arXiv:2509.12626v2 Announce Type: replace-cross Abstract: Agentic workflows promise efficiency, but adoption hinges on whether people can align systems that act on their behalf with their goals, values, and situational expectations. We present DoubleAgents, an agentic planning tool that embeds transparency and control through user intervention, value-reflecting policies, rich state visualizations, and uncertainty flagging for human […]
Quantification of dual-state 5-ALA-induced PpIX fluorescence: Methodology and validation in tissue-mimicking phantoms
arXiv:2510.18387v2 Announce Type: replace-cross Abstract: Quantification of protoporphyrin IX (PpIX) fluorescence in human brain tumours has the potential to significantly improve patient outcomes in neuro-oncology, but represents a formidable imaging challenge. Protoporphyrin is a biological molecule which interacts with the tissue micro-environment to form two photochemical states in glioma. Each exhibits markedly different quantum efficiencies, […]
Dynamic Content Moderation in Livestreams: Combining Supervised Classification with MLLM-Boosted Similarity Matching
arXiv:2512.03553v2 Announce Type: replace-cross Abstract: Content moderation remains a critical yet challenging task for large-scale user-generated video platforms, especially in livestreaming environments where moderation must be timely, multimodal, and robust to evolving forms of unwanted content. We present a hybrid moderation framework deployed at production scale that combines supervised classification for known violations with reference-based […]
Demystifying the Slash Pattern in Attention: The Role of RoPE
arXiv:2601.08297v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) often exhibit slash attention patterns, where attention scores concentrate along the $Delta$-th sub-diagonal for some offset $Delta$. These patterns play a key role in passing information across tokens. But why do they emerge? In this paper, we demystify the emergence of these Slash-Dominant Heads (SDHs) from […]
Tri-Reader: An Open-Access, Multi-Stage AI Pipeline for First-Pass Lung Nodule Annotation in Screening CT
arXiv:2601.19380v2 Announce Type: replace-cross Abstract: Using multiple open-access models trained on public datasets, we developed Tri-Reader, a comprehensive, freely available pipeline that integrates lung segmentation, nodule detection, and malignancy classification into a unified tri-stage workflow. The pipeline is designed to prioritize sensitivity while reducing the candidate burden for annotators. To ensure accuracy and generalizability across […]
FAIRT2V: Training-Free Debiasing for Text-to-Video Diffusion Models
arXiv:2601.20791v1 Announce Type: cross Abstract: Text-to-video (T2V) diffusion models have achieved rapid progress, yet their demographic biases, particularly gender bias, remain largely unexplored. We present FairT2V, a training-free debiasing framework for text-to-video generation that mitigates encoder-induced bias without finetuning. We first analyze demographic bias in T2V models and show that it primarily originates from pretrained […]
FourierCSP: Differentiable Constraint Satisfaction Problem Solving by Walsh-Fourier Expansion
arXiv:2510.04480v2 Announce Type: replace Abstract: The Constraint-satisfaction problem (CSP) is fundamental in mathematics, physics, and theoretical computer science. Continuous local search (CLS) solvers, as recent advancements, can achieve highly competitive results on certain classes of Boolean satisfiability (SAT) problems. Motivated by these advances, we extend the CLS framework from Boolean SAT to general CSP with […]
Recursive Language Models
arXiv:2512.24601v2 Announce Type: replace Abstract: We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Recursive Language Models (RLMs), a general inference paradigm that treats long prompts as part of an external environment and allows the LLM to programmatically examine, decompose, and recursively call itself […]
Membership Privacy Risks of Sharpness Aware Minimization
arXiv:2310.00488v4 Announce Type: replace-cross Abstract: Optimization algorithms that seek flatter minima, such as Sharpness-Aware Minimization (SAM), are credited with improved generalization and robustness to noise. We ask whether such gains impact membership privacy. Surprisingly, we find that SAM is more prone to Membership Inference Attacks (MIA) than classical SGD across multiple datasets and attack methods, […]
Mitigating Sensitive Information Leakage in LLMs4Code through Machine Unlearning
arXiv:2502.05739v2 Announce Type: replace-cross Abstract: Large Language Models for Code (LLMs4Code) have achieved strong performance in code generation, but recent studies reveal that they may memorize and leak sensitive information contained in training data, posing serious privacy risks. To address this gap, this work presents the first comprehensive empirical study on applying machine unlearning to […]
Physics-Guided Multimodal Transformers are the Necessary Foundation for the Next Generation of Meteorological Science
arXiv:2504.14174v2 Announce Type: replace-cross Abstract: This position paper argues that the next generation of artificial intelligence in meteorological and climate sciences must transition from fragmented hybrid heuristics toward a unified paradigm of physics-guided multimodal transformers. While purely data-driven models have achieved significant gains in predictive accuracy, they often treat atmospheric processes as mere visual patterns, […]
LTS-VoiceAgent: A Listen-Think-Speak Framework for Efficient Streaming Voice Interaction via Semantic Triggering and Incremental Reasoning
arXiv:2601.19952v1 Announce Type: cross Abstract: Real-time voice agents face a dilemma: end-to-end models often lack deep reasoning, while cascaded pipelines incur high latency by executing ASR, LLM reasoning, and TTS strictly in sequence, unlike human conversation where listeners often start thinking before the speaker finishes. Since cascaded architectures remain the dominant choice for complex tasks, […]