Knowledge catalogue
arXiv:2608.04776v1 Announce Type: new Abstract: The ability to accurately assess and anticipate risks in safety-critical scenarios is crucial for autonomous driving systems. While existing research ha
arXiv:2608.04210v1 Announce Type: new Abstract: Pose-agnostic Anomaly Detection (PAD) remains challenging as anomalies can appear under arbitrary viewpoints, requiring methods to handle significant po
arXiv:2603.06140v2 Announce Type: replace-cross Abstract: Video object insertion is fundamental to video editing, yet existing diffusion methods often produce visually plausible but physically inconsi
arXiv:2608.04426v1 Announce Type: cross Abstract: We introduce Predictive State Retrieval (PSR), a task in which a model observes a short video prefix and a temporal question about an object's future
arXiv:2608.04905v1 Announce Type: new Abstract: We present PRIMAL3, an ultra-large-scale learning-based framework for multi-agent pathfinding (MAPF) that integrates reinforcement learning, topology-aw
arXiv:2608.04452v1 Announce Type: cross Abstract: High-resolution pixels and crop or zoom tools give multimodal large language models the ability to inspect an image, but they do not provide a reliabl
arXiv:2608.04037v1 Announce Type: cross Abstract: Designing narrative-grounded interactive experiences remains labor-intensive because interactive content must align with the underlying world implied
arXiv:2608.04514v1 Announce Type: new Abstract: Background: Respiratory specialty care requires multimodal interpretation, longitudinal risk assessment, guideline-concordant intervention, and whole-co
arXiv:2608.04527v1 Announce Type: new Abstract: Frozen vision-language-action (VLA) policies generate temporally extended action chunks, but long-horizon manipulation remains vulnerable to accumulated
arXiv:2608.05115v1 Announce Type: cross Abstract: Can computer vision help make classrooms safer? In this pilot study, we investigate privacy-aware and computationally efficient classroom incident rec
arXiv:2608.04832v1 Announce Type: new Abstract: Control policies optimized in simulation can perform poorly in the real system when the parameters x of the simulator are estimated from limited data bu
arXiv:2603.18774v2 Announce Type: replace Abstract: Foundational feed-forward visual geometry models enable accurate and efficient camera pose estimation and scene reconstruction by learning strong sc
arXiv:2507.05173v2 Announce Type: replace Abstract: Generating intermediate video content of varying lengths based on given first and last frames, along with text prompt information, offers significan
arXiv:2608.04084v1 Announce Type: cross Abstract: Mixture-of-experts (MoE) networks pursue specialization through learned routers, gates, and load-balancing losses, yet at matched total-parameter budg
arXiv:2608.04676v1 Announce Type: new Abstract: Surgical procedures unfold as structured and recurring clinical events, whose real-time understanding via intraoperative surgical videos is critical for
arXiv:2608.04127v1 Announce Type: new Abstract: Large language model agents need to perceive human behavior in physical environments. Millimeter-wave (mmWave) radar provides a privacy-friendly and con
arXiv:2508.12480v3 Announce Type: replace Abstract: The ability to cooperate with unknown partners is a central challenge in cooperative AI and widely studied in the form of zero-shot coordination (ZS
arXiv:2608.04879v1 Announce Type: cross Abstract: Vision Transformers (ViTs) achieve strong image-recognition performance, but their parameter count grows linearly with depth when each block is indepe
arXiv:2608.04055v1 Announce Type: cross Abstract: The minimum change principle provides an information-theoretic characterization of the Bayes reversal channel in classical probability theory and has
arXiv:2608.04726v1 Announce Type: new Abstract: Multimodal large language models increasingly reason over screenshots and documents where the task itself may be written in pixels. Yet benchmarks usual
arXiv:2608.03103v1 Announce Type: cross Abstract: Diffusion policies have shown strong performance in learning complex, multi-modal behaviors for robotic manipulation. However, their application to co
arXiv:2608.03159v1 Announce Type: new Abstract: This research proposes a constrained motion planning framework for robot manipulators in human-robot interaction (HRI). For a non-redundant manipulator
Most agent memory setups run a model call on the way in. Something reads the turn, decides whether it's worth keeping, rewrites it into a 'memory', tags it with a type and an importance score. That's
arXiv:2608.03223v1 Announce Type: cross Abstract: Agentic reinforcement learning enables LLM agents to learn through interaction, but sparse trajectory-level rewards reveal success without identifying
arXiv:2608.03779v1 Announce Type: new Abstract: Video anomaly understanding (VAU) focuses on comprehensively interpreting abnormal events in videos, requiring models to identify anomalous occurrences,
arXiv:2608.03715v1 Announce Type: new Abstract: Mean-reverting dynamics are pervasive in finance, and the Cox--Ingersoll--Ross (CIR) process is a standard model for the time series they produce, from
arXiv:2608.02907v1 Announce Type: new Abstract: Multimodal retrievers are essential for knowledge-based visual question answering, where they retrieve external evidence for image-question pairs. Howev
arXiv:2608.02612v1 Announce Type: new Abstract: Formulating an optimization problem strongly affects the quality of the final solution, yet good formulations usually require substantial expertise. Rec
arXiv:2505.14744v3 Announce Type: replace-cross Abstract: Traditionally, in Programming-by-example (PBE) the goal is to synthesize a program from a small set of input-output examples. Lately, PBE has
arXiv:2601.17003v2 Announce Type: replace-cross Abstract: Mental-health AI safety is typically evaluated with small, simulation-based benchmarks that may not reflect the linguistic and contextual dive
arXiv:2608.03938v1 Announce Type: new Abstract: Bimanual manipulation policies trained with imitation learning are typically evaluated on workstation or datacenter-class GPUs, leaving the cost of depl
arXiv:2607.17447v2 Announce Type: replace-cross Abstract: As generative artificial intelligence enters scientific and professional work, its uncertainty must be defined on the states that matter for i
arXiv:2608.03890v1 Announce Type: cross Abstract: A clinically useful chest X-ray system must go beyond fluent report generation: it should classify findings with tunable decision thresholds, localize
arXiv:2608.03031v1 Announce Type: new Abstract: Time series forecasting is fundamental to decision-making in complex systems, where future dynamics are influenced not only by historical observations b
arXiv:2608.03529v1 Announce Type: new Abstract: Biomedical literature is increasingly mined for knowledge beyond the questions it was written to answer. Because the target concepts are not known in ad
arXiv:2402.03904v3 Announce Type: replace Abstract: Deep functional map frameworks are widely employed for 3D shape matching. However, most existing deep functional map methods cannot adaptively captu
arXiv:2608.02678v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) systems are vulnerable to corpus poisoning: an attacker who inserts a crafted document into the retrieval corpus
arXiv:2608.03589v1 Announce Type: new Abstract: We present a method for designing deep neural networks (DNNs) for intermittent, energy-autonomous, on-device learning on microcontroller units (MCUs). I
arXiv:2608.03720v1 Announce Type: new Abstract: Large language models can generate fluent responses to Islamic questions while introducing factual errors that are difficult to identify. This paper pre
arXiv:2608.03591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents offer a promising approach to attack chain reconstruction by retrieving and interpreting heterogeneous telemetry to
arXiv:2608.03025v1 Announce Type: new Abstract: Multimodal named entity recognition (MNER) determines whether each candidate span and entity-type hypothesis is supported by joint textual and visual ev
arXiv:2608.03407v1 Announce Type: cross Abstract: Road network segmentation from satellite imagery remains challenging due to large geographic variation in road appearance, occlusions, and domain shif
arXiv:2608.02712v1 Announce Type: cross Abstract: Kernel generation for hardware accelerators such as GPUs and NPUs has become a proving ground for large language models (LLMs), and state-of-the-art s
arXiv:2608.03597v1 Announce Type: new Abstract: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with increased risks of stroke, heart failure, and mortality.
arXiv:2608.03176v1 Announce Type: new Abstract: Imagined handwriting offers a temporally rich paradigm for non-invasive neural decoding, yet reliable recognition across unseen participants remains dif
arXiv:2607.18303v2 Announce Type: replace-cross Abstract: Identifying which string produces a given pitch in monophonic electric guitar audio is a classification challenge: a single pitch can often be
arXiv:2608.03868v1 Announce Type: cross Abstract: Causal Discovery (CD) from observational data faces two fundamental challenges. First, purely statistical methods often lack the power to resolve stru
arXiv:2608.03071v1 Announce Type: new Abstract: Large language model agents derive much of their capability from tool use. Existing research on tool use has largely focused on selecting the right tool
arXiv:2608.02650v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external tools to complete complex real-world tasks. However, reliable tool-use planning remains
arXiv:2602.17976v2 Announce Type: replace-cross Abstract: In active sequential testing, also termed pure exploration, a learner is tasked with the goal to adaptively acquire information so as to ident
arXiv:2608.03138v1 Announce Type: cross Abstract: Generating a rigorous paper introduction with large language models (LLMs) remains challenging, since it requires coordinating background, gap identif
arXiv:2608.03961v1 Announce Type: new Abstract: Test-time scaling improves LLM reasoning by generating and aggregating multiple candidate answers, yet many pipelines use fixed per-query budgets that s
arXiv:2608.02957v1 Announce Type: new Abstract: Steering vectors (SVs) are widely used to influence the expression of concepts (e.g., truthfulness) in large language model outputs. A key assumption un
arXiv:2608.02641v1 Announce Type: cross Abstract: Large language models (LLMs) can translate natural-language optimization problems into solver-ready formulations, but direct code generation is brittl
arXiv:2608.03974v1 Announce Type: new Abstract: Real-time video editing requires low-latency causal generation with bounded computational resources while preserving source fidelity and long-term tempo
arXiv:2608.02620v1 Announce Type: new Abstract: LLM-as-a-judge evaluation has become a dominant paradigm for ranking language models, yet the ecosystem remains fragmented: most benchmarks ship their o
arXiv:2608.03929v1 Announce Type: cross Abstract: Aligning diffusion models with human preferences usually relies on a sparse terminal reward evaluated on the final generated samples, presenting a sev
arXiv:2608.03838v1 Announce Type: new Abstract: Reasoning-based guard models improve LLM safeguards, but decoding explicit rationales for every interaction makes them costly to deploy. Although latent
arXiv:2608.03489v1 Announce Type: cross Abstract: Combining Bayesian learning and quantitative verification is a powerful toolset for analysing key quantitative properties of software systems, like re
arXiv:2608.03851v1 Announce Type: new Abstract: Real-time 3D perception is crucial for robotics, augmented reality, and embodied intelligence applications. Existing multi-view stereo (MVS) methods pri