Knowledge catalogue
arXiv:2511.20279v3 Announce Type: replace Abstract: End-to-end transformer architectures have driven significant progress in multi-object tracking by unifying detection and association into a single,
arXiv:2609.02116v1 Announce Type: new Abstract: Reverse-logistics operators often decide how to inspect and route returned assets before their condition is fully observed, while full inspection consum
arXiv:2609.02780v1 Announce Type: cross Abstract: Streaming video understanding is a critical capability for real-world applications, including embodied intelligence, autonomous driving, industrial mo
arXiv:2606.15517v2 Announce Type: replace Abstract: Large language models often struggle with sensitive prompts. They may refuse outright, provide generic safety boilerplate, or fail to address the us
arXiv:2602.05605v2 Announce Type: replace-cross Abstract: Diffusion Transformers (DiTs) are costly at high resolution because self-attention scales quadratically with token sequence length. Existing p
arXiv:2609.02224v1 Announce Type: cross Abstract: Post-hoc saliency maps such as Grad-CAM are increasingly used to audit why a deployed vision model made a decision, yet the heatmap drifts when the in
arXiv:2509.00030v4 Announce Type: replace Abstract: Current sign language translation (SLT) systems attempt to learn all aspects of signing---manual gestures, high-speed fingerspelling, and asynchrono
arXiv:2609.01886v1 Announce Type: new Abstract: The objective of this paper is to match dictionary sign videos to corresponding signs in continuous signing videos, where a match is defined by the visu
arXiv:2609.01676v1 Announce Type: new Abstract: Reinforcement learning achieves strong traffic signal control performance in simulation, yet policies trained in simulators often fail once deployed in
arXiv:2609.02241v1 Announce Type: new Abstract: Federated Learning (FL) allows decentralized clients to train models collaboratively while preserving data privacy. However, distribution mismatch acros
arXiv:2509.02191v3 Announce Type: replace Abstract: Predict-Then-Optimize combines machine learning predictions with downstream optimization to support decision-making when problem parameters are unkn
arXiv:2609.02217v1 Announce Type: new Abstract: LLM agents increasingly self-improve by writing and reusing textual skills, kept either as one global document or as a flat pool of per-task entries, th
arXiv:2609.01827v1 Announce Type: new Abstract: Structural magnetic resonance imaging (MRI) images are sometimes corrupted over a contiguous set of slices, where acquisition, motion, hardware, or reco
arXiv:2509.16509v3 Announce Type: replace Abstract: Humans learn in two complementary ways: a slow, cumulative process that builds broad, general knowledge, and a fast, on-the-fly process that capture
arXiv:2609.02203v1 Announce Type: cross Abstract: Time series representation learning (TSRL) has attracted growing research interests in recent years. Two recent explorations in TSRL are: i) exploitin
arXiv:2407.00966v3 Announce Type: replace Abstract: In traditional models of supervised learning, the goal of a learner-- given examples from an arbitrary joint distribution on R^d imes {pm 1}-- is to
arXiv:2609.01641v1 Announce Type: cross Abstract: In the era of digital social interaction, searching friends' posts from massive social streams has become a fundamental user need. However, while mode
arXiv:2505.23713v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in socially grounded applications, where success requires interpreting context, inferring oth
arXiv:2609.02140v1 Announce Type: cross Abstract: Encrypted traffic classification infers semantics beyond the flow record from transport-layer observables, and supervised training rests on labels tha
arXiv:2609.02886v1 Announce Type: new Abstract: We introduce SolarWM, a fully open foundation for building interactive video world models from data preparation through long-horizon inference. Training
arXiv:2609.02343v1 Announce Type: cross Abstract: Recent advances in audio-language modeling have been driven by large-scale audio captioning datasets. However, existing datasets remain limited by low
arXiv:2609.02622v1 Announce Type: new Abstract: Simulation-based science often requires a distribution over simulator parameters whose push-forward reproduces a set of real observations: this is the s
arXiv:2609.02018v1 Announce Type: cross Abstract: Class unlearning aims to remove a model's ability to recognize designated forget classes while preserving performance on retain classes. However, low
arXiv:2609.02741v1 Announce Type: cross Abstract: Signal Phase and Timing (SPaT) messages are a cornerstone of connected vehicle (CV) safety, enabling CVs to perceive and respond to intersection state
arXiv:2609.01936v1 Announce Type: cross Abstract: A language model's prediction of its next token develops across layers, and lens methods track this process by decoding intermediate hidden states int
arXiv:2609.02531v1 Announce Type: new Abstract: World Action Models (WAMs) leverage the capabilities of large-scale pretrained video diffusion models to jointly predict future observations and actions
arXiv:2609.01737v1 Announce Type: new Abstract: Mobile payment applications in Nepal are graphically mediated and largely inaccessible to visually impaired users. This paper presents SpeakPay, a voice
arXiv:2608.25202v3 Announce Type: replace-cross Abstract: Spec-Driven Development (SDD) is a fast-emerging practice in which a structured natural-language specification, written by a developer, or (mo
arXiv:2609.02519v1 Announce Type: cross Abstract: Uncertain knowledge graphs (UKGs) extend knowledge graphs by assigning each triple a continuous confidence score. Since most possible triples lack obs
arXiv:2603.23101v4 Announce Type: replace Abstract: Intelligent spectroscopy serves as a pivotal element in AI-driven closed-loop scientific discovery, functioning as the critical bridge between matte
arXiv:2609.02504v1 Announce Type: new Abstract: With the recent rapid progress in generative models, image editing has made remarkable advances, yet achieving faithful edits that precisely modify only
arXiv:2609.01849v1 Announce Type: new Abstract: This article presents SSAKG 2.0, an open-source software package for constructing and operating Structural Sequential Associative Knowledge Graphs (SSAK
arXiv:2512.15662v4 Announce Type: replace Abstract: Human beings solve complex problems through critical thinking, where reasoning and evaluation are intertwined to converge toward correct solutions.
arXiv:2609.02560v1 Announce Type: new Abstract: Four-dimensional (4D) Radar is a powerful sensing modality capable of detecting surrounding three-dimensional (3D) objects under diverse weather conditi
arXiv:2609.02348v1 Announce Type: new Abstract: Traffic sign detection faces a long-tailed data distribution. Many rare signs matter as much as common ones from a regulatory standpoint, yet they have
arXiv:2609.02344v1 Announce Type: cross Abstract: Existing cell embedding methods predominantly rely on transcriptomic or proteomic measurements and represent each cell as a holistic entity, thereby o
arXiv:2609.01749v1 Announce Type: cross Abstract: Modern generative models, such as GANs, diffusion architectures, and autoregressive systems, now produce facial images that are nearly indistinguishab
arXiv:2609.02118v1 Announce Type: new Abstract: In computational pathology (CPath), developing omni-modal self-supervised learning (SSL) models that integrate histology, genomics, and clinical reports
arXiv:2609.02255v1 Announce Type: new Abstract: Recent text-to-image models have become increasingly capable of rendering explicit text, but reliable localized text control requires more than generati
arXiv:2609.01810v1 Announce Type: new Abstract: Farsi, spoken by more than 120 million people, lacks a comprehensive benchmark for dialogue generation and understanding. We introduce TALKFA, a unified
arXiv:2609.02204v1 Announce Type: new Abstract: Text-Video Retrieval (TVR) retrieves videos that match a natural-language query, but extending image-text models such as CLIP to videos is fundamentally
arXiv:2609.01899v1 Announce Type: new Abstract: Multi-camera systems are increasingly practical for robotics, AR/VR, and autonomous driving because complementary views reduce depth ambiguity and prese
arXiv:2609.02639v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) has become a de facto standard for parameter-efficient fine-tuning (PEFT), yet its performance is highly sensitive to initi
arXiv:2609.02244v1 Announce Type: new Abstract: Large language models (LLMs) often struggle when low-resource training data are ambiguous or incomplete. Task-level natural-language priors can provide
arXiv:2609.02085v1 Announce Type: cross Abstract: We present TropicalCycloneNext (TC-Next), a multimodal deep learning model that forecasts tropical cyclone track and intensity at 6-24 h leads by leve
arXiv:2609.02359v1 Announce Type: new Abstract: Visual grounding maps language referents to spatial targets and is central to open-vocabulary perception with vision-language models. Existing methods h
arXiv:2609.01786v1 Announce Type: new Abstract: Hyperspectral image classification still relies heavily on random pixel splits within a single scene. The Salinas dataset, randomly split, is among the
arXiv:2609.02039v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved remarkable performance by leveraging complementary information from large-scale image-text pairs. However, m
arXiv:2609.02115v1 Announce Type: cross Abstract: Natural language interfaces to databases have traditionally suffered from three structural limitations: exclusive targeting of relational SQL, uncondi
arXiv:2609.01909v1 Announce Type: new Abstract: Clinical prediction can saturate for two different reasons: a fitted learner may fail to extract available information, or the recorded variables may im
arXiv:2609.02411v1 Announce Type: new Abstract: A capable brain-MRI report generator can still be, in effect, diagnostically silent. When a multi-chain chain-of-thought (CoT) reporter built on a medic
arXiv:2609.02049v1 Announce Type: cross Abstract: LLMs latent-state reasoning methods replace discrete intermediate tokens with continuous states, such as weighted mixtures of token embeddings, to ret
arXiv:2609.02852v1 Announce Type: new Abstract: LLMs are trained to generate natural language. However, various strands of evidence indicate that an LLM's externalized linguistic outputs and mechanist
arXiv:2609.01852v1 Announce Type: new Abstract: Persistent memory supports personalized agents, but a stale stored fact can override current authoritative evidence without warning. We study when this
arXiv:2609.02367v1 Announce Type: cross Abstract: Joint audio-video generation models have made substantial progress in visual quality and audio-visual synchronization. However, they still provide lim
arXiv:2609.01627v1 Announce Type: cross Abstract: Explanations play a crucial role in creating trustworthy recommender systems (RS), yet choosing a good explanation method presents challenges. Many ex
arXiv:2609.01867v1 Announce Type: cross Abstract: A major question in cognitive modeling concerns the behavioral alignment between large language models and humans across linguistic and non-linguistic
arXiv:2609.02864v1 Announce Type: new Abstract: Recent advancements in unified generative models (UGMs) and world simulators have achieved unprecedented results in visual perception and synthesis. How
arXiv:2603.03072v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to assist scientists across diverse workflows. A key challenge is generating high-quality figures
arXiv:2609.02067v1 Announce Type: new Abstract: Scientific benchmarks are commonly built by domain experts who write tasks and cross-check one another's work, or who adapt existing material from textb