arXiv:2603.23523v1 Announce Type: new
Abstract: Recent 3D Large-Language Models (3D-LLMs) claim to understand 3D worlds, especially spatial relationships among objects. Yet, we find that simply fine-tuning a language model on text-only question-answer…
arXiv:2510.02392v3 Announce Type: replace
Abstract: Knowledge editing and machine unlearning are two popular approaches for large language models (LLMs) to stay up-to-date. However, the knowledge updating mechanism of LLMs remains largely unexplored d…
arXiv:2603.23508v1 Announce Type: new
Abstract: Retrieval-augmented generation (RAG) is increasingly deployed in enterprise search and document-centric assistants, where responses must be grounded in long and complex source materials. In practice, ver…
arXiv:2603.24535v1 Announce Type: new
Abstract: Adaptive scaffolding enhances learning, yet the field lacks robust methods for measuring it within authentic tutoring dialogue. This gap has become more pressing with the rise of remote human tutoring an…
arXiv:2603.23064v2 Announce Type: replace-cross
Abstract: We identify a critical security vulnerability in mainstream Claw personal AI agents: untrusted content encountered during heartbeat-driven background execution can silently pollute agent memory…
arXiv:2603.22492v2 Announce Type: replace-cross
Abstract: Inference-time scaling has emerged as an effective way to improve generative models at test time by using a verifier to score and select candidate outputs. A common choice is to employ Multimod…
arXiv:2603.24284v1 Announce Type: cross
Abstract: When multiple LLM-based code agents independently implement parts of the same class, they must agree on shared internal representations, even when the specification leaves those choices implicit. We st…
arXiv:2603.24283v1 Announce Type: cross
Abstract: Despite the advancements in cutting-edge technologies, audio signal processing continues to pose challenges and lacks the precision of a human speech processing system. To address these challenges, we …
arXiv:2603.24260v1 Announce Type: cross
Abstract: Diffusion-based video editing has emerged as an important paradigm for high-quality and flexible content generation. However, despite their generality and strong modeling capacity, Diffusion Transforme…
arXiv:2603.21296v3 Announce Type: replace-cross
Abstract: Advanced Persistent Threats (APTs) are stealthy, multi-stage attacks that require adaptive and timely defense. While deep reinforcement learning (DRL) enables autonomous cyber defense, its deci…