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Vladimir Gladshtein

Publications and source records attributed to Vladimir Gladshtein.

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Certified Program Synthesis with a Multi-Modal Verifier

Certified program synthesis (aka vericoding) is the process of automatically generating a program, its formal specification, and a machine-checkable proof of their alignment from a natural-language description. Two challenges make vericoding difficult. First, specifications synthesised from natural language are often either too weak to be meaningful or too strong to be implementable, yet existing approaches lack systematic means to detect such defects. Second, the landscape of program verifiers is fragmented: each tool supports a particular reasoning mode -- auto-active (e.g., Dafny, Verus) or interactive (e.g., Coq, Lean) -- with its own trade-off between automation and expressivity. This forces every synthesis methodology to be tailored to a single verification paradigm, limiting the class of tasks it can handle effectively. We overcome both challenges by structuring the certified synthesis workflow around a multi-modal verifier -- a single tool combining dynamic validation, automated proofs, and interactive proof scripting in one foundational framework. We realise this idea in LeetProof, an agentic pipeline built on Velvet, a multi-modal verifier embedded in Lean. Multi-modality enables LeetProof to validate generated specifications via randomised property-based testing before any code is synthesised, decompose the synthesis task into sub-problems guided by verification conditions, and delegate residual proof obligations to frontier AI provers specialised for Lean. We evaluate LeetProof on benchmarks derived from prior work on certified synthesis. Our specification validation uncovers defects in existing reference benchmarks, and LeetProof's staged pipeline achieves a significantly higher rate of fully certified solutions than a single-mode baseline at the same budget -- consistently across two frontier LLM backends.

cs.SE

Mechanised Hypersafety Proofs about Structured Data: Extended Version

Arrays are a fundamental abstraction to represent collections of data. It is often possible to exploit structural properties of the data stored in an array (e.g., repetition or sparsity) to develop a specialised representation optimised for space efficiency. Formally reasoning about correctness of manipulations with such structured data is challenging, as they are often composed of multiple loops with non-trivial invariants. In this work, we observe that specifications for structured data manipulations can be phrased as hypersafety properties, i.e., predicates that relate traces of $k$ programs. To turn this observation into an effective verification methodology, we developed the Logic for Graceful Tensor Manipulation (LGTM), a new Hoare-style relational separation logic for specifying and verifying computations over structured data. The key enabling idea of LGTM is that of parametrised hypersafety specifications that allow the number $k$ of the program components to depend on the program variables. We implemented LGTM as a foundational embedding into Coq, mechanising its rules, meta-theory, and the proof of soundness. Furthermore, we developed a library of domain-specific tactics that automate computer-aided hypersafety reasoning, resulting in pleasantly short proof scripts that enjoy a high degree of reuse. We argue for the effectiveness of relational reasoning about structured data in LGTM by specifying and mechanically proving correctness of 13 case studies including computations on compressed arrays and efficient operations over multiple kinds of sparse tensors.

cs.PL

Small Scale Reflection for the Working Lean User

We present the design and implementation of the Small Scale Reflection proof methodology and tactic language (a.k.a. SSR) for the Lean 4 proof assistant. Like its Coq predecessor SSReflect, our Lean 4 implementation, dubbed LeanSSR, provides powerful rewriting principles and means for effective management of hypotheses in the proof context. Unlike SSReflect for Coq, LeanSSR does not require explicit switching between the logical and symbolic representation of a goal, allowing for even more concise proof scripts that seamlessly combine deduction steps with proofs by computation. In this paper, we first provide a gentle introduction to the principles of structuring mechanised proofs using LeanSSR. Next, we show how the native support for metaprogramming in Lean 4 makes it possible to develop LeanSSR entirely within the proof assistant, greatly improving the overall experience of both tactic implementers and proof engineers. Finally, we demonstrate the utility of LeanSSR by conducting two case studies: (a) porting a collection of Coq lemmas about sequences from the widely used Mathematical Components library and (b) reimplementing proofs in the finite set library of Lean's mathlib4. Both case studies show significant reduction in proof sizes.

cs.PL