arXiv:2603.13373v2 Announce Type: replace-cross Abstract: Computational models are increasingly embedded in human-centered domains such as healthcare, education, workplace analytics, and digital well-being, where their predictions directly influence individual outcomes and collective welfare. In such contexts, achieving high accuracy alone is insufficient; models must also act ethically and equitably across diverse populations. However, fair AI approaches […]
Role-Augmented Intent-Driven Generative Search Engine Optimization
arXiv:2508.11158v2 Announce Type: replace-cross Abstract: Generative Search Engines (GSEs), powered by Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), are reshaping information retrieval. While commercial systems (e.g., BingChat, Perplexity.ai) demonstrate impressive semantic synthesis capabilities, their black-box nature fundamentally undermines established Search Engine Optimization (SEO) practices. Content creators face a critical challenge: their optimization strategies, effective […]
Edit-As-Act: Goal-Regressive Planning for Open-Vocabulary 3D Indoor Scene Editing
arXiv:2603.17583v1 Announce Type: cross Abstract: Editing a 3D indoor scene from natural language is conceptually straightforward but technically challenging. Existing open-vocabulary systems often regenerate large portions of a scene or rely on image-space edits that disrupt spatial structure, resulting in unintended global changes or physically inconsistent layouts. These limitations stem from treating editing primarily as […]
Scalable Energy-Based Models via Adversarial Training: Unifying Discrimination and Generation
arXiv:2510.13872v4 Announce Type: replace-cross Abstract: Simultaneously achieving robust classification and high-fidelity generative modeling within a single framework presents a significant challenge. Hybrid approaches, such as Joint Energy-Based Models (JEM), interpret classifiers as EBMs but are often limited by the instability and poor sample quality inherent in training based on Stochastic Gradient Langevin Dynamics (SGLD). We […]
Multimodal Emotion Recognition via Bi-directional Cross-Attention and Temporal Modeling
arXiv:2603.11971v2 Announce Type: replace-cross Abstract: Expression recognition in in-the-wild video data remains challenging due to substantial variations in facial appearance, background conditions, audio noise, and the inherently dynamic nature of human affect. Relying on a single modality, such as facial expressions or speech, is often insufficient for capturing these complex emotional cues. To address this […]
APEX-SWE
arXiv:2601.08806v2 Announce Type: replace-cross Abstract: We introduce the AI Productivity Index for Software Engineering (APEX-SWE), a benchmark for assessing whether frontier AI models can execute economically valuable software engineering work. Unlike existing evaluations that focus on narrow, well-defined tasks, APEX-SWE assesses two novel task types that reflect real-world software engineering: (1) Integration tasks (n=100), which […]
Identifying Latent Actions and Dynamics from Offline Data via Demonstrator Diversity
arXiv:2603.17577v1 Announce Type: cross Abstract: Can latent actions and environment dynamics be recovered from offline trajectories when actions are never observed? We study this question in a setting where trajectories are action-free but tagged with demonstrator identity. We assume that each demonstrator follows a distinct policy, while the environment dynamics are shared across demonstrators and […]
CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions
arXiv:2510.14959v5 Announce Type: replace-cross Abstract: Reinforcement learning (RL), while powerful and expressive, can often prioritize performance at the expense of safety. Yet safety violations can lead to catastrophic outcomes in real-world deployments. Control Barrier Functions (CBFs) offer a principled method to enforce dynamic safety — traditionally deployed online via safety filters. While the result is […]
LUMINA: LLM-Guided GPU Architecture Exploration via Bottleneck Analysis
arXiv:2603.05904v2 Announce Type: replace-cross Abstract: GPU design space exploration (DSE) for modern AI workloads, such as Large-Language Model (LLM) inference, is challenging because of GPUs’ vast, multi-modal design spaces, high simulation costs, and complex design optimization objectives (e.g. performance, power and area trade-offs). Existing automated DSE methods are often prohibitively expensive, either requiring an excessive […]
GIFT: Reconciling Post-Training Objectives via Finite-Temperature Gibbs Initialization
arXiv:2601.09233v2 Announce Type: replace-cross Abstract: The prevailing post-training paradigm for Large Reasoning Models (LRMs) – Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) – suffers from an intrinsic optimization mismatch: the rigid supervision inherent in SFT induces distributional collapse, thereby exhausting the exploration space necessary for subsequent RL. In this paper, we reformulate SFT to […]
Unsupervised Symbolic Anomaly Detection
arXiv:2603.17575v1 Announce Type: cross Abstract: We propose SYRAN, an unsupervised anomaly detection method based on symbolic regression. Instead of encoding normal patterns in an opaque, high-dimensional model, our method learns an ensemble of human-readable equations that describe symbolic invariants: functions that are approximately constant on normal data. Deviations from these invariants yield anomaly scores, so […]
JAWS: Enhancing Long-term Rollout of Neural PDE Solvers via Spatially-Adaptive Jacobian Regularization
arXiv:2603.05538v2 Announce Type: replace-cross Abstract: Data-driven surrogate models can significantly accelerate the simulation of continuous dynamical systems, yet the step-wise accumulation of errors during autoregressive time-stepping often leads to spectral blow-up and unphysical divergence. Existing global regularization techniques can enforce contractive dynamics but uniformly damp high-frequency features, causing over-smoothing; meanwhile, long-horizon trajectory optimization methods are […]