2026· International Workshop on Computer Modeling and Intelligent Systems· pp. 212-226· 0 citations· 34 references
Computer Science
TL;DR
The experimental results demonstrate that the LoRA expert achieves a 35% average speedup in code generation while simultaneously improving correctness from 84% to 96%, surpassing the base model by 12 percentage points.
LACE is presented, an LLM-aided multi-agent workflow that translates natural-language ISAX intents into a compact two-level IR, performs retrieval-guided localized RTL edits over large repositories, and closes the loop with a compiler-agnostic riscv-formal checking flow.
Pingqing Zheng, Jiayin Qin, Fuqi Zhang et al.· 0 citations
Natural-language requirements for program synthesis are often incomplete or ambiguous, yet large language models are commonly expected to generate code in a single pass. Prior clarification-based methods address this issue by asking follow-up questions when sampled candidate programs disagree, but fixed clarify-on-disagreement policies can overuse clarification and can also overtrust weak behavioral agreement. We present an adaptive routing framework for LLM-based program synthesis that treats clarification as an inference-time control decision. The framework augments a ClarifyGPT-style pipeline with execution-driven confidence estimation, semanticdifference analysis, and bounded candidate expansion, allowing the system to choose among direct generation, additional evidence gathering, and clarification. We evaluate the framework on MBPP, HumanEval, and extended-test variants using GPT-4.1 mini, Claude Haiku 4.5, and GPT-5.4 mini. Adaptive routing improves pass@1 accuracy by up to 7.60 percentage points over single-pass baselines. Compared with fixed-policy clarification, it preserves accuracy while reducing token usage by up to 57.2% for GPT-4.1 mini, and reallocates computation toward harder cases for Claude Haiku 4.5. These results suggest that clarification is most useful when triggered selectively based on execution evidence and semantic disagreement, even when ambiguity is observed indirectly through candidate behavior rather than through explicitly annotated ambiguous requirements.
Muhammad Ahmed, Edwar Tiu, Niyati Nikunj Kapadia et al.· Annual International Compute...· 0 citations
Large language models (LLMs) have demonstrated strong capabilities in code generation. However, repository-level code generation remains challenging, as it requires effectively identifying and utilizing repository-specific context. While retrieval-augmented generation (RAG) incorporates relevant code snippets, it often introduces redundant context that interferes with the LLM's ability to utilize relevant information, leading to degraded generation quality and increased computational cost. Moreover, existing context selection and compression methods struggle to balance efficiency and quality, either introducing additional computational overhead or failing to effectively select valid context. In this paper, we propose MRCoder, an efficient context selection framework that improves both the effectiveness and efficiency of repository-level code generation. MRCoder adopts a Map-Reduce paradigm: in the Map Phase, a lightweight draft model generates drafts over partitioned contexts, and Structure-Aware Draft-Guided Selection (SADGS) selects informative contexts based on drafts through API consistency and logical similarity; in the Reduce Phase, the refined contexts are aggregated for final generation, with a parallel verification strategy further accelerating decoding. We evaluate MRCoder on two widely used repository-level code generation benchmarks, CoderEval and DevEval, using Qwen2.5-Coder and DeepSeek-Coder as backbone LLMs. Experimental results show that MRCoder improves code generation accuracy over strong baselines while reducing token consumption by 30 to 50% and inference time by up to 52%. These results demonstrate that our proposed structured and draft-guided context selection strategy is crucial for improving both the quality and efficiency of repository-level code generation
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.
The results show that metainfer organizes generation constraints, validation feedback, and knowledge consolidation into a continuous closed loop, enabling runnable customized inference solutions to be generated from explicit knowledge.
Zhenwen Miao, Honglin Wang, Mingheng Mi et al.· 0 citations
Large Language Models (LLMs) are now widely used for code generation, yet even syntactically correct output may contain logical and semantic errors that remain invisible until runtime, particularly in framework-driven applications, where correctness depends on dependency injection, framework conventions, configuration, library compatibility, and database interaction. This paper presents GenTest, an open-source platform for context-aware dynamic generation of Java code using LLMs, runtime compilation and class loading, Spring bean registration, JPA/PostgreSQL execution, and declarative YAML-based test validation. GenTest supports multiple LLM providers through a provider-agnostic interface and evaluates generated components within a live Spring Boot application context. Across 209 test cases and 933 assertions, GenTest achieves a 63.9% assertion pass rate, and 94.2% of failures occur after successful compilation, confirming that execution-centered evaluation reveals failure modes invisible to static analysis.
Muhammed Suphi Şeyhkuruş, Tolga Ovatman· Annual International Compute...· 0 citations