arXiv AI
Jul 17
Local Additive Feature Attribution: A Mathematical Taxonomy and Reporting Checklist
arXiv:2607. 14271v1 Announce Type: cross Abstract: Feature-attribution methods are central to explainable artificial intelligence.
AI safety and alignment
Alignment, interpretability, red-teaming, bias and privacy: the research on what these systems do when they misbehave.
arXiv Machine Learning
Jul 17
PAC Learning in Turn-Based Stochastic Games with Reachability Objectives: A Decentralized Private Approach via Expected Conditional Distance
arXiv:2607. 14877v1 Announce Type: new Abstract: Reachability is the most fundamental logical objective, yet it is notoriously difficult to learn in reinforcement learning settings: even for Markov decision processes, PAC learning of reachability is impossible without additional assumptions.
arXiv AI
Jul 17
InCarEmo: A Multimodal Dataset for In-Cabin Emotion Recognition and Driver State Monitoring
arXiv:2607. 14683v1 Announce Type: new Abstract: Understanding driver emotion and state is critical for the next generation of intelligent in-cabin systems that ensure safety and enhance human-vehicle interaction.
arXiv AI
Jul 17
Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality
arXiv:2607. 14816v1 Announce Type: cross Abstract: Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias.
arXiv Machine Learning
Jul 17
Grad2Fair: A Gradient-driven Approach for Graph Fairness without Demographics
arXiv:2607. 14705v1 Announce Type: new Abstract: Graph neural networks (GNNs) frequently encounter group fairness issues, often yielding biased predictions against specific demographic groups defined by sensitive attributes such as gender or race.
arXiv Machine Learning
Jul 17
Interleaved Noise Injection Improves Clean, Corrupted, and OOD Performance
arXiv:2607. 14466v1 Announce Type: new Abstract: Noise injection is a well-known technique in stochastic optimization.
arXiv AI
Jul 17
Decoupled Alignment for Robust Plug-and-Play Adaptation
arXiv:2406. 01514v4 Announce Type: replace-cross Abstract: We introduce a training-free safety enhancement method for aligning large language models (LLMs) without the need for supervised fine-tuning or reinforcement learning from human feedback.
arXiv AI
Jul 17
Pretraining Data Can Be Poisoned through Computational Propaganda
arXiv:2607. 15267v1 Announce Type: new Abstract: Poisoning pretraining data can introduce harmful behaviors to LMs that are difficult to detect and mitigate.
arXiv AI
Jul 17
Large language models can effectively convince people to believe conspiracies
arXiv:2601. 05050v3 Announce Type: replace Abstract: Large language models (LLMs) have been shown to be persuasive across a variety of contexts.
arXiv Machine Learning
Jul 17
Random Parameter Noise Does Not Make Exact ReLU Verification Easy
arXiv:2607. 14375v1 Announce Type: cross Abstract: We study exact verification of ReLU networks in an adversarial smoothed model.
arXiv AI
Jul 17
How Artificial Intelligence LLM Engines Shape the Global Conflict Information Environment
arXiv:2607. 14197v1 Announce Type: new Abstract: Artificial Intelligence (AI) answer engines now field a growing share of the questions that analysts, scholars, and the public ask about issues of peace and conflict.
arXiv Machine Learning
Jul 17
Dysco: Dynamic Subspace Boosting to Mitigate LoRA Interference in Federated Learning
arXiv:2607. 14367v1 Announce Type: new Abstract: Federated fine-tuning of large pre-trained models increasingly relies on Low-Rank Adaptation (LoRA) to reduce communication and computation, but heterogeneous clients can make adapter aggregation unstable.
arXiv Machine Learning
Jul 17
Diagnosing and Mitigating Domain Shift in Permission-Based Android Malware Detection
arXiv:2605. 09028v3 Announce Type: replace Abstract: Machine learning-based Android malware detectors often fail in real-world deployment due to domain shift, where models trained on one data source perform poorly on applications from another.
arXiv Machine Learning
Jul 17
Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks
arXiv:2607. 14205v1 Announce Type: new Abstract: Federated learning (FL) enables multi-institutional training on clinical text without sharing raw data, but gradient inversion can reconstruct sensitive information from shared model updates.
arXiv AI
Jul 17
Native Extrapolation Awareness in Flow-Based Conditional Generation
arXiv:2602. 13061v2 Announce Type: replace-cross Abstract: The ability of Flow Matching (FM) to model complex conditional distributions has established it as the state-of-the-art for prediction tasks (e.
arXiv Machine Learning
Jul 17
cGAP: Generalized Association Plots with HOMALS-Guided Heatmaps for Visualization of High-Dimensional Categorical Data
arXiv:2607. 15018v1 Announce Type: cross Abstract: High-dimensional categorical data arise in genetics, biomedicine, and the social sciences, yet visualization tools for such data remain far less developed than those for continuous variables.
arXiv AI
Jul 17
Demographically-Conditioned Synthetic Medical Images for Bias Mitigation and Bias Detection in Disease Classifiers
arXiv:2607. 14984v1 Announce Type: new Abstract: Per-subgroup fairness audits of medical image classifiers face a sample-size problem: minority subgroups in held-out test sets have so few samples that the resulting confidence intervals on per-subgroup performance are wider than the bias the audit is meant to detect.
arXiv AI
Jul 17
Analytic Abduction: Causal Decomposition and Governed Commitment for Human--AI Coordination
arXiv:2607. 14641v1 Announce Type: new Abstract: Abductive reasoning operates in two directions.
arXiv AI
Jul 17
Semantic Audio-driven Understanding for Dynamic Humanoid Whole Body Control
arXiv:2607. 14182v1 Announce Type: cross Abstract: Recent advances in humanoid robotics and reinforcement learning have enabled the acquisition of highly expressive whole-body motion policies.
arXiv AI
Jul 17
DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge Graphs
arXiv:2512. 12477v3 Announce Type: replace Abstract: Estimating node importance in heterogeneous knowledge graphs is a fundamental problem underlying recommendation, search, and knowledge decision systems.