Sep 2026· Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence· 0 citations· 24 references
TL;DR
This work proposes VPID, a multi-agent framework for generating complex Verilog that achieves monotonic functional improvement and introduces an experience-guided refinement strategy that distills historical waveform mismatches into constraints, guiding the targeted debugging for the unverified ports.
Abstract
While Large Language Models (LLMs) have shown promise in translating natural-language specifications to Register-Transfer Level (RTL) designs, they often fail on complex, port-rich IPs. Existing frameworks typically separate generation from debugging, relying on static decomposition and iterative repair, which is hard to verify and yields unstable, inefficient maintenance. In this work, we propose VPID, a multi-agent framework for generating complex Verilog that achieves monotonic functional improvement. Given RTL’s inherent concurrency and the alignment between verification and port-level behavior, our key insight is to treat ports as verifiable boundaries for dynamic decomposition, enabling fine-grained analysis to pinpoint root causes and guide targeted debugging. Specifically, we implement a behavior locking mechanism that preserves the behavior of verified ports to ensure monotonicity, preventing regressions in correct functionalities. To accelerate convergence, we introduce an experience-guided refinement strategy that distills historical waveform mismatches into constraints, guiding the targeted debugging for the unverified ports. Together with precise Abstract Syntax Tree (AST)-based code extraction, these mechanisms enable an efficient incremental generation workflow. Experiments on RealBench demonstrate that VPID outperforms both one-pass general-purpose LLMs and existing agent-based frameworks in both syntax and functional correctness, presenting a robust approach for automating Verilog generation for complex RTL designs.
The results indicate that functional correctness in code generation can be meaningfully improved without modifying the backbone architecture, by jointly optimizing how tasks are prompted, how the model is adapted, and how final outputs are selected.
FLEX is presented, a foundational Constrained Horn Clause (CHC) solver implemented in LEAN, that reduces the trusted base to the kernel alone, and allows using LEAN's entire proof ecosystem to verify low-level systems code, via three contributions.
J. Khan, Petros Markopoulos, Nicolás Lehmann et al.· arXiv.org· 0 citations
WasmMend is presented, the first system to automatically repair Native-Wasm functional discrepancies and demonstrates the value of divergence-guided reasoning for cross-platform repair.
Evaluating SEDCoT on a public COBOL-to-C dataset demonstrates that it outperforms state-of-the-art baselines by at least 12% while producing translations with substantially higher readability than rule-based alternatives.
Phillip Entin, Wenchao Gu, Alexander Knapp et al.· 0 citations
VeriRefine progressively refines the prose specification into an explicit, schema-constrained account of design intent, expressed as per-signal Abstract Signal Transition Functions (ASTFs) that commit each signal's logic style, clock domain, and reset behavior before any code exists and ground every behavior in a verbatim specification sentence.
IntentP4 is presented, a formal-methods-aided pipeline that translates an operator's natural-language intent into a P4LTL specification and then into a replayable multi-packet test case, grounded throughout in compiler artifacts via a tool-queryable ProgramContext and gated by deterministic per-stage validators.
Ruonan Feng, Mingming Zhang, Yu Jiang et al.· Conference on Applications,...· 0 citations
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