3447 papers
ChimeraLoRA addresses data scarcity in fine-grained tasks by synthesizing diverse and detail-rich images through a hybrid architecture that combines a class-shared LoRA for semantic priors with per-image LoRAs for specific characteristics, guided by semantic boosting and a Dirichlet-based mixture strategy to improve downstream classification accuracy.
DOCFORGE-BENCH introduces the first unified zero-shot benchmark for document forgery detection, revealing that current methods suffer from severe calibration failures due to the extreme rarity of tampered pixels in documents, which renders standard fixed thresholds ineffective and highlights threshold adaptation as the critical missing step for practical deployment.
This paper proposes the Multimodal Adversarial Quality Policy (MAQP), a framework that enhances safe robot grasping in human-robot interaction by introducing a Heterogeneous Dual-Patch Optimization Scheme and a Gradient-Level Modality Balancing Strategy to effectively generate multimodal adversarial patches that address distribution discrepancies and optimization imbalances between RGB and depth modalities.
This paper establishes the first explicit -hardness results for the approximate Covering Radius Problem on lattices in norms for sufficiently large (specifically ), proving that the problem remains hard even for approximation factors approaching $9/8p$ tends to infinity.
This paper presents a production-oriented, unified multilingual semantic retrieval system for Uber Eats that leverages a fine-tuned Qwen2 two-tower model with advanced loss functions and Matryoshka Representation Learning to achieve significant recall improvements across stores, dishes, and grocery items.
This paper introduces the Hybrid Residue Floating Numerical Architecture (HRFNA), a formally verified numerical system combining carry-free residue arithmetic with lightweight exponent scaling that achieves significantly higher throughput, reduced resource usage, and improved energy efficiency on FPGAs compared to IEEE 754 standards while maintaining rigorous, bounded numerical error.
This paper proposes a column-generation-based algorithmic framework to efficiently solve both splittable and unsplittable variants of the capacitated Multi-Commodity Flow problem with convex, potentially non-differentiable, link cost functions, offering a robust optimization approach for managing traffic in telecommunication networks.
This paper presents an empirical study mapping the interactions between model characteristics and prompt engineering strategies for Verilog code generation, revealing which trends generalize across diverse language models and benchmarks through controlled experiments.
AnalogToBi is a novel framework for automatic device-level analog circuit topology generation that combines bipartite graph representations, grammar-guided decoding, and data augmentation to achieve high validity and novelty while ensuring electrical correctness without human intervention.
This study proposes a novel, context-sensitive AI maturity framework specifically designed for small and medium-sized enterprises (SMEs) that reconceptualizes maturity as a multidimensional, non-linear, and ecosystem-embedded capability comprising eight dimensions, five levels, and four development pathways to better reflect the unique resource constraints and organizational realities of SMEs.
This paper presents an end-to-end, self-hosted pipeline that converts lecture PDFs into multiple-choice questions using a local LLM and deterministic quality control, ensuring privacy and accountability while releasing a validated 24-question dataset with a detailed warning taxonomy for educational use.
This paper proposes a method to significantly reduce hardware resource requirements for matrix multiplications and convolutions by asymptotically replacing real and complex multiplications with fewer squaring operations, which are more efficient to implement in circuits.
The paper presents RSH-SpMM, a fine-grained row-structured hybrid kernel for GPU-based Sparse Matrix-Matrix Multiplication that utilizes adaptive row partitioning, RS-Tile representation, and load-balanced reordering to achieve 1.27x to 6.13x speedups over state-of-the-art methods by effectively handling extreme sparsity irregularity.
FormalRTL is a novel end-to-end multi-agent framework that leverages software reference models as formal specifications to enable scalable, verified, and reliable register-transfer level (RTL) code generation for complex industrial hardware designs.
This paper introduces Kareto, an adaptive multi-objective optimizer that efficiently navigates the complex configuration space of tiered KV cache storage to dynamically balance cost, throughput, and latency, significantly outperforming static strategies in LLM inference services.
This paper proposes extending the ACETONE C code generator, originally limited to sequential code, to support multi-core architectures by formally defining a processor assignment problem and outlining a future implementation of parallel code generation, scheduling heuristics, and synchronization mechanisms.
This paper introduces ChatNeuroSim, an LLM-based agent framework that automates the entire Compute-in-Memory (CIM) accelerator deployment and optimization workflow—including simulation, parameter management, and design space pruning—to significantly reduce the time and effort required for design space exploration.
This paper introduces \texttt{electoral\_sim}, an open-source Python framework that simulates and compares various electoral systems across diverse voter preference distributions using Euclidean distance to the geometric median as a primary performance metric, including a novel theoretical benchmark based on a Boltzmann softmax kernel.
JigsawServe is a novel serving framework that optimizes latency, accuracy, and GPU resource costs for compound inference systems by jointly selecting model variants and spatially partitioning GPUs, achieving up to 11.3x higher throughput and significantly improved resource efficiency compared to prior work.
Granulon is a novel multimodal large language model that leverages a DINOv3-based visual encoder enhanced with a text-conditioned granularity controller and adaptive token aggregation to dynamically unify pixel-level perception with coarse-grained semantics, significantly improving accuracy and reducing hallucinations compared to existing approaches.