Knowledge catalogue
arXiv:2605.27654v1 Announce Type: cross Abstract: Generative translation systems are cultural technologies because they decide how socially meaningful cues are rendered within culturally specific gram
arXiv:2512.00252v4 Announce Type: replace-cross Abstract: Data assimilation (DA) is a cornerstone of scientific and engineering applications, combining model forecasts with sparse and noisy observatio
arXiv:2605.27483v1 Announce Type: cross Abstract: Despite theoretical promise, debate as a scalable oversight protocol has produced mixed empirical results: gains in some settings, and null effects in
arXiv:2605.28167v1 Announce Type: new Abstract: Text-to-image diffusion models, which are theoretically equivalent to score-based generative models, generate images through a multi-step denoising proc
arXiv:2602.03491v2 Announce Type: replace-cross Abstract: Reasoning over table images remains challenging for Large Vision-Language Models (LVLMs) due to complex layouts and tightly coupled structure-
arXiv:2403.11852v5 Announce Type: replace-cross Abstract: Delayed and partially observable state information poses significant challenges for reinforcement learning (RL)-based control in real-world au
arXiv:2605.27931v1 Announce Type: new Abstract: Scientific diagrams are essential for communicating complex methodologies in academic papers. A natural way for researchers to specify such diagrams is
arXiv:2602.16872v2 Announce Type: replace Abstract: Optical Character Recognition (OCR) is a fundamental task for digitizing information, serving as a critical bridge between visual data and textual u
arXiv:2605.28544v1 Announce Type: new Abstract: Pretrained foundation models have become an important basis for end-to-end autonomous driving. In contrast to vision-language models pretrained primaril
arXiv:2605.28018v1 Announce Type: new Abstract: Given the real-time demands of UAV tracking, many methods simplify the backbone to reduce computation, but this often weakens feature representation and
arXiv:2605.28409v1 Announce Type: new Abstract: Post-training using online reinforcement learning (RL) is an important training step for LLMs, including code-generating models. However, online RL for
arXiv:2603.22735v2 Announce Type: replace Abstract: Existing NLP work commonly treats contradictions as errors to be resolved by choosing which statements to accept or discard. Yet a key aspect of hum
arXiv:2605.27736v1 Announce Type: cross Abstract: Online reinforcement learning is becoming increasingly important for aligning diffusion models with non-differentiable objectives. However, existing m
arXiv:2605.27432v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) has emerged as a paradigm for grounding large language models in external knowledge, yet most existing RAG system
arXiv:2605.27774v1 Announce Type: new Abstract: In-context learning -- performing tasks based on examples given in the prompt -- is an important capability that has emerged in large language models an
arXiv:2605.27452v1 Announce Type: new Abstract: Bridge inspection in Japan requires mandatory visual assessments every five years, yet qualitative damage ratings (levels a-e) assigned by different eng
arXiv:2605.28500v1 Announce Type: cross Abstract: Large language models have shown impressive capabilities in code generation, yet they often produce functionally incorrect code. Uncertainty quantific
arXiv:2605.28816v1 Announce Type: new Abstract: World models for interactive video generation have largely focused on single-agent settings, where future observations are generated from a single contr
arXiv:2605.27491v1 Announce Type: new Abstract: We introduce GE-Sim 2.0 (Genie Envisioner World Simulator 2.0), a closed-loop video world simulator for robotic manipulation. Building on the action-con
arXiv:2605.28200v1 Announce Type: new Abstract: Single-cell RNA sequencing (scRNA-seq) profiles large numbers of cells but loses spatial context, whereas spatial transcriptomics (ST) preserves partial
arXiv:2605.28233v1 Announce Type: cross Abstract: Fairness-accuracy trade-offs are a central concern in the deployment of fairness-aware machine learning methods. When sensitive attributes are unavail
arXiv:2605.27866v1 Announce Type: new Abstract: Evaluating AI tutor responses requires more than factual correctness: tutors must identify mistakes, locate errors, provide guidance, and offer actionab
arXiv:2605.28645v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances LLMs by grounding generation in query-relevant external evidence. Beyond unstructured text corpora, Grap
arXiv:2605.27758v1 Announce Type: cross Abstract: Automotive crashworthiness optimization remains a safety-critical challenge, requiring the management of large-scale nonlinear structural deformations
arXiv:2605.28398v1 Announce Type: new Abstract: Hybrid-reasoning large language models (LLMs) expose explicit controls over reasoning effort, allowing users or systems to trade off answer quality agai
arXiv:2605.28802v1 Announce Type: new Abstract: Free-text explanations extend human label variation (HLV) beyond label disagreement by revealing the reasoning and preferences behind annotators' decisi
arXiv:2603.16985v2 Announce Type: replace Abstract: Transformer-based models have been widely adopted for time-series forecasting due to their high representational capacity and architectural flexibil
arXiv:2605.28649v1 Announce Type: cross Abstract: LLMs increasingly require surgical model editing to enhance domain-specific capabilities without incurring the computational cost or catastrophic forg
arXiv:2605.27605v1 Announce Type: new Abstract: We present Laguna M.1 and Laguna XS.2, two Mixture-of-Experts foundation models built for long-horizon, agentic coding: M.1 has 225.8B total parameters
arXiv:2507.06999v2 Announce Type: replace-cross Abstract: Reasoning is essential for large language models (LLMs), especially in complex tasks such as mathematical problem solving. However, multimodal
arXiv:2605.27413v1 Announce Type: cross Abstract: Proteins perform their biological functions through three-dimensional structures encoded by amino acid sequences, and ligand-binding protein co-design
arXiv:2605.27786v1 Announce Type: cross Abstract: Large language models are known to contain representational redundancy across network depth, making depth pruning an effective approach for improving
arXiv:2605.28604v1 Announce Type: cross Abstract: Identifying key individuals in video scenes is essential for applications such as automated video editing and intelligent surveillance. Current method
arXiv:2505.21771v2 Announce Type: replace-cross Abstract: Multimodal tables i.e. tabular layouts interleaved with charts, maps, icons, and color encodings are ubiquitous in real applications yet remai
arXiv:2605.27967v1 Announce Type: cross Abstract: Knowledge distillation is a powerful method for model compression, enabling the efficient deployment of complex deep learning models (teachers), inclu
arXiv:2603.12824v2 Announce Type: replace-cross Abstract: Vision-Language Model (VLM) based retrievers have advanced visual document retrieval (VDR) to impressive quality. They require the same multi-
arXiv:2605.27408v1 Announce Type: cross Abstract: Partial differential equations (PDEs) are central to modeling physical and engineering systems, but repeatedly solving parametric PDEs remains computa
arXiv:2605.28168v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated promising capability in generating reward functions for deep reinforcement learning (DRL)-based building
arXiv:2605.28603v1 Announce Type: cross Abstract: Irregular multivariate time series forecasting is critical in many real-world applications, where time series are irregularly sampled and exhibit dyna
arXiv:2605.28806v1 Announce Type: cross Abstract: Long-term memory is increasingly important for personalized AI agents, yet existing benchmarks and methods remain largely text-centric. Even when imag
arXiv:2605.27631v1 Announce Type: cross Abstract: Code Large Language Models (CLLMs) serve as the core of modern code agents, enabling developers to automate complex software development tasks. In thi
arXiv:2605.27712v1 Announce Type: new Abstract: Long reasoning traces need reliability estimates before final answers are known. We study prefix-conditioned eventual-success estimation, P(y=1 mid o_{1
arXiv:2605.28634v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models offer a promising paradigm for generalist robotic policies, yet their adaptation is hindered by data inefficiency an
arXiv:2605.28058v1 Announce Type: new Abstract: Recent work explored the capabilities of Large Language Models (LLMs) in Aspect-Based Sentiment Analysis (ABSA) through few-shot prompting, requiring su
arXiv:2605.28487v1 Announce Type: new Abstract: Materials process optimization requires reasoning over routes, conditions, tools and causal dependencies, yet most computational formulations flatten sy
arXiv:2603.05642v2 Announce Type: replace-cross Abstract: Open-world interactive object search in household environments requires understanding semantic relationships between objects and their surroun
arXiv:2605.28740v1 Announce Type: cross Abstract: As large language models are increasingly deployed for clinical text, ensuring they can reliably signal their own uncertainty becomes critical. Most e
arXiv:2602.13748v2 Announce Type: replace Abstract: Multimedia Event Extraction (MEE) aims to identify events and their arguments from documents that contain both text and images. It requires groundin
arXiv:2605.27569v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of specific training records from a deployed model without retraining from scratch. Current protocols ve
arXiv:2605.28477v1 Announce Type: new Abstract: Self-supervised depth estimation from monocular sequences relies on the joint learning of a depth and a pose network. Despite abundant research done to
arXiv:2605.28338v1 Announce Type: new Abstract: Large language models(LLMs) increasingly match expert performance on licensing examinations, yet routine clinical use remains limited because governance
arXiv:2605.27947v1 Announce Type: new Abstract: World Action Models (WAMs) improve robot manipulation by using video-based future representations to condition action generation. In pixel-space WAMs, h
arXiv:2605.28583v1 Announce Type: cross Abstract: Ensuring both safety and efficiency in decision-making for autonomous driving systems remains a fundamental challenge. Traditional Deep Reinforcement
arXiv:2605.28358v1 Announce Type: cross Abstract: Error-correcting codes enable reliable communication, yet practical soft decoding remains challenging across code families and block lengths. We propo
arXiv:2605.28442v1 Announce Type: cross Abstract: Self-supervised online traversability estimation enables robots to continuously learn from unlabeled open-world experiences and adapt their navigation
arXiv:2605.27971v1 Announce Type: cross Abstract: When large language models are fine-tuned to generate persona- or tone-conditioned responses, their output diversity is severely limited--a failure we
arXiv:2605.28533v1 Announce Type: new Abstract: We introduce a testing-by-betting framework that leverages predictions on unlabeled data to enhance the power of sequential hypothesis testing. Given li
arXiv:2605.27636v1 Announce Type: new Abstract: Although Large Language Models (LLMs) demonstrate excellent capabilities and performance for general reasoning tasks within the general public domain, t
arXiv:2605.28631v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) can yield large reasoning gains from very few training instances, yet its strong sensitivity to wh
arXiv:2602.13524v2 Announce Type: replace-cross Abstract: Identifying feature representations in language models is a central task in mechanistic interpretability. Several recent studies have made the