MOSAIC: Modular Orchestration for Structured Agentic Intelligence and Composition

arXiv:2606.00708 · cs.AI, cs.LG · Submitted 2026-08-23 · Read on arXiv

Yifan Bao, Xinyu Xi, Xinyu Liu, Wen Ge, Lei Jiang, Kevin Zhang, Raad Khraishi, Yihao Ang, Anthony K. H. Tung, Lukasz Szpruch, Hao Ni

cs.AI, cs.LG

Submitted: 2026-08-23

Updated: 2026-08-25

Code: https://github.com/tjj0502/hackathon_starting_kit

Terminology

• Ablation — 24× in this paper · explained in 1 episode
• Action Masking — 18× in this paper · explained in 1 episode
• Reinforcement Learning — 14× in this paper · explained in 4 episodes
• Feature Engineering — 7× in this paper · explained in 3 episodes
• Verification — 7× in this paper · explained in 2 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Time Series Forecasting — 4× in this paper · explained in 3 episodes
• Trace — 4× in this paper · explained in 2 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Action Space — 3× in this paper · explained in 1 episode
• Agentic Framework — 3× in this paper · explained in 6 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Q-learning — 3× in this paper · explained in 3 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Agentic System — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Iterative Refinement — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Agentic Workflows — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Expected Shortfall — 1× in this paper
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Hybrid Model — 1× in this paper · explained in 2 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Mode Collapse — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Reward Function — 1× in this paper · explained in 2 episodes

Sources

• CodeACT: Code Adaptive Compute-efficient Tuning Framework for Code LLMs
• AlphaEvolve: A coding agent for scientific and algorithmic discovery
• The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
• Automated Composition of Agents: A Knapsack Approach for Agentic Component Selection
• AIDE: AI-Driven Exploration in the Space of Code
• LLM4EFFI: Leveraging Large Language Models to Enhance Code Efficiency and Correctness
• OpenAI GPT-5 System Card
• GPT-4 Technical Report
• Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
• TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation
• Diffusion-TS: Interpretable Diffusion for General Time Series Generation
• FourierFlow: Frequency-aware Flow Matching for Generative Turbulence Modeling

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