arXiv AI
Jun 30
Fairness Attacks on Recommender Systems
arXiv:2606. 29064v1 Announce Type: cross Abstract: The unfairness of recommender systems has become a topic of concern due to its significant social and ethical implications.
AI safety and alignment
Alignment, interpretability, red-teaming, bias and privacy: the research on what these systems do when they misbehave.
arXiv AI
Jun 30
StackingNet: Collective Inference Across Independent AI Foundation Models
arXiv:2602. 13792v2 Announce Type: replace Abstract: Artificial intelligence built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities.
arXiv AI
Jun 30
Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method
arXiv:2304. 11171v5 Announce Type: replace-cross Abstract: To overcome the limitations of point-based inputs, overly fine computation and limited adaptability in existing artificial intelligence methods, Guoyin Wang and Shuyin Xia proposed granular-ball computing as a new artificial intelligence learning paradigm.
arXiv Machine Learning
Jun 30
FinInvest-GTCN: Explainable Graph-Temporal-Causal Modeling for Risk-Aware Investment Decision Optimization
arXiv:2606. 28933v1 Announce Type: cross Abstract: Venture capital (VC) investment decisions face distinct challenges, such as multi-source heterogeneous data, non-stationary time series, and the demand for explainable predictions in high-stakes, low-data settings.
arXiv Machine Learning
Jun 30
Muon learns balanced solutions in matrix factorization without slow saddle-to-saddle dynamics
arXiv:2606. 30509v1 Announce Type: new Abstract: Matrix factorization (i.
arXiv AI
Jun 30
Offline Reinforcement Learning of High-Quality Behaviors Under Robust Style Alignment
arXiv:2601. 22823v2 Announce Type: replace-cross Abstract: We study offline reinforcement learning of style-conditioned policies using explicit style supervision via subtrajectory labeling functions.
arXiv AI
Jun 30
Governance Decay: How Context Compaction Silently Erases Safety Constraints in Long-Horizon LLM Agents
arXiv:2606. 22528v2 Announce Type: replace Abstract: Modern LLM agents increasingly rely on context compaction, summarization, or eviction to keep long-running sessions within a token budget.
arXiv AI
Jun 30
Aristotelian Virtue Profiling of LLMs through Ethical Dilemmas
arXiv:2606. 28683v1 Announce Type: new Abstract: Large Language Models (LLMs) often face ethical tradeoffs in which several responses may be defensible but express different priorities, such as fairness, honesty, courage, or restraint.
arXiv AI
Jun 30
Modelling Human Values for Value-Aware Multi-Agent Systems
arXiv:2402. 06359v2 Announce Type: replace Abstract: One of today's most pressing societal challenges is building AI systems whose behaviour, or the behaviour it enables within communities of interacting human and artificial agents, aligns with relevant human values.
arXiv AI
Jun 30
Pose-Based Fall Detection System: Efficient Monitoring on Standard CPUs
arXiv:2503. 19501v2 Announce Type: replace-cross Abstract: Falls among elderly residents in assisted living homes pose significant health risks, often leading to injuries and a decreased quality of life.
arXiv AI
Jun 30
Transferability for General Reasoning: An Automated Curriculum for Multi-Domain RLVR
arXiv:2606. 25178v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has been extended from single-domain training to multi-domain reasoning suites spanning mathematics, programming, and science.
arXiv Machine Learning
Jun 30
Blackknife: Hard-Label Query-Limited Black-Box Attacks on Heterogeneous Graph Neural Networks
arXiv:2606. 29240v1 Announce Type: new Abstract: Heterogeneous graph neural networks (HGNNs) have achieved strong performance in modeling complex graph-structured data with multiple node and relation types.
arXiv Machine Learning
Jun 30
Sparse Autoencoders are Capable LLM Jailbreak Mitigators
arXiv:2602. 12418v2 Announce Type: replace-cross Abstract: Jailbreak attacks remain a persistent threat to large language model safety.
arXiv AI
Jun 30
Pondering the Way: Spatial-perceiving World Action Model for Embodied Navigation
arXiv:2606. 29908v1 Announce Type: cross Abstract: Existing world model-based planners for visual navigation typically follow a verification-centric paradigm, decoupling goal intent from trajectory synthesis.
arXiv AI
Jun 30
Safety from Honesty in a Disinterested AI Predictor
arXiv:2606. 29657v1 Announce Type: new Abstract: As AI systems become more capable, training procedures that optimize for downstream outcomes risk introducing implicit agency: goal-directed behavior that designers never specified.
arXiv AI
Jun 30
Sequential Fairness Auditing with Limited Output Access
arXiv:2606. 30338v1 Announce Type: new Abstract: External evaluations are becoming increasingly central to the governance of AI systems.
arXiv AI
Jun 30
Mechanistically Eliciting Latent Behaviors in Language Models
arXiv:2606. 29604v1 Announce Type: cross Abstract: We aim to discover diverse, generalizable perturbations of LLM internals that can surface hidden behavioral modes.
arXiv AI
Jun 30
VISTA-DZ: Visual Semantic Trajectory Adaptation for Personalized Dilemma Zone Prediction
arXiv:2606. 29548v1 Announce Type: cross Abstract: Driver decision making in the dilemma zone at signalized intersections is safety critical, as vehicles approaching a yellow signal must decide whether to stop or proceed within limited time and distance margins.
arXiv AI
Jun 30
DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs
arXiv:2602. 23135v2 Announce Type: replace-cross Abstract: Edge classification on directed dynamic graphs requires modeling interactions between source and destination nodes exhibiting asymmetrical behavioral patterns and temporal dynamics.
arXiv AI
Jun 30
Characterizing Large Language Model Agentic Workflows: A Study on N8n Ecosystem
arXiv:2606. 29116v1 Announce Type: new Abstract: Large Language Models (LLMs) are rapidly being adopted in low-code and no-code automation platforms, where non-expert users design workflows that combine natural language understanding with external services and APIs.