FM-Agent is the first framework to automate compositional Hoare reasoning for large systems by having LLMs derive natural-language function specs from caller intent and then generate tests that found 522 new bugs in systems up to 143k lines of code.
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Lakestream provides a consistent brokerless object-store-native data plane for large foundation model training using transactional global batches and decentralized adaptive commit.
AnyPoC introduces a multi-agent system for generating and validating PoC tests from LLM bug reports, producing 1.3x more valid PoCs, rejecting 9.8x more false positives, and discovering 122 new bugs across 12 major projects.
citing papers explorer
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FM-Agent: Scaling Formal Methods to Large Systems via LLM-Based Hoare-Style Reasoning
FM-Agent is the first framework to automate compositional Hoare reasoning for large systems by having LLMs derive natural-language function specs from caller intent and then generate tests that found 522 new bugs in systems up to 143k lines of code.
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Lakestream: A Consistent and Brokerless Data Plane for Large Foundation Model Training
Lakestream provides a consistent brokerless object-store-native data plane for large foundation model training using transactional global batches and decentralized adaptive commit.
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AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detection
AnyPoC introduces a multi-agent system for generating and validating PoC tests from LLM bug reports, producing 1.3x more valid PoCs, rejecting 9.8x more false positives, and discovering 122 new bugs across 12 major projects.