On the Existence of an Inverse Solution for Preference-Based Reductions in Argumentation

• URL: http://arxiv.org/abs/2604.22958v1
• Date: Fri, 24 Apr 2026 19:01:57 GMT
• Title: On the Existence of an Inverse Solution for Preference-Based Reductions in Argumentation
• Abstract summary: preference-based argumentation frameworks (PAFs) extend Dung's approach to abstract argumentation (AAFs)<n>We consider a PAF inverse problem which takes an argumentation graph, a labelling and a semantics as an input, and outputs a yes" or no"<n>This inverse problem has applications in areas including preference elicitation and explainability.
• Score: 3.9373541926236766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Preference-based argumentation frameworks (PAFs) extend Dung's approach to abstract argumentation (AAFs) by encoding preferences over arguments. Such preferences control the transformation of attacks into defeats, and different approaches to doing so result in different reductions from a PAF to an AAF. In this paper we consider a PAF inverse problem which takes an argumentation graph, a labelling and a semantics as an input, and outputs a ``yes" or ``no" as to whether there is a preference relation between the arguments which can yield the desired labelling. This inverse problem has applications in areas including preference elicitation and explainability. We consider this problem in the context of the four most widely-used preference based reductions under the complete semantics. We show that in most cases, the problem can be answered in polynomial time.

Related papers

• The Piggyback Hypothesis of Generalization: Explaining and Mitigating Emergent Misalignment [53.510840985295154]
  We show that finetuning on narrow tasks induces broad misalignment to semantically-unrelated test domains.<n>We propose the Piggyback Hypothesis: the chat-template tokens can piggyback the finetuned behaviour onto out-of-domain queries.<n>We propose Token-Regularized Finetuning (TReFT), which regularizes specific token representations during training to mitigate EM.
  arXiv  Detail & Related papers  (2026-06-04T19:32:00Z)
• AdaptR1: Reinforcement Learning Based Adaptive Interleaved Thinking in Multi-hop Question Answering [76.52199013231484]
  We introduce AdaptR1, a Reinforcement Learning (RL) based framework for adaptive interleaved thinking in multi-hop Question Answering (QA)<n>Under the Graph-R1 setting, AdaptR1 reduces average think tokens by 69.71%, with a 90.35% reduction on HotpotQA.<n>Our analysis reveals that overthinking in multi-hop reasoning is not uniformly distributed but occurs predominantly during the initial planning stages.
  arXiv  Detail & Related papers  (2026-05-29T09:34:45Z)
• Splitting Assumption-Based Argumentation Frameworks [9.546922368727428]
  Assumption-Based Argumentation (ABA) is a well-established formalism for modelling and reasoning over debates.<n>This paper investigates the concept of splitting on the knowledge base rather than on its graph-based instantiation.
  arXiv  Detail & Related papers  (2026-04-30T14:56:26Z)
• ReGuLaR: Variational Latent Reasoning Guided by Rendered Chain-of-Thought [49.203970812338916]
  Explicit reasoning chains introduce substantial computational redundancy.<n>Recent latent reasoning methods attempt to mitigate this by compressing reasoning processes into latent space.<n>We propose Rendered CoT-Guided variational Latent Reasoning (ReGuLaR)
  arXiv  Detail & Related papers  (2026-01-30T17:08:06Z)
• Transform-Augmented GRPO Improves Pass@k [50.3707071191733]
  Group Relative Policy Optimization (GRPO) was designed to improve reasoning, but it worsens this situation through two failure modes.<n>We propose TA-GRPO (Transform-Augmented GRPO), which generates semantically equivalent transformed variants of each question.<n>This pooled computation ensures mixed rewards even when the original question is too easy or too hard, while training on diverse phrasings promotes multiple solution strategies.
  arXiv  Detail & Related papers  (2026-01-30T02:43:29Z)
• Nonconvex Optimization Framework for Group-Sparse Feedback Linear-Quadratic Optimal Control: Non-Penalty Approach [3.585860184121598]
  The distributed linearquadratic problem with fixed communication (DFT-LQ) and the subgradient subgradient feedback problem are studied.
  arXiv  Detail & Related papers  (2025-07-26T09:50:21Z)
• Reverse Preference Optimization for Complex Instruction Following [61.39734201711077]
  We propose a simple yet effective method called Reverse Preference Optimization (RPO)<n>It mitigates noise in preference pairs by dynamically reversing the constraints within the instruction to ensure the chosen response is perfect.<n>RPO scales effectively across model sizes, with the 70B RPO model surpassing GPT-4o.
  arXiv  Detail & Related papers  (2025-05-28T09:44:27Z)
• Understanding the Logic of Direct Preference Alignment through Logic [54.272600416107146]
  We propose a novel formalism for characterizing preference losses for single model and reference model based approaches.<n>We show how this formal view of preference learning sheds new light on both the size and structure of the DPA loss landscape.
  arXiv  Detail & Related papers  (2024-12-23T16:23:13Z)
• Rejection in Abstract Argumentation: Harder Than Acceptance? [18.299322342860513]
  We consider flexible conditions for emphrejecting an argument from an extension, which we call rejection conditions (RCs) Rejection AFs are highly expressive, giving rise to natural problems on higher levels of the hierarchy.
  arXiv  Detail & Related papers  (2024-08-20T09:37:04Z)
• Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering [59.495717939664246]
  Large language models have manifested remarkable capabilities by leveraging chain-of-thought (CoT) reasoning techniques to solve intricate questions. We propose a novel approach called the selective filtering reasoner (SelF-Reasoner) that assesses the entailment relationship between the question and the candidate reasoning chain. SelF-Reasoner improves the fine-tuned T5 baseline consistently over the ScienceQA, ECQA, and LastLetter tasks.
  arXiv  Detail & Related papers  (2024-03-28T06:28:35Z)
• An Extension-based Approach for Computing and Verifying Preferences in Abstract Argumentation [1.7065454553786665]
  We present an extension-based approach for computing and verifying preferences in an abstract argumentation system. We show that the complexity of computing sets of preferences is exponential in the number of arguments. We present novel algorithms for verifying (i.e., assessing) the computed preferences.
  arXiv  Detail & Related papers  (2024-03-26T12:36:11Z)
• Analytical Solutions for the Inverse Problem within Gradual Semantics [3.957174470017176]
  We show how an analytical approach can be used to solve the inverse problem in gradual semantics. Unlike the current state-of-the-art, such an approach can rapidly find a solution, and is guaranteed to do so.
  arXiv  Detail & Related papers  (2022-03-02T15:55:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.