Knowledge catalogue
arXiv:2604.08209v1 Announce Type: new Abstract: To extend the reinforcement learning post-training paradigm to omni-modal models for concurrently bolstering video-audio understanding and collaborative
arXiv:2604.06562v1 Announce Type: new Abstract: Small language models (SLM) are increasingly used as interactive decision-making agents, yet most decision-oriented evaluations ignore emotion as a caus
arXiv:2604.07944v1 Announce Type: new Abstract: Large language models (LLMs) have recently demonstrated strong potential for autonomous vehicle motion planning by reformulating trajectory prediction a
arXiv:2604.06834v1 Announce Type: cross Abstract: Large reasoning models have recently demonstrated strong performance on complex tasks that require long chain-of-thought reasoning, through supervised
arXiv:2604.07296v2 Announce Type: replace Abstract: Spatial understanding is a fundamental cornerstone of human-level intelligence. Nonetheless, current research predominantly focuses on domain-specif
arXiv:2512.03532v2 Announce Type: replace Abstract: Generalizing open-vocabulary 3D instance segmentation (OV-3DIS) to diverse, unstructured, and mesh-free environments is crucial for robotics and AR/
arXiv:2604.08539v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) has emerged as the de facto Reinforcement Learning (RL) objective driving recent advancements in Multimodal
arXiv:2604.07789v1 Announce Type: cross Abstract: Recent advances in language model (LM) agents have significantly improved automated software engineering (SWE). Prior work has proposed various agenti
arXiv:2604.08238v1 Announce Type: new Abstract: The increasing adaptation of vision models across domains, such as satellite imagery and medical scans, has raised an emerging privacy risk: models may
arXiv:2604.08110v1 Announce Type: new Abstract: Training-free open-vocabulary semantic segmentation(TF-OVSS) has recently attracted attention for its ability to perform dense prediction by leveraging
arXiv:2604.08461v1 Announce Type: new Abstract: Open-Vocabulary Segmentation (OVS) aims to segment image regions beyond predefined category sets by leveraging semantic descriptions. While CLIP based a
arXiv:2604.08503v1 Announce Type: new Abstract: Recent advances in generative video modeling, driven by large-scale datasets and powerful architectures, have yielded remarkable visual realism. However
arXiv:2508.21618v2 Announce Type: replace-cross Abstract: We present PhISM, a physics-informed deep learning architecture that learns without supervision to explicitly disentangle hyperspectral observ
arXiv:2603.04165v3 Announce Type: replace-cross Abstract: Large-scale 2D foundation models exhibit strong transferable representations, yet extending them to 3D volumetric data typically requires retr
arXiv:2508.15358v2 Announce Type: replace Abstract: In many planning applications, we might be interested in finding plans that minimally modify the initial state to achieve the goals. We refer to thi
arXiv:2604.07779v1 Announce Type: new Abstract: Pathology foundation models (FMs) have become central to computational histopathology, offering strong transfer performance across a wide range of diagn
arXiv:2604.02073v2 Announce Type: replace Abstract: Universal multimodal embedding (UME) maps heterogeneous inputs into a shared retrieval space with a single model. Recent approaches improve UME by g
arXiv:2512.01236v2 Announce Type: replace Abstract: Personalized generation models for a single subject have demonstrated remarkable effectiveness, highlighting their significant potential. However, w
arXiv:2510.24058v2 Announce Type: replace-cross Abstract: Multi-sensory systems for embodied intelligence, from wearable body-sensor networks to instrumented robotic platforms, routinely face a sensor
arXiv:2601.15356v4 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has empowered Multimodal Large Language Models (MLLMs) to achieve superior human preference alignment in Image Qua
arXiv:2604.07013v1 Announce Type: cross Abstract: Designing quantum neural networks (QNNs) that are both accurate and deployable on NISQ hardware is challenging. Handcrafted ansatze must balance expre
arXiv:2508.07299v2 Announce Type: replace-cross Abstract: The effectiveness of unlabeled data in Semi/Self-Supervised Learning (SSL) depends on appropriate assumptions for specific scenarios, thereby
arXiv:2512.06774v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has become a leading representation for high-fidelity 3D assets, yet protecting these assets via digital watermarking r
arXiv:2604.07165v1 Announce Type: new Abstract: Reinforcement learning for Large Language Model agents is often hindered by sparse rewards in multi-step reasoning tasks. Existing approaches like Group
arXiv:2604.07595v1 Announce Type: cross Abstract: Language model agents reason from scratch on every query: each time an agent retrieves evidence and deliberates, the chain of thought is discarded and
arXiv:2512.12623v3 Announce Type: replace-cross Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have significantly enhanced cross-modal understanding and reasoning by incorpo
arXiv:2512.01925v2 Announce Type: replace-cross Abstract: Recent advancements in large language models (LLMs) have been driven by their emergent reasoning capabilities, particularly through long chain
arXiv:2604.07506v1 Announce Type: cross Abstract: Reward Models (RMs) are critical components in the Reinforcement Learning from Human Feedback (RLHF) pipeline, directly determining the alignment qual
arXiv:2604.07298v1 Announce Type: cross Abstract: Multiple Instance Learning (MIL) is the dominant framework for gigapixel whole-slide image (WSI) classification in computational pathology. However, c
arXiv:2604.05268v2 Announce Type: replace-cross Abstract: Multi-modal retrieval-augmented generation (MM-RAG) relies heavily on re-rankers to surface the most relevant evidence for image-question quer
arXiv:2604.07884v1 Announce Type: new Abstract: High-fidelity generative models are increasingly needed in privacy-sensitive scenarios, where access to data is severely restricted due to regulatory an
arXiv:2604.08536v1 Announce Type: new Abstract: We introduce RewardFlow, an inversion-free framework that steers pretrained diffusion and flow-matching models at inference time through multi-reward La
arXiv:2604.07774v1 Announce Type: cross Abstract: This paper focuses on embodied task planning, where an agent acquires visual observations from the environment and executes atomic actions to accompli
arXiv:2604.06260v1 Announce Type: cross Abstract: Test-time scaling investigates whether a fixed diffusion language model (DLM) can generate better outputs when given more inference compute, without a
arXiv:2604.07922v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) have revolutionized complex problem-solving, yet they exhibit a pervasive 'overthinking', generating unnecessarily long
arXiv:2604.08542v1 Announce Type: new Abstract: This paper addresses the task of large-scale 3D scene reconstruction from long video sequences. Recent feed-forward reconstruction models have shown pro
arXiv:2510.07048v2 Announce Type: replace Abstract: Despite their remarkable natural language understanding capabilities, Large Language Models (LLMs) have been underutilized for retrieval tasks. We p
In an April 9, 2026 Substack post, AI skeptic Gary Marcus argues that Anthropic's announcement of its Claude 'Mythos' model was significantly overhyped, offering three key reasons for skepticism: t...
arXiv:2604.08541v1 Announce Type: cross Abstract: Multimodal Mixture-of-Experts (MoE) models have achieved remarkable performance on vision-language tasks. However, we identify a puzzling phenomenon t
arXiv:2604.08299v1 Announce Type: new Abstract: Chain-of-Thought (CoT) has become a cornerstone of reasoning in large language models, yet its effectiveness is constrained by the limited expressivenes
arXiv:2604.08532v1 Announce Type: new Abstract: Large-scale multi-view reconstruction models have made remarkable progress, but most existing approaches still rely on fully supervised training with gr
arXiv:2604.06389v1 Announce Type: new Abstract: Uncertainty estimation for reasoning language models remains difficult to deploy in practice: sampling-based methods are computationally expensive, whil
arXiv:2604.07766v1 Announce Type: new Abstract: We investigate a fundamental structural question in Grouped Query Attention (GQA) transformers: do the layers most sensitive to task correctness coincid
arXiv:2604.06636v1 Announce Type: cross Abstract: Process supervision has emerged as a promising approach for enhancing LLM reasoning, yet existing methods fail to distinguish meaningful progress from
arXiv:2601.23155v2 Announce Type: replace-cross Abstract: Information-based data selection for instruction tuning is compelling: maximizing the log-determinant of the Fisher information yields a monot
arXiv:2604.07725v1 Announce Type: cross Abstract: We show that verifier-free evolution is bottlenecked by both diversity and efficiency: without external correction, repeated evolution accelerates col
arXiv:2604.03647v2 Announce Type: replace-cross Abstract: In the unsupervised self-evolution of Multimodal Large Language Models, the quality of feedback signals during post-training is pivotal for st
arXiv:2603.04759v2 Announce Type: replace Abstract: The limited context window of contemporary large language models (LLMs) remains a primary bottleneck for their broader application across diverse do
arXiv:2603.11394v2 Announce Type: replace Abstract: Patients and clinicians are increasingly using chatbots powered by large language models (LLMs) for healthcare inquiries. While state-of-the-art LLM
arXiv:2604.07415v1 Announce Type: cross Abstract: Large language models (LLMs) are probabilistic in nature and perform more reliably when augmented with external information. As complex queries often
arXiv:2604.08370v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has demonstrated impressive performance in 3D scene reconstruction. Beyond novel view synthesis, it shows great potential f
arXiv:2010.00638v2 Announce Type: replace-cross Abstract: Generative models for tabular data have evolved rapidly beyond Generative Adversarial Networks (GANs). While GANs pioneered synthetic tabular
arXiv:2604.07916v1 Announce Type: new Abstract: Referring Expression Segmentation (RES) aims to segment image regions described by natural-language expressions, serving as a bridge between vision and
arXiv:2604.08072v1 Announce Type: new Abstract: Convolutional Neural Networks (CNNs) excel at extracting local features hierarchically, but their performance in capturing complex correlations hinges h
arXiv:2604.06613v2 Announce Type: cross Abstract: Modern reasoning models continue generating long after the answer is already determined. Across five model configurations, two families, and three ben
arXiv:2604.06689v1 Announce Type: new Abstract: Accurate classification requires not only high predictive accuracy but also well-calibrated confidence estimates. Yet, modern deep neural networks (DNNs
arXiv:2512.08410v2 Announce Type: replace Abstract: Due to excessive memory overhead, most Multimodal Large Language Models (MLLMs) can only process videos of limited frames. In this paper, we propose
arXiv:2604.06831v1 Announce Type: cross Abstract: Current LLM-based services typically require users to submit raw text regardless of its sensitivity. While intuitive, such practice introduces substan
arXiv:2604.08156v1 Announce Type: new Abstract: Rhyme is deceptively intuitive: what is or is not a rhyme is constructed historically, scholars struggle with rhyme classification, and people disagree
arXiv:2511.19693v3 Announce Type: replace-cross Abstract: Payment networks form the backbone of modern commerce, generating high volumes of transaction records from daily activities. Properly modeling