QuITE: Query-Based Irregular Time Series Embedding
arXiv:2605.28166v2 Announce Type: replace-cross Abstract: Irregular Multivariate Time Series (IMTS) are common in practice, yet their irregular sampling complicates effective…
On Language Generation in the Limit with Bounded Memory
arXiv:2605.30324v1 Announce Type: cross Abstract: We study language generation in the limit under bounded memory. In this task, a learner…
PARCEL: Pool-Anchored Resampling with Conditioned Elastic Queries for Efficient Vision-Language Understanding
arXiv:2605.30126v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) map visual inputs into dense token sequences, imposing a quadratic computational…
CB-SLICE: Concept-Based Interpretable Error Slice Discovery
arXiv:2605.29836v1 Announce Type: cross Abstract: Despite strong average-case performance, deep learning models often exhibit systematic errors on specific population groups,…
HARP: Hadamard-Preconditioned Adaptive Rotation Processor for Extreme LLM Quantization
arXiv:2605.29843v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is essential for deploying LLMs under memory and bandwidth constraints. However, extreme…
The Impact of Semantic Pairs on Self-Supervised Representation Learning
arXiv:2510.08722v3 Announce Type: replace-cross Abstract: Instance discrimination learns visual representations by treating different augmented views of the same image as…
From donor lungs to digital twins
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Learning to Assign Prediction Tasks to Agents with Capacity Constraints
arXiv:2605.27999v1 Announce Type: cross Abstract: We address the problem of learning to assign prediction tasks to one agent from a…
Identifying and Understanding Human Values in Text: A Tailorable LLM-based Architecture
arXiv:2605.27373v1 Announce Type: new Abstract: As intelligent systems become more autonomous, the scientific community focuses on creating decision-making mechanisms that…
LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding
arXiv:2605.27365v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) commonly formulate visual grounding and detection as a coordinate-token generation problem, serializing…