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Core Pattern

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Founder's Pitch

"Develop APEMO, an orchestration tool for maintaining reliability in long-horizon AI agent systems that optimizes resource allocation based on temporal-affective signals."

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Commercial Viability Breakdown

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2.5

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References (21)

[1]
BMAM: Brain-inspired Multi-Agent Memory Framework
2026Yang Li, Jiaxiang Liu et al.
[2]
Beyond Rule-Based Workflows: An Information-Flow-Orchestrated Multi-Agents Paradigm via Agent-to-Agent Communication from CORAL
2026Xinxing Ren, Quagmire Zang et al.
[3]
Orchestrator: Active Inference for Multi-Agent Systems in Long-Horizon Tasks
2025Lukas Beckenbauer, Johannes-Lucas Loewe et al.
[4]
Kairos: Low-latency Multi-Agent Serving with Shared LLMs and Excessive Loads in the Public Cloud
2025Jinyuan Chen, Jiuchen Shi et al.
[5]
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
2023Qingyun Wu, Gagan Bansal et al.
[6]
Let's Verify Step by Step
2023H. Lightman, Vineet Kosaraju et al.
[7]
Self-Refine: Iterative Refinement with Self-Feedback
2023Aman Madaan, Niket Tandon et al.
[8]
How and why humans trust: A meta-analysis and elaborated model
2023P. Hancock, T. Kessler et al.
[9]
Reflexion: language agents with verbal reinforcement learning
2023Noah Shinn, Federico Cassano et al.
[10]
Solving math word problems with process- and outcome-based feedback
2022Jonathan Uesato, Nate Kushman et al.
[11]
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
2020Gagan Bansal, Tongshuang Sherry Wu et al.
[12]
Scaling Laws for Neural Language Models
2020J. Kaplan, Sam McCandlish et al.
[13]
Guidelines for Human-AI Interaction
2019Saleema Amershi, Daniel S. Weld et al.
[14]
Deep Reinforcement Learning from Human Preferences
2017P. Christiano, Jan Leike et al.
[15]
Similarities and differences between human–human and human–automation trust: an integrative review
2007Poornima Madhavan, D. Wiegmann
[16]
Trust in Automation: Designing for Appropriate Reliance
2004John D. Lee, Katrina A. See
[17]
The role of trust in automation reliance
2003Mary T. Dzindolet, S. Peterson et al.
[18]
A model for types and levels of human interaction with automation
2000R. Parasuraman, T. Sheridan et al.
[19]
Toward a Theory of Situation Awareness in Dynamic Systems
1995M. Endsley
[20]
When More Pain Is Preferred to Less: Adding a Better End
1993D. Kahneman, B. Fredrickson et al.

Showing 20 of 21 references