Adversarial Training

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Period ending 2026-09-21

2 new papers

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Period ending 2026-09-14

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69 papers

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Local Inconsistency Resolution: The Interplay between Attention and Control in Probabilistic Models
Apr 18, 2026Oliver E. Richardson, Mandana Samiei, Mehran Shakerinava +4Probabilistic Inference Singular Learning Theory
Improving LLM Code Reasoning via Semantic Equivalence Self-Play with Formal Verification
Apr 18, 2026Antonio Valerio Miceli Barone, Poon Tsz NokEquivalence Mock-Interface Synthesis
Learning from Equivalence Queries, Revisited
Apr 6, 2026Mark Braverman, Roi Livni, Yishay Mansour +2Equivalence Adversarial Training
Data Security in Large Language Models: Risks, Defense, and Directions
Aug 4, 2025Kang Chen, Xiuze Zhou, Yuanhui Yu +4Large Language Model Safety Adversarial Training
Random Walk Learning and the Pac-Man Attack
Aug 1, 2025Xingran Chen, Parimal Parag, Rohit Bhagat +2Worst-Case Adversarial Training
Evaluating the Scalability and Adversarial Generalization of GRPO-Trained NLI Models
Apr 25, 2025Pablo Miralles-González, Javier Huertas-Tato, Alejandro Martín +1Natural Language Inference Adversarial Training
Unified Enhancement of the Generalization and Robustness of Language Models via Bi-Stage Optimization
Mar 19, 2025Yudao Sun, Juan Yin, Juan Zhao +3Adversarial Training Dropout
Adversarial Rademacher Complexity of Deep Neural Networks
Nov 27, 2022Jiancong Xiao, Yanbo Fan, Ruoyu Sun +1Adversarial Training Generalization Bounds
PEARL: Structural Privacy-Utility Control in Human-Centric CPS via Personalized Early-Exit Deep Reinforcement Learning
Date pendingMojtaba Taherisadr, Salma ElmalakiEfficient Privacy Loss Cyber-Physical System

Apr 18, 2026 · cs.AIJ/K move · Enter open · S save

Local Inconsistency Resolution: The Interplay between Attention and Control in Probabilistic Models

Oliver E. Richardson, Mandana Samiei, Mehran Shakerinava, Joseph D. Viviano+3

Université de Montréal · School of Computer Science, McGill University · Institut Polytechnique de Paris · Mila - Quebec Artificial Intelligence Institute

We present a generic algorithm for learning and approximate inference with an intuitive epistemic interpretation: iteratively focus on a subset of the model and resolve inconsistencies using the parameters under control. This framework, which we call Local Inconsistency Resolution (LIR) is built upon Probabilistic Dependency Graphs (PDGs), which provide a flexible representational foundation capable of capturing inconsistent beliefs. We show how LIR unifies and generalizes a wide variety of important algorithms in the literature, including the Expectation-Maximization (EM) algorithm, belief propagation, adversarial training, GANs, and GFlowNets. In the last case, LIR actually suggests a more natural loss, which we demonstrate improves GFlowNet convergence. Each method can be recovered as a specific instance of LIR by choosing a procedure to direct focus (attention and control). We implement this algorithm for discrete PDGs and study its properties on synthetically generated PDGs, comparing its behavior to the global optimization semantics of the full PDG.

Probabilistic Inference Singular Learning Theory Epistemic Actions Adversarial Training

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