Knowledge catalogue
arXiv:2605.14821v1 Announce Type: new Abstract: Face restoration under complex degradations still remains an ill-posed inverse problem due to severe information loss. Although diffusion models benefit
arXiv:2605.14487v1 Announce Type: cross Abstract: Autoregressive video diffusion models support real-time synthesis but suffer from error accumulation and context loss over long horizons. We discover
arXiv:2602.04585v2 Announce Type: replace Abstract: We present ImmuVis, a family of efficient foundation models for imaging mass cytometry (IMC), a high-throughput multiplex imaging technology that ha
arXiv:2511.15407v3 Announce Type: replace Abstract: Humans learn by observing, interacting with environments, and internalizing physics and causality. Here, we aim to ask whether an agent can similarl
arXiv:2605.14907v1 Announce Type: new Abstract: Knowledge graph (KG) foundation models aim to generalize across graphs with unseen entities and relations by learning transferable relational structure.
arXiv:2605.14301v1 Announce Type: new Abstract: Domain adaptation faces a fundamental paradox in the cold-start regime. When target data is scarce, statistical methods fail to distinguish relevant sou
arXiv:2604.05306v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often produce confident yet incorrect answers, which can lead to risky failures in real-world applications. We st
arXiv:2509.01416v2 Announce Type: replace Abstract: The computational overhead of traditional numerical solvers for partial differential equations (PDEs) remains a critical bottleneck for large-scale
arXiv:2605.14120v1 Announce Type: cross Abstract: Geospatial foundation models compress multispectral observations into dense embeddings increasingly used in natural-language environmental reasoning s
arXiv:2602.00520v3 Announce Type: replace Abstract: Event stream data often exhibit hierarchical structure in which multiple events co-occur, resulting in a sequence of multisets (i.e., bags of events
arXiv:2601.22197v3 Announce Type: replace-cross Abstract: Generating clinical reports that summarize abnormal patterns, diagnostic findings, and clinical interpretations from long-term EEG recordings
arXiv:2605.14991v1 Announce Type: cross Abstract: Ovarian cancer is the most lethal gynecologic malignancy: around 60% of patients are diagnosed at an advanced stage, with an associated 5-year surviva
arXiv:2605.14366v1 Announce Type: new Abstract: Extending large language models (LLMs) to low-resource languages often incurs an 'alignment tax': improvements in the target language come at the cost o
arXiv:2510.16196v2 Announce Type: replace-cross Abstract: Understanding how the brain encodes visual information is a central challenge in neuroscience and machine learning. A promising approach is to
arXiv:2605.14037v1 Announce Type: cross Abstract: Under modern test-time compute and agentic paradigms, language models process ever-longer sequences. Efficient text generation with transformer archit
arXiv:2605.14908v1 Announce Type: new Abstract: Video reasoning segmentation requires localizing objects across video frames from natural language expressions, often involving spatial reasoning and im
arXiv:2605.14069v1 Announce Type: new Abstract: Irregularly sampled multivariate event streams remain a stubbornly difficult modality for generative modeling: tokenization-based approaches break down
arXiv:2601.21656v3 Announce Type: replace Abstract: Clustering tabular data is a fundamental yet challenging problem due to heterogeneous feature types, diverse data-generating mechanisms, and the abs
arXiv:2605.13986v1 Announce Type: new Abstract: Tabular data underpins most high-value prediction problems in science and industry, and TabPFN has driven the foundation model revolution for this modal
arXiv:2605.13936v1 Announce Type: cross Abstract: The recent success of large language models (LLMs) has been largely driven by vast public datasets. However, the next frontier for LLM development lie
arXiv:2603.11042v2 Announce Type: replace-cross Abstract: Generating music that temporally aligns with video events is challenging for existing text-to-music models, which lack fine-grained temporal c
arXiv:2012.14425v2 Announce Type: replace-cross Abstract: Cyberattacks cause billions of dollars in damage annually, with malicious hackers often sharing exploit code and techniques on underground for
arXiv:2605.11410v2 Announce Type: replace Abstract: Clinical electroencephalogram (EEG) analysis rests on a hand-crafted feature catalog refined over decades, e.g., band power, connectivity, complexit
arXiv:2511.02776v2 Announce Type: replace Abstract: Recent progress in large-scale robotic datasets and vision-language models (VLMs) has advanced research on vision-language-action (VLA) models. Howe
arXiv:2605.12964v1 Announce Type: new Abstract: Flow-based generation in high-dimensional spaces is difficult because velocity prediction requires modeling high-dimensional noise, even when data has s
arXiv:2605.12736v1 Announce Type: new Abstract: Template based single step retrosynthesis predicts reactants by selecting and applying an explicit reaction template, making each prediction traceable t
arXiv:2605.12980v1 Announce Type: cross Abstract: Molecular structure elucidation from tandem mass spectra (MS/MS) remains challenging, particularly for de novo generation beyond database coverage. A
arXiv:2605.12809v1 Announce Type: cross Abstract: A critical step for reliable large language models (LLMs) use in healthcare is to attribute predictions to their training data, akin to a medical case
arXiv:2605.13540v1 Announce Type: cross Abstract: Dynamic graphs are ubiquitous in real-world systems, and building generalizable dynamic Graph Foundation Models has become a frontier in graph learnin
arXiv:2601.00417v3 Announce Type: replace-cross Abstract: Transformer residual streams evolve by additive accumulation: each layer appends a feature update to a shared hidden state, but has no direct
arXiv:2605.12836v1 Announce Type: new Abstract: Continuous diffusion is a natural framework for non-autoregressive generation but has generally lagged behind masked discrete diffusion models (MDMs) on
arXiv:2605.13179v1 Announce Type: new Abstract: The Engram module -- a hash-keyed, O(1) associative memory injected into Transformer layers -- was recently shown to improve large language model pretra
arXiv:2605.12516v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) often struggle to generate reliable responses in specialized engineering domains due to limited domain gr
arXiv:2605.13568v1 Announce Type: cross Abstract: Myocardial infarction (MI) is a leading cause of death, and its adverse outcomes are urgent to predict. Yet ECG-based prognostic models underperform b
arXiv:2605.13122v1 Announce Type: new Abstract: Instruction-based image editing (IIE) models have recently demonstrated strong capability in modifying specific image regions according to natural langu
arXiv:2605.13194v1 Announce Type: cross Abstract: Electrocardiogram (ECG) arrhythmia classification remains challenging due to signal variability, noise, limited labeled data, and the difficulty in ac
arXiv:2605.13789v1 Announce Type: cross Abstract: Protein structure tokenizers (PSTs) are workhorses in protein language modeling, function prediction, and evolutionary analysis. However, existing PST
arXiv:2605.13581v1 Announce Type: new Abstract: Hyperspectral image (HSI) restoration is crucial for reliable analysis, as real HSIs suffer from degradations like noise, blur, and resolution loss. How
Interesting position paper on agentic AI as a foreseeable pathway to AGI. (bookmark it) There has been strong debate on whether a larger single model get us there or a multi-agent system. The authors
arXiv:2605.12725v1 Announce Type: new Abstract: Recent video anomaly detection research has expanded rapidly with an emphasis on general models of normality intended to work across many different scen
arXiv:2605.13013v1 Announce Type: new Abstract: Diffusion world models have recently become competitive for online model-based reinforcement learning, but current approaches expose a tension: pixel di
arXiv:2605.12556v1 Announce Type: new Abstract: Low-light image enhancement is challenging due to complex degradations, including amplified noise, artifacts, and color distortion. While Retinex-based
arXiv:2605.13711v1 Announce Type: new Abstract: Multimodal irregular time series (MITS) consist of asynchronous and irregularly sampled observations from heterogeneous numerical and textual channels.
arXiv:2507.10797v2 Announce Type: replace Abstract: This paper introduces the framework of multi-armed sampling, which serves as the sampling counterpart to the optimization problem of multi-armed ban
arXiv:2605.13190v1 Announce Type: cross Abstract: Improving the inference efficiency of autoregressive transformers typically means reducing FLOPs per token, usually through approximations that degrad
arXiv:2605.12991v1 Announce Type: cross Abstract: LLM-based multi-agent pipelines flip from correct to incorrect answers under simulated peer disagreement at rates we term yield, a vulnerability widel
arXiv:2601.21033v2 Announce Type: replace Abstract: Diffusion models cannot enforce hard constraints, yet applications in the physical sciences demand exact satisfaction of conservation laws, boundary
arXiv:2605.12855v1 Announce Type: new Abstract: Clinical trial studies indicate benefit of watch-and-wait (WW) surveillance for patients with rectal cancer showing a complete or near clinical response
arXiv:2605.12567v1 Announce Type: cross Abstract: The inherent electronic and speckle noise complicates clinical interpretation of ultrasound images. Conventional denoising methods rely on explicit no
arXiv:2411.15913v4 Announce Type: replace-cross Abstract: Music style transfer blends source structure with reference style to enable personalized music creation. However, existing zero-shot methods o
arXiv:2603.20527v3 Announce Type: replace Abstract: Preconditioned adaptive methods have gained significant attention for training deep neural networks, as they capture rich curvature information of t
arXiv:2605.13672v1 Announce Type: new Abstract: Few-shot classification (FSC) is widely used for learning from limited labeled data, yet most evaluations implicitly assume that target concepts are ind
arXiv:2605.11033v1 Announce Type: cross Abstract: TokaMind is a multi-modal transformer (MMT) foundation model pre-trained on tokamak plasma diagnostics data from MAST, where it was shown to outperfor
arXiv:2511.17001v2 Announce Type: replace Abstract: Cross-embodiment robot learning requires a unified action representation with consistent semantics across robot platforms. Existing representations
arXiv:2605.12904v1 Announce Type: new Abstract: Tabular foundation models (TFMs) have emerged as a powerful paradigm for in-context learning on structured data, enabling direct prediction on new tabul
arXiv:2605.11311v1 Announce Type: cross Abstract: Diffusion models typically generate image batches from independent Gaussian initial noises. We argue that this independence assumption is only one cho
arXiv:2605.11666v1 Announce Type: new Abstract: The reasoning frontier of Large Language Models (LLMs) has advanced significantly through modern post-training paradigms (e.g., Reinforcement Learning f
arXiv:2605.11898v1 Announce Type: new Abstract: Class imbalance is a persistent challenge in visual recognition, particularly in safety-critical domains where collecting positive examples is expensive
arXiv:2505.11356v4 Announce Type: replace Abstract: Graph Contrastive Learning (GCL) relies on semantically consistent graph augmentations, but common local perturbations provide limited control over
arXiv:2605.12167v1 Announce Type: cross Abstract: Video generation models offer a promising imagination mechanism for robot manipulation by predicting long-horizon future observations, but effectively