arXiv Machine Learning
Jun 16
Repeated Bilateral Trade: The Quest for Fairness
arXiv:2606. 15369v1 Announce Type: new Abstract: We study repeated bilateral trade from a fairness perspective.
AI safety and alignment
Alignment, interpretability, red-teaming, bias and privacy: the research on what these systems do when they misbehave.
arXiv AI
Jun 16
Beyond Scalars: Evaluating and Understanding LLM Reasoning via Geometric Progress and Stability
arXiv:2603. 10384v3 Announce Type: replace Abstract: Evaluating LLM reliability via scalar probabilities often fails to capture the structural dynamics of reasoning.
arXiv AI
Jun 16
DifFRACT: Diffusion Feature Reconstruction and Attribution for Circuit Tracing
arXiv:2606. 15796v1 Announce Type: cross Abstract: Mechanistic interpretability seeks to explain neural network behavior by decomposing model computations into interpretable features and circuits.
arXiv AI
Jun 16
Dual-Granularity Orthogonal Disentanglement for Generalizable Audio Deepfake Detection
arXiv:2606. 16532v1 Announce Type: cross Abstract: Audio deepfake detectors often fail to generalize across speakers, as they learn speaker-identity features rather than synthesis artifacts, known as implicit identity leakage.
arXiv AI
Jun 16
Optimizing LLM Inference: Fluid-Guided Online Scheduling with Memory Constraints
arXiv:2504. 11320v4 Announce Type: replace-cross Abstract: Large language models now serve millions of users daily, with providers incurring costs exceeding $700,000 per day.
arXiv Machine Learning
Jun 16
StagePilot: Stage-Level Planning for Long-Horizon Dialogue Simulation in Cybergrooming
arXiv:2602. 05060v2 Announce Type: replace Abstract: Cybergrooming is an evolving threat to youth, requiring proactive educational interventions.
arXiv AI
Jun 16
Are Neuro-Inspired Multi-Modal Vision-Language Models Resilient to Membership Inference Privacy Leakage?
arXiv:2511. 20710v2 Announce Type: replace-cross Abstract: In the age of agentic AI, the growing deployment of multi-modal models (MMs) has introduced new attack vectors that can leak sensitive training data in MMs, causing privacy leakage.
arXiv Machine Learning
Jun 16
Not all Jensen-Shannon Divergence Estimators are Equal
arXiv:2606. 16411v1 Announce Type: new Abstract: The Jensen-Shannon divergence is widely reported as a scalar measure of fidelity for synthetic tabular data.
arXiv Machine Learning
Jun 16
Enhancing LLM Safety Through a Theoretical Minimax Game Lens
arXiv:2502. 05163v2 Announce Type: replace-cross Abstract: The rapid advancement of large language models (LLMs) necessitates effective mechanisms to ensure their responsible deployment by accurately distinguishing unsafe content from benign content.
arXiv Machine Learning
Jun 16
Towards Functional Correctness of Large Code Models with Selective Generation
arXiv:2505. 13553v3 Announce Type: replace-cross Abstract: The hallucination of code generation models hinders their applicability to systems requiring higher safety standards.
arXiv AI
Jun 16
Surprise-Guided MergeSort: Budget-Efficient Human-in-the-Loop Ranking via Adaptive Comparison Scheduling
arXiv:2606. 15623v1 Announce Type: cross Abstract: Pairwise comparison is the gold standard for subjective ranking tasks; however, exhaustive annotation requires a massive number of human comparisons ($O(n^2)$).
arXiv AI
Jun 16
Ranking Abuse via Strategic Pairwise Data Perturbations
arXiv:2604. 17805v2 Announce Type: replace-cross Abstract: Pairwise ranking systems based on Maximum Likelihood Estimation (MLE), such as the Bradley-Terry model, are widely used to aggregate preferences from pairwise comparisons.
arXiv Machine Learning
Jun 16
Assessing Predictive Models for Fairness Based on Movement Patterns
arXiv:2605. 23234v3 Announce Type: replace Abstract: Assessing the spatial fairness of predictive models involves establishing whether they are statistically penalizing (favoring) individuals associated with certain geographical locations.
arXiv Machine Learning
Jun 16
Spatial Priors via Space Filling Curves for Small and Limited Data Vision Transformers
arXiv:2606. 14757v1 Announce Type: cross Abstract: Though Vision Transformers (ViTs) have become the dominant backbone in many computer vision tasks, due to permutation equivariance, their attention mechanism lacks explicit spatial inductive biases.
arXiv Machine Learning
Jun 16
From Tokens to Policy: Causal and Interpretable Heterogeneous Treatment Effects Identification
arXiv:2606. 17010v1 Announce Type: new Abstract: Heterogeneous Treatment Effect (HTE) identification is crucial to explain the impact of an intervention and optimize our policies accordingly.
arXiv AI
Jun 16
Skill-to-LoRA: From Using Skills to Learning Behaviors for Token-Efficient LLM Agents
arXiv:2606. 16769v1 Announce Type: new Abstract: Agent skills are commonly distributed as SKILL.
arXiv Machine Learning
Jun 16
Learning Hybrid Biophysical Neuron Models with Neural ODEs
arXiv:2606. 16693v1 Announce Type: cross Abstract: Biophysical neuron models link measurements of neural activity to underlying cellular mechanisms.
arXiv Machine Learning
Jun 16
Phase Transition in Convex Relaxations for Graph Alignment
arXiv:2606. 15581v1 Announce Type: cross Abstract: We study the graph alignment problem for correlated Gaussian Orthogonal Ensemble (GOE) matrices, where the goal is to recover a hidden vertex permutation given two correlated symmetric Gaussian matrices $(A, B)$ with correlation $1/\sqrt{1+\sigma^2}$.
arXiv Machine Learning
Jun 16
HorusEye: Language as Dynamic Attention for Emergency Visual Analysis
arXiv:2606. 14741v1 Announce Type: cross Abstract: We introduce HorusEye, Language as Dynamic Attention for Emergency Visual Analysis.
arXiv AI
Jun 16
Do You Really Need a GPU to Guard Your LLM? CPU-Class Classifiers and Multi-Stage Pipelines for Safety Enforcement at Scale
arXiv:2512. 19011v3 Announce Type: replace-cross Abstract: Safety classifiers that screen LLM inputs for jailbreak attempts have become standard deployment components, yet almost all production systems rely on GPU-based models: fine-tuned transformers and LLM-as-a-judge pipelines.