Knowledge catalogue
arXiv:2508.06974v2 Announce Type: replace Abstract: 1-bit LLM quantization offers significant advantages in reducing storage and computational costs. However, existing methods typically train 1-bit LL
arXiv:2605.17548v1 Announce Type: cross Abstract: Code review has evolved for decades, from informal peer checking to today's pull request (PR) workflows, yet it remains a largely manual, uneven, and
arXiv:2511.08704v2 Announce Type: replace Abstract: This paper investigates the scaling properties of autoregressive next-pixel prediction, a simple, end-to-end yet under-explored framework for unifie
arXiv:2601.04855v2 Announce Type: replace-cross Abstract: Handling missing node features is a key challenge for deploying Graph Neural Networks (GNNs) in real-world domains such as healthcare and sens
arXiv:2605.17566v1 Announce Type: new Abstract: With the rapid progress of multimodal foundation models and predictive pre-training, an important open question is how to equip 3D point clouds with a p
arXiv:2601.03851v2 Announce Type: replace Abstract: Table Question Answering (TableQA) benefits significantly from table pruning, which extracts compact sub-tables by eliminating redundant cells to st
arXiv:2605.16981v1 Announce Type: new Abstract: Streaming 3D reconstruction under a strict constant-memory budget hinges on how the recurrent state is updated as the stream evolves. We profile TTT3R-s
arXiv:2605.16331v1 Announce Type: cross Abstract: Protein language models are increasingly used to guide experimental and clinical decisions, yet it is often unclear whether a confident prediction ref
arXiv:2605.16767v1 Announce Type: new Abstract: Multi-label legal annotation requires assigning multiple labels from large, evolving taxonomies to long, fact-intensive documents, often under limited s
arXiv:2605.18684v1 Announce Type: cross Abstract: Legacy systems concentrate business rules, architectural decisions, and operational exceptions that often remain implicit in code, data, configuration
arXiv:2602.10134v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are pretrained on corpora containing trillions of tokens and, therefore, inevitably memorize sensitive informatio
arXiv:2305.10721v2 Announce Type: replace-cross Abstract: Introduction: Long-term time series forecasting (LTSF) has gained significant attention in recent years. While various specialized designs exi
arXiv:2605.17787v1 Announce Type: new Abstract: It is widely believed that stochastic gradient descent (SGD) performs significantly worse than adaptive optimizers such as Adam in pre-training Large La
arXiv:2605.18197v1 Announce Type: cross Abstract: Current approaches to 3D scene graph generation rely on dedicated depth sensors, such as LiDAR or RGB-D cameras, for metric 3D reconstruction. This li
arXiv:2605.17014v1 Announce Type: new Abstract: Reconstructing people, objects, and their interactions in 3D is a long-standing goal for intelligent systems. Often the input is RGB video from a moving
arXiv:2603.11276v2 Announce Type: replace-cross Abstract: Real-world contextual bandit problems with complex reward models are often tackled with iteratively trained models, such as boosting trees. Ho
arXiv:2605.16651v1 Announce Type: new Abstract: Explanation mechanisms are increasingly used to support transparency and trust in vision-language models (VLMs), particularly in settings where model de
arXiv:2605.18174v1 Announce Type: new Abstract: Muon has recently emerged as a strong alternative to AdamW for training neural networks, with encouraging large-scale pretraining results and growing ev
arXiv:2605.18004v1 Announce Type: new Abstract: Randomized linear algebra (RLA) algorithms are a modern class of numerical linear algebra techniques that play an essential role in scientific computing
arXiv:2509.21319v3 Announce Type: replace-cross Abstract: Reinforcement Learning with Human Feedback (RLHF) and Reinforcement Learning with Verifiable Rewards (RLVR) are the main RL paradigms used in
arXiv:2605.18729v1 Announce Type: cross Abstract: The ability to navigate and interact with complex environments is central to real-world embodied agents, yet navigation in unseen environments remains
arXiv:2605.17522v1 Announce Type: new Abstract: Planning and acting in 3D environments is a fundamental capability for robotic manipulation in the real world. Although prior work has explored predicti
arXiv:2603.04639v2 Announce Type: replace-cross Abstract: Memory is critical for long-horizon and history-dependent robotic manipulation. Such tasks often involve counting repeated actions or manipula
arXiv:2509.17666v2 Announce Type: replace Abstract: Object insertion tasks are prone to failure under pose uncertainty and environmental variation, often requiring manual fine-tuning or controller ret
arXiv:2605.18338v1 Announce Type: cross Abstract: Champion recommendation in multiplayer online battle arena games is usually framed informally as a problem of metagame strength, personal comfort, or
arXiv:2605.16941v1 Announce Type: new Abstract: Diffusion Large Language Models (DLLMs) promise fast parallel generation, yet open-source DLLMs still face a severe quality-speed trade-off: acceleratin
arXiv:2605.17279v1 Announce Type: cross Abstract: Code merging is a significant challenge, particularly in large-scale projects. Existing solutions, including program analysis and machine learning, sh
arXiv:2508.13977v3 Announce Type: replace Abstract: Depth estimation is a fundamental component of spatial perception for autonomous driving and other unmanned systems operating in open urban environm
arXiv:2603.13708v2 Announce Type: replace Abstract: In this paper, we explore text-guided image editing in the remote sensing domain using generative modeling. We propose rsedit, a collection of model
arXiv:2605.18263v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) enables real-time novel view synthesis with high visual quality. However, existing methods struggle with semi-transparent s
arXiv:2605.16843v1 Announce Type: new Abstract: India's Right to Information Act, 2005 gives every citizen the right to demand information from public authorities, yet in practice most people cannot m
arXiv:2605.17076v1 Announce Type: cross Abstract: Concurrent LLM agents sharing mutable natural-language state produce Structural Race Conditions (SRCs): write-write and cross-shard stale-read conflic
arXiv:2605.18579v1 Announce Type: new Abstract: Pre-training on text-attributed graphs (TAGs) is central to building transferable graph foundation models, where LLM-as-Aligner methods align graph and
arXiv:2605.17526v1 Announce Type: cross Abstract: As autonomous coding agents become capable of handling increasingly long-horizon tasks, they have gradually demonstrated the potential to complete end
arXiv:2605.16871v1 Announce Type: new Abstract: Explainable robots require not only successful task execution but also the ability to expose internal decision-making process in a user-friendly manner.
arXiv:2605.17985v1 Announce Type: cross Abstract: We propose a new method for compressing physics foundation models (PFMs) which is a new trend in AI for Science. While model compression is essential
arXiv:2605.18719v1 Announce Type: new Abstract: Diffusion models have been widely studied for removing unsafe content learned during pre-training. Existing methods require expensive supervised data, e
arXiv:2605.17610v1 Announce Type: cross Abstract: The rapid growth of online video platforms and AI-generated content has made reliable video guardrails a key challenge for safety and real-world deplo
arXiv:2605.18104v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) often fail to transfer safety capabilities learned in the text modality to semantically equivalent non-text inp
arXiv:2510.18822v4 Announce Type: replace Abstract: Due to the varying granularity of target states across different tasks, most existing trackers are tailored to a single task, which specificity limi
arXiv:2605.18613v1 Announce Type: cross Abstract: Latent representations are at the heart of the majority of modern generative models. In the audio domain they are typically produced by a neural-audio
arXiv:2605.18332v1 Announce Type: cross Abstract: Behavioral studies of LLM-based software engineering agents extract operational rules about which trajectory shapes correlate with higher resolution r
arXiv:2605.17189v1 Announce Type: cross Abstract: Low-rank matrix completion is a widely studied problem with many variants. Inductive matrix completion (IMC) incorporates row and column side informat
arXiv:2510.26635v3 Announce Type: replace-cross Abstract: Summary: SAMRI is an MRI-specialized adaptation of the Segment Anything Model achieving superior whole-body MRI segmentation, particularly for
arXiv:2605.17648v1 Announce Type: new Abstract: Generative recommendation treats next-item prediction as autoregressive item-identifier generation. Specifically, items are encoded as semantic identifi
arXiv:2605.18012v1 Announce Type: cross Abstract: Deep neural networks have achieved impressive performance across a wide range of tasks, but this success often comes with substantial computational an
arXiv:2602.02830v3 Announce Type: replace Abstract: Discovering causal structures from multivariate time series is a key problem because interactions span across multiple lags and possibly involve ins
arXiv:2605.17271v1 Announce Type: cross Abstract: Bi-causal optimal transport (OT) is a natural framework for comparing and coupling stochastic processes under nonanticipative information constraints,
arXiv:2605.18005v1 Announce Type: new Abstract: Many real-world combinatorial problems involve uncertain parameters, which can be predicted given contextual features and historical data. These `predic
arXiv:2605.18181v1 Announce Type: new Abstract: Generalizable agents should adapt to diverse tasks and unseen environments beyond their training distribution. This position paper argues that such gene
arXiv:2605.16686v1 Announce Type: new Abstract: Knowledge editing (KE) provides a lightweight alternative to repeated fine-tuning of LLMs. However, most existing KE methods target dense feed-forward l
arXiv:2605.16568v1 Announce Type: new Abstract: Knowledge Graphs are pivotal for semantic data integration. The real-world data they model is often inherently uncertain. Within knowledge graphs, uncer
arXiv:2605.17084v1 Announce Type: cross Abstract: In language models, what a representation encodes is determined by the geometry of its representation space: distances, not activations, carry meaning
arXiv:2605.17582v1 Announce Type: new Abstract: Many natural and engineered time series -- equity returns, climate anomalies, turbulent velocities, neural recordings, packet-level network traffic -- a
arXiv:2605.18528v1 Announce Type: cross Abstract: A growing lesson from neural network optimization is that optimizer design should respect how the model is parametrized. Scale-invariant methods becom
arXiv:2510.26384v2 Announce Type: replace Abstract: The prohibitive cost of evaluating large language models (LLMs) on comprehensive benchmarks necessitates the creation of small yet representative da
arXiv:2605.16562v1 Announce Type: new Abstract: We report on the ongoing development of arXiv's HTML Papers offering, available on every new TeX/LaTeX submission since its initial release in 2023. The
arXiv:2510.08702v2 Announce Type: replace Abstract: Code Large Language Models (LLMs) are revolutionizing software engineering. However, scaling laws that guide the efficient training are predominantl
arXiv:2605.16412v1 Announce Type: cross Abstract: Despite the central role of action in embodied intelligence, learning transferable action representations from visual transitions remains a fundamenta
arXiv:2605.16628v1 Announce Type: new Abstract: The SCARED dataset is a widely used benchmark for endoscopic depth estimation, offering ground-truth 3D reconstructions captured with a structured light