8464 papers
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.
The paper proposes CMMR-VLN, a vision-and-language navigation framework that enhances large language model agents with structured multimodal memory retrieval and reflection-based updates to selectively leverage prior experiences, significantly improving performance in long-horizon and unfamiliar scenarios compared to existing methods.
This paper introduces a Dual-Horizon Hybrid Internal Model that enables stable, continuous quadrupedal jumping under lunar gravity using only proprioceptive sensing, validated through the MATRIX hardware-in-the-loop testbed which emulates reduced gravity and lunar terrain in real time.
SafarDB is a novel FPGA-accelerated distributed transaction system that co-designs a network-attached replication engine with a custom FPGA network interface to achieve significantly lower latency and higher throughput for both Conflict-Free and Well-coordinated Replicated Data Types compared to state-of-the-art RDMA-based implementations.
This paper proposes the ViSA-enhanced framework, a triple-phase collaborative architecture that leverages structured visual prompting to enable Vision-Language Models to perform direct spatial reasoning on image planes, achieving a 70.3% improvement in success rate over state-of-the-art aerial Vision-Language Navigation methods on the CityNav benchmark.
This paper addresses the significant challenge of analog clock reading in state-of-the-art Vision-Language Models by introducing the real-world, diverse TickTockVQA dataset and the Swap-DPO fine-tuning framework, which together substantially improve spatial-temporal reasoning and accuracy under complex visual conditions.
This paper proposes Variable Substitution, a domain-specific graph augmentation technique that preserves the structural and semantic integrity of mathematical formulas, significantly enhancing the performance of graph contrastive learning models for mathematical information retrieval compared to generic strategies.
This paper introduces PIRA-Bench, a novel benchmark and the PIRF baseline framework designed to advance GUI agents from reactive instruction-following to proactive intent recommendation by evaluating their ability to anticipate user needs from noisy, continuous visual inputs.
This paper proposes a unified dynamic framework using "task" and "intent" lenses to reconceptualize human-AI collaboration, arguing that alignment, process structure, and outcome quality are non-linearly related and require a structural analysis beyond simple outcome metrics.
This paper proposes "Missing No More," a novel dictionary-guided framework that addresses the challenge of missing infrared modality in image fusion by learning a shared convolutional dictionary to enable interpretable coefficient-domain inference and fusion, thereby avoiding uncontrolled pixel-space generation while improving perceptual quality and downstream detection performance.
This paper introduces DiffRacing, a novel framework that enhances differentiable policy learning for vision-based drone racing by integrating vector fields to provide stable gradient signals for balancing high-speed gate traversal with obstacle avoidance, while employing a differentiable Delta Action Model to enable robust sim-to-real transfer without explicit system identification.
VSDiffusion is a visibility-constrained two-stage diffusion framework that addresses the ill-posed nature of shadow generation by incorporating visibility priors through a shadow-gated cross-attention branch and a learned soft prior map to produce geometrically consistent and realistic cast shadows.
AffordGrasp is a diffusion-based framework that generates physically stable and semantically accurate human grasps by integrating affordance-aware latent representations with a dual-conditioning process to bridge the modality gap between 3D object geometry and textual interaction instructions.
This paper introduces MambaDance, a novel dance generation framework that replaces Transformers with a Mamba-based diffusion model and employs a Gaussian-based beat representation to effectively capture the sequential, rhythmic, and music-synchronized nature of dance across varying sequence lengths.
This paper proposes a two-stage cascaded framework that generates controllable complex human motion videos by first using an autoregressive model to synthesize 2D skeleton sequences from text descriptions and then employing a pose-conditioned diffusion model with adaptive layer fusion to render high-fidelity videos, supported by a new synthetic dataset designed to overcome limitations in existing benchmarks.
QualiTeacher introduces a novel framework for real-world image restoration that transforms imperfect pseudo-labels into conditional supervisory signals by explicitly conditioning the student model on estimated label quality, thereby enabling the learning of a quality-graded restoration manifold that avoids artifact mimicry and achieves superior generalization.
This paper establishes that the size difference between optimal Boolean circuits and formulas over the AIG basis is strictly limited to 0 or 1, characterizing the precise conditions under which sharing occurs and proving that any non-zero gap arises exclusively from a single gate with fan-out 2.
This paper presents a robust multimodal framework for the 10th ABAW Expression Recognition Challenge that utilizes a dual-branch Transformer with safe cross-attention and modality dropout to dynamically fuse audio and visual data, effectively addressing partial occlusions, missing modalities, and class imbalance to achieve 60.79% accuracy on the Aff-Wild2 validation set.
This paper introduces Samyama, a high-performance, unified graph-vector database written in Rust that integrates persistent storage, vector indexing, native optimization solvers, and agentic LLM enrichment into a single engine, achieving competitive throughput and latency on commodity hardware while offering GPU-accelerated enterprise features.
The paper proposes CEMR, a novel subgraph matching algorithm that significantly improves efficiency on large graphs by eliminating redundant computations through common extension merging and reusing, while further optimizing performance with two pruning techniques.