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ARXIV:2605.16142 · LLM AGENTS · SUBMITTED 18 MAY · 20:30 UTC · FRESHNESS STALE
ARXIV:2605.16142LLM AGENTSSUBMITTED 18 MAY · 20:30 UTCFRESHNESS STALEAugusto B. Corrêa · André G. Pereira · Jendrik Seipp · arXiv
Property-guided LLM program synthesis that uses counterexamples to reduce inference costs and improve program quality for planning tasks.
Opportunity summary
Pain Property-guided LLM program synthesis that uses counterexamples to reduce inference costs and improve program quality for planning tasks.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Property-guided LLM program synthesis that uses counterexamples to reduce inference costs and improve program quality for planning tasks. However, these approaches rely on simple numeric scores to signal program quality, such as the value…
LLMs have shown impressive success in program synthesis, discovering programs that surpass prior solutions. However, these approaches rely on simple numeric scores to signal program quality, such as the value of the solution or…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Whenever a problem admits a verifiable property, property-guided LLM synthesis can reduce cost and improve program quality.
LLM Agents moved forward this cycle; last verified May 2026. Public score 6.0/10.
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Property-guided LLM program synthesis that uses counterexamples to reduce inference costs and improve program quality for planning tasks.
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10.48550/arXiv.2605.16142Property-guided LLM program synthesis that uses counterexamples to reduce inference costs and improve program quality for planning tasks.
Abstract
LLMs have shown impressive success in program synthesis, discovering programs that surpass prior solutions. However, these approaches rely on simple numeric scores to signal program quality, such as the value of the solution or the number of passed tests. Because a score offers no guidance on why a program failed, the system must generate and evaluate many candidates hoping some succeed, increasing LLM inference and evaluation costs. We study a different approach: property-guided LLM program synthesis. Instead of scoring programs after evaluation, we check whether a candidate satisfies a formally defined property. When the property is violated, we stop the evaluation early and provide the LLM with a concrete counterexample showing exactly how the program failed. This feedback drastically reduces both the number of program generations and the evaluation cost, and can guide the LLM to generate stronger programs. We evaluate this approach on PDDL planning domains, asking the LLM to synthesize direct heuristic functions: every state reachable by strictly improving transitions has a strictly improving successor. A heuristic with this property leads hill-climbing algorithm directly to a goal state. A counterexample-guided repair loop generates one candidate program, checks the property over a training set, and returns the first case that violates the property. We evaluate our approach on ten planning domains with an out-of-distribution test set. The synthesized heuristics are effectively direct on virtually all test tasks, and compared to the best prior generation method our approach generates seven times fewer programs per domain on average, solves more tasks without using search, and requires several orders of magnitude less computation to evaluate candidates. Whenever a problem admits a verifiable property, property-guided LLM synthesis can reduce cost and improve program quality.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 6.0
PROBLEM
Property-guided LLM program synthesis that uses counterexamples to reduce inference costs and improve program quality for planning tasks. However, these approaches rely on simple numeric scores to signal program quality, such as the value of the solution or the number of passed...
METHOD
LLMs have shown impressive success in program synthesis, discovering programs that surpass prior solutions. However, these approaches rely on simple numeric scores to signal program quality, such as the value of the solution or the number of passed tests.
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Whenever a problem admits a verifiable property, property-guided LLM synthesis can reduce cost and improve program quality.
WHY NOW
LLM Agents moved forward this cycle; last verified May 2026. Public score 6.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Property-guided LLM program synthesis that uses counterexamples to reduce inference costs and improve program quality for planning tasks. However, these approaches rely on simple numeric scores to signal program quality, such as the value of the solution or the number of passed tests.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLMs have shown impressive success in program synthesis, discovering programs that surpass prior solutions. However, these approaches rely on simple numeric scores to signal program quality, such as the value of the solution or the number of passed tests.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Whenever a problem admits a verifiable property, property-guided LLM synthesis can reduce cost and improve program quality.
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 6.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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Property-guided LLM program synthesis that uses counterexamples to reduce inference costs and improve program quality for planning tasks.
Segment
LLM Agents
Adoption evidence
No public code link in the paper record yet
Commercial read
6.0/10 public viability
Direct
Adjacent
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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
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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
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stale
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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
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
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Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
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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.
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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
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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No CRM or outreach source attached.
People
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Regulatory need unclassified.
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People
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Gaps
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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TIMELINE
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BUZZ
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