Search: “papers”

arXiv:2411.01683v3 Announce Type: replace Abstract: Autonomous Vehicle (AV) perception systems require more than simply seeing, via e.g., object detection or scene segmentation. They need a holistic u

arXiv:2607.07693v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) has emerged as a powerful paradigm for aligning generative models with human preferences. However, a

arXiv:2506.08630v3 Announce Type: replace Abstract: A universal controller for any robot morphology would greatly improve computational and data efficiency. Steps have been made towards such multi-rob

arXiv:2607.07508v1 Announce Type: cross Abstract: Reinforcement learning (RL) is becoming increasingly important for post-training large language models (LLMs). Previous RL pipelines for LLMs were mos

arXiv:2607.06915v1 Announce Type: new Abstract: Scaling down the resolution of input images can greatly reduce the computational overhead of convolutional neural networks (CNNs), which is promising fo

arXiv:2607.06748v1 Announce Type: cross Abstract: Smart home automation platforms increasingly rely on user-authored YAML configuration files to define device behaviors, but these files are prone to s

arXiv:2607.07320v1 Announce Type: new Abstract: The SoccerNet 2026 Challenges constitute the sixth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision rese

Many of the most challenging and valuable problems in the world are related to optimization. Now, AI is now making these problems tractable. If you've ever tried to design a microchip, plan a delivery

arXiv:2607.07510v1 Announce Type: cross Abstract: Generating signals on graphs requires permutation-equivariant models that exhibit stability with respect to relative structural perturbations. While f

arXiv:2607.06614v1 Announce Type: cross Abstract: Accurate station-level demand forecasting is essential for the efficient operation of bike-sharing systems, yet it remains challenging due to complex

arXiv:2601.16946v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used for text analysis tasks, such as named entity recognition or error detection. Unlike encoder-base

arXiv:2502.00037v4 Announce Type: replace-cross Abstract: We introduce Superstate Quantum Mechanics (SQM), a theory that considers states in Hilbert space subject to multiple quadratic constraints, wi

arXiv:2511.16107v3 Announce Type: replace-cross Abstract: Visual in-context learning (VICL) solves visual tasks by conditioning on a few input-output demonstrations without any model training. Recent

arXiv:2607.07169v1 Announce Type: new Abstract: The precise pixel-level localization of 2D material flakes is crucial for high-throughput screening. However, traditional fully supervised methods rely

// The Harness Effect // (bookmark it) Now more that ever pay very close attention to the orchestration harness and its effect on costs and performance. This study ran 22 evaluation tasks on six found

this is awesome. he didn’t import @activegraphai but borrowed design principles to improve his systems (and found improvement opportunities) great walkthrough in the linked post (for your agent to ing

arXiv:2607.07612v1 Announce Type: cross Abstract: Artificial intelligence is rapidly evolving from generative systems to agentic AI capable of autonomously planning and executing tasks. Widely charact

arXiv:2607.07408v1 Announce Type: new Abstract: Automatic prosodic segmentation identifies boundaries between speech units from acoustic and linguistic evidence. Although recent deep learning approach

arXiv:2607.07277v1 Announce Type: new Abstract: Harmful online communication often contains slang, coded terms, abbreviations, and community-specific expressions, which make messages difficult to inte

arXiv:2607.06779v1 Announce Type: new Abstract: Real-time stereo matching is crucial for robotics, autonomous systems, and embedded vision applications, where both computational efficiency and dispari

arXiv:2510.24108v2 Announce Type: replace-cross Abstract: Human demonstrations are widely considered the cornerstone of end-to-end (E2E) autonomous driving despite human demonstration's scarcity for l

arXiv:2607.05467v1 Announce Type: new Abstract: Fog severely degrades the visibility of small unmanned aerial vehicles (UAVs) in skydominant, long-range imagery, reducing the reliability of downstream

arXiv:2607.06101v1 Announce Type: cross Abstract: AI coding agents are rapidly reshaping how software is built, with developers increasingly delegating substantial coding tasks to autonomous agents in

arXiv:2607.06291v1 Announce Type: new Abstract: Game worlds have traditionally been built through labor-intensive production pipelines, making them costly to develop, difficult to customization, and e

arXiv:2607.06484v1 Announce Type: cross Abstract: Poisoning attacks against public datasets lead to major concerns, such as (i) misclassification of perceived objects when the poisoned data is used fo

arXiv:2607.05399v1 Announce Type: cross Abstract: Large language model serving is increasingly limited by KV-cache growth under long-context workloads, yet existing KV-cache compression techniques are

arXiv:2503.17020v2 Announce Type: replace-cross Abstract: Kernel methods compare inputs through feature maps. Quantum kernels follow the same principle: input data are encoded into quantum states, whi

arXiv:2607.05445v1 Announce Type: cross Abstract: Extended Reality (XR) wearables require always-on perception within tight power envelopes of a few watts and motion-to-photon latency budgets below 20

arXiv:2607.06335v1 Announce Type: new Abstract: Diffusion models generate high-quality images, but their inference cost comes from two sources: large denoising networks and repeated denoising steps. E

arXiv:2607.06186v1 Announce Type: new Abstract: Most quadruped loco-manipulation designs trade manipulation capability against stance. A trunk-mounted arm sits high and usually carries a single arm; u

arXiv:2607.05465v1 Announce Type: cross Abstract: Complex image creation and editing often require more than a single generation or editing model. A user request may involve synthesizing images, local

arXiv:2607.05428v1 Announce Type: cross Abstract: We present Charlie, an on-premise multi-agent Retrieval-Augmented Generation (RAG) system for structured evidential processing in digital forensic env

arXiv:2607.06499v1 Announce Type: new Abstract: With the widespread availability of parallel computing hardware, sampling-based motion planning methods such as Model Predictive Path Integral (MPPI) co

arXiv:2607.05927v1 Announce Type: cross Abstract: Multimodal document retrieval aims to retrieve relevant pages while preserving both textual and visual content from the original document. However, ex

arXiv:2607.06447v1 Announce Type: new Abstract: Recent LLM-based mathematical reasoning agents have begun to tackle research-level problems and, in several cases, have contributed to the resolution of

arXiv:2607.05759v1 Announce Type: cross Abstract: Submodular maximization is an important building block for developing algorithms in many areas such as machine learning and data mining. Due to the NP

arXiv:2606.08048v1 Announce Type: cross Abstract: Diffusion language models (DLMs) offer substantial speed advantages through parallel decoding, but the lack of token dependencies limits generation qu

arXiv:2607.05644v1 Announce Type: cross Abstract: Natural language processing (NLP) is a common method for supplying data to clinical research and decision making by extracting information from electr

arXiv:2607.05544v1 Announce Type: new Abstract: Trajectory planning strongly influences tracking accuracy, actuator demand, and overall execution behavior in robotic manipulators. Classical planners s

arXiv:2607.06217v1 Announce Type: new Abstract: Enhancing videos under extreme low-light conditions remains challenging due to the difficulty of balancing restoration quality and computational efficie

arXiv:2607.05457v1 Announce Type: cross Abstract: Deep neural networks often contain substantial hidden-state redundancy, but most compression methods operate directly on weights, neurons, or quantise

arXiv:2607.06407v1 Announce Type: new Abstract: The XAI community has studied a wide range of queries and scores for explaining predictions of ML models. From a data management perspective, this proli

arXiv:2509.11819v2 Announce Type: replace-cross Abstract: Federated Domain Adaptation (FDA) is a federated learning (FL) approach that improves model performance at the target client by collaborating

arXiv:2607.05553v1 Announce Type: new Abstract: Transient stability control in smart grids requires rapid post-fault damping of generator frequency and rotor angle deviations to prevent cascading fail

arXiv:2607.05891v1 Announce Type: cross Abstract: Coreset selection aims to identify a small and highly representative subset of a massive dataset for efficient model training. The problem remains cha

arXiv:2505.19120v2 Announce Type: replace Abstract: Image demoireing remains a challenging task due to the complex interplay between texture corruption and color distortions caused by moire patterns.