Knowledge catalogue
arXiv:2608.29126v1 Announce Type: new Abstract: Referring single-object tracking enables language-grounded target initialization and subsequent tracking by jointly leveraging semantic cues and visual
arXiv:2608.29106v1 Announce Type: new Abstract: While neural rendering methods such as 3D Gaussian Splatting achieve remarkable visual fidelity, traditional polygonal meshes remain the backbone of est
arXiv:2608.29896v1 Announce Type: new Abstract: A robot's effective ``mind'' need not reside in a single policy. It can emerge when specialized components perceive, reason, predict, act, verify, and r
arXiv:2608.28630v1 Announce Type: cross Abstract: Compared with half-duplex dialogue systems where the system waits for user turn completion before it responds, natural full-duplex dialogue systems re
arXiv:2608.30462v1 Announce Type: cross Abstract: Large language models exhibit substantial performance variation across languages, even when solving semantically equivalent tasks. Existing analyses o
arXiv:2608.28709v1 Announce Type: cross Abstract: Accurate boundary delineation of brain tumors in Magnetic Resonance Imaging (MRI) is a critical yet formidable challenge in neuro-oncology due to inhe
arXiv:2512.23765v2 Announce Type: replace-cross Abstract: Speculative decoding (SD) accelerates large language model (LLM) inference by using a lightweight draft model to propose tokens and a stronger
arXiv:2608.30021v1 Announce Type: cross Abstract: Errors in radiology reports can adversely affect patient treatment, yet automated report quality assurance remains challenging because errors are ofte
arXiv:2511.14086v2 Announce Type: replace-cross Abstract: Despite recent progress in 3D-LLMs, they remain limited in accurately grounding language to visual and spatial elements in 3D environments. Th
arXiv:2608.29929v1 Announce Type: new Abstract: Vehicle attribute recognition is an important task in intelligent transportation systems, particularly when Automatic License Plate Recognition (ALPR) i
arXiv:2608.30980v1 Announce Type: cross Abstract: We study self-modeling: an LLM's ability to answer questions about its own behavior. We focus on verifiable behavioral questions, such as whether a pr
arXiv:2608.28716v1 Announce Type: cross Abstract: Deep learning segmentation models are often evaluated using geometric metrics such as Dice, HD95, and ASD, yet it remains unclear to what extent impro
arXiv:2608.29835v1 Announce Type: new Abstract: Large language models (LLMs) often produce fluent but weakly grounded conclusions when reasoning over non-interactive, long-form narratives. A central f
arXiv:2608.31066v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning improves multi-step problem solving, but long reasoning traces inflate inference cost. Token-level CoT compression redu
arXiv:2608.31033v1 Announce Type: new Abstract: Lightstage facial capture produces production-quality digital humans, but it is resource and labor-intensive. Multi-camera setups, hours of computation,
arXiv:2608.28814v1 Announce Type: cross Abstract: Converting scientific figures into executable code has gained increasing attention, yet existing methods primarily focus on reproducing the reference
arXiv:2510.03204v2 Announce Type: replace Abstract: Web agents powered by large language models (LLMs) must process lengthy web page observations to complete user goals; these pages often exceed tens
arXiv:2608.29802v1 Announce Type: new Abstract: Face recognition systems are increasingly deployed in security-critical applications, yet they remain vulnerable to presentation and morph attacks. Pres
arXiv:2608.30392v1 Announce Type: new Abstract: Global food security and sustainable climate action increasingly rely on robust, scalable agricultural monitoring. Earth observation foundation models h
arXiv:2608.29183v1 Announce Type: new Abstract: Conditional flow matching has enabled a step forward in object 6D pose estimation, achieving state-of-the-art performance by progressively denoising and
arXiv:2608.29012v1 Announce Type: new Abstract: Time-series data across physical and biological domains are fundamentally driven by complex, non-stationary oscillatory modes. While deep learning model
arXiv:2608.30419v1 Announce Type: new Abstract: Healthcare workforce scheduling is an NP-hard optimization problem requiring simultaneous satisfaction of labor regulations, coverage requirements, empl
arXiv:2608.28594v1 Announce Type: new Abstract: Conversational analytics systems assume the user already has a well-formed question, leaving a non-expert facing a blank query box on an unfamiliar ente
arXiv:2608.30210v1 Announce Type: cross Abstract: AI image generators now create face portraits that are hard to tell from real photographs. Vision-language models (VLMs) are increasingly proposed to
arXiv:2608.29519v1 Announce Type: new Abstract: We introduce Function-Room Generation, a new indoor 3D scene generation setting that creates rooms supporting explicit functional goals rather than mere
arXiv:2608.30741v1 Announce Type: new Abstract: A central question in modern machine learning is how much a trained model can be compressed without changing its behavior, to reduce the memory, compute
arXiv:2608.29395v1 Announce Type: new Abstract: Vision-language models such as CLIP and SigLIP provide strong zero-shot recognition, but their predictions can degrade when deployed on target data that
arXiv:2608.29483v1 Announce Type: cross Abstract: Modern Vision-Language Models (VLMs) perform well above the human baseline in image geolocalization, a task critically important in disaster response,
arXiv:2608.29755v1 Announce Type: new Abstract: Electroencephalography (EEG) is a promising, non-invasive, and cost-effective modality for Alzheimer's disease (AD) detection, but deep learning methods
arXiv:2608.29113v1 Announce Type: new Abstract: We aim to improve frozen DINOv3 dense-prediction models under distribution shift by adding inference computation inside the visual backbone, without cha
arXiv:2608.29251v1 Announce Type: new Abstract: Privacy protection for live web traffic requires more than detecting private spans. Agent-based privacy protection systems must determine whether an out
arXiv:2608.29193v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) can assign similar confidence to answers that fail for different reasons. We propose HalluPrism, a behavioral d
arXiv:2608.29120v1 Announce Type: cross Abstract: Speech Language Models (SLMs) are increasingly deployed in multi-speaker environments, yet their ability to attribute speech to the correct speaker an
arXiv:2510.00628v3 Announce Type: replace-cross Abstract: Large audio-language models (LALMs) are often used in tasks that involve reasoning over ordered options. An open question is whether their pre
arXiv:2608.29715v1 Announce Type: cross Abstract: Transformers rely on position embedding mechanisms in long context modeling in most cases. Rotary Position Embedding (RoPE) embeds positional informat
arXiv:2608.29198v1 Announce Type: new Abstract: As Large Language Models (LLMs) increasingly encourage users to disclose personal profiles for tailored assistance, measuring their political alignment
arXiv:2601.08626v4 Announce Type: replace Abstract: Large language models (LLMs) excel at semantic understanding, yet their ability to reconstruct internal structure from scrambled inputs remains unde
arXiv:2608.29591v1 Announce Type: new Abstract: Users articulate the same advice-seeking request in different ways: some specify detailed constraints, others gesture at a vague need. Prior work treats
arXiv:2608.30841v1 Announce Type: new Abstract: Large language models can often generate plausible mathematical reasoning traces, but reliably identifying the correct solution among multiple candidate
arXiv:2511.22187v4 Announce Type: replace Abstract: Realistic and controllable simulation is critical for advancing end-to-end autonomous driving, yet existing approaches often struggle to support nov
arXiv:2608.29207v1 Announce Type: new Abstract: Protein structure modeling rests on a single computational primitive: the interaction between what a residue is (sequence content) and where it sits (th
arXiv:2608.29696v1 Announce Type: new Abstract: Evaluating research ideas generated by LLMs is difficult because their scientific value cannot be fully determined by objective criteria, and no single
arXiv:2608.31058v1 Announce Type: new Abstract: When information extraction fails, a natural instinct is to improve the model doing it: for example, by scaling it up or refining its reasoning. In this
arXiv:2608.28666v1 Announce Type: new Abstract: Video-language models (VLMs) remain brittle on tasks that require tracking events over time and grounding answers in specific spatial regions. We propos
arXiv:2608.29783v1 Announce Type: new Abstract: Industrial anomaly detection is a critical component of modern manufacturing. Most traditional unsupervised methods rely on modelling normal feature dis
arXiv:2608.28696v1 Announce Type: new Abstract: Visual in-context learning (ICL) with multimodal large language models (MLLMs) is effective for fine-grained visual classification, but each retrieved i
arXiv:2508.08966v2 Announce Type: replace Abstract: The attention mechanism lies at the core of the transformer architecture, providing an interpretable model-internal signal that has motivated a grow
arXiv:2604.02382v2 Announce Type: replace-cross Abstract: The scale and complexity of modern cloud infrastructure have made 'Infrastructure-as-Code' (IaC) essential for managing deployments through de
arXiv:2602.06181v2 Announce Type: replace Abstract: Post-training quantization reduces the memory needed to run large language models but alters their social biases in ways that aggregate metrics fail
arXiv:2608.29892v1 Announce Type: new Abstract: Learning solution operators for partial differential equations (PDEs) on irregular and geometry-dependent domains remains a central challenge in scienti
arXiv:2608.29616v1 Announce Type: new Abstract: Criminal judgment prediction requires models to infer statutory articles, charges, and sentencing outcomes from case facts. Unlike standard classificati
arXiv:2608.29168v1 Announce Type: new Abstract: The LLM-as-a-Judge paradigm has emerged as a scalable alternative to human evaluation. However, single-model judges are limited by their inherent model
arXiv:2512.16391v2 Announce Type: replace-cross Abstract: Attention is the dominant source of latency during long-context LLM inference, an increasingly popular workload with reasoning models and RAG.
arXiv:2608.30316v1 Announce Type: new Abstract: Existing class-incremental learning methods struggle in multi-label scenarios (MLCIL) due to the inherent contradiction of learning objectives arising f
arXiv:2505.15741v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) and Evolutionary Computation (EC) are increasingly being combined to support automated optimization, algorithm de
arXiv:2608.29257v1 Announce Type: new Abstract: Multiple-choice questions (MCQs) are a standard format for evaluating large language models (LLMs), yet the popularity of answer options can confound ev
arXiv:2505.16305v3 Announce Type: replace Abstract: While CANDECOMP/PARAFAC (CP) decomposition (CPD) is fundamental for tensor reconstruction, Bayesian CPD often scales poorly because variational upda
arXiv:2508.13073v3 Announce Type: replace-cross Abstract: Robotic manipulation, a key frontier in robotics and embodied AI, requires precise motor control and multimodal understanding, yet traditional
arXiv:2608.30679v1 Announce Type: cross Abstract: Large Reasoning Models produce Long Chains-of-Thought (LCoTs) which involve breaking down the problem into smaller reasoning steps before reaching the
arXiv:2608.30955v1 Announce Type: new Abstract: Accurate action models are critical for effective planning. Existing action-model learning methods largely assume simple action representations or becom