Knowledge catalogue
arXiv:2610.01377v1 Announce Type: new Abstract: We study causal logistic bandits with counterfactual fairness constraints. The causal structure is given through known factual and counterfactual featur
arXiv:2610.02136v1 Announce Type: cross Abstract: Unsupervised anomaly detection (UAD) methods for brain MRI are ranked by a single score, yet that score rests on choices that are rarely reported: how
arXiv:2610.00620v1 Announce Type: cross Abstract: Grokking refers to the delayed emergence of validation-set generalization after a model has already overfit the training set. Although first observed
arXiv:2610.01238v1 Announce Type: new Abstract: Unified language models are increasingly expected to combine heterogeneous capabilities, such as mathematics, code, instruction following, and controlla
arXiv:2505.17613v2 Announce Type: replace Abstract: Automatically evaluating multimodal generation presents a significant challenge, as automated metrics often struggle to align with human evaluation,
arXiv:2610.01352v1 Announce Type: new Abstract: Open multimodal reasoning models have benefited from large-scale reasoning supervision, yet reliable post-training remains challenging due to uneven dat
arXiv:2604.01329v2 Announce Type: replace Abstract: Model merging provides a way of cheaply combining individual models to produce a model that inherits each individual's capabilities. While some merg
arXiv:2610.01284v1 Announce Type: cross Abstract: Model validation estimates the performance of a complete learning procedure on new data. However, an invalid split can produce an optimistic and stabl
arXiv:2602.12486v2 Announce Type: replace-cross Abstract: Humans appear to represent objects when reasoning about physics with coarse, volumetric 'bodies' that smooth concavities, trading fine visual
arXiv:2610.01917v1 Announce Type: cross Abstract: Latent visual reasoning equips vision--language models with continuous intermediate states that can process visual evidence without explicit textual r
arXiv:2510.06105v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly shaping how information is created and disseminated, from companies using them to craft persuasive adv
arXiv:2610.01058v1 Announce Type: cross Abstract: Quantized large language models are increasingly deployed on edge devices for their low latency and energy efficiency. However, model quantization wea
arXiv:2610.02210v1 Announce Type: new Abstract: I don't think I have ever done anything as peculiar in my life. Among other things, it shows a young man looking with interest at a print on the wall of
arXiv:2610.00367v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models enable parameter scaling with limited per-token computation by activating only a small subset of experts for each token,
arXiv:2607.15942v2 Announce Type: replace Abstract: Remote sensing vision-language models are increasingly expected to support open-ended reasoning over Earth Observation data and a variety of tasks.
arXiv:2610.00860v1 Announce Type: cross Abstract: Background and Objectives: Fluorescence-labeled cellular arbors provide readouts of neuronal and microglial morphology, but fine and weakly labeled pr
arXiv:2610.02153v1 Announce Type: new Abstract: Long-horizon autoregressive video generation is limited by a finite context window. When an object or scene falls out of context, its fine-grained visua
arXiv:2506.00173v2 Announce Type: replace-cross Abstract: We present MotionPersona, a generative framework for character-aware locomotion control, in which the motion for a command depends on the capt
arXiv:2610.00268v1 Announce Type: new Abstract: Multimodal graph learning faces a fundamental challenge: new classes may emerge after deployment, while models are trained with a fixed label space. Exi
arXiv:2610.00538v1 Announce Type: cross Abstract: A real-time auditory scene analyzer (ASA) aims to carry out the tasks of locating, separating and classifying the sound sources present in a given aco
arXiv:2610.00148v1 Announce Type: cross Abstract: Open-ended artificial life systems must acquire diverse competencies from a single evolving genotype. Biological brains combine neuromodulation, which
arXiv:2610.00287v1 Announce Type: cross Abstract: Identity assurance is the cost a digital system pays for dishonesty and uncertainty: it exists to make acts attributable when not everyone can be trus
arXiv:2610.01488v1 Announce Type: cross Abstract: Backchannel prediction has been studied almost entirely in dyadic conversation. We introduce a multi-party benchmark based on the AMI corpus, comprisi
arXiv:2610.00057v1 Announce Type: new Abstract: Guiding a tractor along a predefined reference path is a key component of precision agriculture. This study develops a path tracking controller based on
arXiv:2610.00279v1 Announce Type: new Abstract: The segmentation of anatomical structures in medical images and particularly in MRI scans, is essential for clinical diagnosis and monitoring disease pr
arXiv:2410.16089v3 Announce Type: replace Abstract: The cost, flexibility, and efficiency of modern UAVs make them attractive across many applications, but their proliferation has driven a rising numb
arXiv:2603.11546v2 Announce Type: replace Abstract: Many real-world machine learning tasks are anti-causal: they require inferring latent causes from observed effects. In practice, we often face multi
arXiv:2610.02158v1 Announce Type: new Abstract: We consider the problem of sampling from Gibbs distributions on matrix spaces whose potential energies are neither convex nor globally gradient-Lipschit
arXiv:2610.01098v1 Announce Type: new Abstract: Illusory matches between distinct yet visually similar 3D surfaces--doppelgangers--remain a fundamental obstacle for large-scale, in-the-wild 3D reconst
arXiv:2610.01434v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) incur substantial inference costs when processing long visual-textual sequences. While existing operation com
arXiv:2610.00981v1 Announce Type: cross Abstract: We focus on language-conditioned flow-based manipulation, where robot flows (robot velocity fields) serve as embodiment-agnostic, motion-centric repre
arXiv:2610.01533v1 Announce Type: new Abstract: Existing work on Semantic IDs (SIDs) for generative recommendation treats SID construction as a representation learning problem: encode items into a qua
arXiv:2610.01048v1 Announce Type: new Abstract: World models, which simulate an environment and predict how it changes under actions, are increasingly used in real-world applications such as robotics.
arXiv:2610.00841v1 Announce Type: cross Abstract: For quantum machine learning, the exact boundary between classical and quantum advantage is still poorly understood. Direct comparison between quantum
arXiv:2511.15445v3 Announce Type: replace-cross Abstract: Accurately simulating wave propagation is crucial in fields such as acoustics, electromagnetism, and seismic analysis. Traditional numerical m
arXiv:2610.00985v1 Announce Type: new Abstract: Kolmogorov-Arnold Networks (KANs) represent a compelling alternative to traditional Multi-Layer Perceptron (MLP)-based neural networks. By employing act
arXiv:2610.00397v1 Announce Type: new Abstract: Identifying which speaker a listener is attending to in a noisy room -- the cocktail-party problem -- is the missing ingredient for next-generation hear
arXiv:2607.15270v3 Announce Type: cross Abstract: The snake-in-the-box problem asks for a longest induced path in the hypercube graph Q_n. We find a length-191 snake in dimension n=9, the lowest dimen
arXiv:2610.01461v1 Announce Type: new Abstract: We often plan ambitiously yet act habitually and wonder, in retrospect, whether we would have planned differently had we known what we would actually do
arXiv:2610.01493v1 Announce Type: cross Abstract: Iterative fine-tuning on synthetic data causes model collapse: output diversity narrows as rare patterns are progressively lost, a signature most visi
arXiv:2610.00557v1 Announce Type: cross Abstract: Autonomous red team agents increasingly stress-test AI-enabled cyber defenses by planning strategy and executing multistage attacks. Reinforcement lea
arXiv:2610.00678v1 Announce Type: cross Abstract: Histological whole-slide images (WSIs) are central to computational pathology but pose severe computational challenges due to their extremely high res
arXiv:2610.01640v1 Announce Type: cross Abstract: Vision-language models (VLMs) face a fixed-budget trade-off between processing more visual information for fine-grained perception and using a larger
arXiv:2610.01787v1 Announce Type: new Abstract: Self-improving GUI agents keep the trajectories they produce and return them to the agent, by fine-tuning or by retrieval into the prompt, and studies t
arXiv:2610.01270v1 Announce Type: new Abstract: Personalization encoders compress evolving interaction histories into preference states used to rank items or condition text generation. A task head ope
arXiv:2609.33149v2 Announce Type: replace Abstract: A common principle of effective learning is to practice material that is neither already mastered nor too difficult to permit progress. We ask how t
arXiv:2610.00094v1 Announce Type: cross Abstract: Belief-based agent memory needs reliable decisions about current state, yet its evidence may be noisy, copied, or stale. Must a memory calibrate its s
arXiv:2610.01291v1 Announce Type: new Abstract: Recent advances in deep learning for shadow removal have significantly enhanced image quality and realism. However, most approaches rely on real-world p
arXiv:2610.02148v1 Announce Type: new Abstract: Extending a text embedding model to new modalities typically degrades text retrieval quality, and existing omni-modal embedders compensate with multi-bi
arXiv:2610.00381v1 Announce Type: new Abstract: Medical models are judged not only on correctness, but on whether reported confidence matches actual accuracy. Generalist multimodal medical models have
arXiv:2610.02181v1 Announce Type: new Abstract: We present OmniSeek, an agentic framework that transforms an Omni Large Language Model (Omni-LLM) into an active, multi-turn reasoning agent with native
arXiv:2610.00007v1 Announce Type: cross Abstract: Named-entity recognition (NER) is increasingly wanted on-device (no API, low latency, data kept local). The practitioner's question is not the leaderb
arXiv:2610.00649v1 Announce Type: cross Abstract: Synthetic speech detection is critical for audio security, but performance can degrade when labeled data are scarce and evaluation conditions differ f
arXiv:2610.02015v1 Announce Type: cross Abstract: Recent advances in LLM reasoning models---driven primarily by the paradigm of post-training via reinforcement learning with verifiable reward (RLVR)--
arXiv:2610.01842v1 Announce Type: new Abstract: A time series world model (TSWM) predicts a controlled system's state from its observed history and planned actions and exogenous inputs. Current approa
arXiv:2509.11337v2 Announce Type: replace Abstract: Adversarial training has been widely studied in recent years due to its role in improving model robustness against adversarial attacks. This paper f
arXiv:2610.00820v1 Announce Type: cross Abstract: General-purpose models can adapt to many tasks from context, while specialised models can execute individual functions with less capacity. Yet obtaini
arXiv:2610.00382v1 Announce Type: cross Abstract: Model quantization reduces the numerical precision of neural network weights and activations to lower storage and computational costs. Model inversion
arXiv:2610.02207v1 Announce Type: cross Abstract: 3D Gaussian avatars support fast rendering, however, their real-time animation is often challenged by the costly neural inference. We address this bot
arXiv:2610.00095v1 Announce Type: new Abstract: Tutoring systems use mastery thresholds to decide when students can stop practicing and advance, but the same numerical threshold can lead to very diffe