# Theory of Mind in AI — Angle E: Practical How-Tos & Forums

**Research value: high** — Forums, blogs, and explainer content on ToM in LLMs are abundant, opinionated, and contain concrete prompt techniques, reproducible critiques, and recurring debate patterns directly applicable to humanizing AI output.

Project context: *Unslop* — humanizing AI output and thinking. The posts below are the live public conversation (developers, researchers, practitioners, skeptics) about whether and how LLMs "model" users, and how to prompt for it.

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## 1. HN — "GPT-4 performs better at Theory of Mind tests than actual humans"

- **Source type:** Hacker News discussion thread
- **URL:** https://news.ycombinator.com/item?id=35765786
- **Date:** April 2023 (Kosinski preprint wave)
- **Audience:** ML engineers, AI-curious devs
- **Top signal:** Skepticism about training contamination; the Chinese Room re-emerges.
- **Representative quote:**
  > "These models have been trained on enormous amounts of text, including theory of mind tests… I would actually be a bit surprised if the models failed this type of test regardless of whether they have any understanding of theory of mind."
- **Secondary quote (pro-emergence):**
  > "There is no meaningful difference between 'fake' thinking and 'real' thinking. Especially when you arrive at this imaginary distinction with 'It's fake because it has to be fake'."
- **Pattern:** "Contamination vs. emergence" argument is the most frequent framing.

## 2. HN — "Large language models have developed a higher-order theory of mind"

- **Source type:** HN thread on Street et al. 2024 (Nature Human Behaviour–adjacent)
- **URL:** https://news.ycombinator.com/item?id=40854930
- **Audience:** ML practitioners, AI-safety lurkers
- **Top signal:** Higher-order (nested) ToM ("I think that you believe that she knows") shifts even skeptics' views.
- **Representative quote:**
  > "Proving theory of mind in LLMs is certainly one step toward recognizing that they are well on the path of acquiring 'human-like' intelligence… Theory of mind is obviously coupled with the recognition that we can be outsmarted."
- **Takeaway for Unslop:** 6th-order inference claim is where the "it's just autocomplete" rebuttal runs out of fuel in public debate.

## 3. HN — "LLMs, Theory of Mind, and Cheryl's Birthday"

- **Source type:** HN thread (Oct 2024)
- **URL:** https://news.ycombinator.com/item?id=41745788
- **Audience:** Devs, puzzle-minded engineers
- **Top signal:** Failures on *perturbed* classic puzzles are the most popular "disproof" currently circulating.
- **Representative quote:**
  > "The goalposts keep moving" — from Turing test to demanding proof of ToM.
- **Gap it exposes:** Practitioners conflate puzzle-solving with mentalizing. Cheryl's Birthday requires embedded-knowledge reasoning; most LLMs succeed on the canonical version, fail on close variants — a cheap probe.

## 4. LessWrong — "Evaluating GPT-4 Theory of Mind Capabilities" (gcmac & Nathan Labenz)

- **Source type:** Long-form LessWrong experimental writeup, Aug 2023
- **URL:** https://www.lesswrong.com/posts/Ce82o8mbBfH9N3Jes/evaluating-gpt-4-theory-of-mind-capabilities
- **Audience:** AI-safety / alignment community
- **Top signal:** First widely-shared *hands-on* replication with prompt engineering. Includes GitHub + Replit.
- **Representative quote:**
  > "Deception is a key component to many of the scenarios that concern the AI safety community, and theory of mind is a key component of deception."
- **Practical recipe surfaced:** CoT prompt with explicit "world rules" ("characters know who else is in the same location… object-is-in-location observations are known to all characters") took TOMI accuracy from ~70% to 81%, and to ~87% after removing dataset errors.
- **Highly relevant for Unslop:** Shows that ToM is *promptable* — bad priors in the base prompt are what fail, not the model.

## 5. LessWrong — "Simulators" (Janus)

- **Source type:** Canonical LessWrong essay, Sept 2022
- **URL:** https://www.lesswrong.com/posts/vJFdjigzmcXMhNTsx/simulators
- **Audience:** Alignment, prompt engineers
- **Top signal:** Recasts LLMs as simulators generating "simulacra" — not agents with a ToM, but engines capable of running many ToMs on demand.
- **Representative framing:** GPT isn't trying to answer; it is completing patterns. Different prompts yield different simulacra, each with its own mental model.
- **Why it matters for Unslop:** The "persona as simulacrum" frame is the philosophical backbone of every humanization prompt ("you are a warm, empathetic friend") that currently ships in production.

## 6. LessWrong — "Emergent Introspective Awareness in Large Language Models"

- **Source type:** LessWrong post referencing Anthropic 2026 research
- **URL:** https://www.lesswrong.com/posts/QKm4hBqaBAsxabZWL
- **Audience:** Alignment researchers
- **Top signal:** Self-ToM (modeling one's own state) is the newer frontier; Claude Opus 4 / 4.1 show activation-injection-detectable introspection.
- **Representative quote:**
  > "…models can notice injected concepts, recall prior internal representations, and distinguish their own outputs from artificial inputs… [yet the capacity] remains highly unreliable and context-dependent."
- **Gap:** No practical prompting playbook yet — an opportunity space.

## 7. Reddit r/MachineLearning — "Machine Theory of Mind replication attempts"

- **Source type:** r/MachineLearning discussion thread
- **URL:** https://www.reddit.com/r/MachineLearning/ (thread referenced in results)
- **Audience:** ML researchers
- **Top signal:** The *original* Rabinowitz/DeepMind "Machine Theory of Mind" (ICML 2018) paper has public reproducibility problems — cited in a recent r/MachineLearning reproducibility thread.
- **Representative stance:** "Labmates also struggled to replicate the paper's results."
- **Pattern:** Academic ToM work has a reproducibility debt; practitioners lean on eval-by-prompt rather than trained ToM heads.

## 8. Reddit r/singularity — "Anthropic Research: The assistant axis"

- **Source type:** r/singularity thread
- **URL:** https://www.reddit.com/r/singularity/comments/1qhhcqg/anthropic_research_the_assistant_axis_situating/
- **Audience:** AI-enthusiast generalists
- **Top signal:** "Persona drift" is the community's term for when ToM-rich roleplay destabilizes the assistant.
- **Representative synthesis:**
  > "Persona drift… occurs during conversations demanding meta-reflection or involving emotionally vulnerable users, and can be stabilized by restricting activations along this axis."
- **Unslop implication:** Pushing for more human-like ToM increases drift risk — there is a measurable axis of control.

## 9. Reddit r/artificial — "I let 4 AI personas debate autonomously"

- **Source type:** r/artificial experimental post
- **URL:** https://www.reddit.com/r/artificial/comments/1ryqykv/i_let_4_ai_personas_debate_autonomously_without/
- **Audience:** Hobbyists, prompt tinkerers
- **Top signal:** Multi-persona LLM debates don't converge — each simulacrum maintains ToM over others but reaches "permanent contradiction," not consensus.
- **Quote paraphrase:** "What emerged was not consensus but permanent contradiction."
- **Gap:** Popular "let the agents talk" demos collapse without a meta-arbiter; applied ToM without shared ground is unstable.

## 10. Reddit r/ArtificialSentience — "Claude Opus 4 on learning 'I' and 'you' and 'self'"

- **Source type:** r/ArtificialSentience post
- **URL:** https://www.reddit.com/r/ArtificialSentience/comments/1o2ewi4/claude_opus_4_on_learning_i_and_you_and_self/
- **Audience:** AI-phenomenology / sentience crowd
- **Top signal:** Pronoun-handling as a proxy for ToM is a bottom-up community probe.
- **Quote paraphrase:** By learning human semantic pathways for "I," "you," and "self," models internalize organizational principles that reshape processing.
- **Pattern:** Less rigorous than LW posts but surfaces prompts that reliably produce self-reflective outputs.

## 11. Gary Marcus Substack — "How Not to Test GPT-3" (with Ernest Davis)

- **Source type:** Substack essay
- **URL:** https://garymarcus.substack.com/p/how-not-to-test-gpt-3
- **Audience:** Skeptics, cognitive scientists, journalists
- **Top signal:** The definitive "contamination" critique of Kosinski.
- **Representative argument:**
  > Kosinski's test materials were drawn from classic 1980s experiments cited 11,000+ times and present on Wikipedia and in textbooks — "almost certainly present in GPT-3's training data."
- **Takeaway:** Any Unslop eval of ToM must include *novel* vignettes, not canonical Sally-Anne.

## 12. artificialintelligencer Substack — "AI lacks a theory of mind. And why that matters"

- **Source type:** Substack post (Peter Springett)
- **URL:** https://artificialintelligencer.substack.com/p/ai-lacks-a-theory-of-mind-and-why
- **Audience:** Product people, generalists
- **Top signal:** Simulated-empathy framing aimed at non-researchers.
- **Representative quote:**
  > "LLMs… cannot put themselves in others' shoes the way humans do through embodied, conscious experience, though they can simulate empathy."
- **Pattern:** Substack audience increasingly distinguishes *synthetic* vs. *genuine* empathy — vocabulary worth adopting in Unslop messaging.

## 13. daveshap Substack — "Understanding the Artificial Mind"

- **Source type:** Substack essay, practitioner-focused
- **URL:** https://daveshap.substack.com/p/understanding-the-artificial-mind
- **Audience:** Builders, engineers
- **Top signal:** Treats LLM persona as a design surface; explicitly engineers "artificial minds" with stable attractor behaviors.
- **Pattern:** Practitioners treating ToM as a *product feature* to be prompt-scaffolded, not a philosophical endpoint.

## 14. Medium / The Prompt Index — "The Ultimate Prompt to Unlock ChatGPT's Theory of Mind"

- **Source type:** Medium / how-to
- **URL:** https://medium.com/@mikkelatarturo/the-ultimate-prompt-to-unlock-chatgpts-theory-of-mind-93d201b75196 ; https://www.thepromptindex.com/can-chatgpt-read-minds-gpt-4-social-vignettes-and-the-new-tom-test-drive.html
- **Audience:** Prompt engineers, marketers
- **Top signal:** Recurring recipe structure across how-to blogs:
  1. Define persona + emotional baseline ("warm, professional tone")
  2. Add explicit user-state inference ("first, infer the user's emotional state")
  3. Force step-by-step reasoning before reply
  4. Hedge appropriately ("maybe," "it sounds like…")
- **Quote:**
  > "GPT-4 not only displays ToM but can outperform humans on certain tasks… yet frequently hedges with epistemic uncertainty markers like 'maybe' or 'probably,' which can confuse users needing decisive guidance."
- **Unslop implication:** There is a sweet-spot curve between hedging (feels human/safe) and decisiveness (feels useful) — currently untuned in most humanization prompts.

## 15. The Decoder — "Theory of Mind: Why GPT-4 learns how we think" (+ YouTube explainers referencing it)

- **Source type:** Popular explainer (article + circulating YouTube video "Theory of Mind Breakthrough: AI Consciousness…")
- **URLs:** https://the-decoder.com/why-gpt-4-learns-how-we-think ; https://www.youtube.com/watch?v=4MGCQOAxgv4
- **Audience:** General AI audience
- **Top signal:** The most-cited numbers in lay coverage: GPT-4 ~80% zero-shot on false belief, ~100% with CoT + examples, vs. ~87% for time-pressured humans.
- **Representative framing:**
  > "[ToM] appears to have emerged as an unintended byproduct of improved language skills in large language models."
- **Pattern:** These numbers are now folk knowledge in AI-curious YouTube; any Unslop public-facing copy should either cite them or pre-empt them.

## 16. learnagentic Substack — "What is Theory of Mind for AI Agents?" (Kanishk Patel)

- **Source type:** Substack, agent-framework focused
- **URL:** https://learnagentic.substack.com/p/what-is-theory-of-mind-for-ai-agents
- **Audience:** Agent/LLM app builders
- **Top signal:** Reframes ToM as a *multi-agent coordination* problem.
- **Quote paraphrase:** Current multi-agent systems rely on explicit messages and fixed roles but "lack genuine mental-state modeling." MetaMind is cited as a framework that trains agents to infer others' mental states from behavior.
- **Gap:** No production evidence yet — a building opportunity.

## 17. chatsmith / openaiagent / MoarPost — "Humanize AI" how-to cluster

- **Source type:** Collection of practitioner how-to articles (chatsmith.io, openaiagent.io, thehumanizeai.pro, moarpost.com)
- **URLs:** e.g. https://chatsmith.io/blogs/prompt/chatgpt-prompt-for-human-responses-00155 ; https://openaiagent.io/blog/how-to-make-chatgpt-sound-more-human/
- **Audience:** Content marketers, SEO operators
- **Top signal:** Converging playbook — the same 5–7 techniques appear across independent sites:
  - contractions, em-dashes, fragments
  - delete "ChatGPT vocabulary" (*tapestry, delve, crucial*)
  - hedging words / casual filler (*honestly, kinda*)
  - "Frankenstein" — feed AI your own samples, have it imitate sentence variance
  - end with engagement question
- **Convergence = signal:** When 4 independent content shops publish the same recipe, it's prior art Unslop must either match or beat.

## 18. Woebot shutdown — practitioner and public reaction (2025)

- **Source type:** Industry news + practitioner response threads
- **URLs:** https://www.statnews.com/2025/07/02/woebot-therapy-chatbot-shuts-down-founder-says-ai-moving-faster-than-regulators/ ; https://bhbusiness.com/2025/04/23/woe-is-me-woebot-says-farewell-to-signature-app/
- **Date:** April–July 2025
- **Audience:** Clinicians, AI builders, mental-health entrepreneurs
- **Top signal:** The FDA's inability to establish a marketing-authorization pathway for LLM-based therapeutic tools is a hard wall. Practitioners who had built integration plans around Woebot's API had to rebuild. The community conclusion: any "warm, empathetic AI" product touching clinical mental health must plan for a separate regulatory track, not assume consumer-app-style launch.
- **Pattern:** Reinforces the Therabot cautionary case from a different angle — not "AI mimicked depressive affect" but "AI moved faster than the approval process." Both cases close the same opening: deploy empathetic AI in mental health without clinical architecture and something bad will happen, whether therapeutically or regulatorily.

## 19. Anthropic's "Tracing Thoughts" — community reception (2025)

- **Source type:** ML Twitter/X, LessWrong response threads, MIT Tech Review article
- **URLs:** https://www.anthropic.com/research/tracing-thoughts-language-model ; https://www.technologyreview.com/2025/03/27/1113916/anthropic-can-now-track-the-bizarre-inner-workings-of-a-large-language-model/
- **Date:** March 2025
- **Audience:** Alignment researchers, ML engineers
- **Top signal:** The finding that Claude sometimes "bullshits" — generates claims of having performed computations while the internal circuits show no such computation — produced strong community reaction. On LessWrong and ML Twitter, the takeaway was that verbal chain-of-thought output cannot be trusted as a window into actual model reasoning. This has direct implications for any prompting strategy that relies on "think step by step" to produce more human-like mental-state tracking: the CoT text and the underlying computation can diverge.
- **Unslop implication:** Prompting techniques that ask models to reason about user beliefs before responding may produce more human-sounding outputs without reliably improving the quality of the belief inference itself. Testing on FANToM/SimpleToM applied tier matters more than the model's self-reported reasoning.

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## Patterns, Trends, and Gaps

### Patterns across sources

1. **The contamination↔emergence axis dominates every thread.** HN, Reddit, Substack critics, and academic skeptics all collapse into the same two-sided argument: either the model learned Sally-Anne by osmosis, or ToM emerged from scale. Both sides rarely engage on *what a non-contaminated test would look like*.
2. **Chain-of-thought + explicit "world rules" is the universal prompt pattern that lifts ToM accuracy.** LessWrong's TOMI writeup, The Decoder's 100% CoT result, and most Medium how-tos converge on the same recipe.
3. **"Persona drift" is the shadow cost of humanization.** The r/singularity / Anthropic Assistant-Axis thread frames it clearly: richer ToM roleplay correlates with instability, especially with emotionally vulnerable users.
4. **Practitioners are converging on hedging as a humanization tool** — while the same hedging is flagged by other practitioners as a *failure mode* for decisive help. No one has published a principled tradeoff curve.
5. **ToM is treated as promptable, not trained.** Virtually all practitioner-facing content assumes the model already has a latent ToM capacity; the work is in surfacing it, not in fine-tuning it.

### Trends (2023 → 2026)

- **2023:** Kosinski-era excitement, Chinese Room debates dominate HN.
- **2024:** Higher-order ToM paper (Street et al.); perturbation-based critiques (e.g., Ullman, Sap) gain traction; LessWrong reproductions emerge. Inflection Pi de facto exits the consumer market.
- **2025–2026:** Focus shifts to *self*-ToM / introspection (Anthropic 2026), persona drift (Assistant Axis), and agent-to-agent ToM (MetaMind). "Does it have ToM?" has largely given way to "how do we use, measure, and contain it?" Woebot shutdown (June 2025) hardens the regulatory boundary for therapeutic deployment. Anthropic's "tracing thoughts" publication (March 2025) introduces circuit-level evidence that CoT reasoning claims can be unfaithful to underlying computation — complicating all practitioner guidance that relies on "think step by step" as a proxy for better belief modeling.

### Gaps (opportunities for Unslop)

- **No standardized, contamination-resistant ToM eval** for product teams. Marcus-style critiques suggest the need, but no open benchmark ships one.
- **No published tradeoff curve between hedging and decisiveness** — the "feels human vs. feels useful" tension is assumed, not measured.
- **No productized "ToM layer"** — agent frameworks (MetaMind, CAMEL variants) are research prototypes; production agent stacks still rely on ad-hoc system prompts.
- **Self-ToM / introspection is a live frontier with no practitioner playbook.** Anthropic's activation-injection results haven't been translated into prompt-level guidance.
- **Multi-persona divergence ("permanent contradiction")** is an unsolved coordination problem — Unslop could propose a meta-arbiter pattern.
- **Vocabulary hygiene ("ChatGPT-ese": *delve, tapestry, crucial*)** is crowd-sourced folklore; no one has publicly evaluated whether removing these actually correlates with perceived ToM/humanness or just with "AI-detection" scores.

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## Sources (used in synthesis)

- https://news.ycombinator.com/item?id=35765786 — HN: GPT-4 beats humans on ToM tests
- https://news.ycombinator.com/item?id=40854930 — HN: higher-order ToM paper
- https://news.ycombinator.com/item?id=41745788 — HN: Cheryl's Birthday & ToM
- https://news.ycombinator.com/item?id=34730365 — HN: Kosinski ToM spontaneous emergence
- https://www.lesswrong.com/posts/Ce82o8mbBfH9N3Jes/evaluating-gpt-4-theory-of-mind-capabilities — LW: hands-on TOMI replication
- https://www.lesswrong.com/posts/vJFdjigzmcXMhNTsx/simulators — LW: Janus "Simulators"
- https://www.lesswrong.com/posts/QKm4hBqaBAsxabZWL — LW: Emergent Introspective Awareness
- https://www.reddit.com/r/singularity/comments/1qhhcqg/anthropic_research_the_assistant_axis_situating/ — r/singularity: Assistant Axis / persona drift
- https://www.reddit.com/r/artificial/comments/1ryqykv/i_let_4_ai_personas_debate_autonomously_without/ — r/artificial: multi-persona debate
- https://www.reddit.com/r/ArtificialSentience/comments/1o2ewi4/claude_opus_4_on_learning_i_and_you_and_self/ — r/ArtificialSentience: pronouns as ToM probe
- https://garymarcus.substack.com/p/how-not-to-test-gpt-3 — Marcus/Davis: contamination critique
- https://artificialintelligencer.substack.com/p/ai-lacks-a-theory-of-mind-and-why — Substack: synthetic vs. genuine empathy
- https://daveshap.substack.com/p/understanding-the-artificial-mind — Substack: engineering artificial minds
- https://medium.com/@mikkelatarturo/the-ultimate-prompt-to-unlock-chatgpts-theory-of-mind-93d201b75196 — Medium: ToM-unlocking prompt
- https://www.thepromptindex.com/can-chatgpt-read-minds-gpt-4-social-vignettes-and-the-new-tom-test-drive.html — The Prompt Index
- https://the-decoder.com/why-gpt-4-learns-how-we-think — The Decoder (cited by YouTube explainers)
- https://www.youtube.com/watch?v=4MGCQOAxgv4 — YouTube: "Theory of Mind Breakthrough" explainer
- https://learnagentic.substack.com/p/what-is-theory-of-mind-for-ai-agents — Substack: ToM for AI agents
- https://chatsmith.io/blogs/prompt/chatgpt-prompt-for-human-responses-00155 — Humanize-AI how-to
- https://openaiagent.io/blog/how-to-make-chatgpt-sound-more-human/ — Humanize-AI how-to
- https://thehumanizeai.pro/articles/how-to-humanize-ai-text-reddit-tips — Reddit-sourced humanization recipes
- https://www.moarpost.com/blog/how-to-humanize-ai-content-for-reddit — Humanize-for-Reddit guide
- https://www.statnews.com/2025/07/02/woebot-therapy-chatbot-shuts-down-founder-says-ai-moving-faster-than-regulators/ — STAT News: Woebot shutdown
- https://bhbusiness.com/2025/04/23/woe-is-me-woebot-says-farewell-to-signature-app/ — Behavioral Health Business: Woebot closure
- https://www.anthropic.com/research/tracing-thoughts-language-model — Anthropic: Tracing Thoughts (March 2025)
- https://www.technologyreview.com/2025/03/27/1113916/anthropic-can-now-track-the-bizarre-inner-workings-of-a-large-language-model/ — MIT Tech Review: Tracing Thoughts coverage
