From 0-to-1 to 1-to-N: Reproducible Engineering Evidence for MetaAI Recursive Self-Design

Paper 1 presents a novel, well-defined technical contribution (READER framework) with concrete experimental results addressing a timely and practical problem—LLM provenance in agentic systems. It introduces a new methodology with rigorous evaluation, demonstrating strong performance gains. Paper 2 is primarily a survey/framework paper that maps existing systems against proposed criteria and offers a protocol without completed experimental results. Paper 1's combination of novelty, methodological rigor, practical relevance to the growing LLM API ecosystem, and empirical validation gives it substantially higher potential impact.

Paper 1 introduces a concrete, well-defined benchmark (200 tasks, 7,118 criteria) for evaluating LLMs on professional Office automation tasks, addressing a practical gap in AI evaluation. It provides rigorous empirical results across 7 frontier models with clear metrics. Paper 2, while addressing an interesting topic (recursive self-design), is primarily a conceptual framework and literature mapping exercise. Its proposed protocol (MetaAI-Mini) lacks experimental results, significantly limiting its immediate scientific impact. Paper 1's benchmark and findings have broader, more immediate utility for the AI research community.

Paper 1 (Visual-SDPO) presents a concrete, novel framework with demonstrated empirical results across multiple benchmarks, showing significant improvements. It addresses a practical and growing problem (visual defects in code-generated artifacts) with a technically rigorous approach combining self-distillation, visual grounding, and credit assignment. Paper 2 is primarily a survey/framework paper that proposes evaluation criteria for recursive self-design but lacks experimental results (MetaAI-Mini is only a protocol, not a completed experiment). Paper 1's methodological contributions and verified improvements give it substantially higher near-term scientific impact.

Paper 2 is more likely to have higher near-term scientific impact: it proposes a concrete, end-to-end method to generate labeled mobility anomaly datasets under realistic kinematic/map constraints, addressing a clear bottleneck (lack of ground truth) with immediate applications in anomaly detection, urban computing, transportation, and privacy-preserving data sharing. The methodology appears more actionable and testable (LLM-driven edits + routing reconstruction + noise model). Paper 1 is timely and conceptually relevant, but is primarily a framework/protocol with limited new experimental evidence (no completed MetaAI-Mini runs), reducing rigor and immediate utility.

Paper 1 addresses recursive self-design in AI, a foundational frontier concept with the potential to accelerate development across the entire AI ecosystem. While Paper 2 offers a robust, multi-agent approach to motor design with immediate industrial value, Paper 1's focus on AGI-adjacent mechanisms, standardized evaluation frameworks, and broad cross-domain applicability gives it a significantly higher potential for widespread, transformative scientific impact.

Paper 1 presents a concrete, validated multi-agent framework (AbaqusAgent) with demonstrated results (86% success rate across 50 problems), open-source code, and clear real-world applications in computational mechanics and engineering education. It addresses a well-defined problem with a working solution. Paper 2 is primarily a conceptual/survey paper proposing an evidence framework for recursive self-design but lacks experimental results (MetaAI-Mini is reported as a protocol only, with no completed model run). Paper 1's practical utility, validated methodology, and immediate applicability give it higher near-term scientific impact.

Paper 1 offers significantly higher scientific impact because it addresses the paradigm-shifting concept of recursive AI self-design. While Paper 2 provides a valuable but incremental optimization for tool-augmented LLMs, Paper 1 tackles foundational questions regarding how AI systems can autonomously improve their own design mechanisms. By introducing an operational evidence framework, analyzing state-of-the-art systems, and providing a reproducible protocol for meta-level modification, Paper 1 lays the groundwork for transformative leaps in AI capabilities and AGI development, demonstrating much broader long-term relevance.

Paper 2 presents a concrete, validated AI framework with clear clinical applications in osteoarthritis research, combining deep learning with interpretable statistical modeling on real patient data (OAI). It demonstrates significant methodological improvements (MCC gains), actionable clinical findings (odds ratios for pain progression), and addresses trustworthiness through conformal prediction. Paper 1 is largely a conceptual/protocol paper about recursive AI self-design without completed experimental results, making it more speculative. Paper 2's rigorous methodology, real-world medical applicability, and quantified results give it substantially higher near-term scientific impact.

Paper 1 explores recursive self-design, a foundational step toward artificial general intelligence. By providing a clear evaluation framework and reproducible protocol for AI systems that can modify their own design space, it addresses a highly profound, paradigm-shifting concept. While Paper 2 offers strong empirical results in agentic delegation, Paper 1's focus on self-improving AI has broader, more transformative long-term implications across the entire field of AI research and safety.

Paper 2 addresses recursive self-improvement in AI, a highly timely topic with transformative potential. By establishing an evidence framework and a reproducible protocol for MetaAI, it lays the groundwork for future breakthroughs in self-designing AI systems. While Paper 1 offers rigorous theoretical advancements in active inference, Paper 2's focus on scalable, self-improving AI presents a significantly broader potential impact, relevance, and real-world applicability in the rapidly evolving landscape of artificial general intelligence.