Knowledge catalogue
arXiv:2609.39071v1 Announce Type: new Abstract: Legal language models require reward signals that capture not only answer correctness but also the multidimensional quality of legal responses. Existing
arXiv:2609.39507v1 Announce Type: new Abstract: General-purpose agents can plan, use tools, and revise their behavior from feedback, but it remains unclear whether these capabilities transfer from dig
arXiv:2609.39151v1 Announce Type: cross Abstract: Does memory-dependent control need nonlinear recurrent dynamics? We study simulated air-hockey defence under temporary loss of puck tracking. A Dreame
arXiv:2609.40286v1 Announce Type: cross Abstract: Unlearning a fact in one language does not guarantee its removal in others as changing the query or even the requested answer language can reopen seem
arXiv:2410.02343v2 Announce Type: replace Abstract: Large language models (LLMs) routinely fail to output the correct option in multiple-choice question answering (MCQA) while encoding the answer inte
arXiv:2609.39882v1 Announce Type: new Abstract: Pre-training equips large language models (LLMs) with a broad repertoire of behavioral patterns associated with roles, styles, values, and goals. Post-t
arXiv:2609.38928v1 Announce Type: new Abstract: Mapless navigation in unknown and partially observable environments remains challenging for mobile robots, particularly when local minima prevent the ro
arXiv:2609.40222v1 Announce Type: new Abstract: When a camera revisits a previously observed region, a video world model should reproduce what was there before. This requires both remembering past obs
arXiv:2609.38852v1 Announce Type: new Abstract: Recent humanoid soccer systems make motion tracking the substrate and derive locomotion from it, typically by steering a motion-reference anchor toward
arXiv:2609.39179v1 Announce Type: new Abstract: High-precision locomotion combines motion-command tracking with precise regulation of task-relevant physical states, enabling robots to interact reliabl
arXiv:2609.38381v1 Announce Type: cross Abstract: AI-enabled Cyber-Physical Systems (CPS) are highly vulnerable to adversarial and anomalous inputs, where small perturbations can induce cascading erro
arXiv:2609.40079v1 Announce Type: cross Abstract: While recent Multimodal Large Language Models (MLLMs) have shown promise in affective computing, their reasoning capabilities are largely confined to
arXiv:2609.38562v1 Announce Type: new Abstract: World models, game simulators, and long-take video creation require coherent scene evolution and sustained dynamics over extended durations. Autoregress
arXiv:2609.39038v1 Announce Type: new Abstract: Generative policies have emerged as a promising paradigm for robot learning, combining expressive generative action modeling with scalable imitation lea
arXiv:2609.38955v1 Announce Type: cross Abstract: Inverse Reinforcement Learning (IRL) aims to recover a reward function that explains expert demonstrations. Existing IRL methods typically rely on a b
arXiv:2609.40305v1 Announce Type: new Abstract: Improving text-to-image models has traditionally relied on increasing model size or the number of denoising steps. In this work, we explore an alternati
arXiv:2609.38426v1 Announce Type: cross Abstract: We introduce LoopVL to study whether Loop Transformers can be effectively extended to vision- language models. LoopVL combines Module-Loop and Model-L
arXiv:2609.39185v1 Announce Type: new Abstract: Mamba-style and hybrid language models compress their past into a fixed-size recurrent state that is rewritten at every generated token. Storing this st
arXiv:2609.38375v1 Announce Type: cross Abstract: Can a constant number of linear minimizations per round improve on the T^{3/4} regret rate of online Frank-Wolfe on general convex sets? Weibel et al.
arXiv:2601.19918v2 Announce Type: replace Abstract: Hallucinations in Large Language Models (LLMs), i.e., plausible but non-factual generations, pose a significant challenge to reliable deployment in
arXiv:2609.39146v1 Announce Type: new Abstract: Multi-agent debate (MAD) can improve large language model (LLM) reasoning by allowing multiple agents to exchange and critique their answers to the same
arXiv:2609.39583v1 Announce Type: cross Abstract: We present the results of extbf{MADGRAV}, a deep-learning-based search for high-mass compact binary coalescences, applied to the data collected by the
arXiv:2609.39870v1 Announce Type: new Abstract: World-action models (WAMs) augment robot policies with action-conditioned environment dynamics, yet existing approaches largely rely on future observati
arXiv:2609.40043v1 Announce Type: cross Abstract: Photospheric vector magnetic fields are foundational to modeling, understanding, and forecasting solar activity. These data are usually produced by in
arXiv:2609.39950v1 Announce Type: new Abstract: Continuum robots are well suited for gentle manipulation because of their inherent compliance and ability to adapt to complex environments. However, the
arXiv:2609.39018v1 Announce Type: cross Abstract: Frontier models can control robots, but reasoning through every reach, grasp, and retreat makes manipulation slow and token-intensive. We revisit code
arXiv:2609.39368v1 Announce Type: new Abstract: A common formalism for constraining the output of autoregressive text generation models involves lexical constraints, words or phrases which are require
arXiv:2609.38972v1 Announce Type: cross Abstract: Chain-of-thought (CoT) traces often serve as a proxy for how Large Language Models (LLMs) arrive at their answers. However, growing evidence shows tha
arXiv:2609.39652v1 Announce Type: new Abstract: Distributed manipulator systems manipulate objects through the coordinated motion of many actuators. However, an object must be supported by several act
arXiv:2609.40170v1 Announce Type: new Abstract: MANETs enable flexible infrastructure-less wireless connectivity in dynamic and resource-constrained environments. As modern MANETs exploit multiple fre
arXiv:2601.23151v2 Announce Type: replace Abstract: Generative models have enjoyed widespread success in a variety of applications. However, they encounter inherent mathematical limitations in modelin
arXiv:2609.39639v1 Announce Type: new Abstract: Tabular learning uses structured data to predict target outcomes. Traditionally, this process has relied on labeled data. However, large language models
arXiv:2609.38627v1 Announce Type: new Abstract: Yoruba is a tonal language in which contour tones pose persistent orthographic challenges. These are especially notable for personal names and lexical i
arXiv:2609.39888v1 Announce Type: new Abstract: Neural trajectory predictors can reach low prediction error while violating dynamics, actuator limits, or state constraints, especially when controls ar
arXiv:2609.39107v1 Announce Type: new Abstract: Large Language Models (LLMs) have been applied in various fields. However, ensuring compliance and safety of LLMs, such as avoiding discrimination and b
arXiv:2609.38278v1 Announce Type: new Abstract: Self-supervised learning (SSL) removes the need for annotations and makes models that are capable across more domains than supervised learning. The auto
arXiv:2602.03537v2 Announce Type: replace Abstract: Matryoshka Quantization (MatQuant), Any-Precision-LLM (AP) and AnyBCQ (AB) are recent quantization approaches showing that a single integer-quantize
arXiv:2609.38746v1 Announce Type: new Abstract: How can a 3D reconstruction system acquire and retain useful information to understand the geometry of a scene from partial views under a limited comput
arXiv:2609.40322v1 Announce Type: cross Abstract: Material generation should produce not only an appearance, but also the rules that construct it. We introduce MatLoom, a compact, layer-oriented langu
arXiv:2609.39681v1 Announce Type: new Abstract: Unsupervised domain adaptation (UDA) reduces the annotation burden in panoptic segmentation by leveraging a cost-effectively labeled source domain (e.g.
arXiv:2609.39920v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) exhibit strong multimodal in-context learning (ICL) capabilities, yet this ability degrades substantially as mode
arXiv:2609.40087v1 Announce Type: cross Abstract: Flow-matching approaches to voice conversion (VC) have gained attention owing to their high speech quality and strong speaker similarity. Among them,
arXiv:2605.06462v2 Announce Type: replace Abstract: Progress in graph learning is hindered by benchmark practices that conflate the contributions of node features and graph structure, making it hard t
arXiv:2609.38543v1 Announce Type: new Abstract: Constantly evolving real-world knowledge necessitates models to be updated continuously. Especially in medicine, as clinical evidence changes over time,
arXiv:2511.00421v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show promise in medical applications, but their ability to detect and correct errors in clinical texts remains un
arXiv:2609.39273v1 Announce Type: new Abstract: This report presents extbf{MegaAvatar}, a controllable talking avatar generation framework built on top of the Wan2.2-TI2V-5B model. Compared with previ
arXiv:2609.39765v1 Announce Type: new Abstract: Agent memory faces heterogeneous access needs: a single-hop question may require one piece of evidence, whereas a multi-hop question must combine eviden
arXiv:2609.40195v1 Announce Type: cross Abstract: Long-term egocentric video enables personalized AI assistants to reason about daily life. However, as video histories grow to hundreds of hours spanni
arXiv:2609.38900v1 Announce Type: new Abstract: Streaming video understanding requires models to process unbounded visual streams while preserving rich visual semantics across vast temporal horizons,
arXiv:2609.38401v1 Announce Type: new Abstract: Training data variation, whether through designing a domain randomization (DR) scheme in simulation or curating demonstrations for imitation learning, i
arXiv:2609.39182v1 Announce Type: cross Abstract: World Models are appearing as the next major frontier in computer vision. However, their robustness is currently largely unexplored. We identify the p
arXiv:2609.38985v1 Announce Type: new Abstract: Generating compact, artist-style meshes with explicit topology typically relies on autoregressive models which incur prohibitive sequential per-token co
arXiv:2609.38392v1 Announce Type: new Abstract: Personas used to seed LLM social simulations face a cold-start problem: existing methods lack a principled basis for deciding which attributes to includ
arXiv:2609.38718v1 Announce Type: new Abstract: Steering large language models typically relies on linear, context-independent interventions in activation space, an assumption that recent work has cha
arXiv:2609.38609v1 Announce Type: cross Abstract: This note is a technical companion to a previously published preprint describing an Attention Residual U-Net that postprocesses deterministic forecast
arXiv:2609.40041v1 Announce Type: new Abstract: We present MGhana-ST, a speech translation dataset for four low-resource Ghanaian language varieties: Ga, Twi (Akuapem and Asante), Ewe, and Fante. MGha
arXiv:2609.39982v1 Announce Type: cross Abstract: Terminal agents act through stochastic model generations, yet the ability to generate a useful action does not ensure its reliable execution. A poor c
arXiv:2609.38349v1 Announce Type: cross Abstract: Modern agentic systems combine an AI model with a harness that controls execution and environmental interactions. Harness design strongly affects long
arXiv:2609.39628v1 Announce Type: new Abstract: This paper proposes MIND, a marginal-invariant neural dependency diffusion model for mixed-type tabular data. MIND does not directly learn the joint dis
arXiv:2609.39147v1 Announce Type: new Abstract: Current image-to-video models achieve visual realism and physical plausibility, but reasoning about mental states remains unexplored. Actions are driven