All papers

This survey paper reviews attacks, defenses, and evaluation methods across 135 studies on secure retrieval-augmented generation systems.

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

Proposes Attr-RAG, an attribution-guided retrieval-augmented generation framework to improve long-form scientific experiment design queries.

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

Surveys the foundations, evolution, practical applications, limitations, and future research directions of retrieval-augmented generation.

Applies Bayesian and search-based hyperparameter optimization to tune retrieval and generation parameters in a RAG chatbot.

Proposes a multi-stage framework combining retrieval-augmented generation and natural language inference verification to reduce model hallucinations.

This paper presents and evaluates a low-code architecture combining Ollama and Open WebUI for self-hosting retrieval-augmented generation systems.

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

The text contains only publisher storefront metadata for a paper on wireless biomedical sensor networks for healthcare.

This paper presents an internet of things voting system using fingerprint authentication on a microcontroller with cloud monitoring.

This paper presents a multiconductor transmission line model for two-dimensional lossy configurations based on mode reciprocity.

This paper presents a framework and a dataset to generate holistic full-body co-speech gestures from audio and masked movement priors.

This paper introduces frequency assignment techniques using partially overlapped channels to improve spectral efficiency in integrated access and backhaul networks.

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 proposes a discrete diffusion model for controllable visual layout generation under structural element constraints.

This paper evaluates the ability of language models to generate non-functional requirements from functional software specifications.

This paper introduces an open-source multi-agent system designed to generate, verify, and debug hardware description code.

This paper introduces an evaluation benchmark using multimodal models to assess generated videos across human preference dimensions.

This paper combines evolutionary computation with language models to optimize register-transfer level hardware code for correctness and efficiency.

This paper surveys synthetic data generation and data augmentation techniques for machine learning with an emphasis on healthcare.

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

This paper introduces an open-source infrastructure and benchmark for multi-fidelity AI-assisted photonic simulation and inverse design.

This paper provides design guidelines and a selection tool to help build augmented reality systems for industrial assembly support.

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.

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

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

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.

Presents AIMA, an autonomous multi-agent system using retrieval-augmented generation to manage Instagram content strategy, generation, and scheduling.

Presents an IEEE publication page stub regarding methods to maximize web service retrieval.

Surveys AI-based phishing detection methods, focusing on hybrid architectures that combine reinforcement learning with retrieval-augmented generation.