arXiv subjects

Mantas Baksys

Publications and source records attributed to Mantas Baksys.

7 recordsLinked to original sources

arXiv2026

WybeCoder: Verified Imperative Code Generation

Recent progress in large language models (LLMs) has substantially advanced automatic code generation and formal theorem proving, yet software verification has not seen comparable gains. To address this gap, we propose WybeCoder, an agentic code verification framework that enables prove-as-you-generate development, in which code, invariants, and proofs co-evolve. WybeCoder builds on a recent framework that combines automatic verification condition generation and SMT solving with interactive proofs in Lean. To enable systematic evaluation, we translate two benchmarks for functional verification in Lean, Verina and Clever, into equivalent imperative code specifications. On complex algorithms such as Heapsort, we observe consistent performance improvements as we scale our approach, synthesizing dozens of valid invariants and dispatching dozens of subgoals, ultimately producing hundreds of lines of verified code and overcoming plateaus reported in previous work. Our best system solves 74% of Verina tasks and 62% of Clever tasks at moderate compute budgets, substantially surpassing previous evaluations and paving the way for the automated construction of large-scale datasets of verified imperative code.

cs.SE↗

arXiv2026

ATLAS: Automated Toolkit for Large-Scale Verified Code Synthesis

Large language models have become proficient at generating functional code, but ensuring the output truly matches the programmer's intent remains difficult. Testing improves trust, yet for safety-critical applications, formal verification provides the only true guarantees through machine-checked proofs. However, verified code remains scarce compared to mainstream languages or mathematical theorem proving, limiting LLM capabilities in this domain. We present ATLAS, an automated pipeline that synthesizes verified programs to address this data bottleneck. Applied to the TACO dataset of Python solutions to LeetCode-style problems, ATLAS generates 2.7K verified Dafny programs, each with high-quality specifications and machine-checked proofs. Through task decomposition, we extract 19K training examples. Fine-tuning Qwen 2.5 7B Coder on this data improves performance from 32.4% to 56.9% on DafnyBench and from 15.8% to 65.8% on DafnySynthesis, demonstrating that synthetic data generation is a viable path to scaling LLM capabilities for formal verification.

cs.SE↗

arXiv2025

Kimina Lean Server: A High-Performance Lean Server for Large-Scale Verification

We introduce the Kimina Lean Server, an open-source project designed as a high-performance verifier for reinforcement learning pipelines. Built on top of the Lean REPL (Read-Eval-Print Loop) maintained by the Lean FRO, our server combines server-side parallelism by managing multiple Lean processes in parallel with a Least Recently Used (LRU) caching mechanism that reuses Lean imports across requests. On the client side, a lightweight Python package enables submitting proof batches and receiving Lean feedback, including extracted tactics and tactic states. Together, these features enable a scalable workflow for large-scale verification and data extraction. In our experiments, the Kimina Lean Server outperforms previous Lean interaction tools, achieving a 1.5 to 2 times speedup in verification time. Moreover, its improved efficiency has enabled its use in the large-scale training of state-of-the-art models such as Kimina-Prover. We hope that our open-source project will support the neural theorem proving community and accelerate future progress by enabling efficient large-scale verification and proof data extraction.

cs.LO↗

arXiv2025

MINIF2F-DAFNY: LLM-Guided Mathematical Theorem Proving via Auto-Active Verification

LLMs excel at reasoning, but validating their steps remains challenging. Formal verification offers a solution through mechanically checkable proofs. Interactive theorem provers (ITPs) dominate mathematical reasoning but require detailed low-level proof steps, while auto-active verifiers offer automation but focus on software verification. Recent work has begun bridging this divide by evaluating LLMs for software verification in ITPs, but the complementary direction, LLMs for mathematical theorem proving in auto-active verifiers, remains unexplored. We present MINIF2F-DAFNY, the first translation of the widely-used mathematical benchmark miniF2F to an auto-active verifier: Dafny. We find that Dafny's automation alone solves 39-44% of problems with empty proofs, whereas many require substantial proof guidance in ITPs. We evaluate 8 off-the-shelf LLMs on proof generation, with the best model (Claude Opus 4.6) achieving 62.7% cumulative pass@4 on the full test set, improving over the 38.9% empty-proof baseline by 23.8 percentage points. These results show that auto-active verification offers a complementary empirical setting for AI-assisted mathematical reasoning, where LLMs provide high-level guidance while SMT automation handles low-level details. Our benchmark and evaluation infrastructure are publicly available on https://github.com/dafny-lang/miniF2F.

cs.LG↗

arXiv2025

Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning

We introduce Kimina-Prover Preview, a large language model that pioneers a novel reasoning-driven exploration paradigm for formal theorem proving, as showcased in this preview release. Trained with a large-scale reinforcement learning pipeline from Qwen2.5-72B, Kimina-Prover demonstrates strong performance in Lean 4 proof generation by employing a structured reasoning pattern we term \textit{formal reasoning pattern}. This approach allows the model to emulate human problem-solving strategies in Lean, iteratively generating and refining proof steps. Kimina-Prover sets a new state-of-the-art on the miniF2F benchmark, reaching 80.7% with pass@8192. Beyond improved benchmark performance, our work yields several key insights: (1) Kimina-Prover exhibits high sample efficiency, delivering strong results even with minimal sampling (pass@1) and scaling effectively with computational budget, stemming from its unique reasoning pattern and RL training; (2) we demonstrate clear performance scaling with model size, a trend previously unobserved for neural theorem provers in formal mathematics; (3) the learned reasoning style, distinct from traditional search algorithms, shows potential to bridge the gap between formal verification and informal mathematical intuition. We open source distilled versions with 1.5B and 7B parameters of Kimina-Prover

cs.AI↗

arXiv2022

Formal Mathematics Statement Curriculum Learning

We explore the use of expert iteration in the context of language modeling applied to formal mathematics. We show that at same compute budget, expert iteration, by which we mean proof search interleaved with learning, dramatically outperforms proof search only. We also observe that when applied to a collection of formal statements of sufficiently varied difficulty, expert iteration is capable of finding and solving a curriculum of increasingly difficult problems, without the need for associated ground-truth proofs. Finally, by applying this expert iteration to a manually curated set of problem statements, we achieve state-of-the-art on the miniF2F benchmark, automatically solving multiple challenging problems drawn from high school olympiads.

cs.LG↗

arXiv2021

On number of different sized induced subgraphs of Bipartite-Ramsey graphs

In this paper, we investigate the set of sizes of induced subgraphs of bipartite graphs. We introduce the definition of $C$-$Bipartite$-$Ramsey$ graphs, which is closely related to Ramsey graphs and prove that in `most' cases, these graphs have multiplication tables of $Ω(e(G))$ in size. We apply our result to give direct evidence to the conjecture that the complete bipartite graph $K_{n,n}$ is the minimiser of the multiplication table on $n^2$ edges raised by Narayanan, Sahasrabudhe and Tomon.

math.CO↗