arXiv:2603.13894v1 Announce Type: cross Abstract: Training deep neural networks with noisy labels remains a significant challenge, often leading to degraded performance. Existing methods for handling label noise typically rely on either transition matrix, noise detection, or meta-learning techniques, but they often exhibit low utilization efficiency of noisy samples and incur high computational costs. In this […]
Learning When to Trust in Contextual Bandits
arXiv:2603.13356v1 Announce Type: new Abstract: Standard approaches to Robust Reinforcement Learning assume that feedback sources are either globally trustworthy or globally adversarial. In this paper, we challenge this assumption and we identify a more subtle failure mode. We term this mode as Contextual Sycophancy, where evaluators are truthful in benign contexts but strategically biased in […]
EchoLVFM: One-Step Video Generation via Latent Flow Matching for Echocardiogram Synthesis
arXiv:2603.13967v1 Announce Type: cross Abstract: Echocardiography is widely used for assessing cardiac function, where clinically meaningful parameters such as left-ventricular ejection fraction (EF) play a central role in diagnosis and management. Generative models capable of synthesising realistic echocardiogram videos with explicit control over such parameters are valuable for data augmentation, counterfactual analysis, and specialist training. […]
Diverse Text-to-Image Generation via Contrastive Noise Optimization
arXiv:2510.03813v3 Announce Type: replace-cross Abstract: Text-to-image (T2I) diffusion models have demonstrated impressive performance in generating high-fidelity images, largely enabled by text-guided inference. However, this advantage often comes with a critical drawback: limited diversity, as outputs tend to collapse into similar modes under strong text guidance. Existing approaches typically optimize intermediate latents or text conditions during […]
NepTam: A Nepali-Tamang Parallel Corpus and Baseline Machine Translation Experiments
arXiv:2603.14053v1 Announce Type: cross Abstract: Modern Translation Systems heavily rely on high-quality, large parallel datasets for state-of-the-art performance. However, such resources are largely unavailable for most of the South Asian languages. Among them, Nepali and Tamang fall into such category, with Tamang being among the least digitally resourced languages in the region. This work addresses […]
From Refusal Tokens to Refusal Control: Discovering and Steering Category-Specific Refusal Directions
arXiv:2603.13359v1 Announce Type: new Abstract: Language models are commonly fine-tuned for safety alignment to refuse harmful prompts. One approach fine-tunes them to generate categorical refusal tokens that distinguish different refusal types before responding. In this work, we leverage a version of Llama 3 8B fine-tuned with these categorical refusal tokens to enable inference-time control over […]
Diffusion Reinforcement Learning via Centered Reward Distillation
arXiv:2603.14128v1 Announce Type: cross Abstract: Diffusion and flow models achieve State-Of-The-Art (SOTA) generative performance, yet many practically important behaviors such as fine-grained prompt fidelity, compositional correctness, and text rendering are weakly specified by score or flow matching pretraining objectives. Reinforcement Learning (RL) fine-tuning with external, black-box rewards is a natural remedy, but diffusion RL is […]
Rough Sets for Explainability of Spectral Graph Clustering
arXiv:2512.12436v3 Announce Type: replace-cross Abstract: Graph Spectral Clustering methods (GSC) allow representing clusters of diverse shapes, densities, etc. However, the results of such algorithms, when applied e.g. to text documents, are hard to explain to the user, especially due to embedding in the spectral space which has no obvious relation to document contents. Furthermore, the […]
Walking Further: Semantic-aware Multimodal Gait Recognition Under Long-Range Conditions
arXiv:2603.14189v1 Announce Type: cross Abstract: Gait recognition is an emerging biometric technology that enables non-intrusive and hard-to-spoof human identification. However, most existing methods are confined to short-range, unimodal settings and fail to generalize to long-range and cross-distance scenarios under real-world conditions. To address this gap, we present textbfLRGait, the first LiDAR-Camera multimodal benchmark designed for […]
The ARC of Progress towards AGI: A Living Survey of Abstraction and Reasoning
arXiv:2603.13372v1 Announce Type: new Abstract: The Abstraction and Reasoning Corpus (ARC-AGI) has become a key benchmark for fluid intelligence in AI. This survey presents the first cross-generation analysis of 82 approaches across three benchmark versions and the ARC Prize 2024-2025 competitions. Our central finding is that performance degradation across versions is consistent across all paradigms: […]
Early stages of collective cell invasion: Biomechanics
arXiv:2602.11813v2 Announce Type: replace-cross Abstract: The early stages of the collective invasion may occur by single mesenchymal cells or hybrid epithelial-mesenchymal cell groups that detach from cancerous tissue. Tumors may also emit invading protrusions of epithelial cells, which could be led (or not) by a basal cell. Here we devise a novel fractional step cellular […]
AEX: Non-Intrusive Multi-Hop Attestation and Provenance for LLM APIs
arXiv:2603.14283v1 Announce Type: cross Abstract: Hosted large language models are increasingly accessed through remote APIs, but the API boundary still offers little direct evidence that a returned output actually corresponds to the client-visible request. Recent audits of shadow APIs show that unofficial or intermediary endpoints can diverge from claimed behavior, while existing approaches such as […]