LLMs can shift people's beliefs. But most persuasion studies only check beliefs before and after a conversation. We built PersuasionTrace to measure beliefs turn by turn, so we can study how belief updates actually unfold.

Interested in how AI is affecting people? Please sign up to review a few papers for our proposed NeurIPS 2026 workshop on Measurement and Models of Psychological Impact (Sydney, Dec 12). Sign up: forms.gle/4v3KiKKAmzgX...

What Does AI Do to the User?

Measurement and Models of Psychological Impact Proposed workshop for NeurIPS 2026 in Sydney

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Disturbing anecdotal reports of "AI psychosis" and negative psychological effects have been emerging in the news. But what actually happens during these lengthy delusional "spirals"? In our preprint, we analyze chat logs from 19 users who experienced severe psychological harm🧵👇

Can LLMs use ToM to genuinely persuade you, or do they just use good rhetoric? In our new preprint, we use the MINDGAMES framework to test this. Surprisingly, LLMs like o3 can be incredibly effective persuaders *without* actually understanding your mental states. 🧵👇

Which, whose, and how much knowledge do LLMs represent? I'm excited to share our preprint answering these questions: "Epistemic Diversity and Knowledge Collapse in Large Language Models" 📄Paper: arxiv.org/pdf/2510.04226 💻Code: github.com/dwright37/ll... 1/10

I'm excited to share work to appear at ‪@colmweb.org‬! Theory of Mind (ToM) lets us understand others' mental states. Can LLMs go beyond predicting mental states to changing them? We introduce MINDGAMES to test Planning ToM--the ability to intervene on others' beliefs & persuade them

LLMs excel at finding surprising “needles” in very long documents, but can they detect when information is conspicuously missing? 🫥AbsenceBench🫥 shows that even SoTA LLMs struggle on this task, suggesting that LLMs have trouble perceiving “negative spaces”. Paper: arxiv.org/abs/2506.11440 🧵[1/n]

🧵I'm thrilled to announce that I'll be going to @facct.bsky.social this June to present timely work on why current LLMs cannot safely **replace** therapists. We find...⤵️

Still looking for a good gift?🎁 Try my book, which just had its first birthday! jaredmoore.org/the-strength... Kirkus called it a "thought-provoking tech tale.” Kentaro Toyama said it "reads less like sci-fi satire and more as poignant, pointed commentary on homo sapiens"

The Strength of the Illusion

jaredmoore.org

"Individually, we are one drop. Together, we are an ocean." --Ryunosuke Satoro So: How can individual preferences be aggregated into collective decisions? 🤔 We investigate this question in a new pre-print! 🧵

🤔What does it mean for a model to have a value? To answer, we first ask, are large language models 🤖 consistent over value-laden questions? 🧵

My novel is out today! In it, a researcher makes an AI like ChatGPT only to lose himself in its advice. The book asks: What kind of relationships do we want with machines? What kind of relationships do we want with other people? Please spread the word! https://t.co/1SLmAZHhB7

My paper, "Language Models Understand Us, Poorly" will appear at EMNLP this December! Read the pre-print here: https://arxiv.org/abs/2210.10684

Language Models Understand Us, Poorly

Some claim language models understand us. Others won't hear it. To clarify, I investigate three views of human language understanding: as-mapping, as-reliability and as-representation. I argue that while behavioral reliability is necessary for understanding, internal representations are sufficient; they climb the right hill. I review state-of-the-art language and multi-modal models: they are pragmatically challenged by under-specification of form. I question the Scaling Paradigm: limits on resources may prohibit scaled-up models from approaching understanding. Last, I describe how as-representation advances a science of understanding. We need work which probes model internals, adds more of human language, and measures what models can learn.

arxiv.org

Uncover the labor hidden beneath the mathematical instruments of power in @katecrawford's Atlas of AI. Take AI's excess carbon, value-laden measurements, and turning of people into time's carcasses as lessons to practice refusal. #ArtificialIdeas

Look to @brianchristian's The Alignment Problem to find a range of mis-specified objectives: from the humdrum but insidious--e.g. racist computer vision--to the catastrophic but speculative--e.g. power-seeking AI. Perhaps we agree more than we thought. #ArtificialIdeas

#ArtificialIdeas 17: Venture into the space of possible minds in @mpshanahan's Embodiment and the Inner Life and find one answer to the static, nonmodular failings of current AI. Search for a framework to understand the mind as a means for us all to better understand the world.

#ArtificialIdeas 16: It is collective intentionality that AI will need to master in order to fulfill the misty dreams of current trumpeters -- as @emilymbender has said. And there is nowhere better to learn how we learn those skills than Michael Tomasello's Becoming Human.

#ArtificialIdeas 15: Look to @margaretomara's The Code to decipher Silicon Valley. Acts like those of the tech guys' congressman, Ed Zschau, to cut capital gains taxes, led us to today. Software eats the world if and only if new legal regimes give that world a chew first.

#ArtificialIdeas 14: Give yourself the chance to consider what causes causes in @yudapearl's The Book of Why. Is it causal thinking that separates you from the grab-bags of correlation dominant in AI?

#ArtificialIdeas 13: Reclaim a bit of yourself in @STurkle's Reclaiming Conversation, when you realize that the good ideas of AI may cover up bad outcomes. In AI, we set out to do the good work of automation. But, as Turkle asks, do we want to be replacing each other?

#ArtificialIdeas 12: Use your brain to search through @benehrlich11's brain to search through Ramón y Cajal's brain to search through the privileged brain itself in this exquisite biography of a scientist. Genius lives on in Cajal's impact, but also in him as a bodybuilder:

#ArtificialIdeas 11: @patchurchland's Conscience is a poignant rejoinder on the hollowness of moral certitude in light of the empirical creatures that we humans are. A truly visceral read. But, then, does one need viscera to be moral?

#ArtificialIdeas 10: In @erikphoel's novel, The Revelations, or a "portrait of the artist as a young neural network," witness the struggle between not just normal and revolutionary sciences but also consciousnesses. A little grotesque. A fair bit incisive. A lot to take in.

#ArtificialIdeas 09: Upgrade the memes of your mind with @danielddennett's From Bacteria to Bach and Bach. Consider: are clam rakes inevitable? "How could a slow, mindless process build a thing that could build a thing that a slow mindless process couldn't build on its own?"

I read this thinking I might tune into the background score of the symbolic composition of thought but then @GaryMarcus mentioned a baby Ibex and thus, my attention elsewhere, I failed to learn any meaning from the words before me. But was my misunderstanding innate or learned? https://t.co/mqVP...

#ArtificialIdeas 08: Get ready for the pop-up when reading @fluffycyborg's Surfing Uncertainty...this kind of predictive processing may well turn your mind, and world, upside down (at least as you try to understand it).

A fascinating new read! From whalesong to babbling to emotion sharing there's much in here to reflect on the future of AI. My only caution: it is still good to focus on the limits of language model understanding -- these errors show us the concepts and values yet acquired. https://t.co/jNx9vh3ArK

#ArtificialIdeas 07: @anilkseth takes us on a kaleidoscopic -- and approachable -- tour of mind, one reined in by reality. And, if his view of a controlled hallucination is so, then, like Borges' Pierre Menard, we have not only read *Being You*--we have written it, as well!

#ArtificialIdeas 06: GPT-N, how do I argue that deep learning alone won't lead to the best of all worlds? Only a book-length Twitter thread of AI failures by @GaryMarcus & Davis can do that; the obvious is what you need when the very problem is that AI models can't spell it out

#ArtificialIdeas 05: X is a BOOK X has "Machines like Us" as a TITLE X has "@ronbrachman" AND "Levesque" as AUTHORS X has "2022" as a YEAR X has "I read it to argue with the symbolists. Now I would join them. If only my knowledge base could represent such concepts" as a REVIEW