Knowledge catalogue
arXiv:2610.14073v1 Announce Type: cross Abstract: Large language models (LLMs) remain unreliable when reasoning about the future. Existing models often overlook the difference among future states, com
arXiv:2510.24469v2 Announce Type: replace-cross Abstract: Personalized text generation requires models not only to produce coherent text but also to align with a target user's style, tone, and topical
arXiv:2610.18773v1 Announce Type: new Abstract: System one models enable direct, low-latency decision-making. Yet recent practice of predicting an answer from serialized text does not, by itself, resp
arXiv:2610.14699v1 Announce Type: new Abstract: The design of high-affinity, target-specific protein binders is central to the development of modern therapeutics. However, existing protein-generation
arXiv:2610.20117v1 Announce Type: new Abstract: KANLab is a browser-based visualization tool designed to interactively teach the core concepts of Kolmogorov-Arnold Networks (KAN), forked from the wide
arXiv:2610.20552v1 Announce Type: new Abstract: We present KinoGen, a framework that synthesizes customizable humanoid loco-manipulation references through kinematically grounded video generation. Kin
arXiv:2610.15081v1 Announce Type: new Abstract: Adapting to a new world and reasoning correctly about which outcomes are possible under rules one has not met before is a basic capacity underlying gene
arXiv:2608.01718v2 Announce Type: replace Abstract: Semantic caching, reusing responses to answer semantically similar queries, has seen growing adoption in LLM serving, offering faster responses and
arXiv:2610.19266v1 Announce Type: new Abstract: Existing illumination estimation methods primarily recover lighting distributions for rendering, but typically leave the contributions of individual phy
arXiv:2610.20102v1 Announce Type: new Abstract: We investigate two aspects of multilingual pre-training with limited data, namely how language exposure is distributed across English, Dutch, and Chines
arXiv:2610.20280v1 Announce Type: new Abstract: Nanosatellites (e.g., CubeSats) have the potential to carry out useful science and exploration activities for outer solar system exploration, but their
arXiv:2610.20462v1 Announce Type: new Abstract: Foundation models have shown strong generalization across scientific domains, but their use in power systems remains largely task-specific. We investiga
arXiv:2610.19946v1 Announce Type: new Abstract: Energy-based models can represent distributions over discrete structures such as molecular graphs, but an energy that is accurate at equilibrium need no
arXiv:2610.15158v2 Announce Type: replace Abstract: We study neuro-symbolic natural language inference (NLI) with independently translated propositional representations of the premise and hypothesis.
arXiv:2610.19786v1 Announce Type: new Abstract: Generating LiDAR point clouds from multi-view camera images is a valuable task with applications in controllable data synthesis, cross-modal simulation
arXiv:2610.19767v1 Announce Type: new Abstract: AI-text detection generally asks whether a machine wrote a text. As AI becomes an increasingly integral and accepted part of writing, the more relevant
arXiv:2610.19331v1 Announce Type: new Abstract: This paper presents LLM-jp-4, a family of Japanese sovereign large language models (LLMs) developed with an emphasis on transparency, reproducibility, a
arXiv:2610.19257v1 Announce Type: new Abstract: Memory for long-horizon video generation must satisfy two competing requirements: it must remain bounded so generation can continue indefinitely, while
arXiv:2603.19714v2 Announce Type: replace Abstract: Looped language models (LoopLMs) perform iterative latent computation to refine internal representations, offering an alternative to explicit chain-
arXiv:2610.19042v1 Announce Type: new Abstract: Understanding the mechanism behind why diffusion models memorize their training data remains an open question in generative modeling. In this paper, we
arXiv:2610.18522v1 Announce Type: new Abstract: Reusable skills let LLM agents load task-specific instructions on demand, but they make execution depend on implicit routing over skill metadata. We tes
arXiv:2610.18877v1 Announce Type: cross Abstract: Time-series language models typically align numerical signals with language using labels, captions, or question-answer pairs. Although such supervisio
arXiv:2610.19138v1 Announce Type: cross Abstract: How well can a language model turn a large, incomplete and partly contradictory body of documents into a plan when no single correct answer exists? We
arXiv:2610.16552v1 Announce Type: new Abstract: An ordered visual code lets a short prefix stand in for an image, and the training loss decides what that prefix contains. Under pixel reconstruction th
arXiv:2610.19827v1 Announce Type: cross Abstract: LLMs absorb vast knowledge from training data, yet their inherently probabilistic nature offers no guarantee that this knowledge will surface in their
arXiv:2610.19647v1 Announce Type: new Abstract: Accurately delineating Arctic coastal polynyas from sea ice concentration data remains challenging because of complex coastal geometry and spatially het
arXiv:2610.16298v1 Announce Type: new Abstract: Training neural networks in reduced precision can yield significant efficiency gains, yet all existing methods that scale to standard computer vision be
arXiv:2610.14727v1 Announce Type: new Abstract: Autoregressive world models have emerged as a powerful paradigm for sample efficient decision making, yet their open loop rollouts often exhibit increas
arXiv:2610.20007v1 Announce Type: cross Abstract: We study the minimax estimation of the pseudo-spectral gap gamma_{ps} of a Markov chain from the observation of a single trajectory. Extending earlier
arXiv:2610.18141v1 Announce Type: new Abstract: Omnimodal large language models (OLLMs) are susceptible to cross-modal hallucinations under audio-visual interference. We find that, for a substantial f
arXiv:2610.20218v1 Announce Type: cross Abstract: Diffusion models are a powerful framework for learning complex probability distributions. While widely used diffusion models for continuous data rely
arXiv:2610.16601v1 Announce Type: cross Abstract: Processing multilingual text requires both identifying a broad range of languages and distinguishing the language of each token in inputs containing m
arXiv:2610.15235v1 Announce Type: new Abstract: Despite remarkable progress in language understanding and generation, decoder language models continue to exhibit striking failures on numerical data. N
arXiv:2610.19071v1 Announce Type: new Abstract: Monocular 3D object detection methods increasingly rely on depth-guided transformers, but they operate entirely in the perspective image plane where spa
arXiv:2610.14507v1 Announce Type: new Abstract: Generative modeling offers a promising solution to the scarcity of labeled clinical electroencephalography (EEG) data under strict privacy constraints.
arXiv:2610.19443v1 Announce Type: new Abstract: Native multimodal pre-training trains a single model from scratch on text and multimodal data, enabling it to understand both language and visual inputs
arXiv:2610.15930v1 Announce Type: new Abstract: Border crossing points are stochastic service systems in which traffic demand, traveler categories, registration status, inspection requirements, staffi
arXiv:2610.15339v1 Announce Type: new Abstract: Current fMRI encoding models frame prediction as deterministic regression, yet identical stimuli evoke a distribution of neural responses (arising from
arXiv:2610.19901v1 Announce Type: new Abstract: Adapting a single vision-language model to medical image parsing requires training choices that reflect the demands of each task. We study task-specific
arXiv:2610.19717v1 Announce Type: new Abstract: Recent advances in diffusion models have enabled high-fidelity, identity-preserving image generation for personalized applications such as digital avata
arXiv:2610.20347v1 Announce Type: new Abstract: We study the finite-sample convergence of Q-learning algorithms for the average-reward formulation of Reinforcement Learning (RL) with options. While op
arXiv:2610.19652v1 Announce Type: new Abstract: Recent work has sought to transfer first-order nonconvex optimization guarantees to zeroth-order methods through gradient estimators, typically incurrin
arXiv:2610.19573v1 Announce Type: cross Abstract: Differential Privacy (DP) provides a rigorous framework for quantifying privacy guarantees. While most methods achieve DP by injecting calibrated rand
arXiv:2610.19861v1 Announce Type: cross Abstract: We study the optimisation landscape of the maximum mean discrepancy (MMD) over weighted atomic measures. For the one-dimensional Gaussian kernel, we s
arXiv:2610.19510v1 Announce Type: new Abstract: Block-diffusion language models denoise a whole block of token positions in parallel so each forward pass can emit many tokens. However, blocks are stil
arXiv:2610.20415v1 Announce Type: new Abstract: Poor generalisation in deep neural networks is frequently associated with convergence to sharp minima. Sharpness-Aware Minimisation (SAM) improves gener
arXiv:2610.19375v1 Announce Type: new Abstract: Latent world models are routinely selected, checkpointed and ablated on one-step prediction loss, on the assumption that a model which predicts the next
arXiv:2610.19139v1 Announce Type: cross Abstract: Tokenization is an essential preprocessing step in almost all large language models (LLMs). One of the most popular frameworks is BPE tokenization, wh
arXiv:2610.17610v2 Announce Type: new Abstract: Recently, the integration of generative priors has revolutionized Monocular Depth Estimation (MDE), achieving state-of-the-art (SOTA) performance with r
arXiv:2610.20170v1 Announce Type: new Abstract: We study the problem of learning accurate and cognitively simple binary classifiers that are directly interpretable as Boolean rules. We propose mixed i
arXiv:2610.18570v1 Announce Type: new Abstract: Latent planners often rank candidate action sequences by terminal Euclidean distance to a goal latent. Exact agreement with transition distance would ma
arXiv:2610.16038v1 Announce Type: cross Abstract: E-commerce user preferences evolve dynamically within sessions, exhibiting concurrent and multi-centric interest distributions. While existing multimo
arXiv:2610.18784v2 Announce Type: new Abstract: Long-horizon agents accumulate progress in persistent task state, yet repeatedly process interaction histories describing how that progress was achieved
arXiv:2610.15797v1 Announce Type: new Abstract: Planning and revision are intended to bring generated code closer to the task specification. Yet a mistaken interpretation can persist across the plan,
arXiv:2610.17059v1 Announce Type: new Abstract: Clinical decisions depend on anticipating how a patient's condition will evolve as treatment is adjusted and new evidence becomes available. Existing me
arXiv:2610.16466v1 Announce Type: cross Abstract: We present PatMT2 (Patent Machine Translation, v2), a two-stage LoRA pipeline that adapts the Hy-MT2-7B dedicated translation model to four-language (
arXiv:2610.18886v1 Announce Type: cross Abstract: Natural supercavitation behind conical cavitators generates a vapor-filled envelope that, when sustained over the full body of a high-speed underwater
arXiv:2506.20642v3 Announce Type: replace Abstract: Chain-of-Thought (CoT) prompting significantly enhances large language models' (LLMs) problem-solving capabilities, but still struggles with complex
arXiv:2610.19605v1 Announce Type: cross Abstract: Long-output inference in large language models suffers from severe KV cache memory and bandwidth bottlenecks. While KV cache quantization alleviates t
arXiv:2610.18917v1 Announce Type: cross Abstract: Urban venues such as restaurants, shops, bars and parks host the daily life of a city, and their collective rise and fall signals how vibrant a neighb