CONTRA is a training‑free method that discovers and qualifies behavior‑changing questions for selective clarification in large language model (LLM) code generation. It first generates candidate questions, filters out those unrelated to required behavior or already resolved, then creates programs conditioned on two plausible answers to check for stable behavioral differences on shared inputs. Experiments on ClarifyCodeBench show that CONTRA achieves the highest F1 across four coding agents, outperforming baselines by 13.88 percentage points, and it is also implemented as a Claude Code plugin for practical use.
arXiv:2607.00711v2 Announce Type: cross Abstract: Large Language Models have emerged as programming assistants. However, the efficacy of code generation is constrained by the quality of input require...
The paper introduces PlanPool, a method for agentic Text-to-SQL systems to manage clarification questions by maintaining a mutable question pool. By requiring agents to explicitly ask or drop each planned question, PlanPool improves ambiguity coverage and reduces silent failures compared to unconstrained or prompt-based approaches. Experiments on benchmarks derived from BIRD-Interact and Spider show that PlanPool achieves competitive execution accuracy while better handling underspecification.
arXiv:2606.13120v2 Announce Type: replace Abstract: Search Agents -- large language models augmented with search tools -- have intensified the need for future-proof evaluation benchmarks. Existing be...
arXiv:2509. 23071v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) agent development is hindered by the lack of executable ground-truth agent-environment interaction trajectories.
arXiv:2506. 13932v3 Announce Type: replace-cross Abstract: The rise of large language models (LLMs) has led to dramatic improvements across a wide range of natural language tasks.
arXiv:2606. 12387v1 Announce Type: cross Abstract: Large Language Models (LLMs) have democratized database access through Text-to-SQL, but moving from prototypes to production remains difficult.
arXiv:2607. 02057v1 Announce Type: cross Abstract: In recent years, it has become increasingly evident that large language models (LLMs) and autonomous agents raise the level of abstraction in software development by shifting the focus from writing precise procedures to expressing intents and goals.
arXiv:2607. 14105v1 Announce Type: cross Abstract: For Large Language Models to reliably answer user queries, users must clearly specify requirements, context, and constraints.
arXiv:2605. 21850v2 Announce Type: replace-cross Abstract: Recent development of agents has renewed demand for long-context reasoning capacity of LLMs.
arXiv:2608. 16742v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable progress in code generation, yet ensuring correctness in complex, repository-level tasks remains challenging.
Large Language Models (LLMs) have achieved remarkable progress in code generation, yet ensuring correctness in complex, repository-level tasks remains challenging. Existing approaches often use generated tests as static post-hoc validators, which limits their ability to guide implementation and may introduce misleading feedback when the tests themselves are incomplete or incorrect.
arXiv:2507. 22580v2 Announce Type: replace-cross Abstract: Automated Program Repair (APR) seeks to automatically correct software bugs without requiring human intervention.