Knowledge catalogue
arXiv:2606.01026v1 Announce Type: new Abstract: Masked diffusion language models (MDLMs) re-predict every position at each denoising step, but standard samplers commit tokens once revealed, leaving th
arXiv:2606.01172v1 Announce Type: new Abstract: Modeling unknown latent functions from finite, irregularly sampled measurements is a recurring challenge across science and engineering. Neural processe
arXiv:2606.00570v1 Announce Type: cross Abstract: Parameter-based knowledge editing updates the internal knowledge of large language models (LLMs) via localized weight modifications and has attracted
arXiv:2606.01610v1 Announce Type: new Abstract: Single-edit updates in large language models can trigger ripple effects across local knowledge neighborhoods: desirable propagation to related facts and
arXiv:2507.02792v5 Announce Type: replace Abstract: Text-to-image (T2I) diffusion models have shown remarkable success in generating high-quality images from text prompts. Recent efforts extend these
arXiv:2606.00559v1 Announce Type: cross Abstract: Neural algorithmic reasoning has emerged as a popular research direction. It aims to train neural networks to mimic the step-by-step behavior of class
arXiv:2606.02328v1 Announce Type: new Abstract: We explore Riemannian optimization techniques for rank-factored matrix parameters, targeting contemporary deep learning applications. We examine ten poi
arXiv:2606.01216v1 Announce Type: new Abstract: The elementwise Hadamard product of two low-rank matrices provides a parameter-efficient model for data with multiplicative structure, but its modeling
arXiv:2606.00413v1 Announce Type: cross Abstract: Sufficient dimension reduction (SDR) makes high-dimensional regression tractable by projecting the covariates onto a low-dimensional subspace that pre
arXiv:2606.02035v1 Announce Type: new Abstract: Medical imaging interpretation is a foundational pillar of modern clinical diagnostics, yet the manual generation of radiology reports remains a time-co
arXiv:2606.01281v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language mo
arXiv:2511.10276v2 Announce Type: replace-cross Abstract: Most existing robotic manipulation benchmarks focus on tabletop or household scenarios. While these setups have driven impressive progress, it
arXiv:2606.02577v1 Announce Type: cross Abstract: Scaling robot learning requires large-scale, diverse demonstrations, yet real-world data collection via teleoperation remains prohibitively expensive
arXiv:2606.02277v1 Announce Type: new Abstract: Vision-language-action (VLA) models are built on the premise that semantic understanding from pretrained language or vision-language backbones should gu
arXiv:2606.00828v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have shown strong visual understanding and are increasingly deployed in embodied AI systems, where reliable perception und
arXiv:2606.01600v1 Announce Type: cross Abstract: Video world models are increasingly used in robotic manipulation, yet existing benchmarks mostly evaluate them under valid, feasible, and safe instruc
arXiv:2606.00981v1 Announce Type: new Abstract: LLMs can plan by either generating action sequences directly as a Planner or translating tasks into domain specific language for an external solver as a
arXiv:2606.01038v1 Announce Type: new Abstract: This paper presents a new robust integrated planning and control (IPC) strategy for multirotor uncrewed aerial vehicles. We propose a nonlinear model pr
arXiv:2601.18115v2 Announce Type: replace Abstract: We study the problem of learning a single neuron under standard squared loss in the presence of arbitrary label noise and group-level distributional
arXiv:2602.19126v2 Announce Type: replace Abstract: We propose a robust Bayesian formulation of random feature (RF) regression that accounts explicitly for prior and likelihood misspecification via Hu
arXiv:2606.00628v1 Announce Type: new Abstract: Self-distillation improves learning efficiency by rewriting reference answers as training data that better matches the model's own distribution. However
arXiv:2606.00270v1 Announce Type: new Abstract: Shielding is an effective approach to formally guarantee the safety of reinforcement learning agents in Markov decision processes (MDPs). However, exist
arXiv:2606.01566v1 Announce Type: new Abstract: Small-to-medium scientific datasets place machine learning pipelines under two compounding pressures. Single-run feature selection produces feature sets
arXiv:2606.00097v1 Announce Type: new Abstract: This work presents RocketSmith, an agentic system capable of the design, manufacturing, and optimization processes in high powered rocket development. T
arXiv:2606.00449v1 Announce Type: new Abstract: Orientation-aware manipulation is essential in post-harvest agricultural processing, where produce must be grasped and placed in consistent configuratio
arXiv:2606.01825v1 Announce Type: new Abstract: Text-Based Person Search (TBPS) aims to retrieve pedestrian images using natural language queries. However, existing TBPS models, especially those based
arXiv:2606.00341v1 Announce Type: cross Abstract: As AI agents are increasingly deployed in real personal and corporate settings (email accounts, development workflows, company databases, etc.), safet
arXiv:2606.01552v1 Announce Type: new Abstract: Role-playing agents(RPAs) are widely used to steer large language models(LLMs) toward role-consistent behavior, yet existing benchmarks mainly evaluate
arXiv:2606.01689v1 Announce Type: cross Abstract: The detection and segmentation of infrared small targets have important application significance in the fields of surveillance and security, maritime
arXiv:2602.20807v2 Announce Type: replace Abstract: Combining 3D Gaussian splatting with Simultaneous Localization and Mapping (SLAM) has gained popularity as it enables continuous 3D environment reco
RubricMiddleware is a LangChain feature that enables agents to verify task completion by delegating grading to a specialized subagent using predefined rubrics. This approach parallels the goal-verific
arXiv:2606.00011v1 Announce Type: cross Abstract: Despite the promise of AI to assist complex decisions, practitioners still lack ways to detect likely failures and inspect the consequences of model e
Running high grade + heavy load feels effortless in the Tesla Semi. Enough power to maintain speed when climbing, and regenerative braking making descents save and controllable. All while in silence.
Bloomberg: Russia's Federal Security Service claims it uncovered a large-scale spyware operation by “foreign intelligence operatives” on senior officials' mobile phones — Russia's Federal Security Ser
arXiv:2511.17502v3 Announce Type: replace Abstract: We introduce RynnVLA-002, a unified Vision-Language-Action (VLA) and world model. The world model leverages action and visual inputs to predict futu
arXiv:2606.00902v1 Announce Type: new Abstract: General-purpose VLMs remain unreliable for biomedical research because valid answers in scientific papers depend on evidence split across figures, table
arXiv:2606.01561v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) with human preferences is often formulated via Direct Preference Optimization (DPO). However, the standard Bradley
arXiv:2606.01332v1 Announce Type: new Abstract: We study the problem of scaling dynamic loco-manipulation from a single free-rolling sphere to multiple spheres transported simultaneously on the back o
arXiv:2606.02151v1 Announce Type: new Abstract: Effective scheduling in the energy sector is essential to ensure the reliable operation of electrical grids and their connected assets by, for instance,
arXiv:2606.00808v1 Announce Type: new Abstract: Source-free graph domain adaptation (SF-GDA) aims to adapt source-trained graph models to unlabeled target graphs when source graphs are no longer acces
arXiv:2606.00191v1 Announce Type: cross Abstract: Recent end-to-end (E2E) autonomous driving policies achieve high driving scores in closed-loop simulations. Yet it remains unclear whether these polic
arXiv:2606.01481v1 Announce Type: new Abstract: With the rapid advancements in text-to-image diffusion models, generative video models (T2V models) like Sora can now produce short synthetic videos fro
arXiv:2512.02342v3 Announce Type: replace-cross Abstract: The stochastic Polyak step size (SPS) has proven to be a promising choice for stochastic gradient descent (SGD), delivering competitive perfor
arXiv:2606.01991v1 Announce Type: new Abstract: As Large Language Model (LLM) agents increasingly leverage the Model Context Protocol (MCP) to operate in complex environments, the expansion of their a
arXiv:2606.02530v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) with human values often degrades their general capabilities, termed the alignment tax. Existing methods mitigate t
arXiv:2512.20806v3 Announce Type: replace Abstract: Ensuring the safety of language models (LMs) while maintaining their usefulness remains a critical challenge in AI alignment. Current approaches rel
arXiv:2510.09330v3 Announce Type: replace Abstract: Ensuring that large language models (LLMs) comply with safety requirements is a central challenge in AI deployment. Existing alignment approaches pr
arXiv:2503.11832v5 Announce Type: replace Abstract: Recent vision language models (VLMs) have made remarkable strides in generative modeling with multimodal inputs, particularly text and images. Howev
arXiv:2606.00773v1 Announce Type: new Abstract: Vision-language-action (VLA) benchmarks measure whether a policy completes a requested manipulation task, but binary success can hide safety-relevant tr
arXiv:2606.00511v1 Announce Type: cross Abstract: Model merging aims to consolidate multiple task-specific models fine-tuned on different datasets into a unified architecture that performs cross-domai
arXiv:2606.00460v1 Announce Type: new Abstract: Speech-aware large language models often generalize poorly to out-of-domain settings. We propose SALSA (Speech-Aware LLM Adaptation via Learned Steering
arXiv:2606.00566v1 Announce Type: cross Abstract: As language models take on agentic roles that span calling external APIs, reading tool outputs, and acting on instructions embedded in third-party con
arXiv:2606.01340v1 Announce Type: new Abstract: We ask: when do Bayesian model averaging (BMA) weights over decision trees carry sufficient epistemic information to justify committed exploitation of t
arXiv:2606.00579v1 Announce Type: new Abstract: As multimodal LLMs increasingly target video and audio, it is often assumed that such tasks require native omnimodal models. We show that this is not al
arXiv:2601.03615v2 Announce Type: replace Abstract: Audio Language Models (ALMs) offer a promising shift towards explainable audio deepfake detections (ADD), moving beyond extit{black-box} classifiers
arXiv:2606.01939v1 Announce Type: new Abstract: Precise 3D representations of industrial environments enable tasks such as robot localization and digital twin generation. We propose SAVMap, a method f
arXiv:2606.01539v1 Announce Type: cross Abstract: Estimating the causal effect of time-varying treatments on survival outcomes in large observational studies is computationally demanding, particularly
arXiv:2503.24183v3 Announce Type: replace Abstract: The expansion of ride-sourcing services such as Uber and Lyft has reshaped urban transportation by offering flexible, on-demand mobility via mobile
arXiv:2603.02478v2 Announce Type: replace-cross Abstract: Attitude estimation methods typically rely on full vector measurements from inertial sensors such as accelerometers and magnetometers. This pa
arXiv:2606.02001v1 Announce Type: new Abstract: General agentic intelligence hinges on the ability to interact with diverse real-world tools to complete complex tasks, a capability fundamentally tied