Knowledge catalogue
arXiv:2502.02345v2 Announce Type: replace Abstract: Subspace inference for neural networks assumes that a subspace of their parameter space suffices to produce a reliable uncertainty quantification. I
arXiv:2604.08952v1 Announce Type: new Abstract: Document Question Answering (DQA) involves generating answers from a document based on a user's query, representing a key task in document understanding
arXiv:2604.09167v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved strong performance in multimodal understanding and reasoning, yet grounded reasoning in 3D scenes remains un
arXiv:2501.15461v4 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have shown great success in various graph-based learning tasks. However, it often faces the issue of over-smoothing as
arXiv:2604.09443v1 Announce Type: cross Abstract: Large language model agents receive instructions from many sources-system messages, user prompts, tool outputs, and more-each carrying different level
arXiv:2604.09124v1 Announce Type: cross Abstract: Deploying DNNs on System-on-Chips (SoC) with multiple heterogeneous acceleration engines is challenging, and the majority of deployment frameworks can
arXiv:2604.04503v3 Announce Type: replace Abstract: Deep research agents (DRAs) integrate LLM reasoning with external tools. Memory systems enable DRAs to leverage historical experiences, which are es
arXiv:2602.15313v2 Announce Type: replace Abstract: AI Memory, specifically how models organizes and retrieves historical messages, becomes increasingly valuable to Large Language Models (LLMs), yet e
arXiv:2604.09253v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are powerful but remain vulnerable to multimodal jailbreak attacks. Existing attacks mainly rely on either explicit visu
arXiv:2604.08782v1 Announce Type: new Abstract: Large language models (LLMs) suffer significant performance degradation when user instructions and context are distributed over multiple conversational
arXiv:2604.08944v1 Announce Type: new Abstract: Multi-agent coordination under partial observability requires agents to share complementary private information. While recent methods optimize messages
arXiv:2604.08582v1 Announce Type: cross Abstract: Multivariate Time Series Anomaly Detection (MTSAD) is critical for real-world monitoring scenarios such as industrial control and aerospace systems. M
arXiv:2604.09232v1 Announce Type: cross Abstract: LiDAR-based perception is critical for autonomous driving due to its robustness to poor lighting and visibility conditions. Yet, current models operat
A new speed-focused LoRA for the Wan 2.2 video generation model, released by the LightX2V project on April 26, 2024, shared on the r/StableDiffusion community. The LightX2V distilled LoRA dramatically
arXiv:2604.09258v1 Announce Type: new Abstract: Pretraining is the cornerstone of Large Language Models (LLMs), dominating the vast majority of computational budget and data to serve as the primary en
arXiv:2604.09030v1 Announce Type: new Abstract: This paper presents NTIRE 2026, the 3rd Restore Any Image Model (RAIM) challenge on multi-exposure image fusion in dynamic scenes. We introduce a benchm
This Reddit post from r/ollama discusses integrating Ollama-served Mistral with MemPalace — a free, locally-run AI memory system — via the Model Context Protocol (MCP). MemPalace runs entirely on a us
arXiv:2604.08641v1 Announce Type: cross Abstract: Interpretation is essential to deciphering the language of art: audiences communicate with artists by recovering meaning from visual artifacts. Howeve
arXiv:2602.01997v2 Announce Type: replace-cross Abstract: Recent work has shown that layer pruning can effectively compress large language models (LLMs) while retaining strong performance on classific
arXiv:2604.09480v1 Announce Type: new Abstract: We present Online3R, a new sequential reconstruction framework that is capable of adapting to new scenes through online learning, effectively resolving
arXiv:2512.03730v2 Announce Type: replace-cross Abstract: Adversarial perturbations are a useful way to expose vulnerabilities in object detectors. Existing perturbation methods are frequently white-b
arXiv:2603.11178v3 Announce Type: replace Abstract: Standard LLM distillation treats all training problems equally -- wasting compute on problems the student has already mastered or cannot yet solve.
arXiv:2604.08804v1 Announce Type: cross Abstract: Digital firms routinely run many online experiments on shared user populations. When product decisions are compositional, such as combinations of inte
arXiv:2601.04884v2 Announce Type: replace Abstract: Executing a multi-agent plan can be challenging when an agent is delayed, because this typically creates conflicts with other agents. So, we need to
arXiv:2604.08941v1 Announce Type: new Abstract: Medical Vision Language Models VLMs suffer from two failure modes that threaten safe deployment mis calibrated confidence and sensitivity to question re
arXiv:2604.09482v1 Announce Type: new Abstract: Reasoning in knowledge-intensive domains remains challenging as intermediate steps are often not locally verifiable: unlike math or code, evaluating ste
arXiv:2604.08703v1 Announce Type: cross Abstract: QoS-QoE translation is a fundamental problem in multimedia systems because it characterizes how measurable system and network conditions affect user-p
arXiv:2603.19929v2 Announce Type: replace-cross Abstract: RAM incorporates a motion-aware semantic tracker with adaptive Kalman filtering to achieve robust identity association under severe occlusions
arXiv:2604.09494v1 Announce Type: cross Abstract: We propose RecaLLM, a set of reasoning language models post-trained to make effective use of long-context information. In-context retrieval, which ide
arXiv:2602.22495v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) post-training has recently driven major gains in long chain-of-thought reasoning large language models (LLMs), but
arXiv:2510.17640v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable performance on complex tasks through imitation learning in recent robotic man
arXiv:2604.09511v1 Announce Type: new Abstract: Universal image restoration (UIR) aims to recover clean images from diverse and unknown degradations using a unified model. Existing UIR methods primari
arXiv:2604.09366v1 Announce Type: new Abstract: Reconstructing dynamic 4D scenes is an important yet challenging task. While 3D foundation models like VGGT excel in static settings, they often struggl
arXiv:2601.02850v2 Announce Type: replace Abstract: Reinforcement Learning (RL) is a well-established framework for sequential decision-making in complex environments. However, state-of-the-art Deep R
arXiv:2601.22160v2 Announce Type: replace-cross Abstract: Human animation aims to generate temporally coherent and visually consistent videos over long sequences, yet modeling long-range dependencies
arXiv:2604.09532v1 Announce Type: cross Abstract: Prompt learning is a parameter-efficient approach for vision-language models, yet its robustness under label noise is less investigated. Visual conten
arXiv:2511.23369v3 Announce Type: replace Abstract: Achieving fully autonomous driving systems requires learning rational decisions in a wide span of scenarios, including safety-critical and out-of-di
arXiv:2604.08690v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) has proven effective in RLVR by using outcome-based rewards. While fine-grained dense rewards can theoretica
arXiv:2604.08865v1 Announce Type: new Abstract: Proximal Policy Optimization (PPO) is central to aligning Large Language Models (LLMs) in reasoning tasks with verifiable rewards. However, standard tok
arXiv:2604.08905v1 Announce Type: new Abstract: Reinforcement learning (RL) is effective in enhancing the accuracy of large language models in complex reasoning tasks. Existing RL policy optimization
arXiv:2604.08597v1 Announce Type: cross Abstract: Extracting structured knowledge from unstructured data still faces practical limitations: entity and event extraction pipelines remain brittle, knowle
arXiv:2512.21334v2 Announce Type: replace Abstract: We present Streamo, a real-time streaming video LLM that serves as a general-purpose interactive assistant. Unlike existing online video models that
arXiv:2604.09000v1 Announce Type: new Abstract: Vision agent memory has shown remarkable effectiveness in streaming video understanding. However, storing such memory for videos incurs substantial memo
arXiv:2511.08798v2 Announce Type: replace-cross Abstract: LLM agents with tool-calling capabilities often fail when user instructions are ambiguous or incomplete, leading to incorrect invocations and
arXiv:2604.08477v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has significantly improved large language model (LLM) reasoning in formal domains such as mathem
arXiv:2604.08555v1 Announce Type: new Abstract: Physician-physician discussions of patient cases represent a rich source of clinical knowledge and reasoning that could feed AI agents to enrich and eve
arXiv:2604.08921v1 Announce Type: new Abstract: Accurate 3D human keypoints localization is a critical technology enabling robots to achieve natural and safe physical interaction with users. Conventio
arXiv:2604.09547v1 Announce Type: new Abstract: Token pruning has emerged as a mainstream approach for developing efficient Video Large Language Models (Video LLMs). This work revisits and advances th
arXiv:2505.12318v2 Announce Type: replace Abstract: Federated Parameter-Efficient Fine-Tuning (Fed-PEFT) enables lightweight adaptation of large pre-trained models in federated learning settings by up
arXiv:2604.09377v1 Announce Type: new Abstract: Large language models (LLMs) exhibit substantial variability in performance and computational cost across tasks and queries, motivating routing systems
arXiv:2604.09150v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) achieve strong performance on complex tasks by leveraging long Chain-of-Thought (CoT), but often suffer from overthinking,
This research paper explores a depth-recurrent transformer architecture designed to improve compositional generalization in language models by increasing computational depth rather than sequence lengt
arXiv:2604.09220v1 Announce Type: new Abstract: Implicit neural video representations encode entire video sequences within the parameters of a neural network and enable constant time frame reconstruct
arXiv:2603.01400v2 Announce Type: replace Abstract: Video Large Language Models (VLLMs) demonstrate strong video understanding but suffer from inefficiency due to redundant visual tokens. Existing pru
arXiv:2603.08146v3 Announce Type: replace Abstract: Existing frameworks for gradient-based training of spiking neural networks face a trade-off: discrete-time methods using surrogate gradients support
arXiv:2604.09320v1 Announce Type: cross Abstract: Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond indi
arXiv:2604.08560v1 Announce Type: cross Abstract: Accurate uncertainty estimation is essential for building robust and trustworthy recognition systems. In this paper, we consider the open-set text cla
arXiv:2604.08701v1 Announce Type: new Abstract: We introduce Unified Multimodal Uncertain Inference (UMUI), a multimodal inference task spanning text, audio, and video, where models must produce calib
arXiv:2604.09305v1 Announce Type: new Abstract: Traffic accidents are a leading cause of fatalities and injuries across the globe. Therefore, the ability to anticipate hazardous situations in advance
arXiv:2604.09531v1 Announce Type: cross Abstract: Vision-language models (VLMs) still struggle with visual perception tasks such as spatial understanding and viewpoint recognition. One plausible contr