An automated predecoder generator for arbitrary qLDPC codes cuts decoder utilization by up to 3963x and supports hardware scaling to tens or hundreds of thousands of logical qubits within power limits.
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Multiple-choice queries synthesized from Hoare triples enable more reliable identification of intended programs than labeled-example supervision in active learning for program disambiguation.
Open-weight LLMs reach 81-91% success generating formally verified Dafny code for complex algorithmic problems when given structural signatures and self-healing verifier feedback.
AutoRocq is an LLM agent that learns proofs on-the-fly by collaborating with the Rocq prover to verify programs on SV-COMP benchmarks and Linux kernel modules.
SANJESH applies bi-level optimization to production traces and reveals VM allocation scenarios that cause 4x worse performance than the operator's existing evaluator detected.
citing papers explorer
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Mitigating Classical Resource Costs in Quantum Error Correction via Generalized qLDPC Predecoding
An automated predecoder generator for arbitrary qLDPC codes cuts decoder utilization by up to 3963x and supports hardware scaling to tens or hundreds of thousands of logical qubits within power limits.
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Choose, Don't Label: Multiple-Choice Query Synthesis for Program Disambiguation
Multiple-choice queries synthesized from Hoare triples enable more reliable identification of intended programs than labeled-example supervision in active learning for program disambiguation.
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From Natural Language to Verified Code: Toward AI Assisted Problem-to-Code Generation with Dafny-Based Formal Verification
Open-weight LLMs reach 81-91% success generating formally verified Dafny code for complex algorithmic problems when given structural signatures and self-healing verifier feedback.
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Agentic Verification of Software Systems
AutoRocq is an LLM agent that learns proofs on-the-fly by collaborating with the Rocq prover to verify programs on SV-COMP benchmarks and Linux kernel modules.
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A Performance Analyzer for a Public Cloud's ML-Augmented VM Allocator
SANJESH applies bi-level optimization to production traces and reveals VM allocation scenarios that cause 4x worse performance than the operator's existing evaluator detected.