7210 papers
The paper presents WeldAR, an Augmented Reality system integrated into welding equipment that provides real-time in-situ guidance to novices, demonstrating through a user study that it significantly improves welding performance and the transfer of embodied skills compared to traditional video instruction.
The paper introduces SGG-R, a structured reasoning framework that combines chain-of-thought-guided supervised fine-tuning with relation augmentation and a novel dual-granularity reward scheme in reinforcement learning to achieve end-to-end unbiased Scene Graph Generation with improved recall and reduced bias on long-tailed distributions.
This paper presents GOMA, a geometrically abstracted, globally optimal framework that uses analytical modeling to efficiently solve the combinatorial GEMM mapping problem for spatial accelerators, achieving significant improvements in energy-delay product and search speed over state-of-the-art methods.
This paper presents a co-designed generative AI tool that enables Deaf and Hard-of-Hearing individuals to engage in music psychotherapy through visual and conversational songwriting, demonstrating that collaborative human-AI interaction can effectively facilitate emotional release and self-understanding for this underserved population.
This paper proposes a model-free Deep Reinforcement Learning control framework for power inverters that utilizes an error energy-guided hybrid reward and adaptive importance weighting to distill a heavy policy into a lightweight neural network, achieving microsecond-level inference, superior transient response, and robust performance on a hardware experimental platform.
This paper introduces EcoG-Bench, a rigorous bilingual benchmark for egocentric co-speech grounding that reveals a significant performance gap between humans and state-of-the-art MLLMs, highlighting how multimodal interface limitations rather than reasoning deficits hinder the alignment of speech with pointing gestures in situated collaboration.
This paper introduces EvoStage, a novel evolutionary paradigm that leverages large language models with a stagewise, multi-agent approach and real-time feedback to overcome the limitations of black-box modeling, successfully generating algorithm designs that outperform both human experts and existing methods in complex industrial tasks like chip placement and black-box optimization.
This paper introduces HILA, a Human-In-the-Loop Multi-Agent Collaboration framework that employs Dual-Loop Policy Optimization to train agents with metacognitive policies for dynamically deferring to human experts and continuously improving their reasoning capabilities, thereby overcoming the static knowledge limitations of purely autonomous systems.
VORL-EXPLORE is a hybrid learning and planning framework for multi-robot exploration in dynamic environments that couples task allocation with motion execution via a shared navigability fidelity signal, enabling adaptive arbitration between global and reactive policies to prevent bottlenecks and ensure robust, collision-free coverage.
ZK-ACE is a post-quantum authorization framework that replaces kilobyte-scale signature artifacts with compact, identity-bound zero-knowledge proofs, achieving an order-of-magnitude reduction in on-chain data while providing formal security guarantees against replay and substitution attacks.
The paper introduces OSExpert, a computer-use agent that leverages a GUI-based depth-first search exploration algorithm to discover action primitives and self-construct a skill curriculum, thereby significantly improving performance and efficiency on complex tasks to approach human expert levels.
This paper presents a device-agnostic surgical tool tracking framework that fuses multiple sensing modalities within a dynamic scene graph to overcome line-of-sight occlusions and enhance the robustness of augmented reality visualization in operating rooms.
The paper introduces AR-ACE, a lightweight mempool propagation protocol for post-quantum blockchains that eliminates on-path validity proofs by having relay nodes forward only objects with compact attestations, thereby achieving an order-of-magnitude bandwidth reduction while shifting proof verification entirely to the builder.
This paper proposes an energy-efficient online scheduling framework for Wireless Powered Mobile Edge Computing networks that utilizes Lyapunov optimization and a relax-then-adjust approach to solve the joint wireless power transfer and computation offloading problem, achieving a fundamental trade-off between latency and energy consumption while ensuring theoretical performance guarantees.
The paper introduces AutoReg3D, an autoregressive 3D object detector that reformulates LiDAR-based detection as a sequence generation task using a near-to-far ordering to eliminate reliance on hand-crafted components like anchors and NMS, thereby achieving competitive performance while enabling the integration of advanced language model techniques such as reinforcement learning.
This paper proposes PreHO, a predictive handover mechanism for Low-Earth Orbit Satellite Networks that leverages the stable and predictable channel states of fast-moving satellites to proactively plan optimal handover strategies, thereby significantly reducing signaling overhead and latency compared to traditional reactive schemes.
TeamHOI is a decentralized framework that leverages a Transformer-based policy and a masked Adversarial Motion Prior strategy to enable a single unified policy to control scalable, physically realistic cooperative human-object interactions among any number of humanoid agents.
AutoTraces is a novel autoregressive vision-language-trajectory model that leverages multimodal large language models with a specialized trajectory tokenization scheme and automated chain-of-thought reasoning to achieve state-of-the-art, long-horizon robot trajectory forecasting in human-populated environments.
This paper proposes SI-ChainFL, a secure and efficient federated learning framework for high-speed rail data sharing that combines Shapley value-based contribution incentives with a blockchain-driven decentralized aggregation protocol to mitigate free-riding and model poisoning while ensuring robust performance against malicious attacks.
This paper introduces a pipeline comprising the static analysis tool tSCAN and the searcher tSEARCH to discover and exploit Token-based Maximal Extractable Value (tMEV) by identifying non-standard supply-control functions in token contracts, demonstrating that this approach can extract ten times more profit than existing MEV strategies on Ethereum.