Agentic interpretation uses lattices to track LLM judgments on decomposed program claims during analysis.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
SAILOR combines static analysis and LLM-orchestrated synthesis to automatically generate symbolic execution harnesses, discovering 379 previously unknown memory-safety vulnerabilities across 10 large open-source C/C++ projects where the strongest baseline found only 12.
FGDM is a sequential multi-agent system using flow graphs, CoT/ToT prompts, and FAISS retrieval that reports mean Levenshtein distance reductions of 24.33 (Python) and 8.37 (C) with cosine similarities of 0.951 and 0.974 on 100 programs from ten open-source projects.
Augmenting LLMs with bug references, few-shot learning, chain-of-thought, and RAG improves MPI error detection accuracy from 44% to 77% and generalizes across models.
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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Guiding Symbolic Execution with Static Analysis and LLMs for Vulnerability Discovery
SAILOR combines static analysis and LLM-orchestrated synthesis to automatically generate symbolic execution harnesses, discovering 379 previously unknown memory-safety vulnerabilities across 10 large open-source C/C++ projects where the strongest baseline found only 12.
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FGDM: Reasoning Aware Multi-Agentic Framework for Software Bug Detection using Chain of Thought and Tree of Thought Prompting
FGDM is a sequential multi-agent system using flow graphs, CoT/ToT prompts, and FAISS retrieval that reports mean Levenshtein distance reductions of 24.33 (Python) and 8.37 (C) with cosine similarities of 0.951 and 0.974 on 100 programs from ten open-source projects.
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Improving MPI Error Detection and Repair with Large Language Models and Bug References
Augmenting LLMs with bug references, few-shot learning, chain-of-thought, and RAG improves MPI error detection accuracy from 44% to 77% and generalizes across models.