The research · the numbers · the sources

Two associative engines with the same four leaks — sailed by the same rig.

Antidote is built on one thesis: your brain and your LLM run the same architecture — brilliant associative pattern-completion with four structural leaks: attention, memory, time, and verbatim truth. Every claim on the main page traces here, to a published study or its coverage. Audit everything. That is the point.

The evidence gallery

Nine numbers the pitch is built on.

Every statistic deployed on sloppyxbaby.com, paired with its source. If a number can't survive this table, it doesn't go on the page.

StatFindingSource
+300%A single irrelevant sentence ("Interesting fact: cats sleep most of their lives") raised wrong-answer rates over 300% in DeepSeek-R1 and OpenAI o1-class models — and made even correct answers up to 3× longer.CatAttack LRM robustness study, 2024
−30%LLM accuracy collapses 30%+ when key information sits in the middle of the context window — even in 128K-token models.Liu et al., Lost in the Middle, Stanford 2023
500–1000%LLMs miss task-duration estimates by 500–1000%; top conversational models score 4.3/100 on the TimeSpeak temporal benchmark.Tan, Tan & Soatto, 2026
d = 0.69–0.74Working-memory deficit in ADHD is among the most replicated findings in clinical science.ADHD working-memory meta-analyses (Kofler et al., 2024)
d = 0.69+Adults with ADHD show elevated false-memory confabulation on DRM-paradigm tests — the same effect-size class as the working-memory deficit.Soliman & Elfar, J. Attention Disorders, 2017
n = 671,000Autistic hyper-systemizing confirmed in a PNAS mega-study — systemizing (isolate one variable, hold the rest constant, observe the output) is the formal definition of prompt engineering.Greenberg, Warrier, Allison & Baron-Cohen, PNAS 2018
n = 1,110Monotropism — the single-channel "attention tunnel" — validated by the Monotropism Questionnaire, with high scores correlating to both autism and ADHD.Garau, Murray et al., 2023 — via monotropism.org
more coherentConfabulated LLM outputs are measurably more narratively coherent than truthful ones — fluency is not evidence of truth.Sui, Duede, Wu & So, ACL 2024
$500KThe NSF funded $500K (2026) for LLM-based strength-based job coaching for autistic adults (Georgia Tech) — institutional validation that this cognitive frame is real.NSF award, Kim & Riedl — Georgia Tech news

The merge map

Why the four rules work.

Andrej Karpathy's public writing catalogued how vibe coding fails; the cognitive science names the architecture producing those failures. Each of the four principles is precisely the accommodation for one isomorphic deficit. Nominative reference only — no endorsement stated or implied.

Karpathy's observed failureThe cognitive analogAntidote's fix
Silent assumptions, hidden confusionConfabulation — gist-track pattern completion that believes itselfSurface-assumptions rule; verification against ground truth
Overcomplication, bloated abstractionsDistractibility — every unneeded token burns attention (CatAttack)Simplicity budgets; scoped context; lean injection order
Orthogonal edits, touching what it shouldn'tMonotropic split — context-breaking intrusionSurgical-changes rule; request-traced diffs
Needs goals, not instructionsPoint of performance — structure must live where behavior happensAGENTS.md constitution; step→verify loops

The vocabulary

Words for things you've felt but couldn't name.

Precise terms are load-bearing. These six run through everything Antidote ships.

Confabulation, not hallucination Hallucination implies a sensory experience. Your agent has no senses. It confabulates — pattern-completes gaps with the most statistically plausible narrative and believes it — and so, sometimes, do you. The fix is the same: external ground truth.
Monotropic split What happens when a session breaks thread: friction, state loss, the original train of thought gone. Names the pain every heavy AI user has felt and never had words for. The Monotropy Protocol exists to prevent it.
The eternal present The LLM's native temporal state: no clock, no elapsed time, no horizon. "Now / Not Now" meets a system with no "when" at all. Measured Time — environment-stamped clocks, never model guesses — is the counterweight.
Gist vs. verbatim Fuzzy-Trace framing for why both brains and models "remember" things that never happened: the verbatim track decays first, the gist track pattern-completes the gap and signs it as true.
The four leaks Attention, memory, time, verbatim truth. The unifying diagnostic frame for both engines: four leaks, one rig.
Point of performance Barkley's term: structure must live exactly where the behavior happens, because internalized rules can't survive a working-memory deficit. It's why Antidote lives in your repo and not in a settings page.

For the human, too

The scaffold holds the loop, not just the agent.

Four field-tested patterns AuDHD users already run through LLMs. Each is on the Antidote roadmap as a future SKILL.md — documented here because the research behind them is the same research behind the kit.

Micro-stepping

Defeat task-initiation paralysis

"Give me only the first 5 physical actions, under 2 minutes each." Overwhelming, abstract projects decompose into the next physical action plus the step after it — the same decomposition Measured Time enforces on the agent.

Social translation

Cut the masking load

Neurodivergent→neurotypical reformatting of blunt, literal text for workplace consumption. The model doesn't suffer social fatigue or judgment — let it carry the translation cost instead of your nervous system.

Synthetic body doubling

A check-in that never tires

10–30 minute accountability loops that pull a wandering mind back to the point of performance — combating time blindness and hyperfocus alike. Already half-built into the Monotropy Protocol's checkpoint cadence.

Metacognitive scaffolding

Plan → Monitor → Evaluate

The agent acts as scaffold, not oracle: it asks the questions that keep your executive function in the driver's seat, instead of answering everything and outsourcing the thinking along with the typing.

Radical transparency

Audit the kit before you buy it.

The domain is young and the refund is honor-based — so instead of testimonials, here is the merchandise: the complete Professional Kit file tree (names, not contents) and the DAY1.md table of contents. This audience buys what it can audit.

antidote-kit/Professional · 31 files
# every file in the kit, by name
AGENTS.md  # agent discovery router
CLAUDE.md  # global guardrails + @-imports
CONTEXT-MAP.md  # every surface mapped
context.json  # injection order + rules
DAY1.md  # 90-minute guided setup
fix_plan.md  # repair-runbook template
index.html  # printable kit overview
PATTERNS.md  # advanced architectural patterns
README.md  # what this is, how to install
.cursor/rules/  # 4 deterministic Cursor rules
  antidote-context-lock.mdc
  antidote-external-memory.mdc
  antidote-surgical-changes.mdc
  antidote-time-anchors.mdc
docs/_registry/  # doc_index.json
docs/4-explanation/  # ARCHITECTURE.md · THEORY.md
memory/  # brand-spec.md · decisions.md · todos.md
skills/  # 11 model-agnostic SKILL.md files
  context-lock/SKILL.md
  dual-mandate-audhd/SKILL.md
  external-memory/SKILL.md
  focus-rails/SKILL.md
  goal-driven-loop/SKILL.md
  hold-the-thread/SKILL.md
  surgical-changes/SKILL.md
  think-before-code/SKILL.md
  time-anchors/SKILL.md
  what-to-test/SKILL.md
  work-truth-control-plane/SKILL.md
templates/  # instance.control-plane.md
DAY1.mdtable of contents
# Day 1 — A 90-minute setup
Your one default path
0–10 minutes — orient
10–25 minutes — make the work record real
25–45 minutes — add the smallest useful context
45–65 minutes — choose an accessibility lens
65–80 minutes — complete one small loop
80–90 minutes — leave a soft landing
Recovery path

The citation chain

References.

The full source chain behind the AuDHD×LLM thesis and the behavioral guidelines, as compiled in the Divergent Harness outline (2026-07-16). Community sources (Reddit, blogs) are marked as such and backstop only the claims they actually support; the peer-reviewed anchors carry the numbers.

  1. Primary sources — the behavioral guidelines
  2. Karpathy, A. Public X post on LLM coding pitfalls ("LLMs are exceptionally good at looping until they meet specific goals..."). x.com/karpathy/status/2015883857489522876 — nominative reference; no endorsement implied.
  3. forrestchang / multica-ai, andrej-karpathy-skills repo (MIT license) — the four behavioral principles. github.com/forrestchang/andrej-karpathy-skills
  4. Peer-reviewed anchors
  5. Liu, N. F., Lin, K., Hewitt, J., et al. (2023). "Lost in the Middle: How Language Models Use Long Contexts." arXiv:2307.03172
  6. Greenberg, D. M., Warrier, V., Allison, C., & Baron-Cohen, S. (2018). "Testing the Empathizing–Systemizing theory of sex differences and the Extreme Male Brain theory of autism in half a million people." PNAS 115(48). doi:10.1073/pnas.1811032115
  7. Kofler, M. J., et al. (2024). "Executive function deficits in ADHD and autism spectrum disorder." Nature Reviews Psychology. PMC11485171
  8. Soliman & Elfar (2017). "False Memory in Adults With ADHD." Journal of Attention Disorders. doi:10.1177/1087054714556814
  9. Sui, P., Duede, E., Wu, S., & So, R. J. (2024). "Confabulation: The Surprising Value of Large Language Model Hallucinations." ACL 2024. aclanthology.org/2024.acl-long.770
  10. Barkley, R. A. "The Important Role of Executive Functioning and Self-Regulation in ADHD." russellbarkley.org factsheet
  11. "Hallucination or Confabulation? Neuroanatomy as metaphor in Large Language Models." PLOS Digital Health (2023). doi:10.1371/journal.pdig.0000388
  12. Garau, V., Murray, A., et al. (2023). "Development and validation of the Monotropism Questionnaire." Autism. Discussed at monotropism.org and the National Autistic Society.
  13. Kim, J. & Riedl, M. (2026). NSF award for LLM-based strength-based job coaching for autistic adults. Georgia Tech news
  14. Tan, Tan & Soatto (2026). LLM temporal-perception error margins and the TimeSpeak benchmark. Via "Time Is the Missing Modality"
  1. The Architecture of Divergence — full 68-entry works-cited
  2. "Large Language Models as Simulative Agents for Neurodivergent Adult Psychometric Profiles" — arXiv. arxiv.org/abs/2601.15319
  3. "I spent months reading ADHD and neuroscience papers..." — r/ADHD_Programmers (community). reddit.com/r/ADHD_Programmers
  4. "LLM failure modes map surprisingly well onto ADHD cognitive science" — r/ClaudeAI (community). reddit.com/r/ClaudeAI
  5. Same discussion — r/artificial (community). reddit.com/r/artificial
  6. "AI as a Metacognitive Scaffold for Neurodivergent Learners" — Structural Learning. structural-learning.com
  7. "3 AI Prompt Strategies for ADHD Productivity" — Medium (community). medium.com/@christianaistudio
  8. "Atypical coupling between posterior regions of the default mode network in ADHD" — PMC. PMC3756117
  9. "ADHD and attentional control: Impaired segregation of task positive and task negative brain networks" — PMC. PMC6130439
  10. "Evidence from 'big data' for the default-mode hypothesis of ADHD: a mega-analysis" — PMC. PMC9751118
  11. "ADHD Symptoms are Associated with the Modular Structure of Intrinsic Brain Networks" — bioRxiv. biorxiv.org/content/10.1101/505891v2
  12. Same study, journal version — Network Neuroscience (MIT Press). direct.mit.edu/netn
  13. "Your AI Agent Isn't Dumb. It Has ADHD" — AI in Plain English (community). ai.plainenglish.io
  14. "LLM failure modes map surprisingly well onto ADHD cognitive science" — r/ChatGPT (community). reddit.com/r/ChatGPT
  15. Barkley, R. A. "The Important Role of Executive Functioning and Self-Regulation in ADHD." russellbarkley.org
  16. "Executive function deficits in ADHD and autism spectrum disorder" — PMC. PMC11485171
  17. "What Are Executive Functions and How Are They Related to ADHD?" — Children's Hospital of Philadelphia. chop.edu
  18. "Cat content disturbs AI models" — Computerworld (CatAttack coverage). computerworld.com
  19. "New Research Shows How a Single Sentence About Cats Can Break Advanced AI Reasoning Models" — r/OpenAI (community). reddit.com/r/OpenAI
  20. "ExtendAttack: Attacking Servers of LRMs via Extending Reasoning" — AAAI Publications. ojs.aaai.org
  21. "Irrelevant facts about cats added to math problems increase LLM errors by 300%" — Hacker News discussion (community). news.ycombinator.com/item?id=44724238
  22. Lohmann, C. "ADHD, Executive Function and the Core Executive Function Working Memory in Students" — University of Groningen thesis. ub.rug.nl
  23. "Working Memory, Processing Speed, and Academic Achievement in Adults with ADHD" — LSU Scholarly Repository. repository.lsu.edu
  24. "Executive Function in Children with ADHD: the NIH EXAMINER battery" — PMC. PMC4425416
  25. Perez, C. E. "People Spirits and Generation-Verification Loops" — Intuition Machine (community). medium.com/intuitionmachine
  26. "Lost-in-the-Middle Problem: Why Position Matters in the Context Window" — Atlan. atlan.com
  27. Liu et al. "Lost in the Middle: How Language Models Use Long Contexts" — Stanford CS (PDF). cs.stanford.edu/~nfliu
  28. "Attention and Limitations" — Moveworks Agent Studio docs. help.moveworks.com
  29. "The 'Lost in the Middle' Problem — Why LLMs Ignore the Middle of Your Context Window" — dev.to (community). dev.to
  30. "Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding" — arXiv. arxiv.org/html/2403.04797v1
  31. "ADHD and Time Blindness: Why Time Management Is So Hard" — Doctronic. doctronic.ai
  32. "Russell Barkley Time Blindness: What ADHD Does to Your Clock" — ReachLink. reachlink.com
  33. "ADHD Time Blindness: What It Is and How to Manage It" — Sommer Psychology Group. sommerpg.com
  34. "ADHD Time Blindness: Why Timers Fail and What Works" — Super Productivity. super-productivity.com
  35. "ADHD Time Blindness: Why It Happens & How to Manage It" — Kantoko. kantoko.com.au
  36. "ADHD Time Blindness: How to Detect It & Regain Control Over Time" — ADDA. add.org
  37. "ADHD Time Blindness: Why It Happens & How to Fight It" — Brain.fm. brain.fm
  38. "Time Is the Missing Modality" — time-aware LLM research notes (Tan, Tan & Soatto, 2026). gogoduck912.github.io
  39. "Understanding the brain with AI-driven explanations and experiments" — Microsoft Research. microsoft.com/research
  40. "Seq2Time: Sequential Knowledge Transfer for Video LLM Temporal Grounding" — CVPR 2025 poster. cvpr.thecvf.com
  41. "TimeExpert: An Expert-Guided Video LLM for Video Temporal Grounding" — ICCV 2025 poster. iccv.thecvf.com
  42. "Mitigating the Discrepancy Between Video and Text Temporal Sequences" — ACL Anthology (COLING 2025). aclanthology.org/2025.coling-main.655
  43. "Development and Validation of a Paradigm for Eliciting Confabulation in Humans and LLMs" — MEI:CogSci. journals.phl.univie.ac.at
  44. "Hallucination or Confabulation? Neuroanatomy as metaphor in Large Language Models" — PLOS Digital Health (printable). journals.plos.org
  45. "Redefining 'Hallucination' in LLMs: Towards a psychology-informed framework for mitigating misinformation" — arXiv. arxiv.org/html/2402.01769v1
  46. "Hallucination or Confabulation? Neuroanatomy as metaphor in Large Language Models" — PLOS Digital Health. journals.plos.org
  47. "False Memory in Adults With ADHD" — Journal of Attention Disorders (Ovid). ovid.com/journals/jatd
  48. "False memory for trauma-related Deese–Roediger–McDermott lists..." — Development and Psychopathology, Cambridge Core. cambridge.org/core
  49. "False Memory in Adults With ADHD: A Comparison Between Subtypes and Normal Controls" — ResearchGate. researchgate.net
  50. "LLM failure modes map surprisingly well onto ADHD cognitive science" — r/ClaudeCode (community). reddit.com/r/ClaudeCode
  51. Ito, A. "Humans and AI Alike Fill Gaps with 'Plausibility' Under Constraints" — Medium (community). medium.com/@poola.vii
  52. "Confabulation: The Surprising Value of Large Language Model Hallucinations" — ResearchGate. researchgate.net
  53. "Confabulation: The Surprising Value of Large Language Model Hallucinations" — ACL Anthology. aclanthology.org/2024.acl-long.770
  54. "ReFACT: A Benchmark for Scientific Confabulation Detection with Positional Error Annotations" — ACL Anthology (EACL 2026). aclanthology.org/2026.eacl-long.381
  55. "Critical Confabulation: Can LLMs Hallucinate for Social Good?" — arXiv. arxiv.org/html/2511.07722v2
  56. "Similar overall expression, but different profiles, of autistic traits, sensory processing, and mental health..." — PMC. PMC10659573
  57. "Social and non-social autism symptom and trait domains are genetically dissociable" — bioRxiv. biorxiv.org/content/10.1101/228254v2
  58. "A Pilot Study of Autistic and Non-Autistic Adults' Systemizing in a Learning Task" — Cambridge Educational Research e-Journal. cerj.educ.cam.ac.uk
  59. "Autism: The Big Picture – A Conversation With Sir Simon Baron-Cohen" — Navigating Neuropsychology. navneuro.com/90
  60. "Monotropism: Understanding Autistic Ways of Being Through the Lens of Attention" — Reframing Autism. reframingautism.org.au
  61. "Monotropism and The Monotropism Questionnaire" — Neurodiverse Connection. ndconnection.co.uk
  62. Monotropism.org — the monotropism research hub. monotropism.org
  63. "What is monotropism? Understanding a neuroaffirming theory of autism" — National Autistic Society. autism.org.uk
  64. "Understanding monotropism: why autistic attention works differently" — Neurodiversity Directory. neurodiversity.directory
  65. "A Comprehensive Survey of Prompt Engineering Techniques in Large Language Models" — TechRxiv. techrxiv.org
  66. "Exploring Large Language Models Through a Neurodivergent Lens: Use, Challenges, Community-Driven Workarounds, and Concerns" — VTechWorks. vtechworks.lib.vt.edu
  67. "Scaffolding Metacognition with GenAI: Design Opportunities to Support Task Management for University Students with ADHD" — CityUHK Scholars. scholars.cityu.edu.hk
  68. "New LLMs Could Provide Strength-based Job Coaching for Autistic People" — Georgia Tech News (NSF award, Kim & Riedl). gatech.edu
  69. "How AI and Large Language Models (LLMs) can help neurodivergent individuals" — Do-IT Profiler. doitprofiler.com

The very traits pathologized as clinical deficits — hyper-systemizing, literal communication, monotropic focus, divergent association — are the exact competencies required to control the most advanced computational systems in human history.