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[2602.13230] Intelligence as Trajectory-Dominant Pareto Optimization
Summary
The paper presents a novel framework for understanding intelligence through the lens of trajectory-dominant Pareto optimization, addressing limitations in long-horizon adaptability in AI systems.
Why It Matters
This research shifts the focus from traditional performance metrics to the optimization geometry of intelligence, offering insights into overcoming developmental constraints in AI. It introduces concepts like Pareto traps and the Trap Escape Difficulty Index, which could influence future AI design and training methodologies.
Key Takeaways
- Intelligence optimization is framed as a trajectory-level phenomenon.
- Pareto traps can hinder access to superior developmental paths in AI.
- The Trap Escape Difficulty Index quantifies the challenges of escaping local optimization traps.
- Dynamic intelligence ceilings are geometric outcomes of trajectory dominance.
- A formal taxonomy of Pareto traps is introduced to aid in diagnosing AI limitations.
Computer Science > Artificial Intelligence arXiv:2602.13230 (cs) [Submitted on 28 Jan 2026] Title:Intelligence as Trajectory-Dominant Pareto Optimization Authors:Truong Xuan Khanh, Truong Quynh Hoa View a PDF of the paper titled Intelligence as Trajectory-Dominant Pareto Optimization, by Truong Xuan Khanh and 1 other authors View PDF HTML (experimental) Abstract:Despite recent advances in artificial intelligence, many systems exhibit stagnation in long-horizon adaptability despite continued performance optimization. This work argues that such limitations do not primarily arise from insufficient learning, data, or model capacity, but from a deeper structural property of how intelligence is optimized over time. We formulate intelligence as a trajectory-level phenomenon governed by multi-objective trade-offs, and introduce Trajectory-Dominant Pareto Optimization, a path-wise generalization of classical Pareto optimality in which dominance is defined over full trajectories. Within this framework, Pareto traps emerge as locally non-dominated regions of trajectory space that nevertheless restrict access to globally superior developmental paths under conservative local optimization. To characterize the rigidity of such constraints, we define the Trap Escape Difficulty Index (TEDI), a composite geometric measure capturing escape distance, structural constraints, and behavioral inertia. We show that dynamic intelligence ceilings arise as inevitable geometric consequences of traject...
