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Jan Hoffmann

Publications and source records attributed to Jan Hoffmann.

At least 19 recordsLinked to original sources
arXiv2026

Potential Functions as Types

Amortized analysis can be framed from the physicist's view, amenable to manual verification in dependent type theory using potential functions, and the banker's view, amenable to automated inference in substructural type theory using type-level credit annotations. In this work, we synthesize these perspectives in Calf, a dependent type theory cost verification. From the physicist's view, we present a fracture and gluing theorem that renders every type as containing a fusion of an abstraction function and a potential function. By construction, every program between two such types must preserve abstraction, to facilitate modularity of behavior, and conserve potential, to facilitate modularity of cost. Incorporating the banker's view, we synthetically construct type operators for credits and debits. We then define Giralf, a graded substructural dependent type theory for programming with credits and debits, which is semantically interpreted as a sub-language of Calf. Finally, we adapt an inference algorithm to transform a limited class of Calf programs into Giralf counterparts, automating the cost analysis of common algorithms in Calf.

cs.PL↗
arXiv2026

LFPL: Revisited and Mechanized

Hofmann (1999) introduced the functional programming language LFPL to characterize the functions computable in polynomial time using an affine type system. LFPL enables a natural programming style, including nested recursion, and has inspired the development of type systems for automatic cost analysis, linear dependent type theories, and efficient memory management in functional programming languages. Despite its prominence, there does not exist a self-contained presentation, let alone a full mechanization, of LFPL and its core metatheory. This article presents a modern account and mechanization of LFPL and its metatheory with the goal of being self-contained and accessible while streamlining the strongest-known soundness and completeness results. The soundness proof works with the language LFPL+, which extends LFPL with additional language features. The proof is novel, adapting a technique by Aehlig and Schwichtenberg (2002) to construct explicit polynomials that bound the cost of an LFPL+ expression with respect to a big-step cost semantics. The completeness proof shows that LFPL programs can simulate polynomial-time Turing machines while only relying on restricted forms of linear functions and lists. It has the same structure as the original proof by Hofmann (2002) but greatly simplifies the core argument with a novel stack-like data structure that is implemented with first-class functions and lists. The mechanization includes the full soundness and completeness proofs, and serves as one of the first case studies of mechanized metatheory in the recently developed proof assistant Istari.

cs.PL↗
arXiv2026

From Dust to Planets -- A Chemical Perspective

Chemical and chronological information preserved in meteorites permits the reconstruction of events and processes in the solar nebula from the formation of the first solids to the accretion of planetary bodies and their subsequent differentiation. The path from a gas-dust cloud to differentiated planets includes intervals of steady evolution interrupted by singular events that dramatically altered this steady path, leading to planetary bodies with distinct chemical compositions and different degrees of internal differentiation. The dominant continuous process in the early Solar System was the cooling of the gas-dust cloud, which caused a steady condensation of elements into solid compounds and a continuous increase in the dust/gas ratio. Planetesimal formation started within less than 1 Ma of Solar System formation and continued for ca. 3 Ma apparently in random regions within the disk. The first planetesimals most likely formed due to streaming instabilities and created gaps in the gas-dust disk that prevented significant element exchange. Later planetesimals formed by accretion of chondrules that had developed in the dust rings by bow shocks. The Earth formed by early accretion of volatile-poor material and a later collision with a Mars-sized volatile richer body after proto-Earth had formed a metal core. This chance event provided the chemical conditions that transformed the Earth into a habitable planet.

astro-ph.EP↗
arXiv2026

Handling Exceptions and Effects with Automatic Resource Analysis

There exist many techniques for automatically deriving parametric resource (or cost) bounds by analyzing the source code of a program. These techniques work effectively for a large class of programs and language features. However, non-local transfer of control as needed for exception or effect handlers has remained a challenge. This paper presents the first automatic resource bound analysis that supports non-local control transfer between exceptions or effects and their handlers. The analysis is an extension of type-based automatic amortized resource analysis (AARA), which automates the potential method of amortized analysis. It is presented for a simple functional language with lists and linear potential functions. However, the ideas are directly applicable to richer settings and implemented for Standard ML and polynomial potential functions. Apart from the new type system for exceptions and effects, a main contribution is a novel syntactic type-soundness theorem that establishes the correctness of the derived bounds with respect to a stack-based abstract machine. An experimental evaluation shows that the new analysis is capable of analyzing programs that cannot be analyzed by existing methods and that the efficiency overhead of supporting exception and effect handlers is low.

cs.PL↗
arXiv2025

Efficient Cost Bounds with Linear Maps

The Automatic Amortized Resource Analysis (AARA) derives program-execution cost bounds using types. To do so, AARA often makes use of cost-free types, which are critical for the composition of types and cost bounds. However, inferring cost-free types using the current state-of-the-art algorithm is expensive due to recursive dependence on additional cost-free types. Furthermore, that algorithm uses a heuristic only applicable to polynomial cost bounds, and not, e.g., exponential bounds. This paper presents a new approach to these problems by representing the cost-free types of a function in a new way: with a linear map, which can stand for infinitely many cost-free types. Such maps enable an algebraic flavor of reasoning about cost bounds (including non-polynomial bounds) via matrix inequalities. These inequalities can be solved with off-the-shelf linear-programming tools for many programs, so that types can always be efficiently checked and often be efficiently inferred. An experimental evaluation with a prototype implementation shows that-when it is applicable-the inference of linear maps is exponentially more efficient than the state-of-the-art algorithm.

cs.PL↗
arXiv2025

Big-Stop Semantics: Small-Step Semantics in a Big-Step Judgment

As is evident in the programming language literature, many practitioners favor specifying dynamic program behavior using big-step over small-step semantics. Unlike small-step semantics, which must dwell on every intermediate program state, big-step semantics conveniently jumps directly to the ever-important result of the computation. Big-step semantics also typically involves fewer inference rules than their small-step counterparts. However, in exchange for ergonomics, big-step semantics gives up power: Small-step semantics describes program behaviors that are outside the grasp of big-step semantics, notably divergence. This work presents a little-known extension of big-step semantics with inductive definitions that captures diverging computations without introducing error states. This big-stop semantics is illustrated for typed, untyped, and effectful variants of PCF, as well as a while-loop-based imperative language. Big-stop semantics extends the standard big-step inference rules with a few additional rules to define an evaluation judgment that is equivalent to the reflexive-transitive closure of small-step transitions. This simple extension contrasts with other solutions in the literature that sacrifice ergonomics by introducing many additional inference rules, global state, and/or less-commonly-understood reasoning principles like coinduction. The ergonomics of big-stop semantics is exemplified via concise Agda proofs for some key results and compilation theorems.

cs.PL↗
arXiv2025

Proceedings 16th International Workshop on Programming Language Approaches to Concurrency and Communication-cEntric Software

This volume contains the proceedings of PLACES 2025, the 16th edition of the Workshop on Programming Language Approaches to Concurrency and Communication-cEntric Software. The workshop is scheduled to take place in Hamilton, Canada, on May 4, 2025, as a satellite event of ETAPS, the European Joint Conferences on Theory and Practice of Software. PLACES offers a forum for exchanging new ideas on how to address the challenges of concurrent and distributed programming and how to improve the foundations of modern and future computer applications. PLACES welcomes researchers from various fields, and its topics include the design of new programming languages, models for concurrent and distributed systems, type systems, program verification, and applications in various areas (e.g., microservices, sensor networks, blockchains, event processing, business process management).

cs.PL↗
arXiv2023

Worst-Case Input Generation for Concurrent Programs under Non-Monotone Resource Metrics

Worst-case input generation aims to automatically generate inputs that exhibit the worst-case performance of programs. It has several applications, and can, for example, detect vulnerabilities to denial-of-service (DoS) attacks. However, it is non-trivial to generate worst-case inputs for concurrent programs, particularly for resources like memory where the peak cost depends on how processes are scheduled. This article presents the first sound worst-case input generation algorithm for concurrent programs under non-monotone resource metrics like memory. The key insight is to leverage resource-annotated session types and symbolic execution. Session types describe communication protocols on channels in process calculi. Equipped with resource annotations, resource-annotated session types not only encode cost bounds but also indicate how many resources can be reused and transferred between processes. This information is critical for identifying a worst-case execution path during symbolic execution. The algorithm is sound: if it returns any input, it is guaranteed to be a valid worst-case input. The algorithm is also relatively complete: as long as resource-annotated session types are sufficiently expressive and the background theory for SMT solving is decidable, a worst-case input is guaranteed to be returned. A simple case study of a web server's memory usage demonstrates the utility of the worst-case input generation algorithm.

cs.PL↗
arXiv2023

Automatic Amortized Resource Analysis with Regular Recursive Types

The goal of automatic resource bound analysis is to statically infer symbolic bounds on the resource consumption of the evaluation of a program. A longstanding challenge for automatic resource analysis is the inference of bounds that are functions of complex custom data structures. This article builds on type-based automatic amortized resource analysis (AARA) to address this challenge. AARA is based on the potential method of amortized analysis and reduces bound inference to standard type inference with additional linear constraint solving, even when deriving non-linear bounds. A key component of AARA is resource functions that generate the space of possible bounds for values of a given type while enjoying necessary closure properties. Existing work on AARA defined such functions for many data structures such as lists of lists but the question of whether such functions exist for arbitrary data structures remained open. This work answers this questions positively by uniformly constructing resource polynomials for algebraic data structures defined by regular recursive types. These functions are a generalization of all previously proposed polynomial resource functions and can be seen as a general notion of polynomials for values of a given recursive type. A resource type system for FPC, a core language with recursive types, demonstrates how resource polynomials can be integrated with AARA while preserving all benefits of past techniques. The article also proposes the use of new techniques useful for stating the rules of this type system and proving it sound. First, multivariate potential annotations are stated in terms of free semimodules, substantially abstracting details of the presentation of annotations and the proofs of their properties. Second, a logical relation giving semantic meaning to resource types enables a proof of soundness by a single induction on typing derivations.

cs.PL↗
arXiv2021

Automatic Amortized Resource Analysis with the Quantum Physicist's Method

We present a novel method for working with the physicist's method of amortized resource analysis, which we call the quantum physicist's method. These principles allow for more precise analyses of resources that are not monotonically consumed, like stack. This method takes its name from its two major features, worldviews and resource tunneling, which behave analogously to quantum superposition and quantum tunneling. We use the quantum physicist's method to extend the Automatic Amortized Resource Analysis (AARA) type system, enabling the derivation of resource bounds based on tree depth. In doing so, we also introduce remainder contexts, which aid bookkeeping in linear type systems. We then evaluate this new type system's performance by bounding stack use of functions in the Set module of OCaml's standard library. Compared to state-of-the-art implementations of AARA, our new system derives tighter bounds with only moderate overhead.

cs.PL↗
arXiv2021

Sound Probabilistic Inference via Guide Types

Probabilistic programming languages aim to describe and automate Bayesian modeling and inference. Modern languages support programmable inference, which allows users to customize inference algorithms by incorporating guide programs to improve inference performance. For Bayesian inference to be sound, guide programs must be compatible with model programs. One pervasive but challenging condition for model-guide compatibility is absolute continuity, which requires that the model and guide programs define probability distributions with the same support. This paper presents a new probabilistic programming language that guarantees absolute continuity, and features general programming constructs, such as branching and recursion. Model and guide programs are implemented as coroutines that communicate with each other to synchronize the set of random variables they sample during their execution. Novel guide types describe and enforce communication protocols between coroutines. If the model and guide are well-typed using the same protocol, then they are guaranteed to enjoy absolute continuity. An efficient algorithm infers guide types from code so that users do not have to specify the types. The new programming language is evaluated with an implementation that includes the type-inference algorithm and a prototype compiler that targets Pyro. Experiments show that our language is capable of expressing a variety of probabilistic models with nontrivial control flow and recursion, and that the coroutine-based computation does not introduce significant overhead in actual Bayesian inference.

cs.PL↗
arXiv2020

Probabilistic Resource-Aware Session Types

Session types guarantee that message-passing processes adhere to predefined communication protocols. Prior work on session types has focused on deterministic languages but many message-passing systems, such as Markov chains and randomized distributed algorithms, are probabilistic. To model and analyze such systems, this article introduces probabilistic session types and explores their application in automatic expected resource analysis. Probabilistic session types describe probability distributions over messages and are a conservative extension of intuitionistic (binary) session types. To send on a probabilistic channel, processes have to utilize internal randomness from a probabilistic branching expression or external randomness from receiving on a probabilistic channel. The analysis for expected resource bounds is integrated with the type system and is a variant of automatic amortized resource analysis. It can automatically derive symbolic bounds for different cost metrics by reducing type inference to linear constraint solving. The technical contributions include the meta theory that is based on a novel nested multiverse semantics and a type-reconstruction algorithm that allows flexible mixing of different sources of randomness without burdening the programmer with type annotations. The type system has been implemented in the language PRast. Experiments demonstrate that PRast is applicable in different domains such as resource analysis of randomized distributed algorithms, verification of limiting distributions in Markov chains, and analysis of probabilistic digital contracts.

cs.PL↗
arXiv2020

Typable Fragments of Polynomial Automatic Amortized Resource Analysis

Being a fully automated technique for resource analysis, automatic amortized resource analysis (AARA) can fail in returning worst-case cost bounds of programs, fundamentally due to the undecidability of resource analysis. For programmers who are unfamiliar with the technical details of AARA, it is difficult to predict whether a program can be successfully analyzed in AARA. Motivated by this problem, this article identifies classes of programs that can be analyzed in type-based polynomial AARA. Firstly, it is shown that the set of functions that are typable in univariate polynomial AARA coincides with the complexity class PTIME. Secondly, the article presents a sufficient condition for typability that axiomatically requires every sub-expression of a given program to be polynomial-time. It is proved that this condition implies typability in multivariate polynomial AARA under some syntactic restrictions.

cs.PL↗
arXiv2020

Liquid Resource Types

This article presents liquid resource types, a technique for automatically verifying the resource consumption of functional programs. Existing resource analysis techniques trade automation for flexibility -- automated techniques are restricted to relatively constrained families of resource bounds, while more expressive proof techniques admitting value-dependent bounds rely on handwritten proofs. Liquid resource types combine the best of these approaches, using logical refinements to automatically prove precise bounds on a program's resource consumption. The type system augments refinement types with potential annotations to conduct an amortized resource analysis. Importantly, users can annotate data structure declarations to indicate how potential is allocated within the type, allowing the system to express bounds with polynomials and exponentials, as well as more precise expressions depending on program values. We prove the soundness of the type system, provide a library of flexible and reusable data structures for conducting resource analysis, and use our prototype implementation to automatically verify resource bounds that previously required a manual proof.

cs.PL↗
arXiv2020

Raising Expectations: Automating Expected Cost Analysis with Types

This article presents a type-based analysis for deriving upper bounds on the expected execution cost of probabilistic programs. The analysis is naturally compositional, parametric in the cost model, and supports higher order functions and inductive data types. The derived bounds are multivariate polynomials that are functions of data structures. Bound inference is enabled by local type rules that reduce type inference to linear constraint solving. The type system is based on the potential method of amortized analysis and extends automatic amortized resource analysis (AARA) for deterministic programs. A main innovation is that bounds can contain symbolic probabilities, which may appear in data structures and function arguments. Another contribution is a novel soundness proof that establishes the correctness of the derived bounds with respect to a distribution-based operational cost semantics that also includes nontrivial diverging behavior. For cost models like time, derived bounds imply termination with probability one. To highlight the novel ideas, the presentation focuses on linear potential and a core language. However, the analysis is implemented as an extension of Resource Aware ML and supports polynomial bounds and user defined data structures. The effectiveness of the technique is evaluated by analyzing the sample complexity of discrete distributions and with a novel average-case estimation for deterministic programs that combines expected cost analysis with statistical methods.

cs.PL↗
arXiv2020

Exponential Automatic Amortized Resource Analysis

Automatic amortized resource analysis (AARA) is a type-based technique for inferring concrete (non-asymptotic) bounds on a program's resource usage. Existing work on AARA has focused on bounds that are polynomial in the sizes of the inputs. This paper presents and extension of AARA to exponential bounds that preserves the benefits of the technique, such as compositionality and efficient type inference based on linear constraint solving. A key idea is the use of the Stirling numbers of the second kind as the basis of potential functions, which play the same role as the binomial coefficients in polynomial AARA. To formalize the similarities with the existing analyses, the paper presents a general methodology for AARA that is instantiated to the polynomial version, the exponential version, and a combined system with potential functions that are formed by products of Stirling numbers and binomial coefficients. The soundness of exponential AARA is proved with respect to an operational cost semantics and the analysis of representative example programs demonstrates the effectiveness of the new analysis.

cs.PL↗
arXiv2020

Central Moment Analysis for Cost Accumulators in Probabilistic Programs

For probabilistic programs, it is usually not possible to automatically derive exact information about their properties, such as the distribution of states at a given program point. Instead, one can attempt to derive approximations, such as upper bounds on tail probabilities. Such bounds can be obtained via concentration inequalities, which rely on the moments of a distribution, such as the expectation (the first raw moment) or the variance (the second central moment). Tail bounds obtained using central moments are often tighter than the ones obtained using raw moments, but automatically analyzing central moments is more challenging. This paper presents an analysis for probabilistic programs that automatically derives symbolic upper and lower bounds on variances, as well as higher central moments, of cost accumulators. To overcome the challenges of higher-moment analysis, it generalizes analyses for expectations with an algebraic abstraction that simultaneously analyzes different moments, utilizing relations between them. A key innovation is the notion of moment-polymorphic recursion, and a practical derivation system that handles recursive functions. The analysis has been implemented using a template-based technique that reduces the inference of polynomial bounds to linear programming. Experiments with our prototype central-moment analyzer show that, despite the analyzer's upper/lower bounds on various quantities, it obtains tighter tail bounds than an existing system that uses only raw moments, such as expectations.

cs.PL↗