Repository for the paper "Large Language Model-Based Agents for Software Engineering: A Survey". Keep updating.
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Updated
Mar 16, 2025
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Repository for the paper "Large Language Model-Based Agents for Software Engineering: A Survey". Keep updating.
Official code for paper "Towards Efficient Online Tuning of VLM Agents via Counterfactual Soft Reinforcement Learning"
We introduce a benchmark for testing how well LLMs can find vulnerabilities in cryptographic protocols. By combining LLMs with symbolic reasoning tools like Tamarin, we aim to improve the efficiency and thoroughness of protocol analysis, paving the way for future AI-powered cybersecurity defenses.
We introduce a benchmark for testing how well LLMs can find vulnerabilities in cryptographic protocols. By combining LLMs with symbolic reasoning tools like Tamarin, we aim to improve the efficiency and thoroughness of protocol analysis, paving the way for future AI-powered cybersecurity defenses.
We introduce a benchmark for testing how well LLMs can find vulnerabilities in cryptographic protocols. By combining LLMs with symbolic reasoning tools like Tamarin, we aim to improve the efficiency and thoroughness of protocol analysis, paving the way for future AI-powered cybersecurity defenses.
A multi-agent system project for literature review as a part of Agentic Technologies for Developers course at University of Jyväskylä.
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