Once4All synthesizes LLM-based generators from extracted SMT grammars and populates formula skeletons to fuzz Z3 and cvc5, discovering 43 confirmed bugs with 40 fixed.
In: Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation
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A ReLU-catalyzed abstraction method yields tighter bounds for transformer verification by converting dot-product constraints into ReLU forms that leverage standard convex relaxations.
AEGIS reduces inter-GPU communication by up to 81.3% in self-attention and reaches 96.62% scaling efficiency with 3.86x speedup on four GPUs for 2048-token encrypted Transformer inference.
citing papers explorer
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Once4All: Skeleton-Guided SMT Solver Fuzzing with LLM-Synthesized Generators
Once4All synthesizes LLM-based generators from extracted SMT grammars and populates formula skeletons to fuzz Z3 and cvc5, discovering 43 confirmed bugs with 40 fixed.
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Precise Verification of Transformers through ReLU-Catalyzed Abstraction Refinement
A ReLU-catalyzed abstraction method yields tighter bounds for transformer verification by converting dot-product constraints into ReLU forms that leverage standard convex relaxations.
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AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systems
AEGIS reduces inter-GPU communication by up to 81.3% in self-attention and reaches 96.62% scaling efficiency with 3.86x speedup on four GPUs for 2048-token encrypted Transformer inference.