Knowledge catalogue
arXiv:2609.39375v1 Announce Type: cross Abstract: A robot that observes people interacting with objects should be able to carry out later requests that refer back to those interactions. Such requests
arXiv:2511.15146v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) constructs uncertainty sets for model outputs with finite-sample coverage guarantees. Yet ranking scores is straight
arXiv:2609.38730v1 Announce Type: new Abstract: Flapping-wing vehicles regulate aerodynamic forces and moments through coordinated variations in stroke, deviation, and pitch motion. Because the result
arXiv:2609.38699v1 Announce Type: new Abstract: A cumulative token cap can fall inside a mathematical derivation, forcing a test-time controller to choose between stopping at the cap (strict) and allo
arXiv:2609.39005v1 Announce Type: new Abstract: Emergency managers need to know where floodwater is, how deep it is, and how it will change over the coming hours across an entire river basin. During a
arXiv:2609.39484v1 Announce Type: cross Abstract: Longitudinal clinical cohorts are multimodal, irregularly sampled and pervasively incomplete: in ADNI, positron emission tomography and cerebrospinal
arXiv:2609.39512v1 Announce Type: new Abstract: The small-sample learning problem remains a fundamental challenge in machine learning because limited training data lead to unstable model estimation an
arXiv:2609.40014v1 Announce Type: new Abstract: Detecting violence after it begins is important from recognizing behavioral cues that appear immediately beforehand. This work studies short-horizon pre
arXiv:2609.40290v1 Announce Type: cross Abstract: In algorithmic statistics a string x is explained by a finite set containing it, and Kolmogorov's structure function records the smallest such model a
arXiv:2609.40208v1 Announce Type: new Abstract: Extending single Unmanned Aerial Vehicles (UAVs) exploration methods to multi-UAV teams can improve coverage speed and robustness, but introduces challe
arXiv:2607.14576v2 Announce Type: replace Abstract: Whether a deep residual architecture trains stably is usually determined by training it, which is expensive and answers the question only for the ar
arXiv:2609.38907v1 Announce Type: cross Abstract: Real-time classroom polling is now routine, yet the data it produces is usually read narrowly, as a correctness score or a headcount. Such readings sa
arXiv:2609.38642v1 Announce Type: new Abstract: Chart editing requires cross-modal edit grounding, realizing a requested visual change in the code that draws it, with necessary related updates and wit
arXiv:2609.39416v1 Announce Type: new Abstract: This paper proposes a communication-free multi-robot task allocation framework based solely on local observations. In this study, tasks are defined as r
arXiv:2609.39572v1 Announce Type: new Abstract: We analyze how lexical ambiguity and underspecification affect language model training. We create artificial homonyms and artificial hypernyms as pseudo
arXiv:2609.39868v1 Announce Type: new Abstract: Exploratory reinforcement learning (RL) on an operating bus fleet is impractical,while policies trained only from historical data cannot acquire new exp
arXiv:2609.40016v1 Announce Type: cross Abstract: Exact incremental BPE maintains the canonical tokenization state after every appended byte. The recent algorithm of Jiang and Gong (2026) does this in
arXiv:2609.38368v1 Announce Type: new Abstract: Vision-language models (VLMs) increasingly reason over visual evidence that is cropped, segmented, retrieved, or revealed over time. Yet most VQA benchm
arXiv:2609.40312v1 Announce Type: new Abstract: Lossy compression is widely used in Federated Learning (FL) but is generally treated as an error source, while conventional poisoning defenses inspect u
arXiv:2609.39263v1 Announce Type: new Abstract: A concept subspace's effect on model behavior does not establish how it relates to the output readout. We introduce a two-sided geometric diagnostic tha
arXiv:2609.39153v1 Announce Type: new Abstract: Planning complex missions in unknown environments requires robots to reason simultaneously about what they should do and what they still need to discove
arXiv:2609.38747v1 Announce Type: new Abstract: Reference-guided camouflaged object detection aims to segment a target whose visual appearance closely resembles its surroundings by exploiting auxiliar
arXiv:2607.09139v2 Announce Type: replace-cross Abstract: We study the optimal transport of optimally controlled agents from a compactly supported absolutely continuous source to a discrete target mea
arXiv:2609.39069v1 Announce Type: new Abstract: Test-time reasoning systems often respond to failure by restarting or revising the latest step, even when an earlier decision caused the error. We intro
arXiv:2609.39488v1 Announce Type: new Abstract: Flow matching generates samples by gradually transforming noise into data. In practice, using a finite number of sampling steps introduces a numerical e
arXiv:2501.18183v3 Announce Type: replace-cross Abstract: We introduce a novel framework for decentralized projection-free optimization, extending projection-free methods to a broader class of upper-l
arXiv:2609.38823v1 Announce Type: new Abstract: Multimodal mixture-of-experts (MoE) models combine sparse expert activation with visual-language capabilities, yet their inference remains costly becaus
arXiv:2609.38510v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive LLMs by having a lightweight drafter propose tokens that the target model verifies in parallel. Diffusio
arXiv:2609.38559v1 Announce Type: new Abstract: This paper examines human-AI interactions (HAIIs) in clinical trials and presents a multidimensional categorisation framework that classifies interactio
arXiv:2609.38560v1 Announce Type: new Abstract: Mycosis fungoides (MF) is a rare form of cutaneous T-cell lymphoma that is often misdiagnosed in early stages due to its visual similarity to benign inf
arXiv:2609.39756v1 Announce Type: new Abstract: This article introduces an algorithm that uses a U-Net architecture to determine vertical ground surface displacements from unmanned aerial vehicle (UAV
arXiv:2509.02109v3 Announce Type: replace Abstract: The Expectation-Maximisation (EM) algorithm is a central tool in statistics and machine learning, widely used for latent-variable models such as Gau
arXiv:2609.38618v1 Announce Type: cross Abstract: We study causal structure learning from observational data in linear Gaussian structural causal models in the presence of directed cycles and an unkno
arXiv:2609.39657v1 Announce Type: new Abstract: Distilling pretrained foundation models into an autoencoder bottleneck improves latent diffusability, enabling diffusion models to converge faster and r
arXiv:2609.39855v1 Announce Type: new Abstract: We study the width required for a randomly initialized hidden layer of a neural network to achieve rank lifting. Namely, given a dataset X in R^{m imes
arXiv:2609.40292v1 Announce Type: new Abstract: How is computation organized and reused across tasks and time in a trained recurrent network? Most analyses emphasize the geometry of neural activity, d
arXiv:2609.39494v1 Announce Type: new Abstract: Self-distillation with privileged context adapts a language model from demonstrations by letting the model, once conditioned on a reference response, te
arXiv:2609.40235v1 Announce Type: cross Abstract: Continuous diffusion language models generate all tokens in parallel, yet high-quality generation can still require hundreds of network evaluations (N
arXiv:2609.38829v1 Announce Type: new Abstract: Multi-path reasoning methods such as self-consistency (SC) sample K reasoning paths and choose the most frequent answer. However, their gains quickly pl
arXiv:2609.38784v1 Announce Type: new Abstract: In probabilistic contrastive learning, a shared temperature is commonly interpreted as a shared similarity scale, but this interpretation does not hold
arXiv:2609.38465v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) are increasingly designed around gradient conflict between understanding and generation objectives. The premise that
arXiv:2609.38879v1 Announce Type: cross Abstract: Large language models rely heavily on human text, which often conveys surface answers rather than the spatial and structural logic behind them. Protei
arXiv:2609.35356v2 Announce Type: replace Abstract: Supervised fine-tuning can teach language models undesired behaviours alongside desired ones. Inoculation prompting (IP) aims to limit unwanted gene
arXiv:2609.31688v2 Announce Type: replace Abstract: In verifiable domains such as math and coding, finding one correct solution among many attempts can matter more than the pass rate of each attempt.
arXiv:2609.40153v1 Announce Type: new Abstract: Video generation models (VGMs) offer strong spatiotemporal priors for embodied observation--action modeling. However, joint-space action vectors lack ex
arXiv:2609.39190v1 Announce Type: new Abstract: A deep classifier is defined not only by the decision it produces, but also by the sequence of transformations through which that decision is formed. Tr
arXiv:2609.39259v1 Announce Type: new Abstract: Modern neural networks scale predictably, yet the mechanisms behind these regularities remain unclear. Neural redundancy is typically characterized by c
arXiv:2509.18349v4 Announce Type: replace-cross Abstract: Meta-learning aims to leverage information across related tasks to improve prediction on unlabeled data for new tasks when only a small number
arXiv:2609.39553v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) enables real-time novel view synthesis, but existing general-purpose acceleration methods suffer severe rendering quality d
arXiv:2609.39371v1 Announce Type: new Abstract: In hospital workflows, electronic health records (EHRs) are often noisy, and may not contain the evidence needed to confirm events or measurements refer
arXiv:2609.39340v1 Announce Type: new Abstract: Electrolyte formulation design requires balancing multiple physicochemical properties, yet existing models often focus on a limited subset. Learning eac
arXiv:2512.06174v3 Announce Type: replace Abstract: Generating realistic cast shadows for inserted foreground objects requires reasoning about scene geometry and illumination. However, most learning-b
arXiv:2609.39227v1 Announce Type: cross Abstract: Over a century ago, Henri Poincare argued that a motionless observer cannot acquire the notion of space. Yet, most visual representation learning meth
arXiv:2609.38689v1 Announce Type: new Abstract: Immersive displays can enable rich and diverse virtual experiences. Manually authoring every possible experience to realize this potential, however, is
arXiv:2609.39829v1 Announce Type: cross Abstract: Modern machine learning depends heavily on massive datasets, but obtaining high-quality annotations at scale is often expensive. As a result, learning
arXiv:2609.38271v1 Announce Type: new Abstract: Morphological characteristics such as spiculation and lobulation play an important role in assessing pulmonary nodules on computed tomography (CT), part
arXiv:2609.38797v1 Announce Type: cross Abstract: As AI systems move from static repositories to agents that are capable of continual adaptation and learning, maintaining their trustworthiness means e
arXiv:2609.38758v1 Announce Type: new Abstract: Referring Video Object Segmentation (RVOS) aims to produce a pixel-accurate mask sequence for an object specified by natural language. Sa2VA combines a
arXiv:2609.39013v1 Announce Type: new Abstract: EVICALC, our system for the DocSem shared task, achieved 8.61% joint accuracy on 1,730 tasks in the official final test evaluation. It reads a PDF, sele
arXiv:2609.38346v1 Announce Type: new Abstract: When a student is stuck, a tutor faces the assistance dilemma: help given too early can hinder productive struggle, while help withheld too long leaves