Knowledge catalogue
arXiv:2605.15571v1 Announce Type: cross Abstract: Estimating the number of distinct elements in a data stream is well understood when repeated elements are identical. In modern settings, however, obse
arXiv:2605.15572v1 Announce Type: new Abstract: The dynamic range of activations is a first-order constraint for low-bit quantization, activation scaling, and stable LLM inference. Prior work characte
arXiv:2603.23433v2 Announce Type: replace Abstract: AI agents are becoming active decision-makers on the Internet. As they make decisions in the same environments as humans, the environments themselve
arXiv:2510.02278v2 Announce Type: replace Abstract: Modeling traffic dynamics is a critical challenge for urban computing, with applications from real-time traffic management to infrastructure plannin
arXiv:2605.15589v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in the mental health domain, yet it remains unclear how well they capture related biomedical knowledg
arXiv:2605.15574v1 Announce Type: new Abstract: Longitudinal chest X-ray (CXR) interpretation requires reasoning over disease evolution across multiple patient visits, yet most existing medical VQA be
arXiv:2605.16081v1 Announce Type: cross Abstract: The paradigm of learning from automatic annotations driven by pre-trained experts and Foundation Models dominates data-hungry applications. However, i
arXiv:2605.16030v1 Announce Type: new Abstract: Model-Based Reinforcement Learning (MBRL) leverages latent imagination for sample efficiency, yet remains constrained by Historical Tethering: imaginati
arXiv:2511.15887v2 Announce Type: replace Abstract: Our ability to interpret others' mental states through nonverbal cues (NVCs) is fundamental to our survival and social cohesion. While existing Theo
arXiv:2605.15342v1 Announce Type: new Abstract: Video reasoning models are a core component of egocentric and embodied agents. However, standard benchmarks for assessing models provide only evaluation
arXiv:2601.21294v2 Announce Type: replace Abstract: Partial Least Squares (PLS) learns shared structure from paired data via the top singular vectors of the empirical cross-covariance (PLS-SVD), but m
arXiv:2605.16064v1 Announce Type: cross Abstract: We study whether simple algorithmic pricing systems can systematically produce collusive-like prices in multi-firm markets. We consider firms using an
arXiv:2605.15831v1 Announce Type: cross Abstract: Autoregressive music generation depends strongly on the audio tokenizer. Existing high-fidelity codecs often use residual multi-codebook quantization,
arXiv:2510.23634v3 Announce Type: replace-cross Abstract: Motivated by applications for set containment problems, we consider the following fundamental problem: can we design set-to-vector functions s
arXiv:2605.15383v1 Announce Type: new Abstract: Microscopy images contain rich information about how cells respond to perturbations, making them essential to applications like drug screening. To quant
arXiv:2605.15423v1 Announce Type: cross Abstract: Modern smart vision sensors need on-device intelligence to process video streams, as cloud computing is often impractical due to bandwidth, latency, a
arXiv:2509.16223v3 Announce Type: replace-cross Abstract: Frequency-modulated continuous wave radars have gained increasing popularity in the automotive industry. Their robustness against adverse weat
arXiv:2605.16118v1 Announce Type: new Abstract: The source distribution in conditional flow matching is a design parameter that can be calibrated to data, not a default isotropic prior. We exploit thi
arXiv:2605.16107v1 Announce Type: new Abstract: Machine-generated texts (MGTs) pose risks such as disinformation and phishing, underscoring the need for reliable detection. Metric-based methods, which
arXiv:2605.16088v1 Announce Type: cross Abstract: Self-supervised pretraining on molecular graphs has emerged as a promising approach for molecular property prediction, yet most existing methods opera
arXiv:2602.17050v3 Announce Type: replace Abstract: Embedding tables are critical components of large-scale recommendation systems, facilitating the efficient mapping of high-cardinality categorical f
arXiv:2605.15326v1 Announce Type: new Abstract: Reliable detection of humans beneath forest canopy remains a difficult remote-sensing challenge due to sparse, structured, and viewpoint-dependent occlu
arXiv:2605.15235v1 Announce Type: new Abstract: Multimodal physiological data powers clinical AI systems from intensive care units to wearable devices, but sensors routinely fail in practice. Two fail
arXiv:2605.15650v1 Announce Type: new Abstract: Athletic performance represents the pinnacle of human motor intelligence, demanding rapid choices, precise control, agility, and coordinated physical ex
arXiv:2605.16134v1 Announce Type: cross Abstract: Sharpness-aware minimization (SAM) encourages flat minima by perturbing parameters along directions of high loss curvature, but treats all parameter d
arXiv:2507.10236v2 Announce Type: replace Abstract: As generative Artificial Intelligence (AI) advances, the realism of AI generated imagery has reached a threshold capable of deceiving even vigilant
arXiv:2605.15559v1 Announce Type: new Abstract: Recent years have witnessed significant progress in autonomous navigation using reinforcement learning. However, existing approaches largely emphasize r
arXiv:2605.15436v1 Announce Type: new Abstract: This paper presents a comprehensive analysis of neural activation patterns across six distinct large language model (LLM) architectures, examining their
arXiv:2601.23030v2 Announce Type: replace-cross Abstract: Inference in nonlinear continuous stochastic processes on trees is challenging, particularly when observations are sparse and the topology is
arXiv:2605.15524v1 Announce Type: cross Abstract: Point cloud learning often rests on the premise that observed samples are noisy traces of an underlying geometric object, such as a manifold embedded
arXiv:2512.04745v3 Announce Type: replace-cross Abstract: The ability to flexibly compose previously acquired skills to execute intelligent behaviors is a hallmark of natural intelligence. Such compos
arXiv:2506.16129v2 Announce Type: replace Abstract: Neurosymbolic learning can use symbolic rules to provide supervision for latent concepts from weak labels, but it commonly assumes that the entities
arXiv:2605.15615v1 Announce Type: new Abstract: Efficient transfer learning of vision-language models (VLMs) commonly suffers from a Base-New Trade-off (BNT): improving performance on unseen (new) cla
arXiv:2605.15227v1 Announce Type: new Abstract: Self-driving laboratories (SDLs) have attracted increasing attention as a means of accelerating scientific discovery; however, developing SDL software r
arXiv:2605.15470v1 Announce Type: new Abstract: Ocean dynamics are inherently chaotic, yet existing machine learning ocean models produce only deterministic forecasts. We introduce Njord, a probabilis
arXiv:2510.02307v2 Announce Type: replace-cross Abstract: Text-to-image diffusion models often degrade when sampled at resolutions outside the final training resolution set. Prior work has largely emp
arXiv:2605.15828v1 Announce Type: new Abstract: Feed-forward 3D reconstruction models, represented by Visual Geometry Grounded Transformer (VGGT), jointly predict multiple visual geometry tasks such a
arXiv:2605.15219v1 Announce Type: new Abstract: Can AI systems discover genuinely new knowledge through iterative self improvement, and if so, at what cost? We introduce the NOVA framework, which mode
arXiv:2605.15726v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a scalable paradigm for improving the reasoning capabilities of large language mode
arXiv:2605.15603v1 Announce Type: cross Abstract: Model-based reinforcement learning (RL) offers a compelling approach to offline RL by enabling value learning on imagined on-policy trajectories. Howe
arXiv:2605.16241v1 Announce Type: cross Abstract: Billion-parameter Vision-Language-Action (VLA) policies have recently shown impressive performance in robotic manipulation, yet their size and inferen
arXiv:2605.15511v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning. Yet most benchmarks focus on the regime n gg p, wher
arXiv:2605.15971v1 Announce Type: new Abstract: While reinforcement learning (RL) enables robots to acquire skills autonomously, its real-world deployment is severely limited by inefficient and unsafe
arXiv:2602.10687v3 Announce Type: replace-cross Abstract: Existing forgery detection methods are often limited to uni-modal or bi-modal settings, failing to handle the interleaved text, images, and vi
arXiv:2605.15240v1 Announce Type: cross Abstract: This paper investigates the critical role of eigenalignments between the kernel matrix and learning targets in achieving robust generalization in lear
arXiv:2605.15860v1 Announce Type: new Abstract: Accurate geometric calibration of RGB-thermal infrared (TIR) stereo camera systems is essential for multimodal building envelope analysis, yet remains c
arXiv:2409.03897v3 Announce Type: replace Abstract: Large-scale multi-agent systems are often deployed across wide geographic areas, where agents interact with heterogeneous environments. There is an
arXiv:2605.15520v1 Announce Type: cross Abstract: Data attribution has become an important component of pricing, auditing, and governance in machine learning pipelines, yet most attribution methods im
arXiv:2605.15435v1 Announce Type: new Abstract: Standard deep-learning pipelines usually choose the network architecture before training and keep it fixed throughout optimization. In contrast, a model
arXiv:2605.15309v1 Announce Type: new Abstract: Despite remarkable progress, image generation is far from solved. The dominant metric, FID, conflates sample fidelity with mode coverage and is close to
arXiv:2511.04484v2 Announce Type: replace-cross Abstract: We study the repeated optimal stopping problem, in which the same optimal stopping instance with an unknown distribution is solved repeatedly
arXiv:2602.03922v3 Announce Type: replace Abstract: Standard sequence mixing layers used in language models struggle to balance efficiency and performance. Self-attention performs well on long context
arXiv:2605.15978v1 Announce Type: cross Abstract: Law enforcement reports contain structured fields and written narratives. However, many incident facts that are needed for review, police training, an
arXiv:2603.05377v2 Announce Type: replace-cross Abstract: Open-world navigation requires robots to make decisions in complex everyday environments while adapting to flexible task requirements. Convent
arXiv:2605.15553v1 Announce Type: cross Abstract: Sixth-generation mobile networks (6G) are approaching a structural inflection point. Five generations of vendor-led architectures have left operators
arXiv:2603.01290v3 Announce Type: replace Abstract: The 2026 Formula 1 technical regulations introduce a fundamental change to energy strategy: under a 50/50 internal combustion engine / battery power
arXiv:2605.16191v1 Announce Type: new Abstract: We present a case study for how AI coding systems can be used to generate novel scientific hypotheses. We combine a generic coding agent (Google's AntiG
arXiv:2605.15765v1 Announce Type: cross Abstract: Optimization problems with drones are widely studied in a variety of civilian tasks, mainly due to their ability to traverse rough terrains and to car
arXiv:2504.11320v3 Announce Type: replace-cross Abstract: Large language models now serve millions of users daily, with providers incurring costs exceeding $700,000 per day. Each request requires toke
arXiv:2506.00182v3 Announce Type: replace-cross Abstract: Will further scaling up of machine learning models continue to bring success? A significant challenge in answering this question lies in under