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Zhejiang University
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Spear accelerates the synthesis of optimal abstract transformers by exploiting parallelism, outperforming state-of-the-art solvers in both efficiency and scalability.
Intention-aware tool discovery can boost LLM agent performance by nearly 60% while slashing unnecessary complexity in tool management.
VerIbmc solves 431 out of 499 software verification problems using local models, proving that high-performance neuro-symbolic reasoning can be achieved without cloud dependency.
