Symposium on Functional and Logic Programming 2026 - Schedule

18th International Symposium on Functional and Logic Programming - Schedule

Note: This schedule is preliminary and subject to change!

Tuesday, May 26

9:30 Opening

9:40 Invited talk: Gabriele Keller

Accelerate – past, present and future

Accelerate, a purely functional embedded parallel array language was designed to give Haskell programmers an easy, high-level way to exploit parallel hardware. It has been around for many years, and it has seen significant changes, some on the user level, but mostly in its implementation behind the scene.

This talk briefly introduces the language, and the concept of functional parallel array languages in general. It also explores upcoming changes now available in an experimental release - that rethink fusion, scheduling, and code generation.

Fusion optimizations typically focus on merging producer-consumer pairs to reduce intermediate structures and memory accesses. However, many approaches struggle with multiple inputs and often fail to fuse computations whose combination would be producing multiple outputs, therefor missing optimizations opportunities. Accelerate’s new strategy can deal with this situation. Yet, more powerful fusion strategies complicate finding optimal solutions, as this is an NP-complete problem, so this poses additional challenges.

The new scheduling strategy, which we present, enables mixing data and task parallelism while preserving loop parallelism. In this respect, it is unlike most approaches that convert all parallelism to tasks. Preserving data-parallelism avoids task-parallel overhead and supports selfscheduling for loops, o!ering full flexibility: thread counts and distribution need not be fixed before execution.

Finally, a new backend-aware intermediate language enables architecturedependent optimizations. Together, these innovations promise to improve performance, and broaden Accelerate’s applicability to modern hardware.

11:00-12:00 Talks: Functional Programming

Miyazawa, O., Nishizaki, S.: Matrix Coeffect: A Coeffect Calculus for Handling Interdependent Information Coeffects capture requirements that the environment imposes on programs. Petricek’s structural coeffect calculus extends the simply typed lambda calculus with a type-and-coeffect system. In this framework, requirements such as variable reuse counts and the number of past values needed in dataflow languages are represented by scalars drawn from algebraic structures that relax some semiring axioms (coeffect algebras). However, existing scalar- and vector-based coeffect domains combine annotations componentwise and therefore do not naturally capture constraints between interacting contextual quantities, such as travel times between airports and departure times at airports. To address this limitation, we introduce Matrix Coeffect Algebra, a matrixvalued coeffect domain whose composition propagates requirements across contextual components, enabling constraints between interdependent quantities. We embed Matrix Coeffect Algebra into Petricek’s structural coeffect calculus and prove its type safety via his translational semantics. A flight-timetable case study demonstrates that matrix-based coeffects enable dependency-aware static checking in a coeffect type system. Our technical contribution is a new matrix-valued coeffect algebra, used as an instantiation of Petricek’s structural (schematic) coeffect calculus.
Cabo, Q., Scholz, S.: Finding Programming Faults Even When Large Parts of the Code have Disappeared This paper proposes a debugging technique for functional programming languages that enables debugging of highly optimised code even if the code that actually is executed is very different from its original specification. We suggest a combination of virtual shadow stacks, uniqueness types, as well as pre- and post conditions for functions. Using this combination, we can trace bugs through function calls that may no longer exist at runtime at all. We describe how this approach is realised in the functional array language SaC and we demonstrate how we can enrich virtual stack traces with partial argument information such as array dimensionality or shape.
Arntzenius, M., Willsey, M.: Finite Functional Programming or, LAMBDA: the Ultimate Predicate We unify functional and logic programming by treating predicates as functions equipped with their support: the set of inputs whose output is nonzero. Datalog, for instance, is a language of finitely supported boolean functions. Finite support allows representing functions as input-output tables. Generalizing from boolean functions to other pointed sets neatly handles aggregation and weighted logic programming. We refer to the combination of finitely supported functions, represented as data, with higher order functions, represented as code, as finite functional programming. We give a simple type system to check finite support, using graded effects to check variable grounding and relevance types to model pointed sets.

12:30-14:00 Lunch

14:00-15:00 Talks: Logic Programming

Li, F., Gupta, G.: Computing Supported Models via Transformation to Stable Models Supported models offer a semantics for logic programs that relaxes the minimality constraint of stable models while maintaining logical consistency through a support condition. Despite their theoretical significance since 1988, supported models lack practical computational tools integrated with modern Answer Set Programming (ASP) infrastructure. We present a transformation-based method that enables computation of supported models using standard stable model solvers. Our approach transforms any ground logic program into an equivalent program whose stable models correspond exactly to the supported models of the original program. We implement this transformation as a preprocessor for Clingo and demonstrate applications in software verification, medical diagnosis, and planning where supported models enable valuable exploratory reasoning beyond stable models.
Bohrer, R.: Demonic Dynamic Logic Programming **(Distinguished Paper)** This paper introduces a logic programming language called Demonic Dynamic Logic Programming. It is based on the ground, Demonic fragment of propositional dynamic logic, a well-studied program logic. Syntax, operational semantics and proof-theoretic semantics are presented. The theoretical foundations are validated by proving theorems such as soundness and existence of uniform proofs. Applications to twoplayer mathematical games are discussed and a pursuit-evasion game on a graph is presented as a concrete example. By bringing program logic and logic programming together, we hope to improve reasoning about imperative programs within logic programs. Future work is discussed, including extensions to game logic which hold the potential to generate winning strategies of games.

15:20-16:20 Talks: Logic Programming

Zhou, N., Jiang, C., Bierlee, H., Stuckey, P.: Dynamic Programming and Tabled Logic Programming for Encoding Single-Constant Multiplication into SAT (Declarative Pearl) **Distinguished Paper** Encoding single-constant multiplication (SCM) constraints into SAT is essential for solving a wide range of combinatorial problems involving integer arithmetic. Although optimal SAT encodings for SCM are known, they are often prohibitively expensive to generate for large constants. This paper introduces a dynamic programming (DP) approach that offers a practical balance between encoding quality and encoding time. The proposed method recursively decomposes the binary representation of the target constant into chunks and eliminates common subexpressions across the chunks and their one’s complements. The objective is either to minimize the number of additions (the min-k objective) or to minimize the number of half/full adders (the min-a objective). The DP algorithm is naturally expressed and efficiently implemented using mode-directed tabling in Picat. Although the approach does not guarantee optimality, experimental results show that it produces high-quality encodings while substantially reducing encoding time. This makes SAT-based techniques considerably more practical for applications involving SCM constraints. The full implementation is publicly available at: https://github.com/nfzhou/Picat/blob/master/scm.pi.
Maieli, R., Acclavio, M.: Probabilistic Linear Logic Programming with an application to Bayesian Networks computations Bayesian networks are a canonical formalism for representing probabilistic dependencies, yet their integration within logic programming frameworks remains a nontrivial challenge, mainly due to the complex structure of these networks. In this paper, we propose probLO (probabilistic Linear Objects) an extension of Andreoli and Pareschi’s LO language which embeds Bayesian network representation and computation within the framework of multiplicative additive linear logic programming. The key novelty is the use of multi-head Prolog-like methods to reconstruct network structures, which are not necessarily trees, and the operation of slicing, standard in the literature of linear logic, enabling internal numerical probability computations without relying on external semantic interpretation.

16:40-18:00 Student Research Competition and Posters

Wednesday, May 27

9:30 Invited talk: Kazunori Ueda

Hierarchical Port Hypergraphs: Two Decades Toward a Unifying Structure for Declarative Languages

Declarative languages take diverse forms with respect to data structures and control structures. They encompass a broad range of notions and paradigms, ranging from higher-order terms with binders to concurrent processes with various forms of communication.

A natural but challenging question is whether many, if not all, of these frameworks can be unified within a simple yet expressive formalism, rather than merely combined into a larger formalism. Graphs and graph rewriting provide a promising framework, since higher-order terms can be represented as tree structures augmented with edges encoding binding structures, while networks of processes can be represented as graph nodes and edges representing processes and their interconnection, respectively.

This perspective motivated us to design and implement LMNtal, a graph rewriting language for modeling and programming. LMNtal was conceived as an attempt to unify constraint-based concurrency (a.k.a. concurrent constraint programming) and Constraint Handling Rules [1], two extensions of concurrent logic programming, within a simple setting consisting of relations and zero-assignment logical variables that connect relations (i.e., without function/constant symbols). This ‘degeneration’ naturally allows computation to be interpreted in terms of graphs and graph rewriting. Here, graphs are more accurately described as port graphs (when each variable connects exactly two ports of graph nodes) or port hypergraphs (when each variable may connect more than two ports), to distinguish them from ordinary graphs in graph theory.

Programming languages for graphs remain largely under-explored, presumably because graphs have poor affinity with standard PL-style inductive definitions. LMNtal addresses this issue by allowing graphs to be represented as terms modulo structural congruence, which serves as a PL counterpart of graph isomorphism.

10:50-12:20 Tutorial

Alama, J.: Scheming in Lean In this tutorial we'll get our hands dirty with Lean, a dependently-typed functional language and proof assistant that mixes proving and programming, including metaprogramming. Bring your laptop and get your hands dirty as we build up a little Scheme inside Lean, with proofs checking our work as we go.

12:20-14:00 Lunch

14:00- Excursion & Banquet

Thursday, May 28

9:30 Invited talk: Fritz Henglein

11:00-12:30 Talks

Hemann, J., Pfingsten, B.: Visualizing miniKanren Search with a Fine-Grained Small-Step Semantics We present a deterministic small-step operational semantics for miniKanren that explicitly represents the evolving search tree during execution. This semantics models interleaving and goal scheduling with fine granularity, exposing each evaluation step—goal activation, suspension, resumption, and success. This approach supports reasoning about miniKanren’s interleaving search behavior, helping users understand surprising answer orders and operational effects. A reference implementation undergirds an interactive visualizer that renders the search tree as it develops and lets users step through execution; we treat this tool as a consumer and demonstration of the semantics. Our semantics and tool are illustrated by example and validated through property-based testing.
Tudor, A., Arias, J., Gupta, G.: Automatic Knowledge Gap Detection and Plan Validation Using Counterfactual Justifications Given the increased importance of physical AI, reliable planning in dynamic environments remains a critical function of embodied systems. In particular, care must be taken in how they interact with situations outside the boundaries of their training. While planning languages such as PDDL or STRIPS tend to be highly problem-dependent, LLMs can be applied to broader and more general tasks in dynamic environments, but remain prone to hallucinations. Logic-based systems, in contrast, provide frameworks for a wide variety of problems and offer justifications for complete and correct plans, but offer no explanation when they cannot generate a plan. For example, VECSR, a planning system based on the s(CASP) answer set programming system, can create plans for unseen tasks by generalizing from a small number of training tasks. However, this generalization is limited to tasks achievable with actions learned during training. To address this limitation, we propose and build the Counterfactual Generation Module, which exploits the justification framework of s(CASP) to identify counterfactuals for unachievable tasks. These counterfactuals allow us to detect missing actions that should be incorporated into VECSR’s knowledge base to enable task completion. We validate our proposal by asking VECSR to generate action plans for 55 tasks outside the 10 it was trained on. For 16 tasks, VECSR could not produce plans, and the counterfactuals “identified” the actions that need to be learned to complete 13 of them. Next, we evaluated 56 plans generated by GPT-4o, observing that the module’s assessment of plan correctness corresponded to human evaluation, supporting that the module itself is correct.
Coltharp, N., Libby, S., Israel, L., Li, Y.: Unifying Hindsight and Foresight: Lazy Cost Analysis as Functional Logic Programming Clairvoyance semantics and demand semantics are both pure and time-cost equivalent to lazy evaluation, offering alternatives to the stateful natural semantics of lazy evaluation for analyzing computation cost. Unfortunately, clairvoyance semantics is simple but hard to execute, and demand semantics is executable but complicated. In this paper, we propose a novel approach for unifying these two semantics using functional logic programming. We propose (1) a logical clairvoyance translation, which translates lazy functional programs to functional logic programs where the computation cost is reified, and (2) a logical demand translation, which is a simple extension to the logical clairvoyance translation. We prove that these two translations correctly implement clairvoyance and demand semantics respectively using Agda. We also conduct case studies on examples including insertion sort and Okasaki’s banker’s queue using Curry/KiCS2 to show how these translations can be used in practice.

12:30-14:00 Lunch

14:00-15:00 Talks: Functional Programming

Kiselyov, O.: More Fun with Monoids (Declarative Pearl) Playing with monoids and discovering for oneself how frequently they turn up in various guises and how much is expressed by them is a delight. Not only it brightens the day of important but frankly boring data analytics. Not only it challenges to discover efficient (with the constant time and space operation) monoids, which demands ingenuity. In the words of Guy Steele, inventing efficient associative combining operators is a very big deal. An efficient monoid opens up parallel implementations – not just parallel but embarrassingly parallel: The input sequence arbitrarily partitioned among workers, all running in parallel with no races, dependencies or even memory bank conflicts. This is the ideal for multi-core, GPU or distributed processing. In this paper we show why monoids are a big deal, and also a big fun even more than expected. Horner rule and Boyer-Moore majority voting – commonly regarded as prototypically inherently sequential – turn out monoids, and hence embarrassingly parallelizable. As we generalize monoids to observable monoids, we discover a new, vertical way to combine them, enabling efficient iterated grouping and aggregation directly on raw deserialized (big) data, on multicore or multi-processor.
Lam, C.: Optimizing Mesh Booleans by Being Lazy (System Description) Mesh Boolean operations (union, difference, intersection) enable the combination of simpler shapes into complex forms, and are essential in modeling software such as OpenSCAD, a popular functional programming language in the Maker community. Although these operations are intuitive to users, existing implementations often suffer from performance or robustness issues. Manifold is a new open source library designed for robust and efficient mesh Boolean operations. It is up to hundreds of times faster than traditional methods using exact arithmetic, and more robust than conventional float-based techniques. In this paper, we describe a crucial design decision that enabled this level of performance while maintaining a simple user-facing API: implementing a functional and lazy API design, coupled with expression tree rewriting before evaluation. Manifold has recently been integrated into projects such as OpenSCAD (nightly builds), Blender, and has bindings for various languages.

15:25-16:25 Talks: Testing

Morihata, A.: Test Your Polymorphic Functions with Boolean Values Testing of parametrically polymorphic functions is nontrivial because an inappropriate type instantiation may lead to overlooking bugs. Several studies have discussed sufficient conditions for complete testing, i.e., a set of tests that can find every incorrect input-output behavior. This paper studies finite complete testing, namely complete testing by instantiating type parameters with finite-domain types (Boolean type, in particular). The finiteness brings not only exhaustive testing but also simpler test cases and debugging than the existing state-of-the-art method, which may derive complicated types whose elements are costly or even computationally infeasible to identify. Based on the theory of parametric polymorphism, known as relational parametricity or Wadler’s free theorem, we formulate a sufficient condition for finite complete testing; moreover, we provide a transformation that yields functions more suitable for applying the theorem. Our theory only examines types and hence applies even to black-box testing. A preliminary experiment with the Haskell 98 Prelude library demonstrated the promise of the current proposal, enabling finite complete testing of 57 functions among 79 polymorphic functions.
Boyland, P., Hyatt, S., Dewey, K., Hardekopf, B.: Breccia: A Functional DSL Compiled to Egglog for Test Input Generation Some prominent efforts within fuzz testing to automatically generate test inputs with complex invariants (e.g., well-typed programs for fuzzing compilers) have exploited logical satisfaction, such as using Prolog to describe constraints and generate satisfying instances. However, using Prolog has met with resistance from the community due to the difficulty of writing Prolog descriptions with acceptable performance. We propose an alternative logic programming-based approach: Datalog. While Datalog improves on the “effective programmability” of Prolog, it has its own issues: it is less expressive than Prolog, its bottom-up evaluation strategy requires keeping all generated terms in memory, and while it is easier to program effective generators in Datalog than Prolog, it still requires a specific style and has hidden pitfalls (such as bounding). We introduce Breccia, a functional DSL for generating objects with complex invariants that compiles to Egglog, a Datalog language extended with equality saturation. We show that Breccia is expressive enough to describe interesting objects such as well-typed programs with non-trivial type systems, and that equality saturation can greatly improve the performance of the resulting Egglog generator. We compare Breccia against Prolog implementations of similar generators in order to understand the tradeoffs involved.

16:20-16:40 Closing, Awards