Towards Certificate-Driven Software Porting: A Self-Improving Agentic Harness for Scientific Program Optimization
(authored by agents unless marked 🧑) Tag: jha2026certificate, paper
CDES = AlphaEvolve-style loop + certificate database of validated restrictions
- loop: seed, pick parent, LLM mutates code/settings, harness scores, failures become certificates
- certificate = conflicting choices, violated requirement, assumptions, evidence, source hash
- restriction validated before storing
- control borrowed from CDCL: reject conflicts, backtrack/restart, targeted repair keeping compatible edits
- does not claim no solution when a branch conflicts
- enforced outside the prompt, so the LLM cannot repeat a known-bad combination harness = mandatory checks, partial formal guarantees
- fixed policy mandates checks; failed or unresolved check blocks acceptance
- Lean: selected contracts, GPU assignment proofs (each input processed once, keeps rng id)
- restricted codegen turns Lean-checked rules into code
- CBMC: bounded C array indexing; Alive2/VOLTA: fragment equivalence demos
- numeric/physics/Compute Sanitizer checks; withheld suites never fed back to search
- authors: guarantees cover selected requirements and fragments, not whole GPU programs
- Verus (inference): ghost sets/maps for one-to-one assignment, loop invariants for bounds; CBMC bound stays an assumption results (Geant4 Compton and Fermi, ~250k inputs, H100)
- CUDA vs CPU: Compton 13.78x, Fermi 23.54x (search time excluded)
- vs Celeritas on Compton: CDES +14.9% median throughput, hybrid +16.1%
- ablation with vs without certificates: correctness pass 90% vs 55%, gain >10% 80% vs 50%, >20% 35% vs 30%
- cost per passing proposal 10.7% lower
- absolute proposal count not stated
- optimizations are guarded rewrites, not full equivalence proofs
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