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Zhi-Yao Xie

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Preprint Oct 2026

CorrectRTL: Boost RTL Generation Quality Through Automated Self-Correction with Instruction Decomposition

Large language models (LLMs) have shown strong promise in specification-to-RTL generation. Among the explorations, the self-correction paradigm has significantly advanced LLM-based RTL generation. However, two challenges limit further progress. Existing benchmarks are saturated by advanced LLMs, leaving little room to...

Jing Wang, Shang Liu, Han Zhou et al. · 0 citations
Jul 2026

CircuitProver: Agentic Lean 4 Theorem Proving with Reusable Circuit Proof Library for Hardware Verification

This paper presents CircuitProver, an agentic Lean 4-based verification framework supporting proof-accumulation and parameterized verification, and introduces the first benchmark suite for evaluating agentic hardware theorem proving, covering diverse parameterized hardware designs, specifications, proof tasks, and eval...

Zi-Yi Yang, Wenji Fang, Chen Chen et al. · 0 citations
Preprint Aug 2026

FABO: Agent-Guided Discovery of Joint Breakpoint Optimization for Timing-Driven Routing Trees

Whether language-model-guided search can discover a constructive algorithm that improves on SALT is asked, and an agent framework that combines parallel exploration with independent checking is developed that combines parallel exploration with independent checking is developed.

Shang Liu, Wenji Fang, Jing Wang et al. · 0 citations
Preprint Aug 2026

G-Power: Architecture-level GPU Power Modeling with Aggregated Knowledge Foundations from Known GPUs

G-Power, an architecture-level GPU power modeling framework that utilizes additional known GPU chips to provide additional knowledge and adopts a three-phase algorithm consisting of pre-training with additional known chips, attention-inspired aggregation, and fine-tuning on the target GPU.

Qi-Jun Zhang, Yao Lu, Shang Liu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Memory Compression for High-Fanout Agent Sandboxes

AgentZip is presented, the first memory compression system designed specifically for AI-agent sandboxes, which broadens the compression scope to any page with a profitable representation and shifts overhead control from compression-time page selection to restore-time prefetching.

Meng-Ming Li, Ce-Yu Xu, Qi-Jun Zhang et al. · 0 citations
Preprint Aug 2026

FSGen: Agile Fused and Sparse Accelerator Generator with Accurate Power Model for LLM Applications

FSGen is proposed, an agile framework for attention-based LLM accelerator generation with an early-stage PPA estimator that supports fused operator dataflows and sparsity with a diverse design space and finds designs with 1.4x better power efficiency or 10x speedup with similar PPA metrics compared to prior work.

J. Mok, Qi-Jun Zhang, Zhi-Yao Xie · 0 citations

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