Large language models (LLMs) are increasingly used as coding assistants, but the programs they generate often fail. Such failures can be addressed by debugging the existing implementation or regenerating a new solution from the problem specification, with existing repair workflows typically defaulting to debugging. We find that the effectiveness of debugging and regeneration depends strongly on initial program quality, suggesting that no single repair strategy is universally optimal. Motivated by this observation, we present PATSSEL (Program-Adaptive Test-guided Repair Strategy Selection), a test-guided repair selection framework that dynamically chooses between debugging and regeneration. Rather than committing to a single repair strategy, PATSSEL generates debugged and regenerated candidates for each buggy program, evaluates them using generated verifier tests, and selects the stronger candidate, leveraging the complementary strengths of debugging and regeneration. Experiments on LeetCodeDataset, LiveCodeBench, and HE+Fix show that PATSSEL achieves average absolute improvements of 2.7% with Qwen3-8B and 3.2% with Llama3-8B over the best single-strategy baselines under the same inference compute budget and routing using gold unit tests. The gains persist across repair models, routing models, and stronger debugger models. Further analysis shows that PATSSEL benefits from the complementary strengths of debugging and regeneration, highlighting the importance of adaptive strategy selection for reliable LLM-based code repair.

Temporal tabular question answering presents a significant challenge for Large Language Models (LLMs), requiring robust reasoning over structured data—a task where traditional prompting methods often fall short. These methods face challenges such as memorization, sensitivity to table size, and reduced performance on complex queries. To overcome these limitations, we introduce TEMPTABQA-C, a synthetic dataset designed for systematic and controlled evaluations, alongside a symbolic intermediate representation that transforms tables into database schemas. This structured approach allows LLMs to generate and execute SQL queries, enhancing generalization and mitigating biases. By incorporating adaptive fewshot prompting with contextually tailored examples, our method achieves superior robustness, scalability, and performance. Experimental results consistently highlight improvements across key challenges, setting a new benchmark for robust temporal reasoning with LLMs. Code and TEMPTABQA-C dataset: https:// coral-lab-asu.github.io/llm_symbolic.