Knowledge catalogue
arXiv:2607.12645v1 Announce Type: new Abstract: Generative modeling of longitudinal Electronic Health Records is increasingly important for privacy-preserving research, yet standard autoregressive mod
arXiv:2607.10203v2 Announce Type: replace-cross Abstract: Adaptive-compute world models -- early-exit or mixture-of-depths predictors that spend variable depth per step -- assume depth buys better pre
arXiv:2607.12293v1 Announce Type: new Abstract: Predicting pedestrian crossing intention is a safety-critical task for autonomous driving, yet existing approaches often rely on single-modal inputs or
arXiv:2511.04689v3 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) typically requires thousands of benchmark items, making the process expensive, slow, and increasingly
arXiv:2607.12500v1 Announce Type: cross Abstract: Deep learning models for online handwriting recognition have been shown effective and are increasingly deployed in practical applications. However, th
arXiv:2510.00506v2 Announce Type: replace Abstract: How can we reconstruct 3D hand poses when large portions of the hand are heavily occluded by itself or by objects? Humans often resolve such ambigui
arXiv:2607.12469v1 Announce Type: cross Abstract: Many agent-safety evaluation results are not yet load-bearing evidence: identical nominal outcomes (task success, attack success, monitor scores) may
arXiv:2607.11098v2 Announce Type: replace-cross Abstract: Tool-using LLM agents are mostly evaluated assuming all tools work. When a tool times out, returns a week-stale value, or has its description
arXiv:2607.12619v1 Announce Type: new Abstract: The rapid emergence of LLM-powered autonomous and semi-autonomous agents is reshaping software systems from static, request-response components into goa
arXiv:2607.12051v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being explored for clinical decision support, but their reliability in complex oncology treatment planning
arXiv:2607.10526v2 Announce Type: replace Abstract: Stateful personal agents increasingly maintain long-term user profiles, episodic memories, and reusable skills. This persistence turns conversationa
arXiv:2607.11183v2 Announce Type: replace Abstract: Large language models increasingly operate as tool-using agents, where small format, argument, or function-call errors can invalidate otherwise plau
arXiv:2607.12122v1 Announce Type: new Abstract: We present an agentic approach to autonomous neural operator discovery based on an AI scientific community, which consists of a swarm of virtual laborat
arXiv:2607.12048v1 Announce Type: cross Abstract: Deploying medical visual question answering (MedVQA) systems in real-world clinical settings requires models that adapt to new clinical tasks without
arXiv:2607.11245v2 Announce Type: replace-cross Abstract: To reduce the substantial engineering effort required to test the corresponding applications from Android to OpenHarmony, migrating existing G
arXiv:2607.12468v1 Announce Type: cross Abstract: We describe our submission to Task 1 of the 2nd MLCSLM Challenge: a cascaded diarization-then-recognition system that combines DiariZen-Large-s80 (Wav
arXiv:2607.12181v1 Announce Type: new Abstract: Robot gaze is a major component of human-robot dialogue coordination. Most studies of gaze in human-robot dialogue focus on face-to-face social conversa
arXiv:2607.12054v1 Announce Type: cross Abstract: Breast ultrasound is widely used for screening, yet automated analysis remains challenging due to speckle noise, acquisition variability, and weak sep
arXiv:2607.11987v1 Announce Type: new Abstract: Perineural invasion (PNI) is associated with poor postoperative outcomes in intrahepatic cholangiocarcinoma, but it is confirmed by surgical pathology.
arXiv:2607.12833v1 Announce Type: cross Abstract: Circular data, representing angles or directions, are frequently encountered in computer vision, biology, geology, and meteorology. Traditional regres
arXiv:2607.11957v1 Announce Type: new Abstract: Maintenance of critical infrastructures, such as railways and power plants, is essential for ensuring operational safety and reliability. However, the d
arXiv:2607.07652v1 Announce Type: cross Abstract: Search engines have long allocated attention on the web by routing users from queries to websites. AI search changes this arrangement because informat
arXiv:2512.14461v2 Announce Type: replace Abstract: Sleep is essential for health, yet studying its dynamics requires manual sleep staging, a labor-intensive step in research and clinical care. Across
arXiv:2603.08862v2 Announce Type: replace-cross Abstract: Autonomous navigation in highly constrained environments remains challenging for mobile robots. Classical navigation approaches offer safety a
arXiv:2607.12433v1 Announce Type: cross Abstract: Diffusion models have recently become the dominant paradigm for monocular depth estimation (MDE). However, they implicitly assume that depth can be re
arXiv:2607.11997v1 Announce Type: cross Abstract: Multi-task model merging combines separately trained expert models into a single model that handles all tasks without co-training. Standard practice m
arXiv:2607.12634v1 Announce Type: new Abstract: This paper proposes a theoretical framework for understanding intelligence as a process of atomic compression and compositional reuse. We argue that cog
arXiv:2510.13698v4 Announce Type: replace Abstract: Even modern AI models often remain vulnerable to multimodal queries in which harmful intent is embedded in images. A widely used approach for safety
arXiv:2607.13013v1 Announce Type: new Abstract: Automatic speech recognition is dominated by autoregressive decoders that emit one token at a time. We ask whether a discrete diffusion language model c
arXiv:2511.23276v2 Announce Type: replace Abstract: Effective HFMD surveillance requires forecasts capturing both time-series patterns and contextual drivers such as school calendars, weather, and pol
arXiv:2607.12278v1 Announce Type: new Abstract: Recent vision-language models (VLMs) for computational pathology report striking zero-shot performance on whole-slide image (WSI) visual question answer
arXiv:2607.10183v2 Announce Type: replace-cross Abstract: Running large language models on consumer devices such as laptops and desktops is challenging because model weights often exceed GPU memory ca
arXiv:2607.12801v1 Announce Type: cross Abstract: This study introduces a unified control framework for fixed-wing unmanned aerial vehicles (UAVs) fitted with a pan-tilt (PT) camera, intended to perfo
arXiv:2607.12058v1 Announce Type: cross Abstract: Given a vulnerability-fixing commit, trigger localization asks which specific statement turns the vulnerable program state into a concrete unsafe oper
arXiv:2607.12789v1 Announce Type: cross Abstract: The O(N^2) complexity of attention over N tokens remains a computational bottleneck in transformer models. Vector-Quantized (VQ) attention reduces thi
arXiv:2607.12820v1 Announce Type: new Abstract: Omni-modal video captioning is not merely combining visual captioning with audio transcription: a useful caption must describe how visual actions, speec
arXiv:2607.11949v1 Announce Type: cross Abstract: We present a clinically deployed end-to-end auto-contouring system for cervical cancer radiotherapy planning, anchored by the Boundary-Aware Transform
arXiv:2607.11943v1 Announce Type: cross Abstract: Long-horizon physics-based simulations of battery degradation provide mechanistic insight but remain computationally expensive, limiting their use for
arXiv:2607.11945v1 Announce Type: new Abstract: Capable language models hold what a character believes apart from what is true: told 'Anna believes the cup is blue; in reality it is red,' they answer
arXiv:2607.11915v1 Announce Type: cross Abstract: Plasma diagnostic models for tokamak fusion devices are almost universally evaluated on clean, complete sensor data. In practice, fusion diagnostics f
arXiv:2607.12383v1 Announce Type: new Abstract: Cancer-related discussions on social media provide an important space for information exchange and peer support, but also facilitate the spread of misin
arXiv:2607.11967v1 Announce Type: cross Abstract: Independently trained neural networks have no shared neuron-index reference frame, so comparing them requires accounting for coordinate freedom. Neura
arXiv:2604.01506v2 Announce Type: replace Abstract: Long-tailed classification, where a small number of frequent classes dominate many rare ones, remains challenging because models systematically favo
arXiv:2607.12071v1 Announce Type: new Abstract: Continuous semantic reconstruction from non-invasive neural recordings remains limited by the representational mismatch between semantic feature spaces
arXiv:2409.10094v3 Announce Type: replace Abstract: Out-of-Distribution (OoD) detection aims to justify whether a given sample is from the training distribution of the classifier-under-protection, i.e
arXiv:2607.12214v1 Announce Type: new Abstract: Reconstructing 4D driving scenes in the wild (e.g., internet and AI-generated videos) is critical for diverse autonomous driving simulation. While recen
arXiv:2607.12818v1 Announce Type: new Abstract: Visual place recognition (VPR) is a key enabler of accurate localization and long-term autonomous navigation in robotics applications, such as loop clos
arXiv:2607.11263v2 Announce Type: replace Abstract: Two competing perspectives on fluid intelligence (gf) measures propose that performance is primarily constrained either by working memory capacity o
arXiv:2607.12723v1 Announce Type: cross Abstract: Filesystem isolation in container ecosystems is often weakened by cross-boundary path misresolution, causing path traversal (PaTra) vulnerabilities. T
arXiv:2607.11914v1 Announce Type: cross Abstract: A central goal of current Spiking Neural Network (SNN) research is to improve their accuracy toward becoming low-power alternatives to Artificial Neur
arXiv:2602.12963v2 Announce Type: replace Abstract: An important question in the field of AI is the extent to which successful behaviour requires an internal representation of the world. In this work,
arXiv:2512.17788v2 Announce Type: replace Abstract: Multi-instance partial-label learning (MIPL) is a weakly supervised framework that extends the principles of multi-instance learning (MIL) and parti
arXiv:2607.12075v1 Announce Type: cross Abstract: Background: Deep learning models can classify thyroid nodules on ultrasound, but reliable clinical decision support also requires calibrated probabili
arXiv:2607.11959v1 Announce Type: new Abstract: Greenhouse reinforcement learning can test climate-control ideas at a speed and scale that is difficult to achieve with crop experiments alone. For smar
arXiv:2607.11020v2 Announce Type: replace Abstract: Continual learning promises a language model that keeps acquiring knowledge after training, with each new fact written into its weights. Whether wei
arXiv:2607.12631v1 Announce Type: cross Abstract: As Large Language Models (LLMs) are increasingly deployed as autonomous agents in high-stakes domains, understanding contextual factors that may modul
arXiv:2607.12835v1 Announce Type: new Abstract: Rubric-based evaluation is a promising approach for assessing open-ended outputs from LLM-based research agents, particularly in paper reproduction, whe
arXiv:2607.11891v1 Announce Type: cross Abstract: The deployment of large language models (LLMs) in specialized domains like medical diagnostics and financial advisory necessitates evaluating capabili
arXiv:2607.11940v1 Announce Type: cross Abstract: As the scale of large pre-trained models continues to grow, fine-tuning them under limited memory budgets has become increasingly challenging. Low-Ran
arXiv:2601.08010v3 Announce Type: replace Abstract: Vision-language models achieve strong performance across a wide range of multimodal understanding and reasoning tasks, yet their multi-step reasonin