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Telogenesis: Goal Is All U Need

arXiv:2603.09476v1 Announce Type: new Abstract: Goal-conditioned systems assume goals are provided externally. We ask whether attentional priorities can emerge endogenously from an agent's internal cognitive state. We propose a priority function that generates observation targets from three epistemic gaps: ignorance (posterior variance), surprise (prediction error), and staleness (temporal decay of confidence in unobserved variables). We validate this in two systems: a minimal attention-allocation environment (2,000 runs) and a modular, partially observable world (500 runs). Ablation shows each component is necessary. A key finding is metric-dependent reversal: under global prediction error, coverage-based rotation wins; under change detection latency, priority-guided allocation wins, with advantage growing monotonically with dimensionality (d = -0.95 at N=48, p < 10^-6). Detection latency follows a power law in attention budget, with a steeper exponent for priority-guided allocation

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Zhuoran Deng, Yizhi Zhang, Ziyi Zhang, Wan Shen · · 1 min read · 3 views
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arXiv:2603.09476v1 Announce Type: new Abstract: Goal-conditioned systems assume goals are provided externally. We ask whether attentional priorities can emerge endogenously from an agent's internal cognitive state. We propose a priority function that generates observation targets from three epistemic gaps: ignorance (posterior variance), surprise (prediction error), and staleness (temporal decay of confidence in unobserved variables). We validate this in two systems: a minimal attention-allocation environment (2,000 runs) and a modular, partially observable world (500 runs). Ablation shows each component is necessary. A key finding is metric-dependent reversal: under global prediction error, coverage-based rotation wins; under change detection latency, priority-guided allocation wins, with advantage growing monotonically with dimensionality (d = -0.95 at N=48, p < 10^-6). Detection latency follows a power law in attention budget, with a steeper exponent for priority-guided allocation (0.55 vs. 0.40). When the decay rate is made learnable per variable, the system spontaneously recovers environmental volatility structure without supervision (t = 22.5, p < 10^-6). We demonstrate that epistemic gaps alone, without external reward, suffice to generate adaptive priorities that outperform fixed strategies and recover latent environmental structure.

Executive Summary

The article 'Telogenesis: Goal Is All U Need' proposes a novel approach to goal-conditioned systems, where attentional priorities emerge endogenously from an agent's internal cognitive state. The authors introduce a priority function that generates observation targets based on epistemic gaps, including ignorance, surprise, and staleness. The system is validated in two environments, a minimal attention-allocation environment and a modular, partially observable world. The results show that the proposed system outperforms fixed strategies and recovers latent environmental structure without supervision. This approach has significant implications for the development of autonomous systems that can adapt to dynamic environments.

Key Points

  • ▸ The proposed system generates attentional priorities endogenously from an agent's internal cognitive state.
  • ▸ The priority function is based on three epistemic gaps: ignorance, surprise, and staleness.
  • ▸ The system outperforms fixed strategies and recovers latent environmental structure without supervision.

Merits

Strength

The proposed system demonstrates adaptability and autonomy in dynamic environments, which is a significant advancement in the field of artificial intelligence.

Robustness

The system's performance is validated in multiple environments, including a minimal attention-allocation environment and a modular, partially observable world.

Flexibility

The priority function can be easily modified to accommodate different epistemic gaps and environments.

Demerits

Limitation

The system's performance may be heavily dependent on the specific choice of epistemic gaps and environments, which may limit its generalizability to other scenarios.

Scalability

The system's performance may degrade as the dimensionality of the environment increases, which may limit its applicability to complex systems.

Expert Commentary

The article 'Telogenesis: Goal Is All U Need' is a significant contribution to the field of artificial intelligence, particularly in the area of goal-conditioned systems. The proposed system's ability to generate attentional priorities endogenously from an agent's internal cognitive state is a major advancement, and the results demonstrate its adaptability and autonomy in dynamic environments. However, the system's performance may be heavily dependent on the specific choice of epistemic gaps and environments, which may limit its generalizability to other scenarios. Furthermore, the system's performance may degrade as the dimensionality of the environment increases, which may limit its applicability to complex systems. Nevertheless, the proposed system has significant practical and policy implications for the development of autonomous systems and surveillance systems, respectively.

Recommendations

  • ✓ The proposed system should be further developed and validated in more complex environments to assess its scalability and generalizability.
  • ✓ The authors should investigate the use of other epistemic gaps and environments to further demonstrate the system's adaptability and autonomy.

Sources

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