Knowledge catalogue
arXiv:2606.05402v1 Announce Type: new Abstract: Large reasoning models (LRMs) produce reasoning traces with non-linear structures, such as backtracking and self-correction, that complicate the evaluat
arXiv:2606.06060v1 Announce Type: new Abstract: Modern diffusion models generate high-quality images and videos, but their iterative denoising process makes inference expensive. Feature caching accele
arXiv:2606.05359v1 Announce Type: new Abstract: In this paper, we propose RePHO, a method to reconstruct physically plausible human-object interactions (HOI) from monocular videos. While existing kine
arXiv:2606.06027v1 Announce Type: cross Abstract: Community-conditioned language model adaptation requires choices about data collection, community definition, and evaluation that are currently made i
arXiv:2606.05901v1 Announce Type: new Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing. Despite these advances, LLMs and LLM-based sys
arXiv:2606.06428v1 Announce Type: new Abstract: Prior work has shown that large language models (LLMs) can translate unseen or low-resource languages by undergoing continued training or even by encodi
arXiv:2606.05895v1 Announce Type: new Abstract: Research attention is widely used as an indicator of visibility, influence, and societal uptake, yet it is typically represented as aggregated counts th
arXiv:2606.06020v1 Announce Type: new Abstract: To address the limited diversity and data scarcity in Pedestrian Attribute Recognition (PAR), we explore image synthesis using diffusion models guided b
arXiv:2606.05896v1 Announce Type: new Abstract: Creating lifelike digital humans with genuine social intelligence requires unifying cognitive reasoning and multimodal generation within a coherent fram
arXiv:2606.05698v1 Announce Type: new Abstract: Parametric retrieval augmentation encodes document information into lightweight, document-specific modules such as LoRA adapters, reducing the need to i
arXiv:2601.02880v2 Announce Type: replace-cross Abstract: Every existing inference-time reasoning framework discards all failure context at problem boundaries, leaving a model solving problem 500 no w
arXiv:2606.05922v1 Announce Type: cross Abstract: AI agents rely on a harness of skills, tools, and workflows to solve complex problems. Continually improving this harness is essential for adapting to
arXiv:2606.05858v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have opened new avenues for generating training-free text embeddings. However, the causal attention in d
arXiv:2606.06443v1 Announce Type: new Abstract: Large language models are increasingly used to simulate social media users and infer how individuals may respond to online discussions. However, it rema
arXiv:2606.06183v1 Announce Type: cross Abstract: Building a lexicon from discovered word-like units is a central goal in zero-resource speech processing. But do our evaluations provide a trustworthy
arXiv:2606.06309v1 Announce Type: new Abstract: Video generation models based on Diffusion Transformers (DiTs) have achieved remarkable performance in video synthesis, yet they suffer from high infere
arXiv:2606.06423v1 Announce Type: new Abstract: Safety-critical traffic scenario generation is essential for evaluating autonomous driving systems under rare but high-risk interactions. Existing diffu
arXiv:2606.05506v1 Announce Type: new Abstract: We propose a sensor-guided adaptive contrastive learning framework for visual representation learning in PointGoal navigation. During training, privileg
arXiv:2506.10145v3 Announce Type: replace Abstract: End-to-end (E2E) autonomous driving has recently emerged as a new paradigm, offering significant potential. However, few studies have looked into th
arXiv:2606.06176v1 Announce Type: new Abstract: Underwater Image Enhancement (UIE) is essential for mitigating degradations caused by water medium. Although learning-based methods have advanced signif
arXiv:2606.05660v1 Announce Type: new Abstract: Embodied AI systems are increasingly expected to reason and act over extended horizons in physical environments. This growing capability brings safety t
arXiv:2606.06228v1 Announce Type: new Abstract: Training-free image editing has recently attracted increasing attention due to its ability to modify real images using powerful pre-trained diffusion an
arXiv:2606.06041v1 Announce Type: new Abstract: As robotic systems become more sophisticated, the growing complexity of their motion planning models and the longer training times pose substantial chal
arXiv:2606.06199v1 Announce Type: new Abstract: Standard segmentation metrics such as Dice and Hausdorff distance measure geometric overlap but say nothing about whether a segmented surface is suitabl
arXiv:2606.06454v1 Announce Type: cross Abstract: Large language models increasingly write, review, and judge code, and a fast-growing practice equips them with prompt 'skills' that ask the model to r
arXiv:2601.08182v2 Announce Type: replace Abstract: Corner detection is widely used in various computer vision tasks, such as image matching and 3D reconstruction. Our research indicates that there ar
arXiv:2510.22768v2 Announce Type: replace Abstract: As autonomous agents increasingly interact, they inevitably attempt to influence one another. While prior work in text-only settings has explored th
arXiv:2606.05702v1 Announce Type: cross Abstract: Recent advancements in Vision-Language Models (VLMs) have significantly enhanced their ability to interpret complex visual semantics, yet their capaci
arXiv:2606.06474v1 Announce Type: new Abstract: Discrete diffusion language models generate text by iteratively denoising an entire response in parallel. At each step, they predict tentative tokens fo
arXiv:2606.05912v1 Announce Type: new Abstract: Modeling dynamic facial expressions using 3D Gaussian representations remains challenging due to their unstructured nature. Conventional Gaussian avatar
arXiv:2503.22929v3 Announce Type: replace Abstract: Face anti-spoofing (FAS) techniques aim to enhance the security of facial identity authentication by distinguishing authentic live faces from decept
arXiv:2606.05336v1 Announce Type: new Abstract: Personalizing large language models (LLMs) has become a central challenge as LLMs are deployed across recommendation, search, dialogue, and content gene
arXiv:2506.10601v2 Announce Type: replace Abstract: Given its ability to reduce annotation costs, weakly supervised learning based on single-point annotations has emerged as a research focus in orient
arXiv:2306.09712v2 Announce Type: replace-cross Abstract: In reinforcement learning (RL), there are two major settings for interacting with the environment: online and offline. Online methods explore
arXiv:2606.05635v1 Announce Type: new Abstract: Prior work on aesthetic composition typically produces a single aesthetically pleasing crop, overlooking the narrative value of composing multiple shots
arXiv:2606.06079v1 Announce Type: new Abstract: Agent skills, which consist of reusable strategies that guide agent reasoning and action, have shown strong potential for improving model capability at
arXiv:2606.05563v1 Announce Type: cross Abstract: Evaluating LLM mediators remains challenging, as mediation unfolds as a real-time trajectory shaped by disputants' shifting emotions, intentions, and
arXiv:2601.22580v2 Announce Type: replace Abstract: The success of Large Language Models (LLMs) hinges on the stable training of deep Transformer architectures. A critical design choice is the placeme
arXiv:2606.05384v1 Announce Type: cross Abstract: LLM-as-judge evaluation is widely used in benchmarking pipelines, where model outputs are compared and ranked using automated evaluators. These pipeli
arXiv:2606.05186v1 Announce Type: cross Abstract: Budget-constrained micro-pretraining often requires triaging many candidate recipes on a shared accelerator before larger search budgets are spent. We
arXiv:2606.05828v1 Announce Type: cross Abstract: As Large Language Model (LLM) capabilities advance, locally deployed personal agents relying on API-based remote models and external skills have emerg
arXiv:2606.05308v1 Announce Type: cross Abstract: With PRECISE, we extended Prediction-Powered Inference to produce bias-corrected estimates of ranking evaluation metrics by combining a small human-la
arXiv:2606.05890v1 Announce Type: new Abstract: LLMs are increasingly deployed as Artificial Moral Advisors (AMA) in a variety of contexts: what kind of conversational patterns should they display? In
arXiv:2606.06338v1 Announce Type: new Abstract: Video question answering (VideoQA) aims to answer questions about given videos. While existing approaches excel on factoid VideoQA, they struggle with d
arXiv:2606.05761v1 Announce Type: cross Abstract: Persistent AI assistants, such as OpenClaw, accumulate large collections of related memories over long-term interactions. As these memories grow, they
arXiv:2606.06224v1 Announce Type: new Abstract: Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology. Existing methods primari
arXiv:2606.06292v1 Announce Type: new Abstract: Robotic manipulation of textiles remains challenging because continuous deformation and self-occlusions hinder the robust visual perception required to
arXiv:2606.05975v1 Announce Type: new Abstract: Open-vocabulary 3D functionality segmentation enables robots to localize functional object components in 3D scenes. It is a challenging task that requir
arXiv:2606.05700v1 Announce Type: new Abstract: We present T-SAR-JEPA, a self-supervised framework for temporal anomaly detection in SAR amplitude stacks via latent prediction. A ViT-Base/16 encoder f
arXiv:2606.05880v1 Announce Type: new Abstract: Agile humanoid locomotion across diverse challenging terrain demands both wide perceptual coverage and precise local geometry understanding. Motivated b
arXiv:2606.06218v1 Announce Type: new Abstract: A policy tuned for one robot often behaves differently on another, whether due to the sim-to-real gap, unknown payloads, or the differing dynamics of tw
arXiv:2605.05367v2 Announce Type: replace Abstract: Existing 3D sign language avatar reconstruction methods are developed and evaluated exclusively on Western sign languages, and no 3D parametric anno
arXiv:2606.05859v1 Announce Type: new Abstract: Latent reasoning has emerged as a promising alternative to discrete Chain-of-Thought (CoT) in large language models (LLMs), enabling more expressive rea
arXiv:2606.05194v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly being deployed to make decisions that require trading off near-term gains against long-term consequences
arXiv:2606.06491v1 Announce Type: new Abstract: Robot manipulation alternates between low-risk transit phases that call for fast execution and high-risk contact stages that demand slow, precise motion
arXiv:2606.05436v1 Announce Type: cross Abstract: Summarizing the latest medical literature to guide clinical decision-making is essential for evidence-based medicine and high-quality patient care. Ye
arXiv:2606.05570v1 Announce Type: new Abstract: Repository-level coding benchmarks face a trade-off between task difficulty and evaluation reliability: tasks that challenge frontier models often invol
arXiv:2606.06039v1 Announce Type: new Abstract: Cone-beam computed tomography (CBCT) frequently suffers from data truncation, which introduces severe artifacts and limits the effective field of view (
arXiv:2606.05730v1 Announce Type: new Abstract: We propose TextWand, a general-purpose framework that unifies scene text removal, generation, and replacement into a single model. By decomposing comple
arXiv:2606.06004v1 Announce Type: new Abstract: Dialect resources occupy a unique position at the intersection of scientific description, cultural preservation, and computational infrastructure. Large