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ARXIV:2605.23262 · LLM AGENTS · SUBMITTED 25 MAY · 20:37 UTC · FRESHNESS STALE
ARXIV:2605.23262LLM AGENTSSUBMITTED 25 MAY · 20:37 UTCFRESHNESS STALEYining Hua · Hongbin Na · Cyrus Ayubcha · Levi Lian · arXiv
A new benchmark design and reporting framework for knowledge work AI that better reflects real-world deployment settings and work product usability.
Opportunity summary
Pain A new benchmark design and reporting framework for knowledge work AI that better reflects real-world deployment settings and work product usability.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A new benchmark design and reporting framework for knowledge work AI that better reflects real-world deployment settings and work product usability. However, current knowledge-work evaluation and benchmark design still largely follow the logic of…
The development of LLM agents has led to a growing body of work on knowledge-work AI, including coding, research, and healthcare. However, current knowledge-work evaluation and benchmark design still largely follow the logic of…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. As a result, higher benchmark performance does not reliably show that a system can carry out knowledge work in real-world deployment settings. Code availability…
LLM Agents moved forward this cycle; last verified May 2026. Public score 5.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score5.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A new benchmark design and reporting framework for knowledge work AI that better reflects real-world deployment settings and work product usability.
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Paper Pack
10.48550/arXiv.2605.23262A new benchmark design and reporting framework for knowledge work AI that better reflects real-world deployment settings and work product usability.
Abstract
The development of LLM agents has led to a growing body of work on knowledge-work AI, including coding, research, and healthcare. However, current knowledge-work evaluation and benchmark design still largely follow the logic of traditional NLP tasks. As a result, higher benchmark performance does not reliably show that a system can carry out knowledge work in real-world deployment settings. This paper contributes a three-step approach for making explicit how benchmarked tasks represent the work claims attached to their scores: defining the work activity under evaluation, specifying the tested setting, and scoring the appropriate work product. We review work studies showing that knowledge work is organized through roles and responsibilities, local materials and tools, and artifacts that must remain usable in downstream workflows. We then translate these concerns into benchmark design and reporting guidance, covering how tasks should be mapped to work activities, how tested settings should specify materials, tools, roles, and constraints, and how scoring should focus on the work product left by the system. To name the work activity being evaluated and distinguish it from common benchmark tasks, we derive an inventory of 18 work activities from the O{*}NET occupational task database. We demonstrate the approach through three benchmark case analyses: GDPval, a non-code occupational deliverable benchmark; OfficeQA Pro, a grounded document-analysis benchmark scored by final answers; and APEX-SWE, a software-engineering benchmark with executable scored products. These cases show how benchmark design choices shape the strongest work claim a score can support, and where gaps arise between the benchmarked task, tested setting, scored product, and broader work claim.
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
unverified0 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 5.0
PROBLEM
A new benchmark design and reporting framework for knowledge work AI that better reflects real-world deployment settings and work product usability. However, current knowledge-work evaluation and benchmark design still largely follow the logic of traditional NLP tasks.
METHOD
The development of LLM agents has led to a growing body of work on knowledge-work AI, including coding, research, and healthcare. However, current knowledge-work evaluation and benchmark design still largely follow the logic of traditional NLP tasks.
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. As a result, higher benchmark performance does not reliably show that a system can carry out knowledge work in real-world deployment settings. Code availability is flagged in the production record; the pu...
WHY NOW
LLM Agents moved forward this cycle; last verified May 2026. Public score 5.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A new benchmark design and reporting framework for knowledge work AI that better reflects real-world deployment settings and work product usability. However, current knowledge-work evaluation and benchmark design still largely follow the logic of traditional NLP tasks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
The development of LLM agents has led to a growing body of work on knowledge-work AI, including coding, research, and healthcare. However, current knowledge-work evaluation and benchmark design still largely follow the logic of traditional NLP tasks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. As a result, higher benchmark performance does not reliably show that a system can carry out knowledge work in real-world deployment settings. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Agents moved forward this cycle; last verified May 2026. Public score 5.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
Materials
Markets
Competitors
A new benchmark design and reporting framework for knowledge work AI that better reflects real-world deployment settings and work product usability.
Segment
LLM Agents
Adoption evidence
No public code link in the paper record yet
Commercial read
5.0/10 public viability
Direct
Adjacent
Substitute
Unknown
No indexed public discussion is attached to 2605.23262 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.
Foundation
Extension
Commercially relevant
Conflicting
Owned Distribution
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2/3 checks · 67%
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 / 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
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 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.