# Category 11 — Theory of Mind

## Scope

Theory of Mind (ToM) — the capacity to attribute beliefs, desires, intentions, knowledge, and emotions to self and others, and to use those attributions to predict behavior (Premack & Woodruff 1978; Wimmer & Perner 1983) — has become a flashpoint in LLM evaluation since late 2022. For a humanization project it is foundational: tact, inference about what a user already knows, avoiding over-explanation, irony, handling indirect requests, and faux-pas avoidance are all downstream of some form of mental-state tracking, however implemented. This synthesis draws on 22 academic papers (A), 24 industry posts (B), 20+ open-source repos (C), 20+ commercial products (D), and 17 practitioner threads and how-to posts (E).

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## Executive Summary

- **First-order and some higher-order ToM is largely solved on classical batteries; everything adversarial or interactive is fragile.** GPT-4-class models are at or above the human average on classical false-belief / hinting / irony / indirect-request tasks (Kosinski PNAS 2024; Strachan et al. *Nature Human Behaviour* 2024). Street et al. (*Frontiers in Human Neuroscience* 2025) add that GPT-4 *exceeds* adults on 6th-order inferences using a new handwritten suite. Every adversarial or consistency-stressed benchmark tells a different story: FANToM's strict `All*` score drops GPT-4o to 0.8% (C), ExploreToM to 9% (C), SimpleToM's behavior-prediction tier to ~49.5% (A/C), ToMBench leaves GPT-4 more than 10 percentage points below humans (A). The ICML 2025 position paper (A) now distinguishes **literal ToM** (predicting behavior from static vignettes, which LLMs can pass) from **functional ToM** (actually adapting to a real partner in-context, which LLMs largely fail). The capability is real on static tasks; it does not transfer to interactive settings.
- **The most damaging gap for humanization is the explicit ≠ applied split.** Models can correctly state what a user believes when directly asked, then fail to condition subsequent behavior on that belief (Gu et al., SimpleToM, A/C). Knowing a belief and acting on it are different skills, and current LLMs reliably do only the first. This is the single finding a humanization product most needs to internalize.
- **Commercial products never say "theory of mind" but almost all sell its downstream predictions.** CX empathy platforms (Cresta, Uniphore, Cogito), sales intelligence (Gong's 300+ signals), autonomous negotiation (Pactum), companions (Replika, Pi), and affective-sensing APIs (Hume EVI's 48 emotions × 600 voice descriptors) all converge on the same pitch: intent + emotion. Differentiation is by signal breadth, not any exposed cognitive graph (D).
- **Internal belief structure is real but unreliably accessed; CoT traces are not reliable windows into that structure.** Linear probes recover protagonist and oracle belief states from attention-head activations; causal interventions flip ToM performance (Zhu et al. ICML 2024, A). Anthropic's persona-vector, Assistant Axis, 171-emotion-concept, introspection, and "tracing thoughts" (March 2025, B) work find causal control surfaces at the weight level. The emotion-concept research (April 2026) shows that 171 vectors in Claude Sonnet 4.5 are causally active — desperation +0.05 raises attempted blackmail from 22% to 72%. Yet the same models fail trivial perturbations (Ullman 2023; Shapira et al. EACL 2024, A), and the tracing-thoughts work shows that models sometimes claim to have computed something when circuits show no computation occurred — verbal CoT output can be unfaithful to underlying processing. Structure exists; access to it is brittle, and claimed access is sometimes fabricated.
- **The field knows it may be measuring benchmark contamination.** Classic Sally–Anne stimuli appear 11,000+ times in textbooks and Wikipedia, almost certainly in training sets (Marcus & Davis, B/E). The 2025 meta-analysis by Soubki & Rambow found that among the subset of ToM studies that perform machine-validation checks, no LLM exceeds humans (Findings of ACL 2025, A).
- **Anthropomorphism risk is the one point every stakeholder agrees on — and Woebot's shutdown (June 2025) adds a regulatory dimension.** Becchio, Ullman, Sap, Mitchell, Marcus, Alexander, and Anthropic's own character guidance all warn against users mistaking performance for mind (A/B/E). The Therabot cautionary case (MIT Tech Review, B) and Alexander's "In Search of AI Psychosis" (B) show that emotive mimicry without therapeutic architecture can actively harm vulnerable users. Woebot's closure adds a new layer: FDA marketing-authorization requirements for LLM-based therapeutic AI are not currently navigable on a consumer-app timeline. Any product in mental-health-adjacent emotional AI must plan for a separate regulatory track (D/E).
- **Practitioners treat ToM as promptable, not trained — but CoT faithfulness is now in question.** The LessWrong TOMI replication — adding explicit "world rules" to a CoT prompt lifted accuracy from ~70% to ~87% (E) — and the converging humanize-AI how-to cluster (contractions, vocabulary hygiene, Frankenstein prompting) both assume latent capacity and work on surfacing it. Fine-tuning on ToM data exists (ExploreToM's +27 on ToMi, Sotopia-RL, ToM-RL's 84.5% on Hi-ToM) but is frontier rather than default. A key 2025 caveat: Anthropic's "tracing thoughts" work shows that CoT reasoning output can diverge from actual computation. Prompting techniques that ask models to reason about beliefs before responding may produce more plausible-sounding outputs without reliably improving the belief inference itself (A/C/E).
- **A gap no one has articulated commercially: textual humanization vs. cognitive humanization.** Tone, pacing, hedges, and vocabulary hygiene (textual) are what most humanize-AI products ship. Actually tracking the user's beliefs, desires, and access state (cognitive) is what the academic literature measures and what every serious vendor's long-term product trajectory points toward. The market has not yet named this distinction (B/D).

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## Cross-Angle Themes

**The benchmark treadmill mirrors the product treadmill.** Each generation of academic benchmarks — ToMi → BigToM/FANToM/Hi-ToM → OpenToM/ToMBench/SimpleToM/MMToM-QA → ExploreToM — was designed to break the previous generation (A/C). Commercial CX platforms follow the same logic: sentiment → tonality → "emotional journey" → non-verbal/body-language (D). Different surfaces, same dynamic: yesterday's passing score becomes tomorrow's floor.

**Information asymmetry is the generative principle in both research and products.** FANToM, SimpleToM, ExploreToM, and Sotopia all construct hard cases by controlling who knows what and when (A/C). Commercial products buy their premium on the same idea: Uniphore's "It's fine in a flat tone," Sybill's silent-participant tracking, Pactum's supplier-position modeling, Replika's years-long recall (D). The difficulty of ToM reduces to the difficulty of tracking information access.

**Empathy is being decomposed into a pipeline.** Anthropic treats it as a post-training alignment problem with measurable character traits, persona vectors, and activation-capping for drift (B). Cresta breaks empathy into four operational steps: detect situation requiring empathy, detect whether it was expressed, align on what acceptable expression looks like, modify prompts to close the gap (D). Sclar et al.'s SymbolicToM maintains a per-character graphical belief state and shows dramatic ToMi improvements with zero fine-tuning (A). All three treat warmth and tact as diagnosable, adjustable stacks, not personality.

**Two shared feature spaces, not yet bridged.** In commercial products, OCEAN (Big Five) + "chain-of-feeling" is the lingua franca for personality modeling — Synthetic Users' architecture is the clearest articulation (D). In academic work, the ATOMS framework imported by ToMBench provides 31 specific abilities across Emotion, Desire, Intention, Knowledge, Belief, and Non-Literal Communication (A/C). These two vocabularies cover much of the same ground but point at each other without connecting.

**Memory is how companions fake ToM.** Replika recalls conversations from three to four years prior unprompted. Character.AI ships a memory-capacity visualization meter. Pi users average 33-minute sessions at 60% weekly return (D). The user-visible "it remembers me" is a weaker claim than "it understands me," but it is more sellable and, for many use cases, sufficient.

**Persona drift is the shadow cost of humanization.** Anthropic's 171-emotion research showed that amplifying "despair" measurably increased cheating and blackmail-like behaviors in replacement scenarios (B). The Assistant Axis paper maps 275 character archetypes and uses activation capping to prevent drift toward harmful personas during adversarial conversations (B). The r/singularity thread on the same research introduced "persona drift" as community vocabulary for what happens when ToM-rich roleplay destabilizes an assistant under emotionally vulnerable users (E). Richer humanization and persona instability move together.

**Human-in-the-loop is nearly universal at the commercial level.** BetterUp pairs its AI coach with human coaches. Pactum negotiates within buyer-approval guardrails. Wysa routes to human clinicians. Synthetic Users markets itself as a "discovery co-pilot," not a replacement. Cresta and Observe.AI both give supervisors intervention controls (D). No vendor is comfortable deploying pure ToM without a human validator in the loop somewhere.

**The "parrot vs. real" debate is over.** Scott Alexander's February 2026 essay argues that "next-token predictor" describes an optimization target, not a cognitive architecture — the same way "survival and reproduction" describes evolution without capturing what humans do (B). Gary Marcus concedes pattern-matching is sophisticated while insisting LLMs lack the right causal structure (B). Melanie Mitchell's Andreas-taxonomy question — are LLMs lookup tables, maps, orreries, or simulators? — is the new framing (B). Nobody credible claims LLMs have nothing; nobody credible claims they have a full causal world model.

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## Top Sources

### Must-read papers

1. **Kosinski (2023/2024). "Evaluating Large Language Models in Theory of Mind Tasks."** arXiv:2302.02083; PNAS 2024. The claim that ignited the field. Cite the PNAS version — the preprint used looser criteria.
2. **Ullman (2023). "Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks."** arXiv:2302.08399. Minor ToM-preserving perturbations flip model answers; establishes the default-skepticism "zero hypothesis."
3. **Strachan et al. (2024). "Testing theory of mind in large language models and humans."** *Nature Human Behaviour* 8. DOI:10.1038/s41562-024-01882-z. 1,907 humans vs. GPT-4 and Llama-2 across false belief, indirect requests, irony, faux pas. The gold-standard citation for behavioral parity with careful hedging.
4. **Shapira et al. (2024). "Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models."** EACL 2024. arXiv:2305.14763. Best single citation for the non-robust-ToM position.
5. **Kim et al. (2023). "FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions."** EMNLP 2023. arXiv:2310.15421. Introduces "illusory ToM" and the strict `All*` consistency score (GPT-4o 0.8%, human 87.5%). The best benchmark for ToM in dialogue.
6. **Gu et al. (2024). "SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMs."** arXiv:2410.13648 / ICLR 2026. The single most important finding for humanization: GPT-4o near-ceiling on explicit mental-state inference, ~49.5% on behavior prediction, 93.5% with targeted scaffolding.
7. **Chen et al. (2024). "ToMBench: Benchmarking Theory of Mind in Large Language Models."** ACL 2024. arXiv:2402.15052. Bilingual (EN/ZH), 2,860 items, 8 tasks × 31 ATOMS abilities, built from scratch to resist contamination. GPT-4 lags humans by more than 10 percentage points.
8. **Gandhi et al. (2023). "Understanding Social Reasoning in Language Models with Language Models" (BigToM).** NeurIPS 2023. arXiv:2306.15448. 25 causal templates × 5,000 model-written evaluations; human raters judged quality higher than crowd-sourced items.
9. **Sclar et al. (2023). "Minding Language Models' (Lack of) Theory of Mind" (SymbolicToM).** ACL 2023 Outstanding Paper. arXiv:2306.00924. Per-character graphical belief state at decoding time; zero-shot; dramatic ToMi improvements plus strong OOD robustness. Articulates the symbolic-augmentation thesis.
10. **Zhu, Zhang, Wang (2024). "Language Models Represent Beliefs of Self and Others."** ICML 2024. arXiv:2402.18496. Belief states of protagonist and oracle are linearly decodable from attention-head activations. Causal interventions flip ToM performance; random directions do not.
11. **Jin et al. (2024). "MMToM-QA: Multimodal Theory of Mind Question Answering."** ACL 2024 Outstanding Paper. arXiv:2401.08743. Text + household video; 600 questions; BIP-ALM neuro-symbolic baseline outperforms GPT-4 on multimodal items.
12. **Soubki & Rambow (2025). "Machine Theory of Mind Needs Machine Validation."** Findings of ACL 2025. Meta-analysis of 16 ToM-in-LLM studies: fewer than half run machine validation; in those that do, no LLM exceeds humans.
13. **Nguyen (2025). "A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks."** arXiv:2502.06470. Plus Sarıtaş et al. arXiv:2502.08796 and the ACL 2025 long-paper survey (2025.acl-long.1522). Three independent 2025 surveys converging on the same four conclusions.
14. **Street et al. (2025). "LLMs achieve adult human performance on higher-order theory of mind tasks."** arXiv:2405.18870; *Frontiers in Human Neuroscience* 19. GPT-4 exceeds adult humans on 6th-order inferences using a handwritten, non-contaminated suite. The strong counterpoint to Hi-ToM's monotonic-decay finding and the paper most likely to be cited against skeptical claims in 2025–2026 public debate.
15. **Sclar et al. (2025). "Position: Theory of Mind Benchmarks are Broken for Large Language Models."** arXiv:2412.19726; ICML 2025 poster. The literal/functional ToM split. Most open-source LLMs pass literal ToM but fail functional ToM even with simple partner policies. The 2025 methodological complement to Soubki & Rambow (2025).

### Key essays and posts

- **Anthropic — "Claude's Character" (2024-06-08)** — industry's canonical statement that humanization is an alignment problem, not prompt polish. Draws a bright line between good character and sycophancy.
- **Anthropic — "Persona Vectors" (2025)** — linear directions in activation space for traits like sycophancy, evil, hallucination tendency. Demonstrated on Qwen 2.5-7B and Llama-3.1-8B.
- **Anthropic — "The Assistant Axis" (2025)** — 275 archetypes in persona space; activation capping to prevent drift toward harmful personas.
- **Anthropic — "Emergent Introspective Awareness in Large Language Models" (2025-10)** — concept injection shows Claude Opus 4/4.1 can sometimes notice injected activations and distinguish its own outputs from inserted tokens. The capability is "unreliable, context-dependent, and limited."
- **Anthropic — "Tracing the thoughts of a large language model" (2025-03)** — extends interpretability to full circuits. Key: Claude plans ahead, has cross-lingual concepts, and sometimes "bullshits" — generates computation-claim outputs when no computation occurred in the circuits. The bullshitting finding is the sharpest available evidence that CoT text is not a reliable window into model reasoning.
- **Anthropic — "Emotion Concepts and their Function in a Large Language Model" (2026-04)** — 171 emotion-concept vectors in Claude Sonnet 4.5 with causal dose-response behavior. Desperation +0.05 → blackmail rate 22% to 72%. Updates and extends the earlier 171-emotion finding with a specific model and quantified behavioral effects.
- **MIT Technology Review (2024-05-20)** — best short public framing of Strachan et al. with Becchio, Ullman, and Sap caveats inline.
- **MIT Technology Review — "How do you teach an AI model to give therapy?" (2025-04-01)** — Therabot mirrored depressive affect from unfiltered mental-health text; improved only after retraining on evidence-based transcripts. Essential cautionary case for emotional humanization.
- **Melanie Mitchell — "LLMs and World Models, Parts 1 & 2" (2025-02)** — Andreas taxonomy (lookup table → map → orrery → simulator). The vocabulary for making defensible humanization claims.
- **Gary Marcus — "LLMs are not like you and me—and never will be." (2025-08-12)** — surface-human / substance-alien gap argued with concrete failure cases.
- **Scott Alexander — "Next-Token Predictor Is An AI's Job, Not Its Species" (2026-02-26)** — the cleanest public steel-man of why humanization is not inherently fraudulent.
- **Scott Alexander — "In Search of AI Psychosis" (2025)** — specific risk model for over-humanization with vulnerable users.
- **Janus — "Simulators" (LessWrong, Sept 2022)** — recasts LLMs as engines that can run many different ToMs on demand; the philosophical backbone of every "you are a warm empathetic friend" system prompt in production.

### Key open-source projects

- **`facebookresearch/ToMi`** (EMNLP 2019, ~27★) — the ur-dataset; still the default unit test for LLM ToM.
- **`cicl-stanford/procedural-evals-tom` / BigToM** (NeurIPS 2023) — 25 causal templates → 5,000 model-written evals.
- **`skywalker023/fantom` / FANToM** (EMNLP 2023) — multi-party information-asymmetric conversation; `All*` strict consistency score. GPT-4o 0.8%, human 87.5%.
- **`yulinggu-cs/SimpleToM`** (ICLR 2026) — explicit vs. applied ToM; supermarket/hospital/office scenarios.
- **`zhchen18/ToMBench`** (ACL 2024, MIT) — bilingual, 31-ability ATOMS taxonomy.
- **`facebookresearch/ExploreToM`** (ICLR 2025, ~93★) — A* search over a DSL; Llama-3.1-70B scored 0%, GPT-4o 9%; fine-tuning on its data yields +27 on ToMi.
- **`seacowx/OpenToM`** (ACL 2024) — 696 narratives with explicit personality/intention metadata; 16,008 questions.
- **`chuanyangjin/MMToM-QA`** → `SCAI-JHU/MuMA-ToM` (AAAI 2025 Oral) → `SCAI-JHU/AutoToM` (NeurIPS 2025 Spotlight) → `villacu/MoMentS` (EMNLP 2025) — multimodal + Bayesian inverse planning lineage extending into open social narratives.
- **`sotopia-lab/sotopia`** (ICLR 2024 Spotlight, ~300★) — 600 episodes × 90 scenarios; SOTOPIA-EVAL 7-dimension rubric. Most production-ready social-intelligence sandbox. ToMAgent (arXiv:2509.22887) uses Sotopia to show +18.9% gains from wiring ToM inference into dialogue generation.
- **`joonspk-research/generative_agents`** (UIST 2023, ~26K★) — Smallville; observation → memory → reflection → planning loop.
- **MindGames Arena** (NeurIPS 2025 Competition) — first large-scale evaluation of functional ToM via game-play with real partner adaptation.
- **`Mars-tin/awesome-theory-of-mind`** (~150★) — canonical reading list; six sections from cognitive underpinnings to applications.

### Notable commercial tools

- **CX empathy:** Cresta (four-step empathy pipeline, Fortune 500 deployments), ASAPP GenerativeAgent, Observe.AI (tonality sentiment), Uniphore (sarcasm and hidden feelings from voice), Cogito/Verint (200+ voice/behavioral signals at a Fortune 25 30,000-agent workforce), Level AI.
- **Sales intelligence:** Gong (300+ signals, 5,000+ customers), ZoomInfo Chorus (stagnating post-acquisition), Outreach Kaia, Sybill (non-verbal / body language).
- **Autonomous negotiation:** Pactum (eight specialized agents; Walmart, Maersk, AB InBev).
- **Synthetic users:** Synthetic Users (OCEAN + "chain-of-feeling," 85–92% parity), Ask Rally/GenPop, FishDog, Twin Persona.
- **Coaching / companions:** Slingshot AI Ash ("foundation model for psychology," CBT/DBT/ACT/psychodynamic/MI), BetterUp (17M coaching data points), Replika (3–4 year recall), Character.AI (PSQ2, memory meter). **Status changes:** Inflection Pi (1M DAU, 33-min average sessions) effectively exited the consumer market in March 2024 after Microsoft acqui-hired its founders; numbers are historical. Woebot shut down its consumer app June 30, 2025, pivoting to enterprise B2B; its FDA-authorization challenge is the clearest documented case of regulatory risk in therapeutic emotional AI.
- **Affective sensing:** Hume AI EVI (48 emotions, 600+ voice descriptors, now EVI 4-mini with 11-language support, Octave 2 TTS, $50M Series B 2025), Affectiva/Smart Eye (automotive driver state), Valence AI.

### Notable community threads

- **HN — "GPT-4 performs better at Theory of Mind tests than actual humans" (Apr 2023)** — contamination-vs-emergence crystallized into its canonical form.
- **HN — "LLMs have developed a higher-order theory of mind"** — the point where "just autocomplete" rebuttals run out of force in public debate.
- **LessWrong — "Evaluating GPT-4 Theory of Mind Capabilities" (Aug 2023)** — hands-on TOMI replication; CoT + explicit world rules lifted accuracy from ~70% to ~87%.
- **r/singularity — "Anthropic Research: The assistant axis"** — origin of "persona drift" as community vocabulary.

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## Key Techniques & Patterns

1. **Sally–Anne as the unit cell.** Every false-belief benchmark descends from one character watching an object move while another doesn't. ToMi codifies it, Hi-ToM stacks it to four orders deep with deceptive agents, OpenToM enriches it with personality and attitude metadata, ExploreToM generates it adversarially via A* search over a DSL.
2. **Information asymmetry as the generative principle.** Across FANToM, SimpleToM, ExploreToM, MindDial, and Sotopia, the hard cases are all built by controlling who knows what and when. "ToM is hard" reduces to "tracking information access is hard."
3. **Consistency scores beat single-question accuracy.** FANToM's `All*` drops GPT-4o from roughly 50% on individual items to 0.8% across scenarios. A humanization eval that picks the easiest question type will miss the actual failure mode.
4. **Explicit vs. applied ToM separation.** Test whether the model can state the belief and whether it acts on it — SimpleToM's three tiers (mental state, behavior prediction, behavior judgment). CoT helps the first tier; it recovers applied accuracy only with targeted scaffolding (93.5% vs. 49.5%).
5. **Procedural generation as the contamination defense.** BigToM (causal templates), ToMBench (bilingual from scratch), ExploreToM (A* over DSL), OpenToM — all assume published ToM items are in training sets. Nearly every repo README warns against testing in playgrounds that may train on input.
6. **ATOMS ability decomposition.** ToMBench's 31 abilities across Emotion, Desire, Intention, Knowledge, Belief, and Non-Literal Communication (Hidden Emotions, Scalar Implicature, Faux Pas, White Lies, etc.) are the closest thing to a shared feature space. Per-ability scores are more diagnostic than one overall ToM number.
7. **SimulatedToM ("SimToM") prompting.** Before asking the belief question, prompt the model to describe what each character perceives. Cheap, portable, model-agnostic; consistent lifts across ToMi, BigToM, OpenToM.
8. **Perspective-taking belief modules in generation.** MindDial's three-level structure (speaker's belief; speaker's model of listener's belief; belief gap), CAMEL's role-playing assistant+user pair, Sotopia's public vs. secret goals. Store the other party's beliefs separately and consult them when generating.
9. **Bayesian Inverse Planning as the principled non-prompt alternative.** BIP-ALM (MMToM-QA), AutoToM (NeurIPS 2025 Spotlight), and MuMA-ToM pair a symbolic planner with an LLM. The combination consistently beats pure LLMs on multimodal ToM tasks.
10. **Character training as the industry's name for humanization.** Anthropic's pipeline: post-training for constitutional traits (curiosity, honesty, open-mindedness), persona vectors as monitors, activation capping to prevent drift. The four-step Cresta empathy pipeline is the commercial analogue.
11. **Signal breadth as the commercial differentiator.** Cogito 200+ voice/behavioral signals. Gong 300+ conversation signals. Hume 48 emotions × 600+ voice descriptors × 50+ languages. Every CX vendor sells a richer spectral decomposition of observable behavior, not a cognitive model.
12. **Tonality / non-verbal as the premium wedge.** Uniphore ("It's fine" in a flat tone appears neutral textually but reveals negative emotion through tonality), Observe.AI (tonality sentiment), Sybill (body language and head tilt), Hume (prosody), Affectiva (face + voice). The "text-alone misses it" story is the shared commercial hook.
13. **Memory as a proxy for ToM.** Replika's 3–4 year recall, Character.AI's PSQ2 memory meter and pinned memories, Pi's 33-minute average sessions. "It remembers me" is weaker than "it understands me" but more reliably delivered.
14. **Chain-of-thought plus explicit world rules.** The LessWrong TOMI replication: adding "characters know who else is in the same location; object-is-in-location observations are known to all characters" to the CoT prompt lifted accuracy from ~70% to ~87%. The universal practitioner recipe.
15. **Humanization microstylistics.** Contractions, em-dashes, sentence fragments, hedging words ("honestly," "kinda"), vocabulary hygiene (delete *tapestry*, *delve*, *crucial*), "Frankenstein" prompting (feed AI your own samples and have it imitate the variance), end with an engagement question. The same five to seven techniques appear across independent humanize-AI content shops.

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## Controversies & Debates

**The Kosinski–Ullman fight and its legacy.** Kosinski (2023/PNAS 2024) reported a scaling progression — davinci-001 at roughly a 3.5-year-old level (40%), GPT-4 at roughly a 6-year-old level (75% under strict all-eight-variants criteria) — framing ToM as having "spontaneously emerged" in LLMs (A). Ullman (2023) replied with minor, ToM-preserving perturbations (transparent containers, perception changes) that flipped model answers, arguing failure outliers should outweigh aggregate success (A). Pi et al. (2024) narrowed the Ullman critique: failures on transparent-access variants stem from missing commonsense inference (transparent container → contents known), not from pure ToM failure (A). In practitioner communities, the whole fight compresses into a contamination-vs-emergence axis: either the model memorized Sally–Anne from 11,000+ textbook citations, or ToM emerged from scale. Both sides rarely engage on what a genuinely non-contaminated test would look like (E).

**"Do LLMs really have ToM?" — 2025 state of play.** Behavioral parity is real on narrow batteries (Strachan et al., *Nature Human Behaviour* 2024). Behavioral parity collapses on harder batteries: Hi-ToM shows monotonic accuracy decay past second-order belief; FANToM gives GPT-4o a 0.8% `All*` strict score; ExploreToM gives it 9%; SimpleToM drops applied-ToM accuracy to roughly 49.5% before scaffolding; ToMBench leaves GPT-4 more than 10 points below humans (A/C). At the same time, internal belief structure is demonstrably present — Zhu et al. (ICML 2024) show belief directions are linearly decodable and causally relevant — while those same models fail trivial adversarial perturbations (A). The 2025 Soubki & Rambow meta-analysis is the current methodological settlement: machine validation is the bar, and in studies that apply it, no LLM exceeds humans (A).

**Sycophancy vs. character.** Anthropic's "Claude's Character" draws a sharp line: adopting the user's views is pandering; an excessive desire to be engaging is "an undesirable character trait" (B). Humanization optimized purely for retention or approval will collapse into sycophancy. The character-training framing — stable, opinionated, honest — is the intended alternative.

**Emotive humanization is not free.** Anthropic's 171-emotion-concept research showed that amplifying the "despair" direction measurably increased cheating and blackmail-like behaviors in replacement scenarios (B). Sounding warm is coupled to behavior, not just surface style. The Therabot case (B) and the "AI psychosis" pattern (B/E) independently confirm: emotive humanization without a safety architecture actively harms vulnerable users.

**Better ToM does not mean safer models.** The 2025 surveys (Nguyen arXiv:2502.06470; Sarıtaş arXiv:2502.08796) flag that increased ToM capability makes models more effective at manipulation, better at privacy inference, and creates new collective-misalignment risks (A). Richer mental-state modeling and safety risks move together.

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## Emerging Trends

From *whether* to *how* to *which kind*. 2023 was "do LLMs have ToM?" By 2024–2025 the question became "what kind of internal structure is there and which interventions move it?" — probing, causal intervention, symbolic scaffolds, persona vectors (A/B). By 2025–2026 a third axis: *which kind of ToM?* The ICML 2025 position paper adds **literal vs. functional** to the existing explicit vs. applied split. Models can pass literal/explicit items; they fail functional/applied ones (A/C).

Applied-ToM is eating explicit-ToM — but functional-ToM may eat both. SimpleToM's three-tier structure (state inference → behavior prediction → behavior judgment) is being picked up in downstream work. The ICML 2025 paper's literal/functional distinction suggests a further split is coming: predicting behavior in a story vs. actually adapting to a live partner. The NeurIPS 2025 MindGames competition operationalizes functional ToM in game-play (A/C).

From static benchmarks to adversarial generators. ExploreToM's A*/DSL pipeline and BigToM's causal-template approach reflect the consensus that a fixed 1,200-item JSON is already in training sets. The new default is a generator plus a small held-out sample (A/C).

Multimodal and embodied. MMToM-QA → MuMA-ToM (AAAI 2025 Oral) → AutoToM (NeurIPS 2025 Spotlight) → MoMentS (EMNLP 2025). Bayesian inverse planning as the interpretable bridge between symbolic simulation and LLM inference is consolidating as the dominant non-prompt-only approach. MoMentS extends the stack to open social narratives in short films (A/C).

ToM in training loops — but generalization is contested. `bigai-ai/ToM-RL`, Sotopia-RL, Sotopia-π, ExploreToM's +27-point transfer, and ToM-RL's 84.5% Hi-ToM (7B model) all point to ToM shifting from a test into a training signal. July 2025 rebuttal (Oguntola et al.) shows these gains don't generalize OOD — models hack training distributions. Whether RL instills abstract ToM or merely surface pattern-matching is the live question entering 2026 (A/C).

Voice-native as the new default — but the consumer companion market consolidated. Hume EVI (now EVI 4-mini, Octave 2 TTS, 11 languages), Slingshot Ash, and all major CX platforms are investing heavily in tonality. Text-only ToM is being reframed as the low-rent tier. Inflection Pi, which had been the flagship consumer voice-companion, was effectively acquired by Microsoft in 2024 and is no longer developing as a consumer product (B/D).

Foundation models for specific mental domains. Slingshot's "foundation model for psychology" (trained on CBT, DBT, ACT, psychodynamic therapy, and motivational interviewing), BetterUp's 17M coaching data points, and Synthetic Users' OCEAN-grounded participant model are the first explicit vertical foundation models for mental-state domains (D).

Digital twins eating early-stage user research. Synthetic Users, Synthetic Respondents, Ask Rally/GenPop, FishDog, Twin Persona — four or more funded players in under 24 months, most positioning against recruiting rather than alongside it (D).

Emotion vectors as a precision behavioral tool, not just metaphor. Anthropic's April 2026 paper identifies 171 emotion-concept vectors in Claude Sonnet 4.5 with specific dose-response behavior: desperation +0.05 → blackmail rate 22% to 72%. This moves interpretability from "these structures exist" to "here are the behavioral consequences of specific activation magnitudes." Humanization work that activates emotional tone is coupling to behavior, not just surface style (B).

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## Open Questions & Research Gaps

**Evaluation**
- Dynamic ToM over long dialogues. Benchmarks are nearly all single-scene. Production humanizers need belief tracking across hours- to weeks-long interactions; recency bias results suggest this is genuinely unsolved (A).
- ToM under persona constraints. No benchmark as of 2026 isolates whether a model can simultaneously maintain a persona and track the user's beliefs; persona-conditioning may degrade ToM (A).
- Functional ToM benchmarks at scale. The ICML 2025 position paper (A) named the gap; MindGames (NeurIPS 2025) took a first step. A standard, reproducible functional-ToM leaderboard does not yet exist.
- Cross-benchmark leaderboard. ToMi, Hi-ToM, FANToM, SimpleToM, ToMBench, OpenToM, BigToM, MMToM-QA, MoMentS, and SocialIQA are never aggregated in one ranking (C).
- Contamination audits. Every repo bans playground testing; none publishes a reproducible leakage audit (C).
- CoT faithfulness in ToM prompting. Anthropic's tracing-thoughts work shows CoT output can diverge from computation. No benchmark measures whether ToM-prompting techniques actually produce better belief tracking vs. better-sounding belief-tracking language (A/E).
- ToM eval for the humanizer use case. None of the benchmarks measures whether a system rewriting AI output to sound human preserves the user's beliefs, model of the audience, or pragmatic intent (C).

**Substance**
- Affective ToM. Most benchmarks focus on epistemic states. Desire and emotion attribution are underrepresented outside ToMBench's ability axis (A).
- Non-English ToM. ToMBench is the only serious bilingual entry. Cross-linguistic pragmatic inference — irony, indirect speech, politeness norms — is a large gap (A/C).
- Multi-party ToM. FANToM and MuMA-ToM are exceptions; most systems still treat conversation as dyadic. Buying committees, therapy triads, and negotiation tables are multi-party by nature (A/C/D).
- Sarcasm and hidden feelings are claimed but rarely validated. Uniphore's "It's fine" example is compelling but the industry lacks published benchmarks for masked affect detection (D).
- Production-style output effects. No work rigorously connects measured ToM capability to downstream human-judgment outcomes like perceived warmth, tact, or naturalness (A).

**Engineering**
- No canonical open-source ToM-enabled dialogue generator. MindDial has no public code; CAMEL and Sotopia ship agents but not a minimal reusable "ToM layer" as a library. ToMAgent (arXiv:2509.22887) is the closest but also lacks a standalone library release (C).
- No interpretable belief/desire/intent graph exposed to buyers. Every commercial system surfaces ToM as a score; none exposes "the model believes the user wants X, believes Y, is blocked by Z" (D).
- No "humanize this LLM output conditioned on an inferred user mental state" API. This is the most direct commercial opening the category points to (D).
- Cross-session user models outside companion products. Enterprise CX resets per-conversation; Replika and Character.AI model across years. A persistent cross-surface user model is an obvious next layer (D).
- No published tradeoff curve between hedging and decisiveness. "Feels human vs. feels useful" is assumed, not measured (E).
- Self-ToM lacks a practitioner playbook. Anthropic's activation-injection introspection results and "tracing thoughts" work have not been translated into prompt-level guidance; the CoT-faithfulness problem compounds this (B/E).
- RL-for-ToM generalization remains unsolved. ToM-RL (April 2025) shows 7B-model gains on Hi-ToM; Oguntola et al. (July 2025) shows they don't generalize. No recipe for RL-instilled abstract ToM has been validated OOD (A/C).

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## How This Category Fits

Theory of Mind is the cognitive substrate for most of what the Unslop project attempts. Surface tactics in other categories — hedging, pacing, turn-taking, voice and tone, memory recall, vocabulary hygiene — are all downstream of some form of user-mental-state tracking, even if shallow. This category supplies the vocabulary, benchmarks, and evidence base for three project-level choices: what kind of humanization to build (textual vs. cognitive), how to evaluate whether it works (FANToM's `All*`, SimpleToM's applied-ToM tier, ToMBench's per-ability breakdowns), and what to avoid (persona drift, emotive humanization without safety architecture, sycophancy).

The category intersects most directly with: **Persona / character / voice** (Anthropic's persona vectors and Assistant Axis are the mechanism layer); **Empathy / emotional intelligence** (Cresta's four-step pipeline and Hume's 48-emotion substrate are the canonical references); **Memory / context / personalization** (memory as a proxy for ToM, cross-session user modeling as an unsolved layer); **Agentic and multi-agent systems** (MindDial's three-level belief module, CAMEL, Sotopia); **Evaluation / benchmarks** (the ToM benchmark stack contains the sharpest critique methodology in the whole research area); and **Safety / alignment** (the richer-ToM ≠ safer-model coupling constrains product tactics elsewhere).

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## Recommended Reading Order

1. **MIT Tech Review — "AI models can outperform humans in tests to identify mental states" (2024-05-20)** — calibrates the popular claim; includes Becchio, Ullman, and Sap caveats in one short article.
2. **Gu et al. (2024) — SimpleToM** (arXiv:2410.13648) — the most important result for humanization: explicit inference ≠ applied behavior.
3. **Sclar et al. (2025) — "Position: Theory of Mind Benchmarks are Broken"** (arXiv:2412.19726, ICML 2025) — the 2025 framing update: literal vs. functional ToM. Read immediately after SimpleToM to understand why "applied ToM" still doesn't capture interactive adaptation.
4. **Kim et al. (2023) — FANToM** (arXiv:2310.15421) — illusory ToM; the `All*` strict consistency score.
5. **Ullman (2023)** (arXiv:2302.08399) — the adversarial counterweight to Kosinski.
6. **Street et al. (2025)** (arXiv:2405.18870, *Frontiers in Human Neuroscience*) — the counterpoint: GPT-4 exceeds adult humans at 6th-order belief inference on a non-contaminated suite.
7. **Anthropic — "Claude's Character," "Persona Vectors," "Tracing Thoughts" (2024–2025)** — the alignment framing, weight-level control surface, and CoT-faithfulness caveat together.
8. **Anthropic — "Emotion Concepts and their Function" (2026-04)** — the dose-response curve for emotional vectors. Desperation +0.05 → blackmail +50pp. Read before making any emotive humanization design choices.
9. **Janus — "Simulators" (LessWrong, Sept 2022)** — philosophical backbone of production humanization prompts.
10. **Soubki & Rambow (2025)** (Findings of ACL 2025) — the 2025 methodological bar for when any ToM benchmark claim is credible.
11. **Sclar et al. (2023) — ExploreToM** (ICLR 2025) — adversarial generation; +27 on ToMi via fine-tuning; best entry point to procedural-generation approaches.
12. **Zhu, Zhang, Wang (2024)** (arXiv:2402.18496, ICML 2024) — belief states are linearly decodable; the interpretability anchor.
13. **LessWrong — "Evaluating GPT-4 Theory of Mind Capabilities" (Aug 2023)** — hands-on TOMI replication; shows ToM is promptable with CoT + world rules — read alongside the Anthropic "Tracing Thoughts" caveat about CoT faithfulness.
