Knowledge catalogue
arXiv:2610.19224v1 Announce Type: new Abstract: Surface navigation on the Moon is subject to extreme illumination contrast and deployment-time perceptual disturbances. While modular navigation pipelin
arXiv:2310.12765v2 Announce Type: replace-cross Abstract: Recently there has been a lot of interest in non-autoregressive (non-AR) models for speech synthesis, such as FastSpeech 2 and diffusion model
arXiv:2610.19855v1 Announce Type: new Abstract: As Large Language Models (LLMs) continue to scale, their energy consumption during inference has become a critical concern. While Autoregressive (AR) mo
arXiv:2610.14525v1 Announce Type: new Abstract: Test-Time Discovery (TTD) aims to recognize known classes and detect novel ones while the model is already deployed, operating directly on a continual u
arXiv:2610.15882v1 Announce Type: new Abstract: A stored fact may stop being valid when a user changes a preference or an event replaces an earlier one. We study whether a language model can learn the
arXiv:2610.17523v1 Announce Type: cross Abstract: With recent developments in Artificial Intelligence (AI), the adoption of distributed learning paradigms such as Federated Learning (FL) has emerged.
arXiv:2610.19683v1 Announce Type: cross Abstract: World models trained via latent dynamics offer a scalable path for embodied planning, yet existing methods suffer from representational collapse, comp
arXiv:2610.17379v1 Announce Type: new Abstract: LLM-based automated heuristic design (AHD) discovers heuristics for combinatorial optimization problems (COPs), where an LLM generates candidates in an
arXiv:2610.20461v1 Announce Type: new Abstract: Character Error Rate (CER), the standard metric for OCR and vision-language models (VLMs), charges a typo as much as a wrong name. We introduce semCER,
arXiv:2610.14750v1 Announce Type: new Abstract: Credit assignment in modular financial reinforcement learning is difficult to evaluate because an attributed signal may improve a component loss without
arXiv:2609.34404v2 Announce Type: replace-cross Abstract: Leveraging the strong generative capabilities and stable training dynamics of diffusion models, diffusion-based recommender systems (RSs) have
arXiv:2610.19373v1 Announce Type: new Abstract: Does a language model answer better because its compressed memory contains useful document information? Replacing memory vectors with zeros can lower a
arXiv:2610.19003v1 Announce Type: new Abstract: Abstractive summarization remains challenging across languages, where linguistic quality and factual consistency may diverge, particularly in low-resour
arXiv:2610.14346v2 Announce Type: replace Abstract: Enterprise customer-support agents increasingly rely on Large Language Models (LLMs) to execute bounded, tool-using workflows. We study this problem
arXiv:2610.20309v1 Announce Type: new Abstract: Synthetic-aperture imaging (SAI) removes occluders such as foliage by integrating many views of a scene along a camera path. It has so far been used mai
arXiv:2610.17404v1 Announce Type: cross Abstract: Earthquake catalogs underpin studies such as fault structure, earthquake processes and seismic hazard, and their value depends on being both complete
arXiv:2610.19556v1 Announce Type: new Abstract: Confirmation of overdose deaths in the United States is often delayed due to the complexities of certifying drug-involved deaths. Death investigation re
arXiv:2610.14828v1 Announce Type: new Abstract: Office workflows are a demanding testbed for self-improving agents because their deliverables must be correct, natively editable, and mutually consisten
arXiv:2610.19799v1 Announce Type: new Abstract: Recent work lets LLM agents evolve their own harness, up to the team itself. Yet in human groups, the same abilities achieve different outcomes under di
arXiv:2610.17130v2 Announce Type: new Abstract: Automated alpha factor discovery is critical for modern quantitative research. While large language models (LLMs) have increasingly been integrated into
arXiv:2610.16402v1 Announce Type: new Abstract: Web application development often proceeds through successive requests that extend an existing implementation. Yet web-generation benchmarks primarily e
arXiv:2610.19281v1 Announce Type: new Abstract: Understanding neural scaling laws is increasingly important for theory and practice, yet first-principles explanations remain limited even in simple set
arXiv:2610.17633v1 Announce Type: new Abstract: Work on debugging LLM agents treats the 'first mistake' marked by a human or LLM judge as ground truth for where a trajectory failed. We ask whether suc
arXiv:2610.20108v1 Announce Type: new Abstract: Neural symbolic regression (SR) models require diverse datasets of mathematical expressions for robust training. However, existing datasets rely predomi
arXiv:2610.19868v1 Announce Type: cross Abstract: Training LLM agents on long-horizon interactive tasks with reinforcement learning typically relies on human-annotated tasks and reliable verification,
arXiv:2610.19270v1 Announce Type: new Abstract: The ecological insurance theory posits that diverse forests are more resilient and better able to adapt to changing environmental conditions. Increasing
arXiv:2610.15062v1 Announce Type: new Abstract: Supporting edge Artificial Intelligence (AI) inference as a network service is challenging because current mobile systems can transport tensors but do n
arXiv:2610.20305v1 Announce Type: cross Abstract: Homogeneous turbulent flows exhibit recurring multiscale structures across spatially separated regions, suggesting that a single flow realization may
arXiv:2610.19635v1 Announce Type: new Abstract: Gaze coordinates attention in human interaction through coupled eye, head, and eyelid motion. A humanoid robot must choose where to look while generatin
arXiv:2610.19618v1 Announce Type: new Abstract: Object-goal navigation requires an embodied agent to explore an unfamiliar environment, identify a target object, navigate toward candidate instances, a
arXiv:2610.20051v1 Announce Type: new Abstract: In recent years, with advances in multimodal interaction technologies and the growing demand for intelligent emotional support, Multimodal Emotional Sup
arXiv:2610.19155v1 Announce Type: new Abstract: Human demonstrations offer a scalable alternative to costly robot data collection for dexterous manipulation, but most human datasets lack force measure
arXiv:2610.18625v1 Announce Type: cross Abstract: Relying on manipulable characteristics to target the allocation of resources is fraught with difficulties. Naively optimal rules lead to falsification
arXiv:2610.18157v1 Announce Type: cross Abstract: Inside organizations, a question's data often sit with several owners, who query their own tables but never hand them over. An LLM coordinator can fan
arXiv:2610.19166v1 Announce Type: new Abstract: Meta-evaluation of text generation metrics and LLM-judges is primarily conducted using metric-human correlations. However, prior work has shown that cor
arXiv:2610.15691v1 Announce Type: new Abstract: Existing reinforcement learning (RL) largely focuses on optimizing learning algorithms within a pre-defined task formulation, while applying RL to new t
arXiv:2310.14040v2 Announce Type: replace-cross Abstract: Diffusion models have shown promising results for a wide range of generative tasks with continuous data, such as image and audio synthesis. Ho
arXiv:2610.14539v1 Announce Type: new Abstract: Real-world business requests often leave key requirements unstated because frontline practitioners struggle to articulate rules they know from experienc
arXiv:2610.20074v1 Announce Type: new Abstract: Catheter obstruction is the commonest cause of shunt failure; freehand insertion at Keen's point leaves the tip free in cerebrospinal fluid in only one
arXiv:2610.18430v1 Announce Type: cross Abstract: Federated fine-tuning of large language models (LLMs) allows multiple clients to collaboratively adapt models without sharing raw data, offering stron
arXiv:2610.20590v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting (3DGS) methods enable efficient reconstruction of 3D scenes from sparse multi-view images in a single forward pass, w
arXiv:2610.19893v1 Announce Type: cross Abstract: Reliable fault detection is crucial for industrial machinery in large-scale facilities, where unexpected failures can cause costly downtime and produc
arXiv:2610.15372v1 Announce Type: new Abstract: Large language models are generally more capable than smaller ones, but their memory requirements can make local deployment difficult. We ask whether pa
arXiv:2610.19240v1 Announce Type: new Abstract: Financial disclosures express the underlying relationships of double-entry accounting through unstructured text. While financial LLM benchmarks predomin
arXiv:2610.19191v1 Announce Type: new Abstract: Wildfire decisions are made in hours, yet most learned spread models predict daily on targets including burned area. We propose FireGraphNet which formu
arXiv:2610.18010v2 Announce Type: replace-cross Abstract: We introduce Fish Audio Drama 3, an open-source speech generation model that turns text into directed performances, built on a 35B-parameter m
arXiv:2610.14970v1 Announce Type: new Abstract: With the rapid evolution of large language models (LLMs), there is an increasing demand for effective personalization techniques that can adapt model be
arXiv:2610.20269v1 Announce Type: cross Abstract: Single-channel radio-frequency (RF) signal separation is important for interference mitigation when a signal of interest overlaps with a co-channel in
arXiv:2402.03694v3 Announce Type: replace-cross Abstract: Network traffic analysis in cloud and internet service provider (ISP) networks increasingly relies on machine learning (ML) models, as encrypt
arXiv:2610.19050v1 Announce Type: cross Abstract: Dense cardiac dynamics are essential for assessing cardiac function, yet in practice clinicians often observe only extremely sparse measurements. Howe
arXiv:2610.20392v1 Announce Type: new Abstract: Speculative decoding accelerates LLM generation through lightweight drafting and parallel verification. It is particularly promising for long inputs, wh
arXiv:2610.18881v1 Announce Type: new Abstract: Voice agents built on large language models (LLMs) now handle customer calls, but in regulated applications completing the task is not enough: the agent
arXiv:2610.19839v1 Announce Type: new Abstract: Robot-free collection with handheld interfaces enables scalable acquisition of human demonstrations for policy learning. However, existing handheld inte
arXiv:2610.16301v1 Announce Type: new Abstract: Self-attention assigns two functions to one mechanism-where to read and how to mix-at the price of O(L^2) training, an O(T) decode state, and position s
arXiv:2610.19604v1 Announce Type: new Abstract: Force feedback provides vision-language-action (VLA) policies with physical observations that can guide action selection and execution. However, differe
arXiv:2501.05559v2 Announce Type: replace Abstract: Have we made mitigating catastrophic forgetting more complicated than it needs to be? Continual adaptation often degrades performance on earlier tas
arXiv:2610.19408v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) have advanced rapidly, yet strengthening their reasoning through reinforcement learning (RL) remains an open cha
arXiv:2610.20064v1 Announce Type: new Abstract: We present FoveaVLA, a Vision-Language-Action (VLA) model that replaces the wrist camera with learned foveation actions. Wrist cameras provide close-up
arXiv:2504.16173v4 Announce Type: replace-cross Abstract: Space missions are becoming increasingly ambitious, necessitating high-performance onboard spacecraft computing systems. In response, field-pr
arXiv:2610.20244v1 Announce Type: cross Abstract: We study multi-robot motion planning in a polygonal environment with polygonal robots. The setting of the problem consists of a polygonal domain W wit