mlsanityData to 14 Oct 2026

Engineering Sentience

arXiv 2506.20504 ↗First posted 25 Jun 2025cs.AI q-bio.NCcited by 0

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Abstract (arXiv)

We spell out a definition of sentience that may be useful for designing and building it in machines. We propose that for sentience to be meaningful for AI, it must be fleshed out in functional, computational terms, in enough detail to allow for implementation. Yet, this notion of sentience must also reflect something essentially 'subjective', beyond just having the general capacity to encode perceptual content. For this specific functional notion of sentience to occur, we propose that certain sensory signals need to be both assertoric (persistent) and qualitative. To illustrate the definition in more concrete terms, we sketch out some ways for potential implementation, given current technology. Understanding what it takes for artificial agents to be functionally sentient can also help us avoid creating them inadvertently, or at least, realize that we have created them in a timely manner.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 44 references that are in the map. First posted on arXiv 25 Jun 2025.

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References · 18

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  3. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 392
  4. 2021GFlowNet Foundationscited by 290
  5. 2021Deep learning: a statistical viewpointcited by 240
  6. 2018To understand deep learning we need to understand kernel learningcited by 187
  7. 2017Optimizing the Latent Space of Generative Networkscited by 157
  8. 2023Consciousness in Artificial Intelligence: Insights from the Science of Consciousnesscited by 104
  9. 2020Predictive Coding Approximates Backprop along Arbitrary Computation Graphscited by 90
  10. 2019Analogies Explained: Towards Understanding Word Embeddingscited by 64
  11. 2024Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AIcited by 62
  12. 2023Contrastive Chain-of-Thought Promptingcited by 52
6 more
  1. 2023Large language models predict human sensory judgments across six modalitiescited by 48
  2. 2020Emergence of Separable Manifolds in Deep Language Representationscited by 40
  3. 2023GFlowNet-EM for learning compositional latent variable modelscited by 40
  4. 2024How Far Are We From AGI: Are LLMs All We Need?cited by 31
  5. 2024Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoningcited by 27
  6. 2023Gromov-Wasserstein unsupervised alignment reveals structural correspondences between the color similarity structures of humans and large language modelscited by 16

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