Knowledge catalogue
arXiv:2602.08986v2 Announce Type: replace-cross Abstract: In hierarchical multi-label classification, a persistent challenge is enabling model predictions to reach deeper levels of the hierarchy for m
arXiv:2606.12207v1 Announce Type: cross Abstract: Embodied intelligence now spans navigation, household assistance, manipulation, autonomous driving, aerial agents, and multimodal large-model control.
arXiv:2606.11295v1 Announce Type: cross Abstract: Recovering cosmological information beyond the power spectrum is a central goal for upcoming cosmological surveys, since late-time non-Gaussian signal
arXiv:2606.11203v1 Announce Type: new Abstract: Structured sequence generation often requires a model to satisfy several input-derived constraints in a single output. Standard decoding methods may ass
arXiv:2510.24515v2 Announce Type: replace Abstract: The Team Orienteering Problem (TOP) generalizes many real-world multi-agent scheduling and routing tasks that occur in autonomous mobility, aerial l
arXiv:2606.11277v1 Announce Type: new Abstract: Reliable extrapolation remains a central challenge for generative models in computational physics, because models trained over finite ranges of time, pa
arXiv:2510.23320v2 Announce Type: replace-cross Abstract: We introduce LibriConvo, a synthetic conversational speech corpus for speaker diarization and automatic speech recognition (ASR), built by ins
arXiv:2606.11220v1 Announce Type: new Abstract: Forecasting human life outcomes is important to gain insights into how individuals attain long and healthy lives. Conventional statistical approaches yi
arXiv:2603.24080v2 Announce Type: replace Abstract: Benchmarks like MMLU suggest flagship language models approach factuality saturation above 90%. LLMpedia shows this picture is incomplete. We materi
arXiv:2606.11258v1 Announce Type: new Abstract: Gradient-based inversion of reaction-diffusion systems is typically approached via surrogate models or physics-informed neural networks (PINNs), while t
arXiv:2606.11922v1 Announce Type: cross Abstract: Recent respiratory sound classification (RSC) studies largely rely on CLS-token driven self-attention architectures such as the Audio Spectrogram Tran
arXiv:2606.11737v1 Announce Type: cross Abstract: Close-in exoplanets exhibit a wide range of orbital architectures and physical properties shaped by both formation conditions and migration processes.
arXiv:2601.21898v2 Announce Type: replace Abstract: The rise of model hubs has made it easier to access reusable model components, making model merging a practical tool for combining capabilities. Yet
arXiv:2509.14860v2 Announce Type: replace-cross Abstract: Image classification has traditionally relied on parameter-intensive model training, requiring large-scale annotated datasets and extensive fi
arXiv:2606.11702v1 Announce Type: cross Abstract: To make clinically grounded decisions, medical AI agents are expected to go beyond simple recognition and be capable of tool retrieval, evidence acqui
arXiv:2606.11868v1 Announce Type: new Abstract: De novo peptide sequencing from tandem mass spectrometry is pivotal in proteomics, enabling identification of novel peptides without reference databases
arXiv:2606.12047v1 Announce Type: cross Abstract: In this paper, we address the problem of zero-shot understanding of accidents from surveillance videos by identifying when an impact event occurs, wha
arXiv:2606.11724v1 Announce Type: new Abstract: Theory of Mind (ToM) reasoning requires inferring agents' beliefs from partial and asymmetric observations, which remains an open challenge for LLMs. Ex
arXiv:2512.08211v2 Announce Type: replace Abstract: Large language models (LLMs) are moving from cloud-centric services toward on-device embedded AI, where models interact with private, longitudinal s
arXiv:2602.22638v2 Announce Type: replace Abstract: Route-planning agents powered by large language models (LLMs) have emerged as a promising paradigm for supporting everyday human mobility through na
arXiv:2606.11870v1 Announce Type: cross Abstract: Machine learning is increasingly applied to accelerate the discovery of novel materials by exploring large compositional and structural design spaces.
arXiv:2606.11826v1 Announce Type: new Abstract: Designing anthropomorphic dexterous robotic hands remains challenging as the design space straddles morphology, actuation, and sensing properties, and p
arXiv:2606.11416v1 Announce Type: cross Abstract: Repository-level benchmarks for evaluating Large Language Model (LLM) code repair on Secure Multi-Party Computation (MPC) software do not yet exist, a
arXiv:2606.12106v1 Announce Type: cross Abstract: This paper presents our solution to the 2026 SoccerNet VQA Challenge. We first develop a cost-effective data synthesis pipeline driven by a Vision-Lan
arXiv:2606.11609v1 Announce Type: new Abstract: Stance detection requires identifying an author's position toward a target, often from short-form texts where stance is implicit, indirect, or rhetorica
arXiv:2606.11739v1 Announce Type: cross Abstract: We introduce a multi-view in-cabin monitoring dataset for public transportation with synchronized RGB and depth images from four inward-facing cameras
arXiv:2606.12300v1 Announce Type: cross Abstract: Temporal grounding--returning the interval [t_s, t_e] for a natural-language query over a video--is the language interface to long-form video, yet has
arXiv:2510.22397v2 Announce Type: replace-cross Abstract: Network operators monitor their infrastructure by collecting telemetry data such as packet counts, byte rates, or flow volumes, yet answering
arXiv:2602.23461v2 Announce Type: replace-cross Abstract: Data assimilation (DA) for compressible flows with shocks is challenging because many classical DA methods generate spurious oscillations and
arXiv:2606.11676v1 Announce Type: cross Abstract: Traditional wildfire models rely on rigid, low-dimensional parameters and static fuel maps, frequently underpredicting fire spread. To address this we
arXiv:2606.11199v1 Announce Type: cross Abstract: We present NightFeats, a structured multi-agent retrieval-augmented generation (RAG) system submitted to the MMU-RAGent competition at NeurIPS 2025, w
arXiv:2507.21164v2 Announce Type: replace-cross Abstract: Unsupervised anomaly detection (UAD) aims to detect anomalies without labeled data, a necessity in many machine learning applications where an
arXiv:2606.11490v1 Announce Type: new Abstract: Indoor localization from wireless measurements remains challenging in large-scale deployments due to substantial variation in building geometry, the set
arXiv:2606.11602v1 Announce Type: new Abstract: Audio-visual Generalized Zero-shot Learning (AV-GZSL) is a challenging task that aims to classify both seen and unseen objects or scenes by integrating
arXiv:2606.12071v1 Announce Type: cross Abstract: LLMs are increasingly used to generate and judge scientific ideas. This makes novelty evaluation a central problem. Full idea evaluation is difficult
arXiv:2606.11949v1 Announce Type: new Abstract: We present an online monitoring system for distributional shift in deployed safety classifiers, using calibrated sequential statistics to detect when a
arXiv:2606.12169v1 Announce Type: cross Abstract: High-stakes clinical use of large vision-language models (LVLMs) requires reasoning that is grounded in visual evidence and clinical knowledge, not ju
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:2606.11386v1 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: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: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: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:2606.11396v1 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
Pop quiz, which of these is no longer true (or at least directionally true), two years later? 9 reasons that OpenAI could someday be seen as the WeWork of AI: 👉 Lots of competitors are catching up. 👉
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: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:2606.12125v1 Announce Type: new Abstract: Long-video understanding remains challenging for multimodal large language models, because temporally extended videos often contain thousands of frames
arXiv:2606.11270v1 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:2606.11260v1 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:2606.12250v1 Announce Type: new Abstract: Large language models (LLMs) in medicine are mainly evaluated using multiple-choice question answering (MCQA), which can overestimate real clinical abil
arXiv:2603.00461v3 Announce Type: replace Abstract: We present ReMoT, a unified training paradigm to systematically address the fundamental shortcomings of VLMs in spatio-temporal consistency -- a cri
arXiv:2606.12412v1 Announce Type: cross Abstract: Vision-language models (VLMs) project images into hundreds to thousands of visual tokens, making decoder inference expensive in both attention computa