This canonical paper page includes Commercialization Proof and Related Resources.
ARXIV:2603.26233 · AGENTS · SUBMITTED 30 MAR · 21:54 UTC · FRESHNESS STALE
ARXIV:2603.26233AGENTSSUBMITTED 30 MAR · 21:54 UTCFRESHNESS STALENicholas Edwards · Sebastian Schuster · arXiv
LLM agents that proactively seek clarification to resolve underspecified instructions, significantly improving task completion rates.
Opportunity summary
Pain LLM agents that proactively seek clarification to resolve underspecified instructions, significantly improving task completion rates.
Evidence 56 refs | 3 sources | 50% coverage
Blocker Evidence unverified
LLM agents that proactively seek clarification to resolve underspecified instructions, significantly improving task completion rates. While human developers naturally resolve underspecification by asking clarifying questions, current agents are largely optimized for autonomous execution.
As Large Language Model (LLM) agents are increasingly deployed in open-ended domains like software engineering, they frequently encounter underspecified instructions that lack crucial context. While human developers naturally resolve underspecification by asking clarifying questions,…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our results demonstrate that this multi-agent system using OpenHands + Claude Sonnet 4.5 achieves a 69.40% task resolve rate, significantly outperforming a standard single-agent…
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Continue into Read for claims, analysis, references, and neighboring papers.
mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
LLM agents that proactively seek clarification to resolve underspecified instructions, significantly improving task completion rates.
Loading BUILD…
Paper Pack
10.48550/arXiv.2603.26233LLM agents that proactively seek clarification to resolve underspecified instructions, significantly improving task completion rates.
Abstract
As Large Language Model (LLM) agents are increasingly deployed in open-ended domains like software engineering, they frequently encounter underspecified instructions that lack crucial context. While human developers naturally resolve underspecification by asking clarifying questions, current agents are largely optimized for autonomous execution. In this work, we systematically evaluate the clarification-seeking abilities of LLM agents on an underspecified variant of SWE-bench Verified. We propose an uncertainty-aware multi-agent scaffold that explicitly decouples underspecification detection from code execution. Our results demonstrate that this multi-agent system using OpenHands + Claude Sonnet 4.5 achieves a 69.40% task resolve rate, significantly outperforming a standard single-agent setup (61.20%) and closing the performance gap with agents operating on fully specified instructions. Furthermore, we find that the multi-agent system exhibits well-calibrated uncertainty, conserving queries on simple tasks while proactively seeking information on more complex issues. These findings indicate that current models can be turned into proactive collaborators, where agents independently recognize when to ask questions to elicit missing information in real-world, underspecified tasks.
Source availability
PDF linkedThe paper record includes a public PDF URL.
Extraction status
Parse run pending anchorsA parse run id is attached, but no public source anchors are materialized yet.
Proof status
unverified56 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
LLM agents that proactively seek clarification to resolve underspecified instructions, significantly improving task completion rates. While human developers naturally resolve underspecification by asking clarifying questions, current agents are largely optimized for autonomous e...
METHOD
As Large Language Model (LLM) agents are increasingly deployed in open-ended domains like software engineering, they frequently encounter underspecified instructions that lack crucial context. While human developers naturally resolve underspecification by asking clarifying quest...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our results demonstrate that this multi-agent system using OpenHands + Claude Sonnet 4.5 achieves a 69.40% task resolve rate, significantly outperforming a standard single-agent setup (61.20%) and closing...
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Our results demonstrate that this multi-agent system using OpenHands + Claude Sonnet 4.5 achieves a 69.40% task resolve rate
This is a direct result stated in the abstract and supported by Figure 2 and the text discussing UA-MULTI's performance.
partial
significantly outperforming a standard single-agent setup (61.20%)
This is a direct comparison of results stated in the abstract and supported by Figure 2 and the text discussing UA-MULTI vs UA-SINGLE.
partial
we propose an uncertainty-aware multi-agent scaffold that explicitly decouples underspecification detection from code execution.
This is a core methodological contribution described in the abstract and illustrated in Figure 1.
partial
we find that the multi-agent system exhibits well-calibrated uncertainty, conserving queries on simple tasks while proactively seeking information on more complex issues.
This is a key finding about the behavior of the proposed system, stated in the abstract and elaborated upon in the findings.
partial
closing the performance gap with agents operating on fully specified instructions.
The abstract states this, and the results in Figure 2 show UA-MULTI (69.40%) is close to the FULL baseline (which is implied to be higher, though its exact value isn't explicitly stated in the provided text, the comparison is made).
partial
In this configuration, a single coding agent is prompted at each turn to check for underspecification and, if detected, to query the user.
This describes the method for the UA-SINGLE baseline, as stated in the text.
partial
Importantly, the task prompt is modified to explicitly inform the agent that the issue description is incomplete, making it compulsory to query the user before proceeding with any execution.
This accurately describes the setup of the INTERACTIVEBASELINE as presented in the text.
partial
Our results demonstrate that this multi-agent system using OpenHands + Claude Sonnet 4.5 achieves a 69.40% task resolve rate
This is a direct result stated in the abstract and supported by Figure 2 and the text comparing UA-MULTI to other baselines.
partial
significantly outperforming a standard single-agent setup (61.20%)
This is a direct comparison of results stated in the abstract and explicitly detailed in the text and Figure 2.
partial
we propose an uncertainty-aware multi-agent scaffold that explicitly decouples underspecification detection from code execution.
This is a core methodological contribution described in the abstract and illustrated in Figure 1.
partial
we find that the multi-agent system exhibits well-calibrated uncertainty, conserving queries on simple tasks while proactively seeking information on more complex issues.
This is a key finding about the behavior of the proposed system, stated in the abstract and elaborated upon in the findings.
partial
closing the performance gap with agents operating on fully specified instructions.
The abstract states this, and the results in Figure 2 show UA-MULTI (69.40%) is close to INTERACTIVEBASELINE (70.40%) and significantly better than HIDDEN (54.80%). The FULL baseline is not explicitly given a percentage in the provided text, but the comparison implies this.
partial
Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.
Concepts
Methods
Materials
Markets
Competitors
LLM agents that proactively seek clarification to resolve underspecified instructions, significantly improving task completion rates.
Segment
Agents
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
Direct
Adjacent
Substitute
Unknown
No indexed public discussion is attached to 2603.26233 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.
Hacker News
Not indexed yet
Not indexed yet
Bluesky
Not indexed yet
Preview the source document here, or use the hero PDF action for a new tab.
Reference metadata is not materialized in the public index yet. The source PDF remains the authority; cache refresh is optional.
CITED BY
No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.
Foundation
Extension
Commercially relevant
Conflicting
Owned Distribution
Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.
3/3 checks · 100%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
No checklist artifact is attached to the Build Passport payload.
Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
56 refs / 3 sources / 50% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
partial
Current read
Research evidence exists; buyer urgency still needs source proof.
Evidence
56 references, 3 sources, 50% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
Next test
Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
Next test
Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
Cost passport has no observed_usd value.
Gaps
Next test
Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
Next verification path
Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
Next verification path
No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
Gaps
Next verification path
Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
No public artifacts yet.
DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
No verified watchtower monitor rows yet.
FORESIGHT
No prediction yet — minted on next Foresight batch.
OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
No verified OpportunityKernel changes since the last view.
COMPETITIVE LANDSCAPE UPDATES
No verified competitive landscape changes yet.
RELATED PAPER UPDATES
No verified related paper changes yet.
SIGNAL CANVAS HISTORY AND DELTAS
No Signal Canvas history deltas yet.
TIMELINE
Save this paper to start tracking momentum - commits, demos, and score changes appear here.
No tracked events yet.
Score trend will appear after multiple data points.
BUZZ
Buzz trend pending.