arXiv:2510.21814v2 Announce Type: replace-cross Abstract: Free-form gesture understanding is highly appealing for human-computer interaction, as it liberates users from the constraints of predefined gesture categories. However, the sole existing solution GestureGPT suffers from limited recognition accuracy and slow response times. In this paper, we propose Gestura, an end-to-end system for free-form gesture understanding. Gestura harnesses […]
Deep Koopman Economic Model Predictive Control of a Pasteurisation Unit
arXiv:2511.04437v1 Announce Type: cross Abstract: This paper presents a deep Koopman-based Economic Model Predictive Control (EMPC) for efficient operation of a laboratory-scale pasteurization unit (PU). The method uses Koopman operator theory to transform the complex, nonlinear system dynamics into a linear representation, enabling the application of convex optimization while representing the complex PU accurately. The […]
OUNLP at TSAR 2025 Shared Task: Multi-Round Text Simplifier via Code Generation
arXiv:2511.04495v1 Announce Type: cross Abstract: This paper describes the OUNLP system submitted to the TSAR-2025 Shared Task (Alva-Manchego et al., 2025), designed for readability-controlled text simplification using LLM-prompting-based generation. Based on the analysis of prompt-based text simplification methods, we discovered an interesting finding that text simplification performance is highly related to the gap between the […]
Addressing divergent representations from causal interventions on neural networks
arXiv:2511.04638v1 Announce Type: cross Abstract: A common approach to mechanistic interpretability is to causally manipulate model representations via targeted interventions in order to understand what those representations encode. Here we ask whether such interventions create out-of-distribution (divergent) representations, and whether this raises concerns about how faithful their resulting explanations are to the target model in […]
Style2Code: A Style-Controllable Code Generation Framework with Dual-Modal Contrastive Representation Learning
arXiv:2505.19442v3 Announce Type: replace Abstract: Controllable code generation, the ability to synthesize code that follows a specified style while maintaining functionality, remains a challenging task. We propose a two-stage training framework combining contrastive learning and conditional decoding to enable flexible style control. The first stage aligns code style representations with semantic and structural features. In […]
Deep Graph Learning for Industrial Carbon Emission Analysis and Policy Impact
arXiv:2507.02912v3 Announce Type: replace-cross Abstract: Industrial carbon emissions are a major driver of climate change, yet modeling these emissions is challenging due to multicollinearity among factors and complex interdependencies across sectors and time. We propose a novel graph-based deep learning framework DGL to analyze and forecast industrial CO_2 emissions, addressing high feature correlation and capturing […]
A Unified Kernel for Neural Network Learning
arXiv:2403.17467v2 Announce Type: replace-cross Abstract: Past decades have witnessed a great interest in the distinction and connection between neural network learning and kernel learning. Recent advancements have made theoretical progress in connecting infinite-wide neural networks and Gaussian processes. Two predominant approaches have emerged: the Neural Network Gaussian Process (NNGP) and the Neural Tangent Kernel (NTK). […]
Transferable & Stealthy Ensemble Attacks: A Black-Box Jailbreaking Framework for Large Language Models
arXiv:2410.23558v3 Announce Type: replace-cross Abstract: We present a novel black-box jailbreaking framework that integrates multiple LLM-as-Attacker strategies to deliver highly transferable and effective attacks. The framework is grounded in three key insights from prior jailbreaking research and practice: ensemble approaches outperform single methods in exposing aligned LLM vulnerabilities, malicious instructions vary in jailbreaking difficulty requiring […]
Efficient Model Development through Fine-tuning Transfer
arXiv:2503.20110v2 Announce Type: replace-cross Abstract: Modern LLMs struggle with efficient updates, as each new pretrained model version requires repeating expensive alignment processes. This challenge also applies to domain- or languagespecific models, where fine-tuning on specialized data must be redone for every new base model release. In this paper, we explore the transfer of fine-tuning updates […]
Autocomp: A Powerful and Portable Code Optimizer for Tensor Accelerators
arXiv:2505.18574v5 Announce Type: replace-cross Abstract: Hardware accelerators, especially those designed for tensor processing, have become ubiquitous in today’s computing landscape. However, even with significant efforts in building compilers, programming these tensor accelerators remains challenging, leaving much of their potential underutilized. Recently, large language models (LLMs), trained on large amounts of code, have shown significant promise […]