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ARXIV:2604.15760 · LLM EVALUATION · SUBMITTED 20 APR · 20:23 UTC · FRESHNESS STALE
ARXIV:2604.15760LLM EVALUATIONSUBMITTED 20 APR · 20:23 UTCFRESHNESS STALEAnkit Maloo · arXiv
KWBench is a new benchmark for evaluating LLMs' unprompted problem recognition in knowledge work, revealing significant gaps in current models' capabilities.
Opportunity summary
Pain KWBench is a new benchmark for evaluating LLMs' unprompted problem recognition in knowledge work, revealing significant gaps in current models' capabilities.
Evidence 0 refs | 5 sources | 67% coverage
Blocker Evidence unverified
KWBench is a new benchmark for evaluating LLMs' unprompted problem recognition in knowledge work, revealing significant gaps in current models' capabilities. Existing frontier benchmarks have saturated, and most knowledge-work evaluations to date reduce to…
We introduce the first version of KWBench (Knowledge Work Bench), a benchmark for unprompted problem recognition in large language models: can an LLM identify a professional scenario before attempting to solve it. Existing frontier…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We release KWBench to shift how frontier models are evaluated on knowledge work, scoring them on whether they recognize the right problem from the…
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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KWBench is a new benchmark for evaluating LLMs' unprompted problem recognition in knowledge work, revealing significant gaps in current models' capabilities.
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10.48550/arXiv.2604.15760KWBench is a new benchmark for evaluating LLMs' unprompted problem recognition in knowledge work, revealing significant gaps in current models' capabilities.
Abstract
We introduce the first version of KWBench (Knowledge Work Bench), a benchmark for unprompted problem recognition in large language models: can an LLM identify a professional scenario before attempting to solve it. Existing frontier benchmarks have saturated, and most knowledge-work evaluations to date reduce to extraction or task completion against a specification. KWBench targets the step before that: recognizing the governing structure of the situation from raw inputs alone. The benchmark contains 223 tasks sourced from practitioners across acquisitions, contract negotiations, clinical pharmacy, organizational politics, fraud analysis, and incentive design. Each task encodes a formal game-theoretic pattern (principal-agent conflict, signaling, mechanism design failure, strategic omission, coalitional dynamics, strategic interdependence) and carries structured ground truth recording the expert reading of the situation and the anticipated failure modes. Models receive raw data and a task prompt with no indication of problem type. Scoring is a three-tier rubric gated by a mandatory conjunctive check. Mandatory criteria encode the predicted wrong paths. We evaluate 16 models. The best model passes on 27.9% of tasks. The top two models agree on only 31.7% of their passes. Among the top 8, 44 tasks are solved by exactly one model; routing across the top 8 covers 50.7% of the benchmark, nearly double the best single model. Conditional on passing, quality scores converge (approx 83% across models); unconditional scores do not. Same models articulate the relevant game-theoretic concept correctly when asked, then fail to apply it unprompted. We release KWBench to shift how frontier models are evaluated on knowledge work, scoring them on whether they recognize the right problem from the situation alone, not only on how well they execute once the problem has been framed for them.
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Proof status
unverified0 refs; 5 sources; 67% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
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Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
KWBench is a new benchmark for evaluating LLMs' unprompted problem recognition in knowledge work, revealing significant gaps in current models' capabilities. Existing frontier benchmarks have saturated, and most knowledge-work evaluations to date reduce to extraction or task com...
METHOD
We introduce the first version of KWBench (Knowledge Work Bench), a benchmark for unprompted problem recognition in large language models: can an LLM identify a professional scenario before attempting to solve it. Existing frontier benchmarks have saturated, and most knowledge-w...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We release KWBench to shift how frontier models are evaluated on knowledge work, scoring them on whether they recognize the right problem from the situation alone, not only on how well they execute once t...
WHY NOW
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
KWBench is a new benchmark for evaluating LLMs' unprompted problem recognition in knowledge work, revealing significant gaps in current models' capabilities. Existing frontier benchmarks have saturated, and most knowledge-work evaluations to date reduce to extraction or task completion against a specification.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
We introduce the first version of KWBench (Knowledge Work Bench), a benchmark for unprompted problem recognition in large language models: can an LLM identify a professional scenario before attempting to solve it. Existing frontier benchmarks have saturated, and most knowledge-work evaluations to date reduce to extraction or task completion against a specification.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We release KWBench to shift how frontier models are evaluated on knowledge work, scoring them on whether they recognize the right problem from the situation alone, not only on how well they execute once the problem has been framed for them. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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KWBench is a new benchmark for evaluating LLMs' unprompted problem recognition in knowledge work, revealing significant gaps in current models' capabilities.
Segment
LLM Evaluation
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Public code linked for build inspection
Commercial read
7.0/10 public viability
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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
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No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 5 sources / 67% 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
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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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, 5 sources, 67% evidence coverage.
Gaps
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Buyer clarity
missing
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Evidence
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Gaps
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Map target operator, economic buyer, and procurement trigger.
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missing
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Defensibility signals are missing.
Evidence
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Gaps
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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
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Evidence
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Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
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Evidence
Build Passport ledger does not include regulatory flags.
Gaps
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
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Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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Gaps
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People
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Gaps
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Regulatory need unclassified.
No clinical or regulatory source attached.
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Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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RELATED PAPER UPDATES
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