Knowledge catalogue
arXiv:2610.03641v1 Announce Type: new Abstract: Masked discrete diffusion models offer a promising alternative to autoregressive generation, but iterative sampling can be costly, and intractable seque
arXiv:2610.02515v1 Announce Type: cross Abstract: We introduce IGNITE, a generative world foundation model for fusion plasma behavior simulation trained in a self-supervised manner from over a decade
arXiv:2610.02626v1 Announce Type: new Abstract: Vision-language-action (VLA) models increasingly incorporate intermediate reasoning to improve robotic manipulation, yet existing approaches primarily r
arXiv:2610.02772v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly serve as general-purpose interfaces to factual knowledge, but their parameters do not automatically reflect
arXiv:2610.02571v1 Announce Type: cross Abstract: As AI-assisted programming becomes increasingly mainstream, the environmental impact of AI-generated software has emerged as an important consideratio
arXiv:2610.03120v1 Announce Type: new Abstract: Modern autoregressive (AR) video diffusion models excel at short-horizon video generation, yet generating long videos remains challenging due to driftin
arXiv:2506.06557v3 Announce Type: replace-cross Abstract: An ultrametric space or infinity-metric space is defined by a dissimilarity function that satisfies a strong triangle inequality in which ever
arXiv:2610.03321v1 Announce Type: cross Abstract: Low-rank approximation can require additional matrix--vector products to verify that its error meets a prescribed tolerance. We characterize this cert
arXiv:2610.02396v1 Announce Type: cross Abstract: Multi-agent systems (MAS) built from large language models coordinate specialized agents to tackle complex tasks, but effective workflows are difficul
arXiv:2610.02738v1 Announce Type: new Abstract: Differentially private training clips each per-example gradient before adding noise. This clipping is radial for each example, yet unequal clipping fact
arXiv:2610.02520v1 Announce Type: cross Abstract: We study online learning in episodic tabular constrained Markov decision processes with step-wise safety constraints. In such a setting, the constrain
arXiv:2610.02302v1 Announce Type: cross Abstract: Recent work has shown that large language models (LLMs) can be vulnerable to jailbreak attacks in which harmful intent is obscured through composition
arXiv:2602.00066v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong capabilities in code generation, but their adherence to fine-grained user intent with multiple constrai
arXiv:2610.03380v1 Announce Type: new Abstract: Deepfake detection in videos remains challenging, as manipulated content may appear visually consistent at the frame level while exhibiting subtle tempo
arXiv:2610.02897v1 Announce Type: new Abstract: A long-running assistant cannot keep everything it has seen, so it summarises. Summarising is not neutral: what is kept is chosen against some notion of
arXiv:2610.03101v1 Announce Type: cross Abstract: Clustering variables in causal graphs reduces the size of the graph and simplifies causal inference. However, arbitrary clustering can alter crucial c
arXiv:2610.03136v1 Announce Type: new Abstract: Reasoning traces improve large language models (LLMs), but current models are trained to reason mostly in English. It has been shown that forcing a mode
arXiv:2610.02815v1 Announce Type: new Abstract: Long chain-of-thought reasoning substantially increases KV-cache memory during autoregressive decoding, as every generated token introduces new key and
arXiv:2610.03414v1 Announce Type: cross Abstract: Consistency models (CMs) have become a leading approach for generating high-quality samples in few steps. However, adding steps can improve or degrade
arXiv:2610.02334v1 Announce Type: cross Abstract: Mobile embodied AI networks (MEAN) enable embodied agents to perceive, reason, communicate, and act in wireless environments. In such networks, agent
arXiv:2610.03296v1 Announce Type: new Abstract: Complex reasoning queries can be decomposed into directed acyclic task graphs and distributed across heterogeneous LLMs, reducing latency through parall
arXiv:2610.03430v1 Announce Type: new Abstract: Efficient request scheduling is increasingly important for reducing completion time in large language model (LLM) serving. Size-based policies such as S
arXiv:2610.02754v1 Announce Type: new Abstract: Diffusion models are highly developed in continuous spaces for image and video domains. Recently, major advances have been made for discrete diffusion m
arXiv:2610.02300v1 Announce Type: new Abstract: Training-free safeguards for text-to-image generation often rely on a reusable safety signal, such as an unsafe direction or global toxic subspace, appl
arXiv:2610.02398v1 Announce Type: new Abstract: A robot demonstration records two things in the same frames: what happened to the objects, and how one particular arm made it happen. We condition on th
arXiv:2610.03137v1 Announce Type: new Abstract: Specifying a goal in language rather than as a goal frame is a natural interface for planning with a latent world model, but testing it needs scenes in
arXiv:2610.03216v1 Announce Type: new Abstract: Kernel Singular Value Decomposition (KSVD) learns a pair of singular vectors w.r.t. an asymmetric kernel matrix, which can be induced by two data source
arXiv:2610.02943v1 Announce Type: new Abstract: Multimodal 3D Human Pose Estimation (3D HPE) combines complementary information from RGB, LiDAR, and mmWave radar, but models trained on correlated obse
arXiv:2610.03564v1 Announce Type: new Abstract: Financial AI agents must do more than retrieve facts: investment workflows require correct quantitative execution, reliable use of procedural resources,
arXiv:2610.03388v1 Announce Type: new Abstract: Video is an abundant, inexpensive source of human motion data that is rich in extreme athletic behaviors. Making it usable for humanoid robots, however,
arXiv:2610.03198v1 Announce Type: new Abstract: The memory footprint of the key-value (KV) cache constrains the practical use of long-context models, and it dominates cost when one prefilled context m
arXiv:2610.02704v1 Announce Type: new Abstract: Time series are produced continuously at enormous scale by industrial equipment, wearables, power grids, and clinical monitors, yet annotation remains m
arXiv:2610.02586v1 Announce Type: new Abstract: A typed decision model answers a fixed question about an input by returning a probability for each of several caller-defined options. Each option carrie
arXiv:2610.03161v1 Announce Type: cross Abstract: Feature selection for imbalanced classification tasks such as credit card fraud and consumer default detection requires balancing predictive relevance
arXiv:2610.03695v1 Announce Type: new Abstract: Modern chess engines are silent experts: they play at a superhuman level, but do not offer explanations for their play. On the other hand, language mode
arXiv:2610.02665v1 Announce Type: cross Abstract: Despite the success of discrete diffusion language models (dLMs) for fast parallel decoding, their non-smooth, high-dimensional space hinders trajecto
arXiv:2610.02684v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly explored for clinical reasoning, but whether they appropriately revise judgments as patient evidence evolv
arXiv:2610.03370v1 Announce Type: new Abstract: CLIP's visual encoder produces only global image representations, limiting its use in region-level tasks. Existing adaptations rely on visual prompting,
arXiv:2610.02381v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on the responses it generates. Existing LLM multi-teacher OPD transfers what specialists predict through t
arXiv:2610.02774v1 Announce Type: new Abstract: Long-form generators for music, motion and video produce sequences chunk by chunk, with each chunk generated by iterative denoising while rewards are de
arXiv:2610.02792v1 Announce Type: new Abstract: Legal systems are increasingly implemented through software, yet scalable methods for translating legal texts into accurate symbolic representations rem
arXiv:2610.02670v1 Announce Type: cross Abstract: LLM agents are known to be slow in rollouts. An agent completes a task one step at a time. At each step, it reasons and then chooses an action to exec
arXiv:2610.03071v1 Announce Type: new Abstract: Chance-constrained programs (CCPs) optimize decisions under uncertainty by limiting the probability of constraint violation. Despite advances in traditi
arXiv:2610.03251v1 Announce Type: new Abstract: A model trained on 'The capital of X is Y' may produce 'Y' after 'The capital of X is' but fail after 'The capital of X:'. We call these different ways
arXiv:2610.02564v1 Announce Type: cross Abstract: We develop a closure modeling framework for identifying missing components of dynamical systems using Kernel Ridge Regression (KRR). The framework add
arXiv:2610.02700v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) supplies dense token-level feedback on trajectories sampled from the student's own policy, a richer training signal
arXiv:2610.03515v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) uses reference solutions as privileged hindsight to supervise student-generated reasoning trajectories. However, refe
arXiv:2610.02918v1 Announce Type: cross Abstract: Jazz pianists develop distinctive traits that experienced listeners can often identify within seconds, yet the features underlying this recognition re
arXiv:2209.14887v3 Announce Type: replace-cross Abstract: Robotic locomotion is often approached with the goal of maximizing robustness and reactivity by increasing motion control frequency. We challe
arXiv:2610.02749v1 Announce Type: cross Abstract: Efficient vector retrieval requires both a corpus geometry that supports retrieving the right documents through vector similarity, and a query encoder
arXiv:2610.02811v1 Announce Type: new Abstract: During contact-rich manipulation, interactions between a robot and its environment carry information about local geometry: a surface prevents penetratio
arXiv:2610.02437v1 Announce Type: cross Abstract: Why does supervised fine-tuning (SFT) lead to more forgetting than reinforcement fine-tuning (RFT), even when all teacher demonstrations are semantica
arXiv:2610.02517v1 Announce Type: new Abstract: Graph-structured data plays a pivotal role in modeling complex relationships. However, real-world graphs are often incomplete due to data collection and
arXiv:2610.03570v1 Announce Type: new Abstract: Contactless heart-rate sensing with millimeter-wave (mmWave) radar requires assessing whether individual measurements support reliable estimation. We st
arXiv:2610.02703v1 Announce Type: new Abstract: On-policy distillation (OPD) improves large language model reasoning by training students on their own rollouts with dense token-wise supervision from t
arXiv:2610.03065v1 Announce Type: new Abstract: Learning control from action-free recordings is challenging because intervention effects are unobserved and policies may exploit errors in reconstructed
arXiv:2610.02525v1 Announce Type: new Abstract: Long-horizon research agents must decide both how to investigate and what to investigate next as evidence accumulates. This is hard to learn because suc
arXiv:2610.02612v1 Announce Type: new Abstract: Simultaneous speech translation must emit useful target text before the source is complete while preserving every committed token. We adapt a full-utter
arXiv:2610.03717v1 Announce Type: cross Abstract: This paper examines the role of Novel View Synthesis (NVS) in geometric representation learning. In principle, NVS should reason about 3D scene struct
arXiv:2610.03702v1 Announce Type: new Abstract: The choice of post-training data for large language models substantially affects downstream performance. Gradient-based data selection is a popular appr