Knowledge catalogue
arXiv:2610.07026v1 Announce Type: new Abstract: The Offline AI Modules workstream enables practical, low-power, and community-accessible deployment of voice-first AI systems that operate fully offline
arXiv:2610.08740v1 Announce Type: new Abstract: Learning when the environment does not belong to the learner's hypothesis class is typically handled using agnostic learning guarantees. However, for an
arXiv:2610.08178v1 Announce Type: new Abstract: Existing latent-based watermarking methods for diffusion models have overestimated their robustness to image distortions, including geometric transforma
arXiv:2610.08737v1 Announce Type: new Abstract: The adoption of humanoid robots in education and research remains limited by high hardware costs, complex sensing systems, and substantial computational
arXiv:2507.02115v3 Announce Type: cross Abstract: Synthesizing second-language (L2) speech is potentially highly valued for L2 language learning experience and feedback. However, due to the lack of L2
arXiv:2508.16821v2 Announce Type: replace Abstract: We introduce PuzzleJAX, a GPU-accelerated puzzle game engine and description language designed to support rapid benchmarking of tree search, reinfor
arXiv:2610.07117v1 Announce Type: new Abstract: Understanding and modeling interactions between autonomous agents is a fundamental challenge in robotics, with broad implications for collaborative syst
arXiv:2610.08578v1 Announce Type: new Abstract: Transformers provide a state-of-the-art modeling framework, yet poor calibration limits their reliability in safety-critical applications. A promising d
arXiv:2610.08561v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a standard tool for post-training language models on reasoning tasks, where the policy is updated by reward feedb
arXiv:2610.08448v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) trains a student on its own generations using teacher feedback. With different tokenizers, comparing teacher and student
arXiv:2512.23073v2 Announce Type: replace-cross Abstract: Fine-tuning has become the dominant paradigm for adapting Vision-Language Models (VLMs), yet most approaches rely on explicit weight updates t
arXiv:2610.08640v1 Announce Type: new Abstract: Long-horizon urban navigation requires sequential local decisions whose errors can compound over time. Imitation learning (IL) rarely learns from failur
arXiv:2606.16193v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated strong performance on vision-language tasks, yet their internal visual representati
arXiv:2604.07925v2 Announce Type: replace-cross Abstract: The self-attention mechanism is central to the success of Transformer architectures. However, standard row-stochastic attention has been shown
arXiv:2610.07645v1 Announce Type: cross Abstract: Self-improving LLM agents increasingly distill successful experiences into persistent, reusable skills. Existing skill attack methods corrupt this lea
arXiv:2610.07091v1 Announce Type: new Abstract: The evolution of machine learning has progressively changed where intelligence resides in an AI system. In conventional machine learning the task, data
arXiv:2610.07204v1 Announce Type: cross Abstract: Recent AI alignment work often frames alignment as a pre-deployment optimization problem: collect human feedback, learn preferences or principles, fin
arXiv:2610.08403v1 Announce Type: new Abstract: Large Language Models (LLMs) require substantial computational resources, limiting their deployment on resource-constrained hardware. Ternary LLMs mitig
arXiv:2610.06874v1 Announce Type: cross Abstract: Reinforcement-learning quadrotor controllers are usually trained under simplified wind models, yet the impact of wind-field fidelity, as opposed to ma
arXiv:2610.08652v1 Announce Type: cross Abstract: Diffusion models are increasingly used as surrogates for expensive simulators in weather prediction, molecular dynamics, and materials design. In thes
arXiv:2610.07348v1 Announce Type: cross Abstract: Training large language models (LLMs) is resource-intensive, and adapting them for diverse deployment scenarios with varying computational constraints
arXiv:2512.23441v2 Announce Type: replace-cross Abstract: Temporally aware image representations are crucial for capturing disease progression in 3D volumes of longitudinal medical datasets. However,
arXiv:2610.08018v1 Announce Type: new Abstract: Reliable tool use requires more than triggering a mechanism or matching a query to an API description. Before selecting a specific tool, an agent must f
As the title suggests I'm currently trying to make a modal to identify which app is being used. The thing is I don't have labelled data and generating it is out of question since that is a lot of work
arXiv:2610.08126v1 Announce Type: cross Abstract: Product Identification has sprung up to become one of the most challenging problems in the automation of the retail industry. With the new industry 5.
arXiv:2610.07098v1 Announce Type: cross Abstract: Large transformer-based models increasingly depend on multi-GPU execution, which requires frequent collective communication among GPUs. Existing commu
arXiv:2610.07559v1 Announce Type: new Abstract: Recent progress in tabular foundation models suggests that training on synthetic tasks can substantially improve in-context learning capabilities, with
arXiv:2610.06994v1 Announce Type: cross Abstract: Backdoor-defense leaderboards print a clean-accuracy drop and read it as removal cost. Measured on the poisoned victim alone, the drop cannot separate
arXiv:2610.06824v2 Announce Type: replace Abstract: We introduce TasteVal, a benchmark to evaluate the experimental research taste of frontier models. We define research taste as the ability to pick i
arXiv:2610.07096v1 Announce Type: new Abstract: Robotic haircutting requires controlled tool motion near the head while simultaneously accounting for communication, visual feedback, tool actuation, an
arXiv:2610.07925v1 Announce Type: new Abstract: Partially Relevant Video Retrieval (PRVR) aims to retrieve untrimmed videos containing moments relevant to a given text query. Despite recent progress,
The largest agentic LLM Inference dataset has arrived! 206B tokens · 12,002 sessions · 1,186,582 LLM requests · 1,213,347 tool calls The traces include tool names, arguments, durations, and more. Expl
arXiv:2610.07038v1 Announce Type: new Abstract: Modern forecasting models are often updated after deployment so they can respond to changing data. These updates can also make predictions worse, so pra
arXiv:2610.08155v1 Announce Type: cross Abstract: Large language model (LLM)-based multi-agent systems (MAS) have become a promising paradigm for complex information-seeking and reasoning tasks by ena
arXiv:2610.08555v1 Announce Type: new Abstract: Network pruning can reduce parameter redundancy in robotic policies. However, generic pruning criteria are tailored for image recognition tasks and comm
arXiv:2610.07778v1 Announce Type: cross Abstract: Attributed graph clustering aims to discover node groups by jointly exploiting node attributes and graph topology, yet its unsupervised nature makes m
arXiv:2610.08331v1 Announce Type: cross Abstract: The rapid integration of Vision Language Models (VLMs) into sensitive systems introduces critical safety vulnerabilities that remain unexplored in exi
arXiv:2603.05670v2 Announce Type: replace Abstract: When humans learn new manipulation skills, they are able to generalize these skills to new contexts and environments. In particular, when learning,
arXiv:2610.07043v1 Announce Type: cross Abstract: Low-precision execution can substantially accelerate reinforcement learning (RL) for large language models, but discrepancies between learner and samp
arXiv:2610.08353v1 Announce Type: new Abstract: While graph neural networks (GNNs) have shown substantial promise in connectome-based diagnostic classification, deterministic models inevitably suppres
arXiv:2610.08677v1 Announce Type: new Abstract: Off-policy evaluation (OPE) for contextual bandit policies becomes challenging when action-level importance weighting incurs excessive variance. Doubly
arXiv:2610.08043v1 Announce Type: new Abstract: Ensuring safety in autonomous driving requires continuous map maintenance supported by reliable quality indicators. In this context, it is crucial to id
arXiv:2610.08526v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have achieved impressive results in robotic manipulation and ground-mobile navigation, yet language-conditioned co
arXiv:2610.07269v1 Announce Type: new Abstract: Cross-view geo-localization is commonly solved as an image retrieval problem, matching a ground-level image against a database of satellite tiles throug
arXiv:2410.05479v2 Announce Type: replace Abstract: Uncertainty-aware explanation methods often produce several alternatives for the same prediction. Selecting among them requires a policy for balanci
arXiv:2610.08720v1 Announce Type: new Abstract: LLM-based agents are increasingly advancing scientific and engineering problem solving, with physics simulation emerging as a challenging yet practical
arXiv:2610.04022v1 Announce Type: new Abstract: Powered neck exoskeletons are developed to restore head-neck motions for those with neck muscle weakness. Current methods using hand-held input devices
arXiv:2610.06464v1 Announce Type: new Abstract: As technology scales to smaller nodes, increasing current densities make electromigration (EM) one of the dominant reliability challenges in on-chip int
arXiv:2610.04949v1 Announce Type: new Abstract: Reinforcement learning controllers for underwater vehicles are usually trained against one physics representation and deployed against another, so repor
arXiv:2610.04787v1 Announce Type: new Abstract: Neural Architecture Search (NAS) has proved to be a strong alternative to manual network design, but applying it to 3D medical image segmentation is lim
arXiv:2610.04039v1 Announce Type: cross Abstract: Applications such as engineering design often require us to optimize a black-box function, i.e., a system whose inner processing is not analytically k
Hi ML Community, I am excited to share our latest framework, AFP-GIC, officially published in IEEE Access (2026). We have released the deployment codebase and hosted an interactive visual playground.
arXiv:2610.05782v1 Announce Type: cross Abstract: Agentic AI is emerging as a promising paradigm for automating complex cybersecurity decisions, yet its use in enforcing zero trust introduces signific
arXiv:2610.04612v1 Announce Type: new Abstract: Soft robots present significant modelling challenges due to their non-linearity, complex dynamics and potentially intricate geometries. These difficulti
arXiv:2610.06596v1 Announce Type: new Abstract: Short-wave infrared (SWIR) imaging has emerged as a promising modality for autonomous driving, yet its practical benefits over RGB remain poorly charact
arXiv:2610.04342v1 Announce Type: cross Abstract: Inter-robot communication is a key enabling technology in cooperative robotics applications. While wireless communication is ubiquitous, simple, and e
arXiv:2610.04187v1 Announce Type: new Abstract: Autoware is an open-source autonomous driving software platform widely adopted by researchers and industry developers. Originally developed primarily fo
arXiv:2610.06167v1 Announce Type: new Abstract: Zero-shot Neural Architecture Search removes the prohibitive cost of traditional NAS, but its search process is typically based on the evolutionary algo
arXiv:2610.03822v1 Announce Type: new Abstract: Tokens have become a unified interface for multimodal foundation models, making visual-token communication a natural paradigm for efficient image delive
arXiv:2610.06809v1 Announce Type: new Abstract: Causal representation learning (CRL) is the process of recovering causally-related latent variables from high-dimensional observations. As a label-free