ControlScope: Workflow Revision and Reliability in LLM Agents

Hugging Face Daily Papers Papers

Summary

ControlScope is a method for LLM agents to revise workflows, comparing repair strategies across filesystem tasks, ALFWorld, and AppWorld to enhance reliability.

How much of a running workflow should a language model agent revise? ControlScope compares continuing generated code, editing the next tool call's data arguments, and replacing the unfinished workflow from the same public execution state. The nested permissions separate available repairs from the actions an agent selects. We evaluate one-time and repeated reviews across filesystem tasks, ALFWorld, and AppWorld. Across two source programs per task and three reasoning-reviewer draws on 20 filesystem tasks, FULL completes 15-16 tasks versus 13 for KEEP; across four fast draws it completes 10-13 versus 13. Fresh student-record confirmation reproduces a batch-read repair. ALFWorld fast panels yield KEEP/ARG/FULL scores of 85/86/87 on 87 tasks across 52 scenes and 134/134/127 on 134 tasks across four scenes; reasoning on the 87-task cohort also yields 85/86/87 with substantial review cost. An AppWorld V1 official-test panel of 585 task instances from 195 scenario templates shows small net differences. Frozen replays expose viable agent-written replacements interrupted by later revision in two failed file-organization runs. An offline source-trajectory midpoint comparison shows later reviews completing an insufficient repair. Five-call protection saves 19.4% of logged model output and loses one success across 20 fresh source runs. An argument-only shortcut shows that the broader sampled policy can overlook a cheaper successful edit available in both operation sets. These outcomes tie repair access to actual choices and subsequent execution.
Original Article
View Cached Full Text

Cached at: 09/29/26, 04:11 AM

Paper page - ControlScope: Workflow Revision and Reliability in LLM Agents

Source: https://huggingface.co/papers/2609.34313

Abstract

Howmuchofarunningworkflowshouldalanguagemodelagentrevise?ControlScopecomparescontinuinggeneratedcode,editingthenexttoolcall’sdataarguments,andreplacingtheunfinishedworkflowfromthesamepublicexecutionstate.Thenestedpermissionsseparateavailablerepairsfromtheactionsanagentselects.Weevaluateone-timeandrepeatedreviewsacrossfilesystemtasks,ALFWorld,andAppWorld.Acrosstwosourceprogramspertaskandthreereasoning-reviewerdrawson20filesystemtasks,FULLcompletes15-16tasksversus13forKEEP;acrossfourfastdrawsitcompletes10-13versus13.Freshstudent-recordconfirmationreproducesabatch-readrepair.ALFWorldfastpanelsyieldKEEP/ARG/FULLscoresof85/86/87on87tasksacross52scenesand134/134/127on134tasksacrossfourscenes;reasoningonthe87-taskcohortalsoyields85/86/87withsubstantialreviewcost.AnAppWorldV1official-testpanelof585taskinstancesfrom195scenariotemplatesshowssmallnetdifferences.Frozenreplaysexposeviableagent-writtenreplacementsinterruptedbylaterrevisionintwofailedfile-organizationruns.Anofflinesource-trajectorymidpointcomparisonshowslaterreviewscompletinganinsufficientrepair.Five-callprotectionsaves19.4%ofloggedmodeloutputandlosesonesuccessacross20freshsourceruns.Anargument-onlyshortcutshowsthatthebroadersampledpolicycanoverlookacheapersuccessfuleditavailableinbothoperationsets.Theseoutcomestierepairaccesstoactualchoicesandsubsequentexecution.

View arXiv pageView PDFAdd to collection

Get this paper in your agent:

hf papers read 2609\.34313

Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash

Models citing this paper0

No model linking this paper

Cite arxiv.org/abs/2609.34313 in a model README.md to link it from this page.

Datasets citing this paper0

No dataset linking this paper

Cite arxiv.org/abs/2609.34313 in a dataset README.md to link it from this page.

Spaces citing this paper0

No Space linking this paper

Cite arxiv.org/abs/2609.34313 in a Space README.md to link it from this page.

Collections including this paper0

No Collection including this paper

Add this paper to acollectionto link it from this page.

Similar Articles

Operational Reframing and Approval-Framed Delegation in Multi-Agent LLM Safety

arXiv cs.AI

This paper introduces a five-condition controlled contrast design to disentangle the effects of operational reframing, planner behavior, and approval-framed delegation in multi-agent LLM safety evaluations, showing that aggregate pipeline safety measures are not interpretable as stable architectural properties.

Five failure cases I’d test before trusting a multi-agent coding workflow

Reddit r/AI_Agents

A design checklist of five failure cases to test before trusting multi-agent coding workflows: post-review requirement changes, stale worker claims, lost external write responses, approval scope drift, and restart without conversation context. The author frames these as tests to distinguish 'a run ended' from 'this result was accepted against the current task' while building Backplane.