Rui Meng
Rui Meng is a Research Scientist on Google Cloud AI Research. His current work focuses on building autonomous agents that solve complex, real-world tasks end-to-end. His current focus is on agents for scientific research -- systems that conduct literature review, hypothesis generation, experimentation, and paper writing with verifiable evidence chains.
His broader research interests center on the nature of learning and representation: how humans and AI systems compress, understand, and reason about the world across modalities.
Previously, he worked on training and evaluating large language models and multimodal embedding models.
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Automating AI research differs from general software engineering due to computationally expensive evaluation (e.g., model training) and opaque performance attribution. Current LLM-based agents struggle here, often generating monolithic scripts that ignore execution costs and causal factors. We introduce MARS (Modular Agent with Reflective Search), a framework optimized for autonomous AI research. MARS relies on three pillars: (1) Budget-Aware Planning via cost-constrained Monte Carlo Tree Search (MCTS) to explicitly balance performance with execution expense; (2) Modular Construction, employing a "Design-Decompose-Implement" pipeline to manage complex research repositories; and (3) Comparative Reflective Memory, which addresses credit assignment by analyzing solution differences to distill high-signal insights. MARS achieves state-of-the-art performance among open-source frameworks on MLE-Bench under comparable settings, maintaining competitiveness with the global leaderboard's top methods. Furthermore, the system exhibits qualitative "Aha!" moments, where 63% of all utilized lessons originate from cross-branch transfer, demonstrating that the agent effectively generalizes insights across search paths.
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TUMIX: Augmenting LLM Reasoning with a Dynamic Tool-Use Mixture
Chuchu Fan
Na Li
Chi Wang
Ji Yin
Yongchao Chen
2025
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Integrating tools like Code Interpreter and Search has significantly improved Large Language Models (LLMs) reasoning, as shown by leading models such as OpenAI's ChatGPT Agent, Google's Gemini-Pro, and XAI's Grok4. However, the research community still lacks practical guidance on fully leveraging these tools. The main challenge lies in finding an effective method to fully exploit the benefits of textual reasoning, coding, and searching when facing distinctive questions. To address this, we propose an ensemble-based framework that runs multiple agents in parallel, each exploring different answer paths with distinct tool-use strategies. Agents iteratively share and refine their answers by considering the original question and previous responses. Our proposed method Tool-Use Mixture (TUMIX) achieves significant gains over other representative tool-augmented test-time scaling methods such as Self-MoA, Symbolic-MoE, DEI, SciMaster, and GSA. With near equal inference costs, TUMIX delivers an average +3.55% accuracy improvement over the best baseline on Gemini-2.5-Pro and Gemini-2.5-Flash across key reasoning benchmarks (HLE, GPQA, AIME 24&25), where coding and search can effectively support reasoning when applied properly. We find that agent diversity and quality are crucial, and can be further improved by querying LLMs to automatically optimize agent designs. To reduce costs, TUMIX halts refinement once sufficient confidence is reached, preserving nearly the same performance at just 49% of the inference cost. With further scaling, TUMIX can achieve even higher performance, though at substantially greater cost.
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