Knowledge catalogue
arXiv:2601.04068v3 Announce Type: replace Abstract: Aligning text-to-video diffusion models with human preferences is crucial for generating high-quality videos. Existing Direct Preference Otimization
arXiv:2604.07085v1 Announce Type: new Abstract: In electronic health records (EHRs), clustering patients and distinguishing disease subtypes are key tasks to elucidate pathophysiology and aid clinical
arXiv:2604.07747v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) can improve low-k reasoning accuracy while narrowing solution coverage on challenging math que
arXiv:2604.06798v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) based large language models (LLMs) offer strong performance but suffer from high memory and computation costs. Weight binariz
arXiv:2603.20654v4 Announce Type: replace-cross Abstract: Classical Amdahl's Law conceptualized the limit of speedup for an era of fixed serial-parallel decomposition and homogeneous replication. Mode
arXiv:2601.02535v2 Announce Type: replace Abstract: Selecting a single high-quality output from multiple stochastic generations remains a fundamental challenge for large language models (LLMs), partic
arXiv:2604.07664v1 Announce Type: new Abstract: Monocular Depth Estimation (MDE) is a fundamental computer vision task with important applications in 3D vision. The current mainstream MDE methods empl
arXiv:2604.07348v1 Announce Type: cross Abstract: Generating motion-controlled videos--where user-specified actions drive physically plausible scene dynamics under freely chosen viewpoints--demands tw
arXiv:2604.06390v1 Announce Type: cross Abstract: Background: Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. Accurate survival prediction is essential for treat
arXiv:2603.28253v2 Announce Type: replace-cross Abstract: Time series forecasting is vital across many domains, yet existing models struggle with fixed-length inputs and inadequate multi-scale modelin
arXiv:2604.06465v1 Announce Type: cross Abstract: Reasoning models have demonstrated remarkable capabilities in solving complex problems by leveraging long chains of thought. However, this more delibe
arXiv:2603.11633v2 Announce Type: replace Abstract: Recent unified 3D generation models have made remarkable progress in producing high-quality 3D assets from a single image. Notably, layout-aware app
arXiv:2509.07673v4 Announce Type: replace Abstract: Deep neural networks have exhibited impressive performance in image classification tasks but remain vulnerable to adversarial examples. Standard adv
arXiv:2604.06956v1 Announce Type: cross Abstract: Modern recommendation models have increased to trillions of parameters. As cluster scales expand to O(1k), distributed training bottlenecks shift from
arXiv:2501.10806v4 Announce Type: replace-cross Abstract: Two-time-scale stochastic approximation algorithms are iterative methods used in applications such as optimization, reinforcement learning, an
arXiv:2405.11619v2 Announce Type: replace-cross Abstract: Phishing emails continue to pose a significant threat, causing financial losses and security breaches. This study addresses limitations in exi
arXiv:2604.07980v1 Announce Type: new Abstract: Accurate depth estimation is critical for autonomous driving perception systems, particularly for long range vehicle detection on highways. Traditional
arXiv:2604.08171v1 Announce Type: new Abstract: Accurate ocean mapping is essential for applications such as bathymetry estimation, seabed characterization, marine litter detection, and ecosystem moni
arXiv:2604.06413v1 Announce Type: new Abstract: Diffusion and flow matching models generate samples by learning time-dependent vector fields whose integration transports noise to data, requiring tens
arXiv:2602.16005v2 Announce Type: replace-cross Abstract: We introduce ODYN, a novel all-shifted primal-dual non-interior-point quadratic programming (QP) solver designed to efficiently handle challen
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:2603.25898v2 Announce Type: replace-cross Abstract: LLM-assisted modeling holds the potential to rapidly build executable Digital Twins of complex systems from only coarse descriptions and senso
arXiv:2508.20340v4 Announce Type: replace-cross Abstract: Satisfiability Modulo Theory (SMT) solvers are foundational to modern systems and programming languages research, providing the foundation for
arXiv:2604.07658v1 Announce Type: cross Abstract: Linear recurrent models offer linear-time sequence processing but often suffer from suboptimal long-range memory. We trace this to the decay spectrum:
arXiv:2604.06492v1 Announce Type: new Abstract: We study stochastic convex optimization (SCO) with heavy-tailed gradients under pure epsilon-differential privacy (DP). Instead of assuming a bound on t
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:2604.07901v1 Announce Type: new Abstract: 360 video object segmentation (360VOS) aims to predict temporally-consistent masks in 360 videos, offering full-scene coverage, benefiting applications,
arXiv:2604.07912v1 Announce Type: new Abstract: Finding parking consumes a disproportionate share of food delivery time, yet no system addresses precise parking-spot selection relative to merchant ent
arXiv:2506.17212v2 Announce Type: replace Abstract: Articulated objects are common in the real world, yet modeling their structure and motion remains a challenging task for 3D reconstruction methods.
arXiv:2604.08000v1 Announce Type: cross Abstract: Proactivity is a core expectation for AGI. Prior work remains largely confined to laboratory settings, leaving a clear gap in real-world proactive age
arXiv:2604.06620v1 Announce Type: new Abstract: Quantitative estimation of wheel polygonal roughness from axle-box vibration signals is a challenging yet practically relevant problem for rail-vehicle
arXiv:2604.07427v1 Announce Type: new Abstract: Modern text-to-image (T2I) models generate high-fidelity visuals but remain indifferent to individual user preferences. While existing reward models opt
arXiv:2604.07230v2 Announce Type: replace Abstract: Achieving physically accurate object manipulation in image editing is essential for its potential applications in interactive world models. However,
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:2604.08125v1 Announce Type: new Abstract: Human-like multimodal reaction generation is essential for natural group interactions between humans and embodied AI. However, existing approaches are l
arXiv:2604.07016v1 Announce Type: new Abstract: A key challenge in scaling up Reinforcement Learning is generalizing learned behaviour. Without the ability to carry forward acquired knowledge an agent
arXiv:2510.05921v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved remarkable success in a wide range of natural language processing tasks and can be adapted through prompt
arXiv:2604.06912v1 Announce Type: cross Abstract: MLLMs require high-resolution visual inputs for fine-grained tasks like document understanding and dense scene perception. However, current global res
QueryData launches in preview today. It is a tool for translating natural language into database queries with near-100% accuracy. With QueryData, you can build agentic experiences across AlloyDB, Clou
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:2505.24848v4 Announce Type: replace Abstract: To enable egocentric contextual AI in always-on smart glasses, it is crucial to be able to keep a record of the user's interactions with the world,
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:2505.24499v2 Announce Type: replace Abstract: Generating high-quality Scalable Vector Graphics (SVGs) is challenging for Large Language Models (LLMs), as it requires advanced reasoning for struc
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:2604.07882v1 Announce Type: new Abstract: Reconstructing non-rigid objects with physical plausibility remains a significant challenge. Existing approaches leverage differentiable rendering for p
arXiv:2604.07036v1 Announce Type: cross Abstract: Recently, LLM-based agents have become increasingly popular across many applications, including complex sequential decision-making problems. However,
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:2601.04268v2 Announce Type: replace Abstract: Weather and climate models rely on parametrisations to represent unresolved sub-grid processes. Traditional schemes rely on fixed coefficients that
arXiv:2604.06663v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used to simulate social attitudes and behaviors, offering scalable 'silicon samples' that can approximat
arXiv:2511.23158v2 Announce Type: replace-cross Abstract: The rapid progress of visual generative models has made AI-generated images increasingly difficult to distinguish from authentic ones, posing
arXiv:2604.08282v1 Announce Type: new Abstract: Radar perception models are trained with different inputs, from range-Doppler spectra to sparse point clouds. Dense spectra are assumed to outperform sp
SiFive Inc., a startup that sells chip designs based on the open-source RISC-V architecture, has raised $400 million in funding at a $3.65 billion valuation. Atreides Management led the Series G round
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:2604.06638v1 Announce Type: cross Abstract: Effective detection of unknown network security threats in multi-class imbalanced environments is critical for maintaining cyberspace security. Curren
arXiv:2604.06247v1 Announce Type: cross Abstract: Large Language Models (LLMs) and Vision-Language Models (VLMs) remain highly vulnerable to textual and visual jailbreaks, as well as prompt injections
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:2604.06603v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong knowledge reserves and task-solving capabilities, but still face the challenge of severe hallucination,
arXiv:2604.08501v1 Announce Type: cross Abstract: Science currently offers two options for quality assurance, both inadequate. Journal gatekeeping claims to verify both integrity and contribution, but
arXiv:2604.03134v2 Announce Type: replace Abstract: Few-Shot Medical Image Segmentation (FSMIS) aims to segment novel object classes in medical images using only minimal annotated examples, addressing
arXiv:2604.06254v1 Announce Type: cross Abstract: With the rapid growth of interconnected devices in Industrial and Medical Internet of Things (IIoT and MIoT) ecosystems, ensuring timely and accurate