Isil Dillig

The Proceedings of the ACM series presents high-quality research across diverse areas of computer science, as represented by the ACM Special Interest Groups (SIGs). The Proceedings of the ACM on Programming Languages (PACMPL) focuses on research on all aspects of programming languages, from design to implementation and from mathematical formalisms to empirical studies. The journal operates in close collaboration with the Special Interest Group on Programming Languages (SIGPLAN) and is committed to making high-quality peer-reviewed scientific research in programming languages free of restrictions on both access and use. This issue of PACMPL publishes 93 articles selected for OOPSLA2 2026, plus two articles from the first round. These articles represent a broad cross-section of current programming-languages research, spanning language design, verification, program analysis, compilers, systems, security, software engineering, AI for programming, and human-centered aspects of PL. Collectively, these papers reflect both the technical depth and the intellectual breadth of the community.

Predicate pushdown is a long-standing performance optimization that filters data as early as possible in a computational workflow. In modern data pipelines, this transformation is especially important because much of the computation occurs inside user-defined functions (UDFs) written in general-purpose languages such as Python and Scala. These UDFs capture rich domain logic and complex aggregations and are among the most expensive operations in a pipeline. Moving filters ahead of such UDFs can yield substantial performance gains, but doing so requires semantic reasoning. This paper introduces a general semantic foundation for predicate pushdown over stateful fold-based computations. We view pushdown as a correspondence between two programs that process different subsets of input data, with correctness witnessed by a bisimulation invariant relating their internal states. Building on this foundation, we develop a sound and relatively complete framework for verification, alongside a synthesis algorithm that automatically constructs optimal pushdown decompositions by finding the strongest admissible pre-filters and weakest residual post-filters. We implement this approach in a tool called Pusharoo and evaluate it on 150 real-world pandas and Spark data-processing pipelines. Our evaluation shows that Pusharoo is significantly more expressive than prior work, producing optimal pushdown transformations with a median synthesis time of 1.6 seconds per benchmark. Furthermore, our experiments demonstrate that the discovered pushdown optimizations speed up end-to-end execution by an average of 2.4× and up to two orders of magnitude.

Match-action tables are the core abstraction underlying network packet-processing systems, from fixed-function switches to eBPF-based software dataplanes. However, their concrete syntax and semantics vary widely across programming environments, reflecting differences in hardware generations, engineering practices, and vendor design choices. This syntactic and semantic variation renders portability of match-action tables across environments a persistent challenge. This paper presents MatchBox, a system for translating match-action tables across heterogeneous environments. At its core is the Match Algebra , a compositional formalism for concisely and declaratively expressing transformations on match-action tables. To ensure unambiguous semantics, MatchBox introduces a static type system based on guarded functional dependencies (GFDs) that guarantees that every well-typed Match Algebra expression denotes a well-defined function. From such specifications, the MatchBox compiler efficiently computes compact target tables that are semantically faithful. Across case studies in programmable switches, multi-cloud firewalls, and eBPF systems, MatchBox enables concise, declarative portability specifications and realizes them as compact target tables.

High-level specifications of code are inherently ambiguous, and prior systems have explored interactive techniques to help users clarify their intent and resolve such ambiguities. However, most existing approaches elicit supervision through labeled examples, which are often error-prone and may fail to capture user intent. This paper introduces a new active learning paradigm for program disambiguation based on multiple-choice queries. In this paradigm, the system presents a small set of high-level behaviors as multiple-choice options, and the user simply selects the intended one. Technically, each answer option corresponds to a Hoare triple that characterizes a cluster of semantically similar candidate programs. This formulation enables formal reasoning about the informativeness and interpretability of queries, and supports systematic construction of optimal queries. Building on this insight, we develop a new active learning algorithm and implement it in a tool called Socrates, which automatically synthesizes informative multiple-choice queries for program disambiguation. We evaluate Socrates across four domains spanning both symbolic and neurosymbolic settings and show that it produces intuitive, easy-to-answer queries and achieves efficient convergence. Most importantly, Socrates identifies the intended program more reliably than existing methods, while maintaining competitive runtime performance.

Data processing frameworks like Apache Spark and Flink provide built-in support for user-defined aggregation functions (UDAFs), enabling the integration of domain-specific logic. However, for these frameworks to support efficient UDAF execution, the function needs to satisfy a homomorphism property , which ensures that partial results from independent computations can be merged correctly. Motivated by this problem, this paper introduces a novel homomorphism calculus that can both verify and refute whether a UDAF is a dataframe homomorphism. If so, our calculus also enables the construction of a corresponding merge operator which can be used for incremental computation and parallel execution. We have implemented an algorithm based on our proposed calculus and evaluate it on real-world UDAFs, demonstrating that our approach significantly outperforms two leading synthesizers.

Enabling more concise and modular proofs is essential for advancing formal reasoning using interactive theorem provers (ITPs). Since many ITPs, such as Rocq and Lean, use tactic-style proofs, learning higher-level custom tactics is crucial for proof modularity and automation. This paper presents a novel approach to tactic discovery, which leverages Tactic Dependence Graphs (TDGs) to identify reusable proof strategies across multiple proofs. TDGs capture logical dependencies between tactic applications while abstracting away irrelevant syntactic details, allowing for both the discovery of new tactics and the refactoring of existing proofs into more modular forms. We have implemented this technique in a tool called TacMineR and compare it against an anti-unification-based approach (Peano) to tactic discovery. Our evaluation demonstrates that TacMineR can learn 3× as many tactics as Peano and reduces the size of proofs by 26% across all benchmarks. Furthermore, our evaluation demonstrates the benefits of learning custom tactics for proof automation, allowing a state-of-the-art proof automation tool to achieve a relative increase of 172% in terms of success rate.

Probabilistic extensions of logic programming languages, such as ProbLog, integrate logical reasoning with probabilistic inference to evaluate probabilities of output relations; however, prior work does not account for potential statistical correlations among input facts. This paper introduces Praline, a new extension to Datalog designed for precise probabilistic inference in the presence of (partially known) input correlations. We formulate the inference task as a constrained optimization problem, where the solution yields sound and precise probability bounds for output facts. However, due to the complexity of the resulting optimization problem, this approach alone often does not scale to large programs. To address scalability, we propose a more efficient δ-exact inference algorithm that leverages constraint solving, static analysis, and iterative refinement. Our empirical evaluation on challenging real-world benchmarks, including side-channel analysis, demonstrates that our method not only scales effectively but also delivers tight probability bounds.

The goal of active learning for program synthesis is to synthesize the desired program by asking targeted questions that minimize user interaction. While prior work has explored active learning in the purely symbolic setting, such techniques are inadequate for the increasingly popular paradigm of neurosymbolic program synthesis, where the synthesized program incorporates neural components. When applied to the neurosymbolic setting, such techniques can -- and, in practice, do -- return an unintended program due to mispredictions of neural components. This paper proposes a new active learning technique that can handle the unique challenges posed by neural network mispredictions. Our approach is based upon a new evaluation strategy called constrained conformal evaluation (CCE), which accounts for neural mispredictions while taking into account user-provided feedback. Our proposed method iteratively makes CCE more precise until all remaining programs are guaranteed to be observationally equivalent. We have implemented this method in a tool called SmartLabel and experimentally evaluated it on three neurosymbolic domains. Our results demonstrate that SmartLabel identifies the ground truth program for 98% of the benchmarks, requiring under 5 rounds of user interaction on average. In contrast, prior techniques for active learning are only able to converge to the ground truth program for at most 65% of the benchmarks.

Circuit languages like Circom and Gnark have become essential tools for programmable zero-knowledge cryptography, allowing developers to build privacy-preserving applications. These domain-specific languages (DSLs) encode both the computation to be verified (as a witness generator ) and the corresponding arithmetic circuits , from which the prover and verifier can be automatically generated. However, for these programs to be correct, the witness generator and the arithmetic circuit need to be mutually consistent in a certain technical sense, and inconsistencies can result in security vulnerabilities. This paper formalizes the consistency requirement for circuit DSLs and proposes the first automated technique for verifying it. We evaluate the method on hundreds of real-world circuits, demonstrating its utility for both automated verification and uncovering errors that existing tools are unable to detect.