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ARXIV:2605.09764 · EVOLUTIONARY SEARCH · SUBMITTED 12 MAY · 20:14 UTC · FRESHNESS FRESH
ARXIV:2605.09764EVOLUTIONARY SEARCHSUBMITTED 12 MAY · 20:14 UTCFRESHNESS FRESHTemoor Tanveer · arXiv
LEVI is an open-source evolutionary search framework that uses stronger search architectures and smarter LLM allocation to achieve state-of-the-art results at a fraction of the cost.
Opportunity summary
Pain LEVI is an open-source evolutionary search framework that uses stronger search architectures and smarter LLM allocation to achieve state-of-the-art results at a fraction of the cost.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
LEVI is an open-source evolutionary search framework that uses stronger search architectures and smarter LLM allocation to achieve state-of-the-art results at a fraction of the cost. We argue this is largely an artifact of…
LLM-guided evolutionary methods such as AlphaEvolve have proven effective in domains like math, systems research, and algorithmic discovery, but their reliance on frontier models makes each run expensive. We argue this is largely an…
ScienceToStartup currently rates this 9.0/10 on the public viability pass. LEVI improves on three core components of evolutionary search: a solution database that establishes diversity from the beginning, and then maintains it throughout the…
Evolutionary Search moved forward this cycle; last verified May 2026. Public score 9.0/10. Implementation evidence is present through a linked repository.
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Score9.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
LEVI is an open-source evolutionary search framework that uses stronger search architectures and smarter LLM allocation to achieve state-of-the-art results at a fraction of the cost.
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10.48550/arXiv.2605.09764LEVI is an open-source evolutionary search framework that uses stronger search architectures and smarter LLM allocation to achieve state-of-the-art results at a fraction of the cost.
Abstract
LLM-guided evolutionary methods such as AlphaEvolve have proven effective in domains like math, systems research, and algorithmic discovery, but their reliance on frontier models makes each run expensive. We argue this is largely an artifact of how existing frameworks allocate search: archives that fail to preserve solution diversity force compensation through stronger mutation models; blind model use spends frontier dollars on local edits a smaller model could handle; and full-set evaluation wastes rollouts on redundant examples. We introduce LEVI, a harness-first evolutionary framework built on the bet that stronger search architectures can substitute for or even outperform larger LLMs in evolutionary search. LEVI improves on three core components of evolutionary search: a solution database that establishes diversity from the beginning, and then maintains it throughout the run; a smarter mutation router that plays into the strengths of large and small LLMs; and a rank-preserving proxy benchmark for rollout-heavy settings. Across systems-research benchmarks LEVI attains the highest score on a budget 3.3-6.7x smaller than the published frontier-model runs of existing frameworks like ShinkaEvolve, GEPA, and AdaEvolve; on one problem, LEVI matches the existing best at a 35x lower cost. On prompt optimization, LEVI matches or exceeds GEPA at less than half of its rollout budget on four different benchmarks. LEVI is available as an open-source framework at https://github.com/ttanv/levi.
Source availability
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Extraction status
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Proof status
unverified0 refs; 0 sources; 0% 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 9.0
PROBLEM
LEVI is an open-source evolutionary search framework that uses stronger search architectures and smarter LLM allocation to achieve state-of-the-art results at a fraction of the cost. We argue this is largely an artifact of how existing frameworks allocate search: archives that f...
METHOD
LLM-guided evolutionary methods such as AlphaEvolve have proven effective in domains like math, systems research, and algorithmic discovery, but their reliance on frontier models makes each run expensive. We argue this is largely an artifact of how existing frameworks allocate s...
RESULT
ScienceToStartup currently rates this 9.0/10 on the public viability pass. LEVI improves on three core components of evolutionary search: a solution database that establishes diversity from the beginning, and then maintains it throughout the run; a smarter mutation router that p...
WHY NOW
Evolutionary Search moved forward this cycle; last verified May 2026. Public score 9.0/10. Implementation evidence is present through a linked repository.
Across systems-research benchmarks LEVI attains the highest score on a budget 3.3-6.7x smaller than the published frontier-model runs of existing frameworks like ShinkaEvolve, GEPA, and AdaEvolve
Directly stated in abstract with specific numeric comparison.
partial
on one problem, LEVI matches the existing best at a 35x lower cost
Directly stated in abstract with specific numeric claim.
partial
On prompt optimization, LEVI matches or exceeds GEPA at less than half of its rollout budget on four different benchmarks
Directly stated in abstract with specific comparison.
partial
LEVI improves on three core components of evolutionary search: a solution database that establishes diversity from the beginning, and then maintains it throughout the run; a smarter mutation router that plays into the strengths of large and small LLMs; and a rank-preserving proxy benchmark for rollout-heavy settings
Directly stated in abstract as the key methodological contributions.
partial
archives that fail to preserve solution diversity force compensation through stronger mutation models
Stated as a problem that LEVI addresses, implying it is a limitation of prior work.
partial
blind model use spends frontier dollars on local edits a smaller model could handle
Stated as a problem that LEVI addresses, implying inefficiency in prior work.
partial
full-set evaluation wastes rollouts on redundant examples
Stated as a problem that LEVI addresses, implying inefficiency in prior work.
partial
LEVI is available as an open-source framework at https://github.com/ttanv/levi
Directly stated in abstract with a URL.
partial
Across systems-research benchmarks LEVI attains the highest score on a budget 3.3-6.7x smaller than the published frontier-model runs of existing frameworks like ShinkaEvolve, GEPA, and AdaEvolve
Directly stated in abstract with specific numeric range and named baselines.
partial
on one problem, LEVI matches the existing best at a 35x lower cost.
Directly stated in abstract with specific multiplier.
partial
On prompt optimization, LEVI matches or exceeds GEPA at less than half of its rollout budget on four different benchmarks.
Directly stated in abstract with specific comparison and number of benchmarks.
partial
LEVI improves on three core components of evolutionary search: a solution database that establishes diversity from the beginning, and then maintains it throughout the run; a smarter mutation router that plays into the strengths of large and small LLMs; and a rank-preserving proxy benchmark for rollout-heavy settings.
Directly stated in abstract as the key methodological contributions.
partial
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Concepts
Methods
Materials
Markets
Competitors
LEVI is an open-source evolutionary search framework that uses stronger search architectures and smarter LLM allocation to achieve state-of-the-art results at a fraction of the cost.
Segment
Evolutionary Search
Adoption evidence
Public code linked for build inspection
Commercial read
9.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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CITED BY
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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 / 0% coverage
fresh
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
fresh
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
fresh
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, 0% 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
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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
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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.