Takeshi Tsukada

Satoshi Kura, Hiroshi Unno, Takeshi Tsukada

Many quantitative properties of probabilistic programs can be characterized as least fixed points, but verifying their lower bounds remains a challenging problem. We present a new approach to lower-bound verification that exploits and extends the connection between the uniqueness of fixed points and program termination. The core technical tool is a generalization of ranking supermartingales, which serves as witnesses of the uniqueness of fixed points. Our method provides a simple and unified reasoning principle applicable to a wide range of quantitative properties, including termination probability, the weakest preexpectation, expected runtime, higher moments of runtime, and conditional weakest preexpectation. We provide a template-based algorithm for automated verification of lower bounds and demonstrate the effectiveness of the proposed method via experiments.

Takeshi Tsukada, Kazuyuki Asada, Kengo Hirata

A semantic model enjoys full definability if every semantic element in the model is a denotation of some proof or program. Full definability indicates that the model captures programs and proofs in a highly detailed manner. This paper studies full definability in a model based on the (bi)category of profunctors on groupoids, which is a proof-relevant variant of the relational model. Despite the fact that a profunctor is far more complicated than a relation, we show that a rather straightforward application of the ideas for the relational model, together with the notion of stability in profunctors, provides a complete characterisation of definable profunctors. More precisely, all logical families of stable and total profunctors are definable by proof-nets of multiplicative linear logic with MIX. As a part of the full definability proof, we show that the stability serves as a correctness criterion, which we think is of independent interest.

Minchao Wu, Takeshi Tsukada, Hiroshi Unno et al.

Loop-invariant synthesis is the basis of program verification. Due to the undecidability of the problem in general, a tool for invariant synthesis necessarily uses heuristics. Despite the common belief that the design of heuristics is vital for the performance of a synthesizer, heuristics are often engineered by their developers based on experience and intuition, sometimes in an \emph{ad-hoc} manner. In this work, we propose an approach to systematically learning heuristics for template-based CounterExample-Guided Inductive Synthesis (CEGIS) with reinforcement learning. As a concrete example, we implement the approach on top of PCSat, which is an invariant synthesizer based on template-based CEGIS. Experiments show that PCSat guided by the heuristics learned by our framework not only outperforms existing state-of-the-art CEGIS-based solvers such as HoICE and the neural solver Code2Inv, but also has slight advantages over non-CEGIS-based solvers such as Eldarica and Spacer in linear Constrained Horn Clause (CHC) solving.

Mayuko Kori, Takeshi Tsukada, Naoki Kobayashi

A cyclic proof system allows us to perform inductive reasoning without explicit inductions. We propose a cyclic proof system for HFLN, which is a higher-order predicate logic with natural numbers and alternating fixed-points. Ours is the first cyclic proof system for a higher-order logic, to our knowledge. Due to the presence of higher-order predicates and alternating fixed-points, our cyclic proof system requires a more delicate global condition on cyclic proofs than the original system of Brotherston and Simpson. We prove the decidability of checking the global condition and soundness of this system, and also prove a restricted form of standard completeness for an infinitary variant of our cyclic proof system. A potential application of our cyclic proof system is semi-automated verification of higher-order programs, based on Kobayashi et al.'s recent work on reductions from program verification to HFLN validity checking.