Knowledge catalogue
arXiv:2609.15863v1 Announce Type: new Abstract: Video diffusion models are stochastic and hard to control: precise content often requires repeated sampling without guaranteed success, and long-horizon
arXiv:2609.13247v1 Announce Type: cross Abstract: Calibrating an agent-based model (ABM) is difficult because its objective landscape is stochastic and rugged, and can be evaluated only through costly
arXiv:2609.13919v1 Announce Type: new Abstract: This chapter explores how machine learning (ML) is transforming aquaculture, with a particular focus on enhancing decision-making processes and improvin
arXiv:2609.13914v1 Announce Type: new Abstract: Machine-learning models are commonly developed under an assumption that training and test data are sufficiently complete, balanced, labelled, and drawn
arXiv:2609.13195v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) have recently shown strong capabilities in speech understanding and question answering (QA), but they also inherit
arXiv:2609.15676v1 Announce Type: new Abstract: Accurate marine pollution detection (MPD) is essential for protecting coastal ecosystems and marine biodiversity. Vision Mamba models have shown promise
arXiv:2609.13717v1 Announce Type: new Abstract: Masked reconstruction is widely used for EEG foundation models, but optimizing reconstruction on low-SNR waveforms does not necessarily produce the most
arXiv:2609.14660v1 Announce Type: cross Abstract: Split computing constitutes a widely used distributed inference approach, where a lightweight head model is onloaded onto the device and a heavier tai
Many assume that agent spend goes toward output tokens. When we ran DeepSWE on Astra vs. DeepSeek V4.1-Flash, input tokens outnumbered output 174 to 1. 99.6% were cache hits. Those hits are 60% of the
arXiv:2609.13879v1 Announce Type: new Abstract: An acquired representation can enlarge a system's cognitive repertoire without transferring the capacities exercised in producing that representation. T
arXiv:2609.15359v1 Announce Type: new Abstract: The surge of large language model (LLM) applications on personal devices imposes massive, bursty workloads on cloud serving infrastructure. While prefil
arXiv:2609.13665v1 Announce Type: new Abstract: Accurate cell segmentation remains a major bottleneck in subcellular spatial transcriptomics (SST), in which morphological images and spatially resolved
arXiv:2603.22179v2 Announce Type: replace Abstract: Cardiovascular disease remains the leading cause of global mortality, with progress hindered by human interpretation of complex cardiac tests. Curre
arXiv:2609.15408v1 Announce Type: cross Abstract: Long-video understanding remains challenging for multimodal large language models (MLLMs) because densely encoding long frame sequences is computation
arXiv:2606.13989v2 Announce Type: replace-cross Abstract: Recent alignment-free non-autoregressive (NAR) text-to-speech (TTS) models formulate synthesis as a conditional infilling task, bypassing expl
arXiv:2609.15221v1 Announce Type: cross Abstract: Passive acoustic monitoring can measure biodiversity at larger scales, but time--frequency annotation of animal vocalizations is expensive, site-speci
arXiv:2609.15289v1 Announce Type: cross Abstract: Artificial intelligence threatens to outrun human understanding and control. New mathematics is needed to design AI that is legible, steerable, and co
arXiv:2609.14495v1 Announce Type: new Abstract: Image colorization is an inherently ill-posed task, since a single grayscale image may correspond to multiple plausible colorized results. Consequently,
arXiv:2512.24565v4 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly serving as autonomous agents, and their utilization of external tools via the Model Context Protocol (
arXiv:2609.13847v1 Announce Type: new Abstract: In this paper, we extend FLORES+ with Portuguese-source evaluation sets for three Mozambican Bantu varieties: Xichangana, Mozambican Nyanja, and Sena. W
arXiv:2609.14057v1 Announce Type: new Abstract: Frontier LLMs are increasingly capable of conducting automated research, yet their creativity in this setting has not been systematically evaluated. In
arXiv:2609.15647v1 Announce Type: new Abstract: Medical counterfactual generation modifies images to change model predictions for interpretability. However, existing diffusion-based approaches are oft
arXiv:2609.15129v1 Announce Type: new Abstract: Complex medical information is often difficult for patients to understand, making effective medical knowledge simplification essential for improving pat
arXiv:2511.19046v2 Announce Type: replace-cross Abstract: Medical image segmentation is fundamental for biomedical discovery. Existing methods lack generalizability and demand extensive, time-consumin
arXiv:2609.14823v1 Announce Type: new Abstract: Multimodal clinical decision-making requires reliable reasoning over heterogeneous evidence from electronic health records, medical images, and physiolo
arXiv:2609.14874v1 Announce Type: cross Abstract: Medical volume visualization requires selecting regions of interest (ROIs) and carefully controlling their relative visual emphasis according to a giv
memory has been a hot topic for the past ~2 years every time we bring it up or do anything there, gets ton of interest, but nothing sticks few takes on why below: - how to update and then use memory n
arXiv:2609.14976v1 Announce Type: new Abstract: Long-horizon LLM agents accumulate memory across sessions, creating sparse but high-impact risks: stale facts, conflicting updates, cross-user leakage,
arXiv:2609.15743v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems, trained on paired speech-text data, have been improved by leveraging language models (LMs) trained on text-o
arXiv:2511.12041v4 Announce Type: replace-cross Abstract: Super-resolution flow reconstruction using state-of-the-art data-driven techniques is valuable for a variety of applications, such as subgrid/
arXiv:2609.15976v1 Announce Type: new Abstract: Mobile manipulators deployed across many rooms and visits should improve with experience: after discovering that a cabinet is locked or finding an objec
arXiv:2609.14795v1 Announce Type: cross Abstract: There is a tradeoff in machine translation meta-evaluation between prioritizing alignment with adequacy versus fluency. The balance depends on the com
arXiv:2609.13991v1 Announce Type: new Abstract: Reconstructing 3D objects from brain signals offers a promising avenue for understanding human visual cognition. While prior work has shown initial succ
arXiv:2609.15972v1 Announce Type: new Abstract: As language models become more capable, long-term collaboration in learning, reasoning, and decision-making calls for a deeper understanding of the peop
arXiv:2609.13922v1 Announce Type: new Abstract: Minibatch persistency reuses data instead of reading it: rather than drawing a fresh minibatch at every optimizer step, it takes K consecutive steps on
arXiv:2609.15045v1 Announce Type: cross Abstract: Prompt echoing is a recognized failure mode of instruct language models, in which a model instead of generating a response, mirrors the provided promp
arXiv:2408.09838v3 Announce Type: replace Abstract: A continual learning agent builds on previous experiences to develop increasingly complex behaviors by adapting to non-stationary and dynamic enviro
arXiv:2609.13486v1 Announce Type: cross Abstract: Recent work has shown that fine-tuning decoder-only large language models (LLMs) for retrieval yields strong first-stage retrievers, with effectivenes
arXiv:2609.13980v1 Announce Type: cross Abstract: Arabic large-language-model (LLM) evaluation has matured around Modern Standard Arabic (MSA): aggregated leaderboards such as the Open Arabic LLM Lead
arXiv:2609.13585v1 Announce Type: cross Abstract: Communication has become a bottleneck in distributed training and inference of large models. Overlapping communication with computation at the granula
The Financial Services Working Group's flagship initiative advances to the final round of the C:>DIR Global 'Agentic Regulator' Hackathon, with winners announced September 18. The post MLCommons Agent
arXiv:2609.15037v1 Announce Type: cross Abstract: Low-rank gradient projection reduces the optimizer-state memory cost of large language model (LLM) pretraining, but the steps and wall-clock time need
arXiv:2609.14347v1 Announce Type: new Abstract: Computed tomography (CT) plays an essential role in clinical workflow to improve patient outcomes. However, access to CT imaging and specialist interpre
arXiv:2609.14058v1 Announce Type: new Abstract: In this work, we developed a nonlinear model predictive control (NMPC) framework that employs learned dynamics via the Koopman Operator theory for mobil
arXiv:2609.15635v1 Announce Type: cross Abstract: A radiology report can already answer a clinical question, so it is hard to tell whether a vision-language model also uses the image. ModaLens, a pair
arXiv:2602.20723v3 Announce Type: replace Abstract: Multimodal recommenders combine collaborative behavior with visual and textual item evidence, whose usefulness varies across user-item interactions.
arXiv:2609.14231v1 Announce Type: cross Abstract: Controllable synthesis of nonverbal vocalizations (NVVs) is es- sential for natural and expressive speech, but remains challeng- ing due to their acou
arXiv:2608.00929v2 Announce Type: replace Abstract: Social dynamics encode the process in which individual network and discourse interactions aggregate into collective influence, narrative dominance a
arXiv:2503.18460v2 Announce Type: replace-cross Abstract: Modelica is a widely adopted language for simulating complex physical systems, yet effective model creation and optimization require substanti
arXiv:2609.14857v1 Announce Type: new Abstract: Recent work extends recursive self-improvement (RSI) to agent harnesses for long-horizon coding and terminal tasks, enabling agents to improve execution
arXiv:2609.15126v1 Announce Type: cross Abstract: Scaling large language models efficiently has motivated sparse capacity mechanisms such as Mixture-of-Experts and, more recently, conditional memory:
arXiv:2609.13691v1 Announce Type: new Abstract: Most existing visual odometry (VO) systems treat feature correspondences as deterministic measurements or assign uniform uncertainty, ignoring the inher
arXiv:2609.14716v1 Announce Type: new Abstract: Autonomous agents are typically obliged to follow user-assigned tasks. However, strict obedience may conflict with moral obligations that arise during e
arXiv:2609.15696v1 Announce Type: cross Abstract: Generative AI (GenAI) tools are increasingly woven into how blind and low-vision (BLV) people communicate, not only with digital information, but with
arXiv:2609.14189v1 Announce Type: new Abstract: Human motion may be viewed as a combination of action content, style, and body morphology. Existing motion style transfer methods transfer a reference s
arXiv:2609.14399v1 Announce Type: new Abstract: Natural language prompts and skills serve as the strategic backbone of LLM-based agents. Recent advances in prompt and skill optimization have achieved
arXiv:2609.15780v1 Announce Type: new Abstract: Understanding and predicting wildlife movement is critical for ecology and conservation. While trajectory forecasting has advanced for human and vehicle
arXiv:2609.14965v1 Announce Type: new Abstract: Text-driven 3D human motion generation models face significant challenges in responding to diverse and unconstrained textual prompts, primarily due to t
arXiv:2609.14992v1 Announce Type: new Abstract: Recently, the rapid development of large language models (LLMs) has reshaped software engineering by enabling autonomous code agents that plan, execute,
arXiv:2512.07673v2 Announce Type: replace Abstract: Effective motion representation is crucial for enabling robots to imitate expressive behaviors in real time, yet existing motion controllers often i