3,698 items tracked: 33 reported by two or more outlets, the rest by a single source. Most-corroborated first.
arXiv:2506.23101v2 Announce Type: replace-cross Abstract: Ensuring the trustworthiness of Multimodal Large Language Models (MLLMs) requires rigorous evaluation of gender bias, particularly in socially sensitive domains.
arXiv:2508.02773v4 Announce Type: replace-cross Abstract: The convergence of Web3 technologies and AI agents is a rapidly evolving frontier that is reshaping decentralized ecosystems.
arXiv:2508.04903v4 Announce Type: replace-cross Abstract: Large language model (LLM) agents are increasingly organized into multi-agent systems for complex reasoning and decision-making tasks.
arXiv:2508.06163v2 Announce Type: replace-cross Abstract: Merging multiple task-specific fine-tuned checkpoints into a single multi-task model provides an attractive alternative to joint multi-task training.
arXiv:2510.03245v3 Announce Type: replace-cross Abstract: State-of-the-art attribution methods such as AttEXplore rely on adversarial sample generation that applies an all-pass filter across the frequency spectrum: they…
arXiv:2511.13510v2 Announce Type: replace-cross Abstract: This paper presents Naga, a deep State Space Model (SSM) encoding approach inspired by structural concepts from Vedic mathematics.
arXiv:2511.19548v2 Announce Type: replace-cross Abstract: Neuroeconomics appears to offer welfare economics an evidential supplement, since neural data may help distinguish choices that express genuine preferences from c…
arXiv:2511.20088v3 Announce Type: replace-cross Abstract: In recent years, VAD (Visual Anomaly Detection) has attracted significant attention due to its high interpretability, as it provides visual explanations by highli…
arXiv:2601.07632v5 Announce Type: replace-cross Abstract: Discrete motion tokenization has recently enabled Large Language Models (LLMs) to serve as versatile backbones for motion understanding and motion-language reason…
arXiv:2601.09041v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often produce judgments similar to those of humans, but their interpretation of figurative language remains unclear.
arXiv:2601.14660v3 Announce Type: replace-cross Abstract: Agentic Large Language Models (LLMs) are models able to reason, plan, and execute tools over unstructured data.
arXiv:2601.16333v3 Announce Type: replace-cross Abstract: Foundation models are used for many real-world applications involving language understanding and generation from temporally-ordered multimodal events.
arXiv:2601.17673v2 Announce Type: replace-cross Abstract: Unified remote sensing multimodal models exhibit a pronounced spatial reversal curse: Although they can accurately recognize and describe object locations in imag…
arXiv:2601.19060v2 Announce Type: replace-cross Abstract: Multimodal Retrieval-Augmented Generation (MM-RAG) augments visual question answering (VQA) with external knowledge, but many systems rely on modular pipelines th…
arXiv:2602.02481v2 Announce Type: replace-cross Abstract: Likelihood-based policy gradient methods are the dominant approach for training robot control policies from rewards.
arXiv:2602.06219v3 Announce Type: replace-cross Abstract: World models offer a promising avenue for more faithfully capturing complex dynamics, including contacts and non-rigidity, as well as complex sensory information,…
arXiv:2602.08302v2 Announce Type: replace-cross Abstract: Delayed generalization, where training accuracy becomes nearly perfect substantially before test accuracy, has often been studied in the context of the depth and…
arXiv:2602.16708v4 Announce Type: replace-cross Abstract: As agentic computing systems grow increasingly popular, they raise serious security concerns due to their susceptibility to various attacks.
arXiv:2602.23135v3 Announce Type: replace-cross Abstract: Edge classification on directed dynamic graphs is critical for understanding evolving user-item interactions and behavioral patterns in recommender systems and so…
arXiv:2602.24111v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) show promise in drafting radiology reports, yet they frequently suffer from logical inconsistencies, generating diagnostic impressio…
arXiv:2603.07815v2 Announce Type: replace-cross Abstract: Iterative diffusion generation requires different amounts of model capacity at different token positions.
arXiv:2603.11479v5 Announce Type: replace-cross Abstract: Time Series Event Detection (TSED) aims to localize semantically meaningful events in time series data, with critical applications in high-stakes domains.
arXiv:2603.15046v2 Announce Type: replace-cross Abstract: In this study, we address the problem of language-guided robotic manipulation, where a robot is required to manipulate a wide range of objects based on visual obs…
arXiv:2603.26515v2 Announce Type: replace-cross Abstract: Efficient and robust turn-taking detection remains a significant challenge in industrial-grade voice AI agents.
arXiv:2603.29602v2 Announce Type: replace-cross Abstract: Existing multi-turn image editing paradigms are often confined to isolated single-step execution.
arXiv:2604.14025v2 Announce Type: replace-cross Abstract: Reconstructing 3D representations from 2D inputs is a fundamental task in computer vision and graphics, serving as a cornerstone for understanding and interacting…
arXiv:2604.14228v3 Announce Type: replace-cross Abstract: Claude Code is an agentic coding tool that can run shell commands, edit files, and call external services on behalf of the user.
arXiv:2604.15414v3 Announce Type: replace-cross Abstract: Continual reinforcement learning must balance retention with adaptation, yet many methods still rely on \emph{single-model preservation}, committing to one evolvi…
arXiv:2604.18235v3 Announce Type: replace-cross Abstract: Search agents achieve strong question-answering performance through multi-turn interactions with search engines, with Group Relative Policy Optimization (GRPO) be…
arXiv:2604.19734v2 Announce Type: replace-cross Abstract: Scaling humanoid foundation models is bottlenecked by the scarcity of robotic data.
arXiv:2604.23238v3 Announce Type: replace-cross Abstract: Distillation via reasoning traces exposes frontier models to adversarial third parties, who can bypass their guardrails and misappropriate their capabilities.
arXiv:2605.12483v5 Announce Type: replace-cross Abstract: In settings where labeled verifiable training data is the binding constraint, each checked example should be allocated to the model and reward density where it is…
arXiv:2605.13119v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models are effective robot action executors, but they remain limited on long-horizon tasks due to the dual burden of extended closed-…
arXiv:2605.15077v2 Announce Type: replace-cross Abstract: Function calling, also known as tool use, is a core capability of modern LLM agents but is typically constrained by synchronous execution semantics.
arXiv:2605.16343v2 Announce Type: replace-cross Abstract: Looped language models (LoopLMs) increase effective computational depth by repeatedly applying shared Transformer blocks without increasing the parameter count.
arXiv:2605.17862v2 Announce Type: replace-cross Abstract: Scaling on-policy distillation (OPD) for large language models (LLMs) confronts a fundamental tension: asynchronous execution is necessary for system efficiency,…
arXiv:2605.17938v2 Announce Type: replace-cross Abstract: Training data attribution (TDA) should enable generative model interpretability and foster a variety of related downstream tasks.
arXiv:2605.29790v2 Announce Type: replace-cross Abstract: LLM-based multi-agent systems (MAS) have emerged as an effective paradigm for complex and long-horizon tasks.
arXiv:2606.00082v2 Announce Type: replace-cross Abstract: Explainability of deep learning algorithms is critical for computer-vision applications with high-stakes decisions.
arXiv:2606.02859v2 Announce Type: replace-cross Abstract: How can a population of agents self-orchestrate and self-adapt into stronger collective intelligence without centralized control?
arXiv:2606.03161v5 Announce Type: replace-cross Abstract: OpenAgenet, abbreviated as OAN, is an open infrastructure project for trusted Agent interconnection.
arXiv:2606.07001v3 Announce Type: replace-cross Abstract: High-quality training data is essential to large language models (LLMs) and typically requires extensive and costly manual curation.
arXiv:2606.11260v2 Announce Type: replace-cross Abstract: Humans process rich auditory environments through various cognitive capabilities such as audio perception, audio reasoning and memory.
arXiv:2606.11889v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) are increasingly used for scene understanding in autonomous driving, but robustness analysis often relies on task-agnostic embedding…
arXiv:2606.14715v2 Announce Type: replace-cross Abstract: LLM agents are increasingly used to simulate real-world interactions, but it remains unclear whether simulated behaviors preserve the content patterns and interac…
arXiv:2606.19164v4 Announce Type: replace-cross Abstract: Model merging enables training-free multi-task learning by aggregating knowledge from models fine-tuned from a shared checkpoint, but their task updates can inter…
arXiv:2606.20235v2 Announce Type: replace-cross Abstract: Academic paper search is a core step in scientific research, and LLM-based search agents are emerging as a promising paradigm for iterative, intent-driven literat…
arXiv:2606.22027v5 Announce Type: replace-cross Abstract: Reinforcement learning for robot manipulation is often bottlenecked by reward design, especially in long-horizon tasks: sparse success rewards provide weak superv…
arXiv:2606.25451v2 Announce Type: replace-cross Abstract: Group Relative Policy Optimization (GRPO) typically requires multiple rollouts per prompt to estimate relative credit, incurring substantial sampling cost and exa…
arXiv:2606.28385v2 Announce Type: replace-cross Abstract: Robot world models now generate synthetic manipulation videos, but evaluating them is hard: realistic-looking outputs often violate physical laws, temporal consis…