Knowledge catalogue
arXiv:2502.05073v4 Announce Type: replace-cross Abstract: Recent works explore deep learning's success by examining functions or data with hierarchical structure. To study the learning complexity of f
arXiv:2610.03621v1 Announce Type: cross Abstract: There has been a surge of recent work on correlated equilibrium concepts in Markov games. However, existing results focus on concepts weaker than norm
arXiv:2610.02388v1 Announce Type: new Abstract: Existing 3D large language models (LLMs) compromise on two fronts: they compress shapes into latent codebook indices or coordinate text, which removes s
arXiv:2610.03015v1 Announce Type: cross Abstract: Accurate 3D detection is essential for mobile embodied agents, while Vision Foundation Models (VFMs) offer transferable visual and geometric priors. Y
arXiv:2603.13911v2 Announce Type: replace Abstract: A language model can give the wrong answer even when the correct answer is decodable from its intermediate states. To study this gap between decodab
arXiv:2610.02675v1 Announce Type: cross Abstract: Single-photon avalanche diode (SPAD) cameras operate fundamentally differently from conventional cameras due to their photon-counting nature. Each fra
arXiv:2606.14289v2 Announce Type: replace-cross Abstract: Population-based optimizers combine update rules such as mutation, selection, and recombination. When one rule changes, it is often unclear wh
arXiv:2610.03432v1 Announce Type: cross Abstract: Online data selection has demonstrated substantial efficiency gains for LLM pretraining by training on the most valuable candidates within each batch.
arXiv:2610.02561v1 Announce Type: new Abstract: Camera-based physiological monitoring can produce multiple estimates from several facial regions, extraction methods, and processing settings. Signal qu
arXiv:2610.02552v1 Announce Type: cross Abstract: Machine learning-based intrusion detection systems (IDS) are critical for securing Industrial Internet of Things (IIoT) environments. Most adversarial
arXiv:2610.02254v1 Announce Type: cross Abstract: Interpretive Structural Modeling (ISM) is a well-known process for multi-criteria decision making. The success of ISM over other methodologies is its
arXiv:2610.03135v1 Announce Type: new Abstract: Serving long-context autoregressive language models is constrained by the key-value (KV) cache. Most dynamic eviction methods score token importance per
arXiv:2610.02872v1 Announce Type: new Abstract: Evaluating network interventions requires understanding how treatment affects people through their social relationships. Counting treated neighbors with
Over the summer I talked to the CEO of Springboards, a startup building an LLM that’s designed to come up with a wider variety of responses than its mainstream rivals do. At the start of the call, he
arXiv:2510.13210v2 Announce Type: replace Abstract: We compare Ising ({-1, +1}) and QUBO ({0, 1}) encodings for Boltzmann machine learning under controlled protocols that fix the sampler, optimizer, a
arXiv:2610.03017v1 Announce Type: new Abstract: This work presents an automatic speech recognition (ASR) system personalized for a Czech speaker with a permanent tracheal stoma and severe dysarthria r
arXiv:2610.03665v1 Announce Type: cross Abstract: Masked diffusion language models (dLMs) offer a promising parallel alternative to autoregressive models for complex reasoning. However, they face a di
arXiv:2610.03192v1 Announce Type: new Abstract: Mobile Gaussian reconstruction must satisfy two requirements: the reconstruction model must execute within a device resource envelope, and the resulting
arXiv:2602.10273v3 Announce Type: replace-cross Abstract: Reasoning ability in large language models is often attributed to distribution sharpening: concentrating output probability on high-likelihood
arXiv:2610.03199v1 Announce Type: cross Abstract: Large language models fine-tuned from a shared base can be merged by averaging their task vectors, but some merges collapse far below the base model,
arXiv:2610.03163v1 Announce Type: cross Abstract: Adapting large language models to an individual author's style from a few examples is challenging, and scientific writing sharpens the difficulty: for
arXiv:2610.03201v1 Announce Type: cross Abstract: Gaussian Processes (GPs) are a powerful tool for modelling and quantifying uncertainty in functional relationships. However, they require practitioner
arXiv:2610.03467v1 Announce Type: cross Abstract: Accurate colon segmentation from CT images is essential for colorectal disease analysis, yet deep learning based methods often produce disconnected pr
arXiv:2610.03326v1 Announce Type: new Abstract: Diffusion language model (dLLM) compression faces a known challenge because calibration is typically performed on clean, fully visible activations, wher
arXiv:2610.03512v1 Announce Type: new Abstract: Humans draw progressively: a few strokes, a look at the result, a stroke erased, a prompt revised. Image generators do not work this way. They typically
arXiv:2610.02299v1 Announce Type: cross Abstract: We introduce Psi-Resilience, a model-free feature importance method that derives explanations directly from the data itself via 1D topological signals
arXiv:2610.02615v1 Announce Type: new Abstract: Learning research, as one of its central goals, tries to measure, model, and understand how learning-problem properties affect average-case learning per
arXiv:2610.02251v1 Announce Type: new Abstract: Speculative sampling accelerates diffusion generation by verifying inexpensive draft states in parallel while preserving the target law. Recent tree-bas
arXiv:2610.02683v1 Announce Type: new Abstract: Can programmable radio propagation serve as computational depth rather than only as a communication channel or one-shot analog transform? We introduce t
arXiv:2610.02979v1 Announce Type: new Abstract: Unstructured discharge notes in Electronic Health Records (EHRs) often carry signal complementary to structured medical codes, holding patient-specific
arXiv:2610.02601v1 Announce Type: new Abstract: Event cameras have microsecond-level temporal resolution and high dynamic range which make them more resilient to motion blur and poor illumination than
arXiv:2607.16310v2 Announce Type: replace Abstract: Motor impairments affecting the upper limb significantly reduce autonomy in daily activities, particularly for tasks involving object manipulation.
arXiv:2610.03548v1 Announce Type: new Abstract: Generating progressively harder reasoning problems requires synthesis procedures that adapt as the task distribution evolves. Existing task-level recurs
arXiv:2610.03002v1 Announce Type: new Abstract: Unified multimodal models (UMMs) understand and generate both text and images, which lets a model produce its own training data. Existing self-improveme
arXiv:2610.03320v1 Announce Type: new Abstract: Diffusion language models for text-to-speech combine two forms of computation: model depth (parameters) and refinement steps (inference budget). We ask
arXiv:2610.02976v1 Announce Type: new Abstract: Audio-Visual Large Language Models (AV-LLMs) remain prone to cross-modal hallucinations, where one modality incorrectly affects predictions about anothe
arXiv:2606.12997v2 Announce Type: replace Abstract: Two dominant approaches have emerged for generating probabilistic forecasts of physical systems: generative models, such as diffusion or flow matchi
arXiv:2610.03418v1 Announce Type: cross Abstract: Epistemic uncertainty should decrease as additional information about the data-generating process (DGP) becomes available to the predictor. Yet, exist
arXiv:2610.03012v1 Announce Type: new Abstract: Most human motion generation methods encode motion as tokens on a uniform temporal grid, where every token spans the same fixed time window. Human motio
arXiv:2610.03577v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) can generate high-quality images and videos, but generating each sample requires multiple costly DiT forward passes. Two
arXiv:2610.03656v1 Announce Type: cross Abstract: Multi-pitch estimation in vocal ensembles is challenging because singers occupy overlapping pitch ranges and often sing at closely spaced fundamental
arXiv:2506.05937v3 Announce Type: replace-cross Abstract: Reliability of deep learning models is critical for deployment in high-stakes applications, where out-of-distribution or adversarial inputs ma
arXiv:2602.15013v2 Announce Type: replace Abstract: Text style transfer (TST) is naturally a supervised task - rewrite a sentence in a target style while preserving its meaning - yet the parallel corp
arXiv:2610.02469v1 Announce Type: new Abstract: Robotic manipulators operating over long durations often experience uneven joint degradation, which causes the weakest actuator to fail prematurely, lea
arXiv:2610.03013v1 Announce Type: new Abstract: Category-level object pose estimation predicts the rotation, translation, and metric size of unseen instances within known categories. Many accurate RGB
arXiv:2610.03303v1 Announce Type: new Abstract: Uncertainty quantification (UQ) for random partial differential equations (PDEs) is ubiquitous in computational science and engineering. However, classi
arXiv:2610.03106v1 Announce Type: cross Abstract: The subseasonal-to-seasonal (S2S) timescale, roughly from two weeks to two months ahead, is a critical forecast window for sectors such as agriculture
arXiv:2610.02343v1 Announce Type: new Abstract: Geometric understanding supports endoscopic navigation and robotic assistance, but learning reliable endoscopic geometry faces two challenges: scarce ge
arXiv:2610.02361v1 Announce Type: cross Abstract: Large language model (LLM)-driven evolutionary search is a powerful paradigm for automated program and algorithm discovery, yet existing systems are l
arXiv:2610.03268v1 Announce Type: new Abstract: Touche 2026 extends causality extraction to counter-causal claims: news sentences whose surface form appears causal but whose meaning denies the causati
arXiv:2610.03689v1 Announce Type: new Abstract: We examine the transfer of a pretrained SigLIP2 image encoder to seven class fire risk classification from aerial imagery. We introduce a reproducible p
arXiv:2610.03019v1 Announce Type: new Abstract: Hybrid statistical-neural pipelines often assume that a successful statistical first stage leaves a cleaner and more learnable residual target. We exami
arXiv:2610.03679v1 Announce Type: new Abstract: The dynamics of cells, organisms, and fluids are often modeled as probability distributions evolving over time. Reconstructing and extrapolating this ev
arXiv:2610.03482v1 Announce Type: new Abstract: Hallucination detection is particularly important for medical language models, but repeated-sampling approaches are expensive and existing uncertainty-h
arXiv:2610.02953v1 Announce Type: new Abstract: Long-context LLM serving is increasingly bottlenecked by KV-cache memory, especially in resource-constrained scenarios. Among existing KV-cache compress
arXiv:2610.02338v1 Announce Type: cross Abstract: A growing body of work suggests that tactile sensing gives robot policies contact information that complements vision in dexterous manipulation. Howev
arXiv:2610.02521v1 Announce Type: new Abstract: Long-video generation and world models have shown strong potential for interactive entertainment and embodied simulation by predicting future observatio
arXiv:2610.03265v1 Announce Type: cross Abstract: Large-scale pre-training has improved the generalization of neural operators across diverse PDEs. However, existing PDE foundation models still strugg
arXiv:2610.02660v1 Announce Type: new Abstract: Diffusion-based world models enable high-quality interactive environment generation but suffer from substantial inference overhead due to repeated Trans
arXiv:2610.03215v1 Announce Type: new Abstract: StanceEval 2026 is the second edition of the StanceEval shared task series on stance detection in Arabic social media text. Stance detection aims to ide