All papers

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

Proposes an agent memory architecture that uses biologically inspired adaptive decay functions to selectively retain and forget context.

The paper compares GGUF and GPTQ post-training quantization methods for running Llama 3.1 on x86 and ARM edge hardware.

This paper proposes a non-interactive secure inference framework for Transformer models using fully homomorphic encryption to reduce user overhead.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

This paper presents AVNet, an optimized convolutional network designed for efficient FPGA deployment to classify circuit board defects.

Proposes a distillation framework and structural loss to speed up diffusion-based 3D LiDAR scene completion for autonomous driving perception.

Develops a hardware-dataflow co-optimization framework using LPDDR PIM and draft token pruning for mobile LLM speculative inference.

The authors introduce a training loop where multimodal models use text and visual abilities alongside program execution to generate their own training data.

The paper introduces a method to reduce redundant external knowledge retrieval by estimating what a language model already knows.

The paper presents a flexible triple-band rectenna designed for ambient radio-frequency energy harvesting to power passive Internet of Things devices.

The paper introduces an improved A-star algorithm to optimize serverless resource allocation and job scheduling for MapReduce workloads.

The paper evaluates and compares the power consumption and resource utilization of five 64-bit multiplier architectures implemented on an FPGA platform.

The paper proposes a noise-enrichment data augmentation methodology and curated dataset to improve bird sound recognition models under limited data conditions.

The paper presents an efficient two-pass encoding scheme using low-complexity spatial and temporal features for adaptive live video streaming.

The paper proposes an improved fast Gauss transform algorithm for efficient kernel density estimation in computer vision applications.

This paper surveys foundation models, agent systems, datasets, and computational tools applied to materials science discovery.

The paper evaluates twenty-one language models used as judges across three benchmarks, finding widespread discrepancies between agreement metrics and underlying validity.

The authors introduce CharXiv, a benchmark of realistic scientific charts designed to test descriptive and reasoning capabilities in multimodal language models.

This paper evaluates an ensemble deep learning framework of two convolutional networks for robust and efficient deepfake video detection.

This paper introduces a dual-branch hybrid deep learning model combining spatial features and facial embeddings for deepfake detection.

This paper presents a mid-training method, corpus, and benchmark to help language models understand and reason about time series data.

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Uses coarse CTC decoding to filter relevant hotwords into LLM prompts for improved contextual speech recognition on rare words.

The paper introduces a zero-shot LLM pipeline to generate counterfactual examples for stress-testing NLP models.