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ARXIV:2603.07978 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.07978AGENTSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
OSExpert enhances computer-use agents with a GUI-based exploration algorithm, achieving near-expert performance and closing the efficiency gap with humans, making it a valuable tool for automating complex digital tasks.
Opportunity summary
Pain OSExpert enhances computer-use agents with a GUI-based exploration algorithm, achieving near-expert performance and closing the efficiency gap with humans, making it a valuable tool for automating complex digital tasks.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
OSExpert enhances computer-use agents with a GUI-based exploration algorithm, achieving near-expert performance and closing the efficiency gap with humans, making it a valuable tool for automating complex digital tasks. However, our new benchmark, OSExpert-Eval,…
General-purpose computer-use agents have shown impressive performance across diverse digital environments. However, our new benchmark, OSExpert-Eval, indicates they remain far less helpful than human experts.
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Although inference-time scaling enables adaptation, these agents complete complex tasks inefficiently with degraded performance, transfer poorly to unseen UIs, and struggle with fine-grained action…
Agents moved forward this cycle; last verified April 2026. Public score 8.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score8.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
OSExpert enhances computer-use agents with a GUI-based exploration algorithm, achieving near-expert performance and closing the efficiency gap with humans, making it a valuable tool for automating complex digital tasks.
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Paper Pack
10.48550/arXiv.2603.07978OSExpert enhances computer-use agents with a GUI-based exploration algorithm, achieving near-expert performance and closing the efficiency gap with humans, making it a valuable tool for automating complex digital tasks.
Abstract
General-purpose computer-use agents have shown impressive performance across diverse digital environments. However, our new benchmark, OSExpert-Eval, indicates they remain far less helpful than human experts. Although inference-time scaling enables adaptation, these agents complete complex tasks inefficiently with degraded performance, transfer poorly to unseen UIs, and struggle with fine-grained action sequences. To solve the problem, we introduce a GUI-based depth-first search (GUI-DFS) exploration algorithm to comprehensively explore and verify an environment's unit functions. The agent then exploits compositionality between unit skills to self-construct a curriculum for composite tasks. To support fine-grained actions, we curate a database of action primitives for agents to discover during exploration; these are saved as a skill set once the exploration is complete. We use the learned skills to improve the agent's performance and efficiency by (1) enriching agents with ready-to-use procedural knowledge, allowing them to plan only once for long trajectories and generate accurate actions, and (2) enabling them to end inference-time scaling earlier by realizing their boundary of capabilities. Extensive experiments show that our environment-learned agent takes a meaningful step toward expert-level computer use, achieving a around 20 percent performance gain on OSExpert-Eval and closing the efficiency gap to humans by around 80 percent
Source availability
PDF linkedThe paper record includes a public PDF URL.
Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% 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 8.0
PROBLEM
OSExpert enhances computer-use agents with a GUI-based exploration algorithm, achieving near-expert performance and closing the efficiency gap with humans, making it a valuable tool for automating complex digital tasks. However, our new benchmark, OSExpert-Eval, indicates they r...
METHOD
General-purpose computer-use agents have shown impressive performance across diverse digital environments. However, our new benchmark, OSExpert-Eval, indicates they remain far less helpful than human experts.
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Although inference-time scaling enables adaptation, these agents complete complex tasks inefficiently with degraded performance, transfer poorly to unseen UIs, and struggle with fine-grained action sequen...
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 8.0/10.
our new benchmark, OSExpert-Eval, indicates they remain far less helpful than human experts
Directly stated in the abstract with clear comparison to human experts
partial
these agents complete complex tasks inefficiently with degraded performance
Directly stated in the abstract as a key limitation of existing approaches
partial
we introduce a GUI-based depth-first search (GUI-DFS) exploration algorithm to comprehensively explore and verify an environment's unit functions
Directly stated as a core method contribution in the abstract
partial
The agent then exploits compositionality between unit skills to self-construct a curriculum for composite tasks
Directly stated in the abstract as part of the method description
partial
achieving a around 20 percent performance gain on OSExpert-Eval
Directly stated with specific numeric result in the abstract
partial
closing the efficiency gap to humans by around 80 percent
Directly stated with specific numeric result in the abstract
partial
enabling them to end inference-time scaling earlier by realizing their boundary of capabilities
Directly stated as a benefit of the method in the abstract
partial
struggle with fine-grained action sequences
Directly stated in the abstract as a key limitation of existing approaches
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
OSExpert enhances computer-use agents with a GUI-based exploration algorithm, achieving near-expert performance and closing the efficiency gap with humans, making it a valuable tool for automating complex digital tasks.
Segment
Agents
Adoption evidence
No public code link in the paper record yet
Commercial read
8.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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Bluesky
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CITED BY
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Extension
Commercially relevant
Conflicting
Owned Distribution
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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
0 refs / 0 sources / 17% 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
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 17% 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
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RELATED PAPER UPDATES
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SIGNAL CANVAS HISTORY AND DELTAS
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TIMELINE
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BUZZ
Buzz trend pending.