arXiv:2607.10430v1 Announce Type: cross Abstract: Dimensionality reduction has proven powerful for identifying neural manifolds, which are low-dimensional structures underlying high-dimensional neural activity. These low-dimensional representations have…
arXiv:2607.10548v1 Announce Type: new Abstract: Pseudo-labeling based on Optimal Transport (OT) has become an effective mechanism for enhancing short text clustering. Existing OT methods are short…
arXiv:2607.09678v1 Announce Type: new Abstract: When LLM agents hand off information to one another, does the message format matter? Two literatures disagree: format-optimization work reports that…
arXiv:2601.06861v2 Announce Type: replace Abstract: Background: Large language models (LLMs) harbor systematic biases that are particularly consequential in workplace and HR contexts, where their outputs increasingly…
arXiv:2607.10198v1 Announce Type: new Abstract: Search APIs are the fundamental retrieval layer for many agents and are often their most frequently used tool. Traditional search APIs…
arXiv:2607.09759v1 Announce Type: new Abstract: Building assistants that can continually watch the world, remember what they see, and reason over their accumulated experience is a long-standing…
arXiv:2602.00620v2 Announce Type: replace Abstract: The zero-shot evaluation of time series foundation models (TSFMs) for classification typically uses a frozen encoder followed by a task-specific classifier.…
arXiv:2607.09695v1 Announce Type: new Abstract: This paper addresses the challenging problem of dynamic feature drift in federated learning, where data distributions evolve across clients and over…
arXiv:2607.09889v1 Announce Type: cross Abstract: Fixed-state sequence models compress an unbounded past into a bounded state, which caps their associative recall at roughly the state dimension;…
arXiv:2607.10540v1 Announce Type: new Abstract: We propose a two-stage estimator for structural mediation parameters that combines deep representation learning with G-estimation under the "no essential heterogeneity"…
arXiv:2607.10963v1 Announce Type: cross Abstract: We study the problem of efficient online proportional sampling from a high-dimensional domain under a $sigma$-smoothed adversary, where the sampling distribution…
arXiv:2604.06205v2 Announce Type: replace Abstract: The growth of online platforms and user content requires strong content moderation systems that can handle complex inputs from various media…
arXiv:2606.06065v4 Announce Type: replace Abstract: Second-language (L2) speech recognition often requires transcriptions of pronunciations and intended meanings. Multi-task learning (MTL) is a natural approach because it…
arXiv:2607.09757v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become a cornerstone of parameter-efficient fine-tuning (PEFT); however, the conventional practice of uniform rank assignment ignores the…
arXiv:2606.01374v3 Announce Type: replace Abstract: Observable performance is commonly used to characterize biological systems, yet aggregated outputs may remain insufficient for uniquely resolving observational conditions, and…
arXiv:2509.24653v2 Announce Type: replace Abstract: Large Language Models (LLMs) excel at multi-hop reasoning in distribution, yet fail on unseen compositions, a phenomenon known as the curse…
arXiv:2607.11289v1 Announce Type: new Abstract: Backpropagation is the computational engine of deep learning, yet its mathematical structure is typically treated as a procedural traversal of computational…
arXiv:2607.10456v1 Announce Type: new Abstract: Expert background knowledge is often available in practical applications of causal discovery. Such constraints on the true causal graph can help…
arXiv:2607.09713v1 Announce Type: new Abstract: A key step toward autonomous industrial operation is the ability to create and reconfigure control policies from natural-language requirement specifications, with…
arXiv:2607.10020v1 Announce Type: new Abstract: We present FindMyText, an open-source Python package designed to efficiently assess whether a given text appears, in part or in full,…
arXiv:2607.10235v1 Announce Type: new Abstract: Previous work in group recommender systems has demonstrated a sensitivity to the distribution of preferences within a group. Specifically, the selection…
arXiv:2602.18757v3 Announce Type: replace Abstract: Human driving behavior is inherently diverse, yet most end-to-end autonomous driving (E2E-AD) systems learn a single average driving style, neglecting individual…
arXiv:2607.11388v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) and vision-language models (VLMs) have enabled increasingly capable digital agents for computer use. However,…
arXiv:2607.09696v1 Announce Type: new Abstract: Deep neural models for click-through rate prediction often exhibit a sharp decline in validation performance immediately after the first training epoch…