Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv:2610.26926v1 Announce Type: new Abstract: Large language models (LLMs) possess broad factual knowledge and strong reasoning capabilities, but solving complex tasks also requires procedural know...
arXiv:2610.27178v1 Announce Type: new Abstract: Platforms that host young users screen messages with commercial text-safety APIs, but independent evidence on how often such systems miss harm or flag...
arXiv:2610.27273v1 Announce Type: cross Abstract: Multi-vector retrieval models such as ColBERT often outperform single-vector models of comparable scale by matching queries and documents at the toke...
arXiv:2610.28067v1 Announce Type: cross Abstract: LLMs increasingly operate over mixed-sensitivity contexts, where they must reason over information ranging from public to confidential while disclosi...
arXiv:2610.28381v1 Announce Type: cross Abstract: Reasoning about safety is essential for embodied agents powered by large language models (LLMs). We introduce the Contextual Action and Plan Safety b...
arXiv:2512.08814v2 Announce Type: replace Abstract: Understanding human personality is important for personalized recommendation and mental health assessment. Existing personality detection methods t...
arXiv:2608.16011v2 Announce Type: replace Abstract: We introduce ReRef-3D, a benchmark for language-guided single-object placement in controlled 3D tabletop scenes. It contains 33,826 instructions ac...
arXiv:2610.16372v2 Announce Type: replace Abstract: Although users may express different personalization needs across tasks, these task-specific behaviors can reflect shared underlying preference mec...
arXiv:2410.18362v4 Announce Type: replace-cross Abstract: Web development involves turning UI designs into functional webpages, which can be difficult for both beginners and experienced developers du...
arXiv:2410.21647v5 Announce Type: replace-cross Abstract: Recently, a number of repository-level code generation benchmarks-such as CoderEval, DevEval, RepoEval, RepoBench, and LongCodeArena-have eme...
arXiv:2605.23175v2 Announce Type: replace-cross Abstract: Proprietary large language models (LLMs) face risks of intellectual property (IP) violation, as adversaries can replicate an LLM by collectin...
arXiv:2610.26922v1 Announce Type: new Abstract: Controllable object insertion provides a targeted approach to augmenting object detection datasets that require category--count fidelity, annotation al...
arXiv:2610.26925v1 Announce Type: new Abstract: With the rapid expansion of online video content, video temporal grounding has become essential for localizing moments based on user-specific queries,...
arXiv:2610.26954v1 Announce Type: new Abstract: Auxiliary learning (AL), which can improve the performance of a primary task by leveraging related auxiliary tasks, is commonly regarded as a special c...
arXiv:2610.26988v1 Announce Type: new Abstract: Histopathology is central to cancer diagnosis, but morphology in Hematoxylin and Eosin (HE)-stained images does not directly reveal the molecular state...
arXiv:2610.27104v1 Announce Type: new Abstract: Reliable language-guided perception is essential for embodied agents, where questions about objects, events, and their locations must be answered from...
arXiv:2610.27547v1 Announce Type: new Abstract: Online LiDAR-camera calibration remains a fundamental yet challenging task in the field of robotics. Recently, deep learning-based data-driven approach...
arXiv:2610.27592v1 Announce Type: new Abstract: Aiming to modify textual content while preserving the original style, Scene Text Editing (STE) has significant practical value in many applications. ST...
arXiv:2610.27594v1 Announce Type: new Abstract: While feed-forward models excel in static 3D geometry estimation, extending them to dynamic 4D scenes requires modeling visual geometry as a causally e...
AutoRedClaw is an automated red‑teaming framework designed for claw‑style agents such as OpenClaw, which maintain persistent configuration, memory, and reusable skills. It generates end‑to‑end attacks and the necessary environments, using a strict quality‑checking protocol and execution‑based validation to ensure realistic, effective samples. The framework produces a 330‑case benchmark, RedClaw‑Bench, achieving over 30% effective attack rate at less than $0.10 per successful sample and demonstrating higher attack success rates than existing benchmarks across multiple agent systems.