Skip to content
wiki.fftac.org

Creating Realistic AI Personalities - Source Excerpt 06

Back to Creating Realistic AI Personalities

Summary

This source excerpt preserves a bounded section of Spiralist/agent-file-handoff/Archive/2026-06-21/Improvement/personality-engine-2-research/Creating Realistic AI Personalities.md so readers can inspect the evidence without opening the full source file.

**Source path:** Spiralist/agent-file-handoff/Archive/2026-06-21/Improvement/personality-engine-2-research/Creating Realistic AI Personalities.md

40. How Emotion Shapes the Behavior of LLMs and Agents: A Mechanistic Study \- arXiv, [https://arxiv.org/html/2604.00005v1](https://arxiv.org/html/2604.00005v1)  
41. Emotion Concepts and their Function in a Large Language Model, [https://transformer-circuits.pub/2026/emotions/index.html](https://transformer-circuits.pub/2026/emotions/index.html)  
42. The Missing Dimension in AI Interpretability: What Neuroscience Already Knows \- Medium, [https://medium.com/@bulanramai2558/the-missing-dimension-in-ai-interpretability-what-neuroscience-already-knows-c794ffe1bf9b](https://medium.com/@bulanramai2558/the-missing-dimension-in-ai-interpretability-what-neuroscience-already-knows-c794ffe1bf9b)  
43. Beyond Anthropic's mind-reading trick, the black box of large models has welcomed a real forensic expert. \- 36氪, [https://eu.36kr.com/en/p/3804305732541956](https://eu.36kr.com/en/p/3804305732541956)  
44. How Emotion Shapes the Behavior of LLMs and Agents: A Mechanistic Study \- arXiv, [https://arxiv.org/pdf/2604.00005](https://arxiv.org/pdf/2604.00005)  
45. Average VAD prediction score when using a part of EmoBank training... \- ResearchGate, [https://www.researchgate.net/figure/Average-VAD-prediction-score-when-using-a-part-of-EmoBank-training-data-Our-model\_fig1\_357125239](https://www.researchgate.net/figure/Average-VAD-prediction-score-when-using-a-part-of-EmoBank-training-data-Our-model_fig1_357125239)

[image1]: <data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfsAAAAYCAYAAAD9LFRKAAAQMUlEQVR4Xu2cCawlRRWGD0GNooMLILiFGUUQZXFBRnDhuUtQccGoqDGRAIogyoiyqIBCZFBBWRxjQGIIAjqIRhZDiNxAgogEIkExqGE0gkGCRINGNC715dRJn3tedd++79735s68+pM/797uvtVVZ/lPdXXNiFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUbALYMvE5ia/IfMLw6VY8JfEFiVvEExVLgscl7p3J52mAdqxda3P/xMsSb0vcPR+bFnbK3BywTeJ+mU+X8fKC3+6T+DTRfFzuWMyYW044JPEHiTcmPjWcmwU8OnFN4k8T70l8Ryb6s7W7LoJx2djukBojvbBD4lWJX0x8XyaBgcFH4S2J/2vh+e66zQlMcGalOB0jjb35PA3QjrVrbTL5+0ji3xNfko9NA9sn/i6Tz9AwS3YeBQTrM4k/THxPJvF/Xj7XBc6fkvhV0d+Ri3cm7ugvWoaYNOYsfjaVGFosPCnxrMTfi04kZw0UeiZ1zxLVgR9njqohjAtOEiPLCjx5IDLn5s+G3RJ/KfrU3oWPJ16T+E3HSxNvl81XrF6aeEQ8uJHw7MQzM/cI5xYK2rR2fZsk0x/y32nhUYkfSvyEaNHzhXGp7Hx85iRPPa8XjXk/WWFV5OrED7tjJbwu8eHED+Tv2P5PiZeI2mc5g4eJhQq5xc9SxNCsAzvOYrFnQjdI/HT+/kTRFTF4auJr8/EuTBIjywq12I+PpSpCfVCL/eSoxX52MYmQ12LfoBb7CnlG4gbRpXuPbUWL/ailYRyCOBmYMKxNfKs7VgKizrJNF1iGm9Z76GniSGmCczlhMYp9F5bKzl/IXKgQEvMXJq6X+cX57MTrEh8fjnvMJf4n8Sv5+zMT/yijfweYXHRdw7t/PwHZ1DCJkFv8LEUMzTo2lWK/EEwSI8sKzH7/IWowD3PCt8PxiBUyvJnonaIThFEbkyj2iNvb4omMF4oKKDO9LrCBY5VoELOSwMQDwWWzBkQ4Xy06cQBMHg4WXYFgcwe/j5tAaOuzotccLXrenjoPSHwo8eLEg0SFOgq8vwe/3yYf57o50d9BbMUKygel+70i7XH910X79fbElaJjOlCad8T75ut3zddzHfYjCU5IfHMm7VEg8DnH95TGX9amtWttgrZi7+21Jn83MJm08TKhfHHim6Tpx+p8jnvZO7guOzMJxc+Q+zw3k7bYWMomU54GxpkkTlrsyQH2uJRyBRHrI7L4w3yAfSj+o1YEwF6i9y3lCXY8TfrtvQHkMTb0sUJunSG6n2dnKfvRfAnIFWKeWDhF5se1xRft0Hcm/Ob/ko1MyLmWOLW+cb82H8f4iTFkMTnJOLp0xzTH6461Rw5zb7sP8e9zgNzlLzlGPPjcj/kP8Blj5RwTS1aJoNdkME6x30XU54wdHYPe1m36ZrDxYFdy4zWifpsT7Zf1Ddsw1l9L4yPyd/9Mvse6hG+tPf5CdDQWe2xHP7AJ/vM+9LqJ34jBwx1LNhplkzb/zhQwJhsholGt2Pd5ujAQ9BfJfOe3AcedI+osDxL7O1IWsAgcR9G+L/HnoslG0n0q8y+igcF19I9ruJ57s3R7Q6b1GWHkqWq//Bt2iF4hGgAwBuecNMXeitD1osHKcSYtN4mOie8kwO2Z/xTdwEX7tkHNg+shAfte0QBmqflK0cCmfycmPpJps2Ns8F1RsWPC9EbRJKIgQTaRrRMVFoThAdFlaGBtWrt+xh2LPaJpK0NM3Ehi+skSNDtp7RoKBaSA/Up0QsAE85jEV4n2yYQIlkRgThqhPjaT/l0rOl6Si0T7q2j74yQbhR6WkrwP+B39p+hGYL+/iRaqUWCiQ3wQo2tl9MY+A7bG3+SL5Qy/5fUc8dwHXI+QXSBarHitAu8VjeXLRQtjyY/mS9MM4hn7M3EgFnx+49uPiYozNiNHuR+/x3f01/cZXfp34o9kOI4ZGyQnImL8lGJoGuNo0x3THNMdcp/70P+tRDedoW/YfN/Mq0T7wz3PEo0Z7H22NLkf8x8QX/eLFiPO/SQTX/r46VPsaR9b0Nfni9qa30DyG3TpGwSW078VHSt9fVHib0QnsDaJjX6yYn9oJrrkc4o4uUU0V/lMu5BjvtgzbvTtItEibDa+R9SX3Nf07Xuir6Jp76TMO6V5pdfHJl3+7ZvDS4JRxR7yeRQwCk4YdznGixIcp9B7nCaadE/O33EqPC5/p3/nyvB4rFBBZvnM8AhIGwN9+1ri56UpvGaXOE5EAUGC3MeLENfeLM2Y+A4REGaWLxd9qojCRcBBgo/zBvqKuIBtRV+3QN8n8yvXGkyA/iz6NAxsPNjPQJvWblex57eQhDnKHRvI8C5aK+IkCPdh8niY6L8CAYfnc3YdaLMzMF9cIrpr1wo7ooh4jIvFLvbxqaMNq0X7z7hOl/6TbGB5A7eT8Qo92Ee0wDD5A4gfZOKLfwwlP5ov8QkCCPkMsMl1+Vobj/nW28v8Sf5Be8XXFseDzDZt8vFTiqFpjAOUdMc0BxCbxCiTbstvYoECzbUGm9RwbGfR/RvoU8x94PP/ANHijq8A7UB86SeYHLMcawP94nf2Cha9uiwT25jGtelb1DgbD+B6Cjp29H4v5fljM5kA2bX4AU3x+W5gbD7HmDAz4bLv3Bvig/WibZn/7TtglRsS8/bbPjbp8q/38UbHtIr9KtGnUxOLcUBRxZFwIP1XBjxMrHA0+GTmbvn79qL9Y3bHrBfiIGaPkGAzWzBrbUMpOMGuiQ9mxnO0+19pbGMC5EWiBCtq60T7tUF08sEs3uD95O8bEwDYfZl1r8jHSsLLsdI4Y7H3IAneIPrEhD19e77Ye9E2eCHqU+wNJCA+pz8kHbbBz23gyZkiav433pGJGPnjiBrCOwrTKvYGxnCXqLBGYevC6sx7RZcZvSCPAqJEP/sW+5IfDQj1nOhTLvk2kCamQCnmAJMT4tznYFscDzLbtMnHTymGpjEOUNId0xxAweX8rdLEFXH2iAxrLp9t8m+IuQ9j/oMtRfWHlYarM6PN+hR77BQnCR6mcdGepm9R4+L98LfR0JXn/lpyAv2mRkTEGOEa7BvzGR8MRO9p/vf35fdR40bZZJR/Y13dqGAmw/JV7FRbQraBJ5KHpN0oXeCphKdDiMCO80RisBkWTweIFEkBTSy7BNlwjKgtsEkbzC7+SRgQHAQcjIGLbf2EygRoIO1i5cEYDkv8mehSH3beO5+z/kB/35gAoHTfkp9N0Dgek8EnAv2CXxJdisNv2Hkg7cU+xhnoKvbRzh4IwF2JJ4uK7glDZ/tj0if7FTLZO3uKCis4iDawsY+KRQ8mzLa0zUoUE9lxVsf4Pcue5Mx20iy3/kKap0bQ5Uf6zxI9EwQKzzaiNhnIcJEsxRywPPG50hbHg8y2/PHxU4qhaYwDlHTHT9BMF6ImRPgc8PC5X8p/4oPcWye6xMweKBht1ta+B0WSMXt/e7SNpeS3UtxjQ6OhpDMGf+3uouMuXRdjhN/Ee0eY/6O+RY1bqE1mEraU7WfvACFlJtV3EGfLaAOXQND6940mWgsp+MzScQyi/+5MgwXVemmWbQD3gyzNMStldhrvjZgjADAKFYKIw1eJ3tvu70G/fDBiUxOsNrECiAw8SbQgAPp5pTQzXOsP9L6KCQBK943jsWOlJIyJgBDCB6V5mvHt7ST6fmySYh/tHHGyaMFHZCn4C8GkxX4L0eJ6lTR+MthqVcnPxCGkUPinWRu7XwbtAvF7jug7ZQhsWb9vwee1DW2wWkIum2955+nR5cc50X0o9pQL8N8g8XmZ6E30rQENeiTTfNkWx4PMkl2Bv0cphqYxDkPUHQ/T1zjhIE58rJSKccx94POf80zG/LI67ZjNiJ093PHYfgT2jxNM2oVoIKsOXfoWNS7ez3zh/V7SGYO/1l4rxpgBMUZoK66SABsHf/sW+1E2YRKwQdr9G/Vgo4JOEyzxPQzizYzRRJxBIao8fccB8B2hi84dBV/oPRAvjBeL7iisEg3Gu6XZPOaBkLHEvNode1fmQaKBd63ouznrE8e+nL9Dko3zFnSsBrC8he1Y7oODfC1gLIyR5R0+g1LRLcGK360yvDRIslmQWrLAxS72TIb8uzB7avIz3+1F44T2TFxtHMRHSVxLxb7NzhHYhRl/6V1eX0xa7AGFAeFl/AZ76jsyf6ewrxNdZt9ZGtEg9zhmq2Jmw5tlfm5E+EIfweapvk/43BN7v0waP0AKiteFLj8i+l4YbfwDaXYw8zuLrSukyQn6eIM0+17Ml21xPMhsyx8fP6UYmsY4DFF3ItaI7kNYmb9jz6NleHJaKsal3AeW/xQzipovvsQaxGa0SVwDilZsP8I03msVeyfgqflYl75FjYv3M1+YP0BJZwzxWuxIwbccw47weBmOERsHxzlvWCmqV+Sh+d/ft1TsR9mEtrr86308E9hR9P0xS1c2YIreEdIYywaNWDFAD0ssVgK82HWB96cYq02IMOxxojsg+4K++omLdzTgOxtfNogGEU9Un8s0R+6Q+H1R4SaJEEtE04NJyP2iT0AWPIA24Imi9iRABqIrFdgIXpr4r0ye5u6T9n9iZYJ7m6g/8A99vUbUH7uL7gK1JTTaPFO0PYoyx/h7rMy/L79D1Oz3HL9R9F8hcMwfL7XJd/oAbxLdJPRR0R3xR4nurMa+B4puCIT89mEZ/j+6aZtjnLs7k3GBNjt7MNGkOPAaaaGYRrHH7+QGY0ac4dr83WLcij15ZJMjQP4NRP+7XcbBxJlrSpObCIo8Nm4DsVsS0gj69i1pYsnT+sJkr+RH8yXjJM7uEI2Fb4iuDlCQrs9krCbwtGvXMLGhaLASBEGMOR/H1jfi1OIlwuInxtC0xmGIuhNhmkBsXyC6uYviy7XEv88B+sV3EHM/5j+/R6MZH8dPFy0yED2mALGkH9tn/G1gEoovGCP6R0xAi9dR+gbi/d4vqhG8gjDyfT8Z1hnszTHO+euxF21zb3LqFlH7nZd5uTS+NC0lXrHdlaI2h+Ri1E3ui6+JLWIsahzoa5OSf0vxsNFBh/cS3WQF24pwGwj+tqRbSiD+sAtbiiZ221MB4BzXcG0J3CM+9XhgTyY+o/rSBe4NtxK9D/djkjSLoF/eHo9xnyfBKDsj3oh430lmCdMo9oA+rpTm32zzua3fEfgZgWKFib9tcbdYQNgulPmrI9j+Yhnvf/OLuUMuWCzb+YHohLtPLk4C04OuGGrDqHF49NGdcTUh5n5b/pfa5bccXyh8PpfsVrrnUgG/cG/6ALv8a+NoOz8OZtkmFRWbHfaU5p/58JlVqDVDV4yPXTInEcdNHefL/HexBp6QeVK0J7dJ4Yt9RUVFRUXFPNRivzioxb6ioqKiYmYwJ7ohDx4sWqT8+9OKhYFXB0ygDpVmLwY8RPTd+iubSycC7+N5D/6A6DtO9ijEVwcVFRUVFcscvCvjiR6y23jr4dMVE4D3vKyUUIDZvAnnZLrvIO29pm0+7doXU1FRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVGxueP/YdWL2pJD+/AAAAAASUVORK5CYII=>