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COMPANY · ENTITY #4380

Dafny

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2

RESEARCH · 1 SOURCE · arXiv cs.AI

arXiv paper: corrective feedback reduces discovery of specification counterexamples in Verus/Rust

The arXiv preprint (arXiv:2610.20676v1) shows that corrective correctness feedback can suppress discovery of programs that meet written contracts but violate intended behavior. On 12 Verus/Rust tasks across four model families under matched call budgets, independent no-feedback discovery more than doubles certified-witness yield versus IntentShield’s corrective-feedback loop (59.0% vs 27.1%); a phase-separated execution matches the aggregate yield without reusing responses, and preserving those witnesses yields stronger specification repair (Phase-Revised: 81.3% implementation success vs IS-Revised: 62.5%). The effect did not reproduce in available Dafny and Lean evaluations, suggesting an empirical boundary around the studied Verus/Rust workflow.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

MAGS: multi-agent auto-formalization provides formal safety guarantees for agentic code

The paper introduces MAGS, a multi-agent framework that auto-formalizes LLM-generated programs into Dafny, repairs verifier-detected violations, and compiles verified code back to executables. Evaluated on 100 CUDA kernels, 100 terminal scripts, and 20 robotic-arm tasks (220 examples), MAGS produced programs with machine-checkable safety guarantees against frozen specifications in all cases, while noting failures when the auto-formalized semantics did not fully capture intended behavior.

8.0