All papers

The paper proposes an edge-cloud collaborative inspection framework using multimodal LLMs dynamically scheduled based on scene complexity.

The paper systematically reviews deep learning methods for image anomaly detection in industrial manufacturing inspection.

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 paper presents an engineering framework combining hybrid retrieval, reranking, guardrails, and automated regression testing to improve retrieval-augmented generation in regulatory systems.

The paper analyzes 33,000 GitHub pull requests to examine why AI coding agent contributions succeed or fail.

Proposes Agentless, an agent-free approach to software development issues that compares favorably against autonomous software engineering agents.

The paper proposes an executable routing contract that separates ranking from context deletion for coding agents.

Examines operational challenges of maintaining and deploying fleets of software variants generated by large language models.

Studies how chain-of-thought reasoning affects social bias and code quality during LLM code generation.

The paper studies how outdated code comments systematically mislead language models during automated code completion.

This paper presents a user study evaluating how programmers interact with and perceive AI code generation tools in daily workflows.

The paper introduces an interactive web-based tool for analyzing and visualizing execution trajectories of software engineering agents.

The paper cleans code smells from training datasets to evaluate and improve the quality of LLM-generated code.

The paper proposes injecting reverse-engineered architectural and domain knowledge into coding agents to prevent conceptual and structural code errors.

This paper introduces a multi-agent system that evaluates code extensibility and detects SOLID design violations through automated code extension.

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

The paper presents a multi-agent framework that dynamically generates execution context to enable partial code execution.

This paper compares the quality and coverage of unit test cases generated by ChatGPT against an automated generation tool.

The paper presents an oversampling method using a large language model to improve malicious JavaScript code detection.

This paper explores how consensus planning methods affect the code generation capabilities of language models.

The paper presents a multi-agent framework that improves code generation by planning programming blueprints and repairing generated code.

Proposes AutoReview, a multi-agent system combining retrieval-augmented generation and code slicing for security-focused code review.

This review surveys research on using language models for code verification and assessing the safety of model-generated code.

The paper presents a tool that uses language models and static analysis to generate valid visual flowcharts from legacy codebases.

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

This paper presents an empirical study evaluating open-source language models for automated unit test generation in software development.

This paper presents the design and real-world developer evaluation of an automated code generation assistant for Ansible automation scripts.

This paper presents an agentic framework to compile large multi-modal requirement documents into runnable software systems using test-driven workflows.

This paper investigates using large language models to generate structured commit messages for software security patches.

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.

Specializes compact video segmentation models on live camera streams by intermittently distilling knowledge from a larger teacher model.

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

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

This paper analyzes visual token utilization in high-resolution vision-language models to optimize token pruning across network layers.

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

The paper proposes an edge-cloud collaborative inspection framework using multimodal LLMs dynamically scheduled based on scene complexity.

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

The paper introduces a decomposed attention mechanism for vision-language models to separate visual and textual token processing.

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

The paper introduces a visual and multimodal knowledge graph grounding model designed for visual navigation tasks.

The paper proposes a modular vision-and-language navigation framework combining dynamic memory pruning and language-model spatial reasoning.

The paper presents a multimodal vision-language model for Alzheimer's disease classification and question answering capable of handling missing imaging or clinical data.

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

The paper reviews language-vision fusion architectures and deployment strategies for medical diagnosis in low-resource healthcare settings.

The paper presents a framework for balancing multimodal integration using conditional dependency modeling across diverse data signals.