LACE: Large Language Model Aided Multi-Agent Framework for Agile RISC-V Instruction Extension

HINT: Toward an Executable Hardware-Intent Representation Layer for LLM-Driven RTL Generation

Generating implementation-quality RTL with large language models (LLMs) remains difficult because direct generation must resolve microarchitecture while simultaneously producing and debugging low-level code. We present HINT, an executable hardware-intent intermediate representation layer between behavioral specifications or executable oracles and RTL. HINT makes RTL-relevant microarchitecture explicit, supports pre-RTL checking, and supplies explicit RTL-lowering obligations. We evaluate HINT using both a minimal single-agent flow and a full staged workflow. Across seven operator cases, the HINT-mediated route, with no post-synthesis QoR refinement, produces contract-compliant synthesizable RTL on 7/7 cases; Direct C2RTL and C2HLSC apply to five cases and succeed on 5/5 and 1/5, respectively. Under matched Design Compiler synthesis, HINT reduces area by 5.0\%--26.2\% relative to five manual RTL implementations and by 8.9\%--86.1\% relative to five accepted Direct C2RTL results. RealBench AES and SDC, together with a Vortex VPU synthesizing to 561.67k~$\mu\mathrm{m}^2$, further demonstrate specification-driven, protocol-rich, memory-rich, and hierarchical designs. In the controlled operator study, the HINT-mediated route shows better observed convergence and avoids the severe implementation-quality degradation seen in several direct-generation results.

MACE: Modular Adaptive Code Engine with Context-Aware LoRA Expert Routing and Hierarchical Retrieval-Augmented Generation

QiMeng-VPID: Verification-Grounded Port-Level Iterative Decomposition for Complex Verilog Generation

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.

PerfAgent: Profiler-Guided Iterative Refinement for Repository-Level Code Optimization

PerfAgent is presented, a profiler-guided, verifier-in-the-loop workflow that gives an off-the-shelf coding agent the feedback needed to find real hotspots, improve beyond the first passing patch, and use profiler evidence rather than timing alone to decide what to optimize next.

Route-Align-Verify for Functional Correctness in Code Generation

Large language models (LLMs) have substantially improved code generation, yet achieving strong functional correctness remains difficult, especially for heterogeneous programming tasks where a single prompting strategy and a single directly generated output are often insufficient. In this paper, we present RAV, a lightweight and modular framework that improves code generation with a fixed backbone model through three coordinated stages: Route, which applies task-aware prompt routing before generation; Align, which reduces the mismatch between fine-tuning prompts and inference-time prompts through aligned LoRA adaptation; and Verify, which selects the final output by executing multiple candidates against visible public tests. We evaluate RAV on the MBPP benchmark under both the sanitized and full settings. The complete RAV pipeline achieves the best performance among all evaluated configurations, reaching 0.8911 on MBPP Sanitized and 0.8520 on MBPP Full. Compared with the base model, these results represent improvements of 6.35 and 9.92 percentage points, respectively. Component-wise ablation experiments further show that task-aware routing and aligned adaptation become substantially more effective when combined with execution-based verification. Additional robustness and contamination analyses support the reliability of the observed improvements. Overall, 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.

TriArchitect: A Shared-State Multi-Agent Framework for Safe Java Code Migration

As the global software infrastructure ages, the modernization of legacy systems—particularly the migration of monolithic Java 8 applications to contemporary Long-Term Support (LTS) versions like Java 17 or 21—has become a critical imperative for security, performance, and maintainability. While Large Language Models (LLMs) have demonstrated exceptional proficiency in isolated code generation tasks, their application to repository-scale migration is severely constrained by "contextual hallucinations"—a phenomenon where models generate syntactically correct but structurally invalid code due to a lack of global state awareness. Recent empirical studies (e.g., Collu-Bench [1]) indicate that determining the hallucination status of generated code remains an unsolved problem, with prediction accuracy often below 33%. In this work, we introduce TriArchitect, a novel shared-state multi-agent framework designed to bridge the gap between probabilistic reasoning and deterministic structural integrity. TriArchitect introduces three key innovations: (1) the Typed Migration Graph (TMG), a formal directed acyclic graph that serves as a persistent semantic memory of the migration state; (2) a specialized role-based multi-agent architecture comprising an Archeologist (analysis), Architect (synthesis), and Validator (verification); and (3) a rigorous Validator-Veto Protocol, which enforces a "compilation-as-consensus" rule to reject hallucinated proposals before they corrupt the codebase. We evaluate TriArchitect on J8-to-J17-Bench, a comprehensive benchmark of 1,000 method-level and class-level migration tasks. Results demonstrate a System Success Rate (SSR) of 68.4%, statistically outperforming significant baselines including OpenRewrite (62.0%), SWE-Agent (63.5%), AutoCodeRover (64.8%), and raw GPT-5.1 (65.2%), while reducing token consumption by 40% through state-aware prompting.