Large language models (LLMs) can generate functionally correct code, but their ability to optimize programs is underexplored. We introduce OEISPerf, a code-to-code benchmark for algorithmic program optimization built from alternative implementations of integer sequences in the On-Line Encyclopedia of Integer Sequences. For each sequence we mine multiple mathematical formulations, synthesize and validate Python implementations, and pair a substantially slower program with the fastest validated reference found by our construction pipeline, producing instances that often require substantial structural transformations and can yield substantial speedups. Evaluating 15 open-source and closed-source LLMs shows that even the strongest evaluated setting, Gemini-3-Flash with its thinking mode enabled, matches the reference efficiency on only 67.9% of test instances. Across LLMs, we observe both incorrect “optimizations” that break correctness and superficial refactorings that preserve suboptimal complexity. Finally, we fine-tune Qwen2.5-Coder-3B-Instruct via reinforcement learning, markedly improving reference attainment on OEISPerf while slightly improving performance on standard code-generation benchmarks.

Large language models (LLMs) face challenges in logical reasoning where correctness requires strict deductive procedures. Purely model-based approaches often suffer from hallucinations, while neuro-symbolic methods typically delegate deduction to external solvers, reducing the LLM to a mere translator. To address this, we propose MAC-Reasoner, a multi-agent framework that constructs a Logic-Augmented Context to enhance LLMs’ reasoning. In this framework, a translator agent converts problems into executable symbolic programs. Symbolic information from solver execution is transformed into the Logic-Augmented Context, serving as a verification reference where logical conflicts trigger heightened attention to violated constraints. We evaluate MAC-Reasoner with three backbone LLMs on four challenging benchmarks. Results show consistent and robust improvements over baselines. Furthermore, reasoning traces from MAC-Reasoner can be used for supervised fine-tuning of LLMs to achieve more accurate and efficient logical reasoning.