating point, executes a minimum viable path, and expands scope only when verification fails. On MSE-Bench—a deterministic benchmark of 121 edits in a capability-controlled simulator—E3 matches the strongest baseline's 100% success while cutting cost by 85%, tokens by 91%, and inspected files by 92%, and further beats a strong adaptive retrieval baseline by 16%. A companion real-model harness (LLM-Case) corroborates the effect on a live gpt-4o agent editing a real open-source library: E3 is the leanest and fastest policy at comparable task success, with its one shortfall a provider rate-limit, not a wrong edit. The paper frames this as a controlled probe of execution redundancy and positions task-aware execution as a step toward engineering-grounded AI (EGAI).

## Key points

- Proposes task-aware execution-scope estimation to judge task difficulty and information needs
- Defines ACRR metric and designs E3 framework for minimum-sufficient execution
- E3 matches 100% success of the strongest baseline on MSE-Bench while cutting cost by 85%
- Validated on live gpt-4o agent editing open-source library; E3 is the leanest policy

## Full text

Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires. They often follow a maximum-context-first strategy--re-reading files and dependencies they have already seen--turning a one-line edit into a small code-base audit. We argue the missing capability is task-aware execution-scope estimation: judging a task's difficulty, the information it truly needs, and the shortest reliable path before committing budget. We formalize minimum-sufficient execution and the Agent Cognitive Redundancy Ratio (ACRR), and propose E3 (Estimate, Execute, Expand): the agent estimates an initial operating point, executes a minimum viable path, and expands scope only when verification fails. On MSE-Bench--a deterministic benchmark of 121 edits in a capability-controlled simulator--E3 matches the strongest baseline's 100% success while cutting cost by 85%, tokens by 91%, and inspected files by 92%, and further beats a strong adaptive retrieval baseline by 16%; the gains survive held-out instruction wording and essentially every cost weighting. A companion real-model harness (LLM-Case) corroborates the effect on a live gpt-4o agent editing a real open-source library, with every candidate patch graded by actually running the project's real pytest suite against a measured oracle: the over-reading is milder but real, and E3 is the leanest and fastest policy at comparable task success--its one shortfall a provider rate-limit, not a wrong edit. We frame this as a controlled probe of execution redundancy, not a measurement of any deployed agent, and position task-aware execution as a step toward engineering-grounded AI (EGAI)--agents whose effort is anchored in the engineering reality of the task. We release the framework and benchmark.

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Verify against the original before citing; curated by Astral Feed: https://feed.astralor.com/items/itm_e48ef37c99836611e72edebb