Knowledge catalogue
arXiv:2610.15001v1 Announce Type: new Abstract: Multi-robot systems that combine the complementary capabilities of specialised platforms are critical for complex missions in challenging, inaccessible
arXiv:2610.16598v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning enables multimodal large reasoning models (MLRMs) to solve complex visual problems, but also exposes them to CoT hijack
arXiv:2610.18575v1 Announce Type: new Abstract: Post-training INT8 quantization is typically validated by checking accuracy retention, which requires ground-truth labels often unavailable in low-resou
arXiv:2609.15308v1 Announce Type: cross Abstract: We present a deterministic machine-learning method for denoising candidate signals from all-sky searches for continuous gravitational waves from rotat
arXiv:2607.09507v2 Announce Type: replace Abstract: Global Structure-from-Motion (SfM) is an efficient paradigm for recovering camera poses and sparse 3D structure from unordered images. However, its
arXiv:2502.05505v4 Announce Type: replace-cross Abstract: Differentially private (DP) synthetic data enables the use of sensitive datasets while providing strong privacy guarantees. Recently, Private
arXiv:2610.14999v1 Announce Type: cross Abstract: Steering candidate generation toward user-defined objectives is central to inverse materials design, yet remains difficult when objectives are black-b
arXiv:2610.17015v1 Announce Type: new Abstract: Scaling up data and model capacity has driven substantial progress in visual representation learning. However, conventional pretext tasks exhibit dimini
arXiv:2610.15006v1 Announce Type: cross Abstract: We present DisCo algebra---a compositional framework for joint probability built from a symmetric cooperad (disintegration) and a symmetric operad (co
arXiv:2610.14013v1 Announce Type: cross Abstract: Drug development is an expensive and lengthy process requiring developers to select drugs for human trials and diseases to target. Computational tools
arXiv:2610.14853v1 Announce Type: new Abstract: Optimal transport (OT) is widely used to align distributions and transfer labels between unpaired source and target data, but source-target composition
arXiv:2610.17576v1 Announce Type: cross Abstract: Answering questions from radiology reports involves two decisions: whether the report contains an answer and which span gives it. A false answer prese
arXiv:2610.18130v1 Announce Type: cross Abstract: The distance of a quantum code is a fundamental metric that dictates its error-correction capability. While quantum low-density parity-check (qLDPC) f
arXiv:2610.15739v1 Announce Type: new Abstract: A deterministic method for the problem of distributed area coverage path planning subject to the inclusion of a headland path is presented. Given a, in
arXiv:2610.16142v1 Announce Type: cross Abstract: This paper develops a unified framework for distributional regression with arbitrary response variables. Rather than relying on scalar thresholds or E
arXiv:2610.17968v1 Announce Type: new Abstract: Diffusion MRI (dMRI) probes water diffusion within the brain, providing unique information about tissue microstructure and white matter connectivity. Hi
arXiv:2605.25437v2 Announce Type: replace Abstract: Visual reasoning through reinforcement learning with verifiable rewards (RLVR) has achieved remarkable progress. However, when dealing with multi-so
arXiv:2610.14376v1 Announce Type: new Abstract: Test-time adaptation (TTA) enables time series forecasters to update after deployment as new observations become available. However, adaptation objectiv
arXiv:2610.14016v1 Announce Type: new Abstract: Most existing sports datasets are individual-centric or ball-centric, focusing on individual player actions while ignoring interactions among teammates.
arXiv:2610.16822v1 Announce Type: new Abstract: Architecture attribute predictors reduce the cost of neural architecture search by estimating accuracy or latency from a small set of evaluated candidat
arXiv:2610.18518v1 Announce Type: new Abstract: Four-dimensional cone-beam computed tomography (4D CBCT) provides phase-resolved volumetric images for image-guided radiotherapy. Yet good reconstructio
arXiv:2610.17853v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved strong performance on diverse vision-language tasks, but their computational demands limit deploy
arXiv:2610.15384v1 Announce Type: new Abstract: Compressing the key-value (KV) cache is essential for serving long-context large language models (LLMs) under memory constraints. While current scalar q
arXiv:2610.14120v1 Announce Type: cross Abstract: A defined-benefit pension is only as safe as its sponsor, yet sponsor distress is under-instrumented relative to plan-side modelling. We present a tra
arXiv:2610.14926v1 Announce Type: new Abstract: Multi-finger hands contact objects at different times, so a global close command can overload early-contact fingers while others are still moving. Dense
arXiv:2610.16792v1 Announce Type: new Abstract: Adaptive optimization methods have received considerable attention in recent years due to their strong empirical performance. However, they generally re
arXiv:2610.17572v1 Announce Type: new Abstract: While expert demonstrations on real robots are difficult to collect, human ego-centric videos are easy to collect and abundant on the internet. However,
arXiv:2610.18872v1 Announce Type: cross Abstract: Learning humanoid loco-manipulation from human demonstrations rests on observing how locomotion, posture, and object interaction evolve together as ta
arXiv:2512.01495v3 Announce Type: replace Abstract: Video instance segmentation (VIS) for low-light content remains highly challenging for both humans and machines alike, due to noise, blur and other
arXiv:2610.18742v1 Announce Type: cross Abstract: Gauge-equivariant graph neural networks generate ordered products of gauge links through local message passing, but the geometry of these internal str
arXiv:2610.17686v1 Announce Type: cross Abstract: Longer contexts allow large language models (LLMs) to condition their predictions on more information, but inference costs grow prohibitively with con
arXiv:2610.16149v1 Announce Type: cross Abstract: Learning equilibria from data is difficult when several decision makers interact indefinitely and the system state has no prescribed bounds. We examin
arXiv:2610.18534v1 Announce Type: new Abstract: Flow Matching has recently become popular as a technique for generative modeling with applications in many domains. In the standard Flow Matching formul
arXiv:2610.17898v1 Announce Type: new Abstract: Sparse autoencoders decompose language model representations into interpretable features, but feature interpretation in this literature remains predomin
arXiv:2610.14948v1 Announce Type: new Abstract: Temporal point processes are used both to measure market mechanisms, for example how fast prices absorb news and how much of the reaction is the market
arXiv:2610.17980v1 Announce Type: new Abstract: Plurality-voting ensembles aggregate the predictions of multiple base classifiers and output the most voted class, with ties typically broken at random.
arXiv:2610.18856v1 Announce Type: new Abstract: Persistent homology summarizes the topological evolution of a graph as persistence diagrams (PDs). Generating graphs whose topology matches a target des
arXiv:2511.20088v3 Announce Type: replace Abstract: In recent years, VAD (Visual Anomaly Detection) has attracted significant attention due to its high interpretability, as it provides visual explanat
arXiv:2610.16447v1 Announce Type: cross Abstract: The estimation of heterogeneous treatment effects is a central objective of causal inference. However, state-of-the-art algorithms to solve this task
arXiv:2610.15241v1 Announce Type: cross Abstract: Hoffman constants quantify the distance to the solution set of a polyhedral system in terms of its residual; they control error bounds, geometric cond
arXiv:2610.16187v1 Announce Type: cross Abstract: Existing cumulative regret guarantees for Bayesian optimization (BO) do not necessarily characterize the quality of the algorithm's final output. This
arXiv:2610.14817v1 Announce Type: cross Abstract: State-of-the-art beat tracking is based on neural networks that model beats and downbeats as individual events, often followed by a separate Dynamic B
arXiv:2610.16173v1 Announce Type: new Abstract: Parallel wire-driven systems are a framework in which multiple wires are connected to an end effector, and the end effector is driven by the tensions of
arXiv:2610.16574v1 Announce Type: cross Abstract: Accurately assessing the convergence of Monte Carlo Markov Chains is crucial for ensuring their validity in Bayesian inference problems. However, conv
arXiv:2610.16289v1 Announce Type: new Abstract: Quantization has emerged as one of the primary techniques for reducing the memory footprint of large language models during inference. A prominent examp
arXiv:2610.17951v1 Announce Type: new Abstract: Head circumference (HC) measured from 2D ultrasound is a routine marker of fetal growth, and automating it needs segmentations whose boundaries hold up
arXiv:2610.16161v1 Announce Type: new Abstract: A fruitful approach towards understanding generalization in machine learning involves characterizing how models separate generalizable structure from id
arXiv:2610.16914v1 Announce Type: new Abstract: Intraoperative frozen-section diagnosis of central nervous system (CNS) lesions guides surgical decisions but is challenged by artefacts, diagnostic com
arXiv:2610.17818v1 Announce Type: new Abstract: We give the first adversarial linear bandit algorithm based on Follow-the-Perturbed-Leader (FTPL) that achieves sqrt{T} regret over general convex actio
arXiv:2610.14787v1 Announce Type: new Abstract: Online continual learning (OCL) requires a learner to acquire a sequence of tasks in a single pass over the data, and catastrophic forgetting of earlier
arXiv:2610.15284v1 Announce Type: new Abstract: Drug discovery requires efficient exploration of an astronomically large chemical space. Molecular graph diffusion models offer a promising approach to
arXiv:2610.17191v1 Announce Type: new Abstract: Linear-time sequence models typically store long-context information in a recurrent state. A fixed-size state limits how much information can be stored,
arXiv:2610.15922v1 Announce Type: new Abstract: Autoregressive (AR) video generation enables long-horizon synthesis but requires repeated forward passes through large diffusion transformers. Training-
arXiv:2610.16058v1 Announce Type: new Abstract: Reliable scene understanding requires deciding which predictions to trust, retain, or inspect. In semantic segmentation, component failure predictors su
arXiv:2607.15433v2 Announce Type: replace Abstract: Quantum machine learning ideas are typically absent from traditional machine learning curricula and are instead introduced in upper-level courses on
arXiv:2610.14245v1 Announce Type: new Abstract: An LLM-directed excavator can execute mechanically feasible motion whose destination or authority no longer matches the operator's approval. We propose
arXiv:2601.00978v2 Announce Type: replace Abstract: Human-robot collaboration (HRC) in structured assembly requires reliable state estimation and adaptive task planning under noisy perception and huma
arXiv:2610.16236v1 Announce Type: new Abstract: Morphogenesis is driven by the interplay of genetic regulation and tissue mechanics. In vivo video data is limited, particularly under genetic mutations
arXiv:2610.17706v1 Announce Type: new Abstract: Large vision-language models often exhibit object hallucination, generating claims that are not supported by the input image. To mitigate this issue, we
arXiv:2511.22150v3 Announce Type: replace Abstract: Studying the shape of text embedding spaces enhances our understanding of model behavior and how geometry affects downstream task performance. While