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arXiv:2509.17265v2 Announce Type: replace Abstract: Recommender systems have been shown to exhibit popularity bias by over-recommending popular items and under-recommending relevant niche items. We seek...
arXiv:2509.17429v4 Announce Type: replace Abstract: Accurate temporal prediction is the bridge between comprehensive scene understanding and embodied artificial intelligence. However, predicting multiple fine-grained states of...
arXiv:2509.17466v3 Announce Type: replace Abstract: Journaling can potentially serve as an effective method for autistic adolescents to improve narrative skills. However, its text-centric nature and...
arXiv:2509.17743v4 Announce Type: replace Abstract: Large language models (LLMs) have shown promise in generating program workflows for visual tasks. However, previous approaches often rely on...
arXiv:2509.18052v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed to simulate human collective behaviors, yet the methodological rigor of these "AI societies"...
arXiv:2509.18135v2 Announce Type: replace Abstract: Accurate multivariate time series forecasting hinges on inter-series correlations, which often evolve in complex ways across different temporal scales. Existing...
arXiv:2509.18403v3 Announce Type: replace Abstract: Wikipedia serves as a key infrastructure for public access to scientific knowledge, but it faces challenges in maintaining the credibility...
arXiv:2509.18413v2 Announce Type: replace Abstract: Voice anonymization aims to conceal speaker identity and attributes while preserving intelligibility, but current evaluations rely almost exclusively on Equal...
arXiv:2509.19163v2 Announce Type: replace Abstract: AI "slop" is an increasingly popular term used to describe low-quality AI-generated text, but there is currently no agreed upon...
arXiv:2509.19391v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) is widely used to efficiently adapt Transformers by adding trainable low-rank matrices to attention projections. While effective,...
arXiv:2509.20529v3 Announce Type: replace Abstract: Model discovery aims to uncover governing differential equations of dynamical systems directly from experimental data. Benchmarking such methods is essential...
arXiv:2509.20677v2 Announce Type: replace Abstract: In-context learning (ICL) is a pivotal capability for the practical deployment of large-scale language models, yet its stability heavily depends...
arXiv:2509.20787v4 Announce Type: replace Abstract: Driven by the simple and effective Dense O2O, DEIM demonstrates faster convergence and enhanced performance. In this work, we extend...
arXiv:2509.20808v2 Announce Type: replace Abstract: Hardware Fuzzing emerged as one of the crucial techniques for finding security flaws in modern hardware designs by testing a...
arXiv:2509.21155v3 Announce Type: replace Abstract: For an LLM to correctly respond to an instruction it must understand both the semantics and the domain (i.e., subject...
arXiv:2509.21981v2 Announce Type: replace Abstract: Effective real-world multi-agent collaboration requires not only accurate planning but also the ability to reason about collaborators' intents--a crucial capability...
arXiv:2509.22500v2 Announce Type: replace Abstract: Constrained optimization is a powerful framework for enforcing requirements on neural networks. These constrained deep learning problems are typically solved...
arXiv:2509.23516v4 Announce Type: replace Abstract: Delay-coupled systems often require low-latency decisions from sparse telemetry, where dense fixed-step neural inference is wasteful and can degrade near...
arXiv:2509.23525v2 Announce Type: replace Abstract: AI creates and exacerbates privacy risks, yet practitioners lack effective resources to identify and mitigate these risks. We present Privy,...
arXiv:2509.24386v2 Announce Type: replace Abstract: We have recently observed the commercial roll-out of robotaxis in various countries. They are deployed within an operational design domain...
arXiv:2509.24765v5 Announce Type: replace Abstract: Logical reasoning is a fundamental capability of large language models. However, existing studies often overlook the interaction between logical complexity...
arXiv:2509.25041v3 Announce Type: replace Abstract: Sparse Mixture of Experts (SMoE) performs conditional computation by selectively activating a subset of experts, thereby enabling scalable parameter growth...
arXiv:2509.25178v2 Announce Type: replace Abstract: Object hallucination in Multimodal Large Language Models (MLLMs) is a persistent failure mode that causes the model to perceive objects...
arXiv:2509.25204v2 Announce Type: replace Abstract: Entropy-based inference methods have gained traction for improving the reliability of Large Language Models (LLMs). However, many existing approaches, such...