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ARXIV:2604.19667 · LLM AGENTS · SUBMITTED 22 APR · 20:32 UTC · FRESHNESS STALE
ARXIV:2604.19667LLM AGENTSSUBMITTED 22 APR · 20:32 UTCFRESHNESS STALEYi Zhong · Buqiang Xu · Yijun Wang · Zifei Shan · Shuofei Qiao · Guozhou Zheng · +1 at arXiv
A benchmark and agentic framework for generating executable visual workflows from natural language, aiming to automate costly manual development.
Opportunity summary
Pain A benchmark and agentic framework for generating executable visual workflows from natural language, aiming to automate costly manual development.
Evidence 0 refs | 4 sources | 83% coverage
Blocker Evidence partial
A benchmark and agentic framework for generating executable visual workflows from natural language, aiming to automate costly manual development. However, in current practice, such workflows are almost entirely constructed through manual engineering: developers must…
At present, executable visual workflows have emerged as a mainstream paradigm in real-world industrial deployments, offering strong reliability and controllability. However, in current practice, such workflows are almost entirely constructed through manual engineering: developers…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Experimental results show that while state-of-the-art language models can often capture high-level intent, they struggle to generate correct, stable, and executable workflows, especially under…
LLM Agents moved forward this cycle; last verified April 2026. Public score 6.0/10. Implementation evidence is present through a linked repository.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score6.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A benchmark and agentic framework for generating executable visual workflows from natural language, aiming to automate costly manual development.
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Paper Pack
10.48550/arXiv.2604.19667A benchmark and agentic framework for generating executable visual workflows from natural language, aiming to automate costly manual development.
Abstract
At present, executable visual workflows have emerged as a mainstream paradigm in real-world industrial deployments, offering strong reliability and controllability. However, in current practice, such workflows are almost entirely constructed through manual engineering: developers must carefully design workflows, write prompts for each step, and repeatedly revise the logic as requirements evolve-making development costly, time-consuming, and error-prone. To study whether large language models can automate this multi-round interaction process, we introduce Chat2Workflow, a benchmark for generating executable visual workflows directly from natural language, and propose a robust agentic framework to mitigate recurrent execution errors. Chat2Workflow is built from a large collection of real-world business workflows, with each instance designed so that the generated workflow can be transformed and directly deployed to practical workflow platforms such as Dify and Coze. Experimental results show that while state-of-the-art language models can often capture high-level intent, they struggle to generate correct, stable, and executable workflows, especially under complex or changing requirements. Although our agentic framework yields up to 5.34% resolve rate gains, the remaining real-world gap positions Chat2Workflow as a foundation for advancing industrial-grade automation. Code is available at https://github.com/zjunlp/Chat2Workflow.
Source availability
PDF linkedThe paper record includes a public PDF URL.
Extraction status
Parse run linkedA document parse run is attached to this paper.
Proof status
partial0 refs; 4 sources; 83% 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 6.0
PROBLEM
A benchmark and agentic framework for generating executable visual workflows from natural language, aiming to automate costly manual development. However, in current practice, such workflows are almost entirely constructed through manual engineering: developers must carefully de...
METHOD
At present, executable visual workflows have emerged as a mainstream paradigm in real-world industrial deployments, offering strong reliability and controllability. However, in current practice, such workflows are almost entirely constructed through manual engineering: developer...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Experimental results show that while state-of-the-art language models can often capture high-level intent, they struggle to generate correct, stable, and executable workflows, especially under complex or...
WHY NOW
LLM Agents moved forward this cycle; last verified April 2026. Public score 6.0/10. Implementation evidence is present through a linked repository.
{"file name": "input.pdf", "number of pages": 15, "author": "Yi Zhong; Buqiang Xu; Yijun Wang; Zifei Shan; Shuofei Qiao; Guozhou Zheng; Ningyu Zhang"
Implication not extracted yet.
partial
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Concepts
Methods
Materials
Markets
Competitors
A benchmark and agentic framework for generating executable visual workflows from natural language, aiming to automate costly manual development.
Segment
LLM Agents
Adoption evidence
Public code linked for build inspection
Commercial read
6.0/10 public viability
Direct
Adjacent
Substitute
Unknown
No indexed public discussion is attached to 2604.19667 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
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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.
Extension
Commercially relevant
Conflicting
Owned Distribution
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2/3 checks · 67%
Prototype path
partialhttps://sciencetostartup.com/api/v1/paper/2604.19667v1/build-passport
Source: Build Passport tarball route.
Required assets
verifiedDockerfile.minimal, RUN.sh, EXPECTED_OUTPUT.json, sbom.spdx.json
All required asset routes are present.
Dependencies
partialSBOM route attached; dependency contents require artifact review.
https://jdeeoknqehdvwmyoyayl.supabase.co/storage/v1/object/public/build-passports/2604.19667v1/b3a5932940c3c37a972e93a3b145631abd25c5543af37cde439f2514f8412e5a/sbom.spdx.json
Regulatory flags
missingNo regulatory classification attached.
Build Passport payload does not include regulatory flags.
Validation plan
verifiedProof chip reports VERIFIED.
Proof status VERIFIED; computed 2026-04-26T17:11:49.166933+00:00.
Blockers
verifiedNo Build Passport artifact blocker recorded.
Observed cost $0.00.
Build brief generated from Build Passport metadata.
Computed 2026-04-26T17:11:49.166933+00:00.
Prototype path is verified; live transcript still needs attachment.
Proof status VERIFIED.
Validation checklist missing until 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 / 4 sources / 83% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport present; proof VERIFIED
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
verified
Current read
Build Passport proof is verified.
Evidence
Build Passport proof status VERIFIED.
Gaps
No gap recorded.
Next test
Re-run Proof Lab smoke before release.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 4 sources, 83% 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
partial
Current read
Observed Proof Lab cost $0.00.
Evidence
Source: Build Passport cost passport.
Gaps
No gap recorded.
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.