Knowledge catalogue
arXiv:2606.11680v1 Announce Type: new Abstract: Large language model (LLM) agents struggle with long-horizon tasks due to their inherent statelessness, requiring all task-relevant information to be en
arXiv:2606.11233v1 Announce Type: new Abstract: Supervised Contrastive Learning (SupCon) has achieved strong performance by explicitly modeling pairwise relationships among samples. However, existing
arXiv:2606.11719v1 Announce Type: cross Abstract: Spatial reasoning remains a persistent challenge for multimodal large language models (MLLMs). Existing approaches largely rely on large-scale, static
arXiv:2606.11386v1 Announce Type: cross Abstract: Full-duplex spoken language models (FD-SLMs) enable seamless speech interaction by allowing models to listen and speak simultaneously, yet the interna
arXiv:2606.11682v1 Announce Type: new Abstract: Tabular-image multimodal learning aims to improve predictive modeling by jointly using structured tabular attributes and visual data. Although pretraine
arXiv:2606.11977v1 Announce Type: new Abstract: In this report, we present our third-place solution for the DataMFM Challenge Track 1: Document Parsing. This track requires models to recover structure
arXiv:2505.15201v5 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) algorithms sample multiple n>1 solution attempts for each problem and reward them independently. This optimizes fo
arXiv:2606.11982v1 Announce Type: new Abstract: Preference-based reinforcement learning (PbRL) learns policies from human trajectory-level comparisons, avoiding explicit reward design and expert demon
arXiv:2606.12141v1 Announce Type: new Abstract: Accurate forecasting of sea surface temperature (SST) in regional seas such as the East Sea is crucial for monitoring marine ecosystems, assessing clima
arXiv:2505.08784v2 Announce Type: replace-cross Abstract: As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety. In this paper we int
arXiv:2606.12112v1 Announce Type: new Abstract: This paper presents the design, development, and experimental verification of PEBRE, an open-hardware add-on for fast software development on the Pepper
arXiv:2606.12066v1 Announce Type: new Abstract: In modern vehicular systems, robust performance under harsh conditions has become a critical problem of autonomous driving. Our study delivers a compreh
arXiv:2506.21855v2 Announce Type: replace Abstract: In this paper, we propose Periodic-MAE, a self-supervised framework for learning generalizable spatio-temporal representations of periodic physiolog
arXiv:2606.11262v1 Announce Type: cross Abstract: Access control in large language models (LLMs) requires modular mechanisms to enable domain-specific behavior without retraining or cross-domain inter
arXiv:2507.03065v2 Announce Type: replace Abstract: Why do some macroscopic structures remain identifiable even though their microscopic constituents continually change? Vortices persist while fluid p
arXiv:2511.08299v2 Announce Type: replace Abstract: Salamander-like robots are designed inspired by the skeletal structure of their biological counterparts. However, existing controllers cannot fully
arXiv:2606.12058v1 Announce Type: cross Abstract: Attention is the key mechanism underlying in-context learning in transformers, and attention patterns have been observed empirically to emerge abruptl
arXiv:2606.11284v1 Announce Type: cross Abstract: Real-world multi-agent systems, from traffic coordination to resource allocation, are often modeled as general-sum games where individual incentives c
arXiv:2606.11240v1 Announce Type: cross Abstract: Accurate modeling of quantum many-body systems often requires computationally expensive simulations such as Density Matrix Renormalization Group (DMRG
arXiv:2606.11605v1 Announce Type: cross Abstract: Predicting process-property relationships in manufacturing is often challenged by high experimental costs and the limited interpretability of complex
arXiv:2510.08073v2 Announce Type: replace Abstract: AI-generated videos have achieved near-perfect visual realism (e.g., Sora), urgently necessitating reliable detection mechanisms. However, detecting
arXiv:2606.11247v1 Announce Type: cross Abstract: Generative models are increasingly used to propose designs, data, and control actions for physical systems, yet many such systems are governed by hard
arXiv:2606.12282v1 Announce Type: cross Abstract: Expressive performance rendering (EPR) aims to generate realistic performances constrained on sequences of notes. However, flow matching audio editing
arXiv:2511.13207v2 Announce Type: replace-cross Abstract: Object navigation in unseen indoor environments requires agents to perform semantic search under partial observability. Vision-language models
arXiv:2606.11838v1 Announce Type: new Abstract: Reward models for text-to-video (T2V) generation guide post-training but often fail at fine-grained semantic alignment. We trace this to two structural
arXiv:2602.23545v2 Announce Type: replace Abstract: In the real world, planning is often challenged by distribution shifts. As such, a model of the environment obtained under one set of conditions may
arXiv:2606.11396v1 Announce Type: new Abstract: Dexterous manipulation with multi-finger hands can be sensitive to physical parameters such as object shape, pose, and friction coefficients. While simu
arXiv:2606.12048v1 Announce Type: new Abstract: High-risk applications in robotics, such as robot-assisted surgery, present unique challenges. These systems must be both highly precise and interpretab
arXiv:2603.15158v2 Announce Type: replace Abstract: Addressing the domain adaptation problem becomes more challenging when distribution shifts across domains stem from latent confounders that affect b
arXiv:2606.11196v1 Announce Type: cross Abstract: Decentralized LLM inference networks need lightweight, reference-free quality evaluation for Proof of Quality (PoQ). We present PoQ-Judge, a framework
arXiv:2606.11245v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, raising expectations for Artificial General Intelligence (A
arXiv:2504.09762v4 Announce Type: replace Abstract: Intermediate token generation (ITG), where a model produces output before the solution, has become a standard method to improve the performance of l
arXiv:2603.13854v2 Announce Type: replace-cross Abstract: We introduce power term polynomial algebra, a representation language for Boolean formulae designed to bridge conjunctive normal form (CNF) an
arXiv:2606.11619v1 Announce Type: new Abstract: Vision sensors provide a lightweight solution for spacecraft proximity operations, but monocular spacecraft 6D pose estimation remains difficult under i
arXiv:2602.02229v2 Announce Type: replace Abstract: We study the problem of monitoring model performance in dynamic environments where labeled data are limited. To this end, we propose prediction-powe
arXiv:2606.11217v1 Announce Type: cross Abstract: The proliferation of large language models (LLMs) and autonomous AI agents has given rise to a rapidly growing methodological paradigm: 'in silico' be
arXiv:2606.11542v1 Announce Type: cross Abstract: Modern pretrained self-supervised automatic speech recognition models are trained on large-scale audio data to encode speech into contextualized repre
arXiv:2511.19314v2 Announce Type: replace Abstract: Information-seeking is a core capability for AI agents, requiring them to gather and reason over tool-generated information across long trajectories
arXiv:2606.11556v1 Announce Type: cross Abstract: Continuous electrocardiography (ECG) monitoring could surface rhythm abnormalities before they escalate into cardiovascular events. However, a deploya
arXiv:2606.11508v1 Announce Type: new Abstract: Accurate prediction of absorption, distribution, metabolism, and excretion (ADME) properties is critical to drug discovery, but remains challenging beca
arXiv:2606.11663v1 Announce Type: cross Abstract: Accurate salary prediction is critical for bridging the information gap between employers and job seekers in modern labor markets. Existing approaches
arXiv:2606.11209v1 Announce Type: cross Abstract: Visual question answering increasingly requires multi-step reasoning. Recent post-training with reinforcement learning under verifiable rewards (RLVR)
arXiv:2603.22934v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) improves large language model applications by grounding generation in retrieved evidence, but also introduces c
arXiv:2606.11243v1 Announce Type: cross Abstract: De novo protein generation has transformative potential in therapeutic design, enzyme engineering, and synthetic biology. While diffusion-based and fl
arXiv:2410.24145v3 Announce Type: replace-cross Abstract: We apply conformal prediction techniques to regression problems with circular responses, producing prediction sets with adaptive arc length an
arXiv:2606.12329v1 Announce Type: new Abstract: AI coding assistants now support a growing share of software work, from quick scripts to production applications. Yet these agents remain largely statel
arXiv:2512.11081v2 Announce Type: replace-cross Abstract: Feature and Interaction Importance (FII) methods are essential in supervised learning for assessing the relevance of input variables and their
arXiv:2606.11466v1 Announce Type: new Abstract: Point cloud semantic segmentation requires architectures that capture both fine-grained local geometry and broad global scene structure. Transformer-bas
arXiv:2606.12125v1 Announce Type: new Abstract: Long-video understanding remains challenging for multimodal large language models, because temporally extended videos often contain thousands of frames
arXiv:2606.11915v1 Announce Type: cross Abstract: We present a quality-adaptive angular-margin learning framework that improves feature generalization by enforcing intra-class compactness and inter-cl
arXiv:2606.11270v1 Announce Type: cross Abstract: Distillation of a language model intended to transfer benign behavior to a student model may also transfer undesirable characteristics, if they are pr
arXiv:2606.11339v1 Announce Type: cross Abstract: We study distributed optimization with stochastic gradients and finite-bit communication modeled by random (unbiased) quantization. We propose q-PDGD,
arXiv:2606.12211v1 Announce Type: cross Abstract: A central principle in quantum machine learning is that an ansatz should be expressive enough to represent the quantum data of interest. Yet, the expr
arXiv:2606.11260v1 Announce Type: cross Abstract: Humans process rich auditory environments through tightly integrated cognitive capabilities such as audio perception, audio reasoning, and memory. Des
arXiv:2606.11574v1 Announce Type: new Abstract: In many materials and product design problems, desirable candidates exhibit properties that fall within an acceptable range rather than achieve a single
arXiv:2606.11689v1 Announce Type: new Abstract: Composed Image Retrieval (CIR) constitutes a pivotal paradigm requiring models to perform joint reasoning on reference images and modification texts. Ho
arXiv:2606.11761v1 Announce Type: new Abstract: Dynamic data pruning techniques aim to reduce computational cost while minimizing information loss by periodically selecting representative subsets of i
arXiv:2606.12232v1 Announce Type: new Abstract: Masked diffusion language models (dLLMs) have recently emerged as a competitive alternative to autoregressive language models, with the promise of faste
arXiv:2606.11857v1 Announce Type: cross Abstract: Multi-channel mixed-SNR training improves out-of-distribution (OOD) generalisation of deep learning channel estimators for IEEE 802.11p vehicular comm
arXiv:2606.11683v1 Announce Type: cross Abstract: Spatial reasoning from egocentric videos is inherently challenging because the observable evidence is constrained by the camera trajectory. Existing m