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Pratyabhijñā Creative Engine v0.4

References

Bibliography for the v0.4 paper. Every entry is verified against Crossref, arXiv, or the official publisher record. The verification log lives at audit/v0.4/lit_verification.jsonl; new entries added during Phase 8 are recorded in audit/v0.4/lit_new_entries.jsonl.

Pratyabhijñā philosophy

  1. Abhinavagupta. Īśvarapratyabhijñāvimarśinī, eds. K. A. Subramania Iyer & K. C. Pandey. Motilal Banarsidass, 1986.
  2. Singh, J. Pratyabhijñāhṛdayam: The Secret of Self-Recognition. Motilal Banarsidass. ↗ link
  3. Lawrence, D. P. Rediscovering God with Transcendental Argument: A Contemporary Interpretation of Monistic Kashmiri Śaiva Philosophy. SUNY Press.

Active inference

  1. Friston, K. The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010). doi:10.1038/nrn2787
  2. Friston, K., & Penny, W. Post hoc Bayesian model selection. NeuroImage 56, 2089–2099 (2011). doi:10.1016/j.neuroimage.2011.03.062
  3. Di Paolo, L. et al. Active inference for autonomous LLM agents (2024). ↗ link

LLM-as-judge

  1. Zheng, L. et al. Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. NeurIPS (2023). ↗ link
  2. Liu, Y. et al. G-Eval: NLG evaluation using GPT-4 with Better Human Alignment. EMNLP (2023). ↗ link
  3. Sellam, T., Das, D., & Parikh, A. P. BLEURT: Learning robust metrics for text generation. ACL (2020). doi:10.18653/v1/2020.acl-main.704
  4. Wang, P. et al. Large language models are not fair evaluators (2023). ↗ link

Commit policy

  1. Madaan, A. et al. Self-Refine: iterative refinement with self-feedback. NeurIPS (2023). ↗ link
  2. Shinn, N. et al. Reflexion: language agents with verbal reinforcement learning. NeurIPS (2023). ↗ link
  3. Bai, Y. et al. Constitutional AI: harmlessness from AI feedback (2022). ↗ link
  4. Stiennon, N. et al. Learning to summarize from human feedback. NeurIPS (2020). ↗ link

Creativity benchmarks

  1. Organisciak, P. et al. Beyond semantic distance: automated scoring of divergent thinking with LLMs. Thinking Skills and Creativity (2023). ↗ link
  2. Cao, M. et al. CreativityPrism: a holistic benchmark for LLM creativity (2024). ↗ link
  3. Beaty, R. E., & Silvia, P. J. Why do ideas get more creative across time? Psychology of Aesthetics, Creativity, and the Arts 6 (2012). doi:10.1037/a0030672

Computational Sanskrit

  1. Hellwig, O. The Digital Corpus of Sanskrit (DCS). University of Düsseldorf. ↗ link
  2. Hellwig, O. ByT5-Sanskrit: a Sanskrit segmenter (2023). ↗ link

Hopfield networks

  1. Weber, T. et al. Untapped Potential in Self-Optimization of Hopfield Networks (2025). doi:10.48550/arXiv.2501.04007
  2. Waldron, W. S. The Buddhist Unconscious: The Ālayavijñāna in the Context of Indian Buddhist Thought. Routledge, 2003. doi:10.4324/9780203451175

Benchmarks

  1. Suzgun, M. et al. Challenging BIG-Bench tasks and whether chain-of-thought can solve them. ACL Findings (2023). doi:10.18653/v1/2023.findings-acl.824
  2. Tian, X. et al. MacGyver: are large language models creative problem solvers? NAACL (2024). doi:10.18653/v1/2024.naacl-long.297

Philosophy of language

  1. Wittgenstein, L. Philosophical Investigations. Trans. G. E. M. Anscombe et al., 4th edition. Wiley-Blackwell, 2009.

Companion

  1. Sathish, S. Pratyākṣa: direct perception for long-context LLM agents (2026). ↗ link