arXiv:2606.02609v1 Announce Type: new Abstract: Activation Oracles (AOs) are promising methods for interpreting residual stream activations. However, current AOs face important issues, such as hallucinations and vagueness. Additionally, text-inversion confounds make them hard to evaluate. To this end, we improve…
arXiv:2606.02607v1 Announce Type: new Abstract: Tabular synthesis is critical for privacy-preserving sharing and augmentation, yet diffusion models rely on implicit mechanisms to capture inter-column relationships. We introduce Geometry-Aware Tabular Diffusion (GATD), which augments tabular diffusion denoisers with pairwise angles and…
arXiv:2606.02609v1 Announce Type: new Abstract: Activation Oracles (AOs) are promising methods for interpreting residual stream activations. However, current AOs face important issues, such as hallucinations and vagueness. Additionally, text-inversion confounds make them hard to evaluate. To this end, we improve…
arXiv:2606.02605v1 Announce Type: new Abstract: Coronary artery stenosis is a common cardiovascular disease, with severe, untreated cases posing significant risks of heart attack. Although coronary (X-ray) angiograms remain the standard for stenosis diagnosis, they are invasive, time- and resource-intensive, and…
arXiv:2606.03736v1 Announce Type: cross Abstract: Resource-constrained pricing controllers can make fixed-price inference impossible: the controller’s resource state may remove the target price neighborhood from the feasible set, even when every realized action has a known positive density. We formalize this…
arXiv:2606.01111v2 Announce Type: replace Abstract: Modern industrial recommender systems rely on thousands of heterogeneous features — ranging from low-dimensional scalars (e.g., statistical value) to high-dimensional embeddings (e.g., user-id embeddings, MLP representations) — to achieve high-precision predictions. Given the immense computational…
arXiv:2509.26169v2 Announce Type: replace Abstract: Alignment of large language models remains a central challenge in natural language processing. Preference optimization has emerged as a popular and effective method for improving alignment, typically through training-time or prompt-based interventions. In this paper,…
arXiv:2606.01111v2 Announce Type: replace Abstract: Modern industrial recommender systems rely on thousands of heterogeneous features — ranging from low-dimensional scalars (e.g., statistical value) to high-dimensional embeddings (e.g., user-id embeddings, MLP representations) — to achieve high-precision predictions. Given the immense computational…
arXiv:2606.02606v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed as continuously evolving services, where frequent base-model updates may invalidate previously deployed task-specific Low-Rank Adaptation (LoRA) adapters. For service providers managing numerous downstream model services, retraining each LoRA…
arXiv:2507.21638v2 Announce Type: replace-cross Abstract: The development of reinforcement learning (RL) algorithms has been largely driven by ambitious challenge tasks and benchmarks. Games have dominated RL benchmarks because they present relevant challenges, are inexpensive to run and easy to understand.…
