Agentic interpretation uses lattices to track LLM judgments on decomposed program claims during analysis.
E&V: Prompting Large Language Models to Perform Static Analysis by Pseudo-code Execution and Verification
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NESA presents a neuro-symbolic framework that decomposes static analyses into policy-defined sub-problems solved by parsers and LLMs to enable compilation-free customizable analysis with reduced hallucinations.
A survey classifying RAG foundations for AIGC, summarizing enhancements, cross-modal applications, benchmarks, limitations, and future directions.
A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.
RAG-enhanced LLMs show generally positive effects on automated test generation and code inspection by supplying supplementary context that reduces hallucinations.
citing papers explorer
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Agentic Interpretation: Lattice-Structured Evidence for LLM-Based Program Analysis
Agentic interpretation uses lattices to track LLM judgments on decomposed program claims during analysis.
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NESA: Relational Neuro-Symbolic Static Program Analysis
NESA presents a neuro-symbolic framework that decomposes static analyses into policy-defined sub-problems solved by parsers and LLMs to enable compilation-free customizable analysis with reduced hallucinations.
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Retrieval-Augmented Generation for AI-Generated Content: A Survey
A survey classifying RAG foundations for AIGC, summarizing enhancements, cross-modal applications, benchmarks, limitations, and future directions.
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Large Language Model-Based Agents for Software Engineering: A Survey
A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.
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Enhancing Large Language Models with Retrieval Augmented Generation for Software Testing and Inspection Automation
RAG-enhanced LLMs show generally positive effects on automated test generation and code inspection by supplying supplementary context that reduces hallucinations.