• Publications
  • Influence
A big-step operational semantics via non-idempotent intersection types
We present a typing system of non-idempotent intersection types that characterises strongly normalising λ-terms and can been seen as a big-step operational semantics: we prove that a stronglyExpand
  • 3
  • 2
  • PDF
Psyche: a proof-search engine based on sequent calculus with an LCF-style architecture
TLDR
Psyche is a modular proof-search engine designed for either interactive or automated theorem proving, and aiming at two things: a high level of confidence about the output of the theorem proving process and the ability to apply and combine a wide range of techniques. Expand
  • 7
  • 1
  • PDF
Polarities & Focussing: a journey from Realisability to Automated Reasoning
TLDR
Polarities and focussing play a key role in the interpretation of proofs as programs, a.k.a. the Curry-Howard correspondence, in the context of classical logic. Expand
  • 9
  • PDF
Conflict-Driven Satisfiability for Theory Combination: Transition System and Completeness
TLDR
We propose a new method for satisfiability modulo a combination of theories, named CDSAT, for Conflict-Driven SATisfiability . Expand
  • 5
  • PDF
An MCSAT treatment of Bit-Vectors (preliminary report)
TLDR
We propose a general scheme for treating the theory of bit-vectors in the MCSAT framework, complementing the approach by Zeljic, Wintersteiger, and Rummer. Expand
  • 7
  • PDF
Centralizing equality reasoning in MCSAT
TLDR
This paper broaches the topic of how to reason about equalities in a centralized way, so that the theory reasoners can avoid replicating equality reasoning steps, and even benefit from a centralized implementation of equivalence classes. Expand
  • 4
  • PDF
Interpolating bit-vector arithmetic constraints in MCSAT
We present an interpolation mechanism for a fragment of bit-vector arithmetic. Given a conjunction of constraints under one existential quantifier, and given an interpretation of its free variablesExpand
  • 1
  • PDF
Guiding SMT solvers with Monte Carlo Tree Search and neural networks
TLDR
Monte Carlo Tree Search (MCTS) is a technique to guide search in a large decision space by taking random samples and evaluating their outcome. Expand
  • 1
  • PDF
...
1
2
...