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University of Gothenburg

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Research library302linked papers
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Selected work

Representative Papers

LeWiDi-2025 at NLPerspectives: The Third Edition of the Learning with Disagreements Shared Task

Oct 09, 2025

This work addresses the challenge of modeling and evaluating AI systems’ capacity to capture human judgment variability—such as disagreement and subjectivity. Methodologically, we (1) extend the LeWiDi benchmark to four tasks (paraphrase identification, irony/sarcasm detection, natural language inference) with ordinal annotations and individual-perspective prediction; (2) introduce the first integration of soft-label learning and annotator modeling, moving beyond hard-classification paradigms; and (3) propose a multi-task training framework jointly optimizing distributional prediction, individual annotator modeling, and population-level judgment distribution learning. Contributions include two novel evaluation metrics that surpass conventional measures like cross-entropy, and comprehensive empirical analysis revealing strengths and limitations of existing approaches in modeling judgment variability. These advances significantly enhance LeWiDi’s utility and extensibility as a benchmark platform for controversy-aware AI.

2 citationsRead paper

The Limit of Recursion in State-based Systems

Nov 02, 2025Electronic Proceedings in Theoretical Computer Science

This paper addresses the problem of determining the exact least upper bound—i.e., the closure ordinal—on the number of iterations required to compute least fixed points of modally definable functions over all countable structures. Specifically, it resolves a long-standing open boundary problem for alternation-free modal μ-calculus. The authors develop *conservative well-founded induction*, integrating Kozen’s ordinal analysis with model-theoretic techniques to construct a direct pumping argument. They rigorously prove that ω² is a tight upper bound on the closure ordinal and further eliminate the possibility of any closure ordinal strictly between ω² and the first uncountable limit ordinal. This result corrects and substantially extends prior work, establishing—for the first time—that ω² is a universal, optimal, and non-improvable upper bound on the recursion depth of modal fixed-point convergence over countable structures, thereby fully characterizing the minimal iterative complexity required for such fixed-point computations.

1 citationsRead paper

A Theory of Initialisation's Impact on Specialisation

Mar 04, 2025

This work challenges the necessity of neuron specialization for mitigating catastrophic forgetting in continual learning, revealing that specialization is primarily governed by network initialization rather than intrinsic task properties. Method: Through theoretical analysis and empirical validation, the authors demonstrate that weight imbalance and high weight entropy actively induce localized representations; they provide the first theoretical proof that specialization is not inherent but contingent. They further derive a quantitative relationship between specialization degree and initialization parameters, and reproduce the monotonic relationship between task similarity and forgetting rate even in non-specialized networks. Contribution/Results: Specialized initialization significantly enhances Elastic Weight Consolidation (EWC) performance, an effect attributable to initialization-induced prior shaping of representation structure. These findings establish a novel theoretical foundation for regularization design in continual learning and yield principled guidelines for initialization strategy selection.

1 citationsRead paper

Automating Boundary Filling in Cubical Agda

Feb 19, 2024International Conference on Formal Structures for Computation and Deduction

This work addresses the challenge of automating proofs involving higher-dimensional homotopical structures in cubical type theory, focusing on boundary filling—the core task underlying both contortion solving (single-cube filling) and Kan solving (multi-cube gluing). Methodologically, it models contortion as a poset-mapping problem and introduces, for the first time, a constraint satisfaction programming (CSP) framework to solve Kan filling. To mitigate combinatorial explosion in high dimensions and undecidability inherent in the theory, it designs lightweight heuristic algorithms. The prototype solver, implemented in Haskell, integrates poset-based modeling, CSP solving, and an interface to Cubical Agda. It successfully automates the verification of the Eckmann–Hilton theorem and demonstrates efficiency across multiple benchmark suites. This work establishes a novel paradigm and delivers key technical foundations for practical proof automation in cubical type theory.

1 citationsRead paper

Compositionality in algorithms for smoothing

Mar 24, 2023

This paper addresses the lack of compositional semantics in probabilistic algorithms—particularly the bilateral filter-based Gaussian (BFFG) algorithm. To resolve this, the authors establish, for the first time, a rigorous connection between BFFG and optics in category theory: they model BFFG as a functor from the category of Markov kernels to the category of optics and prove that this functor carries a lax monad structure. This categorical construction exposes BFFG’s intrinsic compositional mechanism and provides it with a principled, category-theoretic semantic foundation. The key contribution lies in elevating classical stochastic algorithms to higher-order abstractions endowed with algebraic structure—enabling modular design, formal verification, and cross-model reuse. By unifying probabilistic computation with optic-based compositional principles, the work opens a new theoretical pathway for compositional reasoning about probabilistic programs and randomized algorithms.

1 citationsRead paper
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