Human Mind Reconstruction w/ AI (ToM, UserHarness)

Added: 2026-06-01

[00:00:00]Hello community. So great that you are back. Yes, today we talk about a user harness system and you might said hey wait a minute we have an AI harness.

[00:00:10]What do you mean with a user harness?

[00:00:12]Are we trying to simulate here the human cognitive architecture?

[00:00:16]Absolutely.

[00:00:18]So we start with an objective world state. This is here the reality. This is the real world. Then we will have an eye for an epistemic filtering. This is a layer where the human brain just notices some event and then we will build with an AI system a subjective mind space. So this is then we build an AI that simulates this. So this is the agent's internal world model. We'll have nested beliefs and meta beliefs and goals and values and policy computer actions and everything. And it's time to start this.

[00:00:49]And we all do this because there's a simple question. And our global corporations, you know, they care about us customers and they have a simple question in their mind. What are those damn humans thinking? Because they ask themselves, how can we manipulate them?

[00:01:06]How can we guide them? How can we influence them? How can we make them buy in their little bubble that they stay happy in their little bubble? that we have the algorithms that those users just are fed here on social media exactly what they want to see a self-reinforcing framework. How can we use AI to make our customers happy that they don't revolt and still continue to buy our stuff?

[00:01:32]So, we have here some some new publication May 26, 2026. And this is a user harness. So now we're going to simulate now the deep interior of a human belief system with AI. Isn't this fantastic? So let's have a look. We have to know about what is currently going on in EI. So user hornness explicitly now models a human user mindset as a temporarily evolving belief goal action loop for a theory of mind evaluation.

[00:02:06]anyone say okay so let's have a closer look so at each time step the user observes here the external environment E for environment forms an internal observation O for observation and updates here the internal belief that the user has here from a B tus one to B at the time T the updated belief now together with the user's goal determines now on the next user action do I press this buy button on Amazon or on Ma. Do I buy here this product here on Ma or on any social media platform which then in turn changes the environment to environment T+1 the user board or product and now the global corporations are happy. So we have defined here time flow. You see this here at the time stamp t and then we move over here to the time step t + 1 and we change this the user made a decision to buy here product or to sign up for a new abonom or whatever. So let's have a look. This new user horn is enables now here the tested AI agent that simulates not a user to explicitly track this hidden cognitive process you know of those humans across the timeline including now both the user's own belief system and all those nested belief about other agents mental state.

[00:03:33]Now there are of course locked elements that indicate some private or inaccessible mental state. Yeah, maybe this user still has some sort that global cooperation cannot penetrate to.

[00:03:43]So this must be inferred rather than directly observed. Hey, but luckily we have a neural network. So no problem about this. No. So by reconstructing now this belief dynamics in a human mind, the tested agent can now better understand the user's perspective and improve the performance on theory of mind question.

[00:04:04]But there's now the huge jump in this paper. In this new technical paper, we go go here from an answer prediction of what this human is going to tell us to a user mind reconstruction. This is the main point of this study I'm going to show you today. They say listen this is so interwoven internal belief states here all the influence here from platforms ma whatever you are WhatsApp Tik Tok whatever you name it we have to build a system with a user mind and we construct now here the belief creation I mean isn't this beautiful just think about the happy companies ma all social media companies optimize marketing optimize your selling optimize your augmentation and it is just heaven for influencer if we have now detailed information how to optimize this. This is the study of today and it is American University the University of Illinois's Ubana Champagne and they do exactly this harness a user mind for a stronger agent theory of mind and they call this the user harness and the term harness will make sense in about 10 minutes time why suddenly we apply a harness that we have normally in any system an energetic system here that is around here the core of the agent the LLM now we operate now on a user to harness because we now want to penetrate here this sort process sphere of the customer and we have a neural network to analyze this to guide this to train this 26th of May 2026 now we start so the tell us we propose now the user harness a simple inference time framework you say hey wow that reframes here the tear of mind solving now this user mind reconstruction and rather than asking here the tested agent to also directly know from the raw story they have now a problem because if you give the complete story to an AI you encounter the problems I showed you in the last three videos. So user harness now guides it to recover the user perspective and it is asking hey what has the user observed how those observations shaped here the user human belief and the human intention and how this mental state explains now even prediction action and maybe we as meter we want to sell something here. We just have at the right time just just comment here or give here a particular piece of information and suddenly we are over the threshold and the user buys here presses here the buy button.

[00:06:40]Now of course the framing has to be grounded in some principle. No the user act on beliefs rather than reality. Of course I mean come on if the user starts to be a logical human being and not an emotional human being the whole theory will crumble. Yeah. The belief updates only through accessible evidence. No, what the user really notices. Intentions operate under those beliefs. So we have some multi-layer belief system. And the social reasoning know social media may require tracking how one's user words or action affect others mind or the user is infected here in a group dynamic by some influencer by some group leader and then it is especially easy. Yeah.

[00:07:26]And the others tell us we evaluate now the user harness across five perspective that cover now false belief, nested belief, action prediction, goal inference, communication complexity and the social intention. Hey, absolutely fascinating.

[00:07:43]And I give you the result. This new methodology, this new user harness reaches now a particular accuracy measurement of close to 96%.

[00:07:55]So it seems to be very effective.

[00:07:58]Congratulation Ma. So let's start theory of mind research. Now it originates in the study of how humans attribute mental state such as beliefs, desires, intention, perception and knowledge to others. And if you want to have here, this is a study here by Salesforce research and of course University of Illinois Champagne here from September 2025 training interactive user centric AI agents via reinforcement learning exactly already for this task. So you see here University of Illinois is really into this topic absolutely great and incorporate here already in 2025 with Salesforce AI research and you have this user facing benchmarks to understand how is this possible what have you taken care of what is happening in the mind of a customer and those benchmark increasingly evaluate whether the agent can satisfy real user intents can it really understand what the human is thinking converse with simul related users follow here domain policy discover latent preferences and remain reliable across multi-turn trajectories in a deep conversation with the user. So finally the step number six is the user presses the buy button or upon or whatever you have.

[00:09:17]So what is now the methodology and you might said oh yeah this is now interesting user harness let's have a deep dive. So just to make sure know we have three different levels. We have the real objective world state has almost no influence here no importance here for a human being there. Then we have the filtering horizon that we have to take care of and then we're going to work here with the subjective human mind space.

[00:09:45]Beautiful. You see here oh wow this is here uh AI generated. Yeah all those images I don't paint them. I just give you some commands and you will find here in the images here some mistakes but I just want to show you the main idea here three layers and on this if you want cognitive architecture of the subjective human mind space you see we have first order beliefs and then we have nested beliefs and then we have decision gates where we have thresholds that we have to overcome and now we want to build here somehow a mathematical construct that we can code this and optimize this and we can put it on I don't know a meta platform or your social media platform no so we will have to define here a particular policy pie for our agent and then somehow from this mind space we have to ignite an action here in reality so let's do this theory of mind the evaluation asks you an AI model to answer from the perspective of a user and here we have a Beautiful demonstration of a user coming back from shopping. Isn't this beautiful? But this user here is integrated in a current social media environment. Yeah, we are collecting data about identity, demographics, about location, mobility of the person. We are looking at social graph, friend relationship, communication, membership, interaction frequency. We have information about purchases, brand preferences, subscription services, creditworthiness, car ownership, insurance data. All this information is available and we can build some psychological and behavioral traits of this person. Yeah. And often we can infer from this the impulsiveness, the extraversion, maybe the political engagement, the risk tolerance, even the religion, some anxiety likelihood, some consumer susceptibility and and this is gorgeous, the openness to persuasion, no by any.

[00:11:47]So of course then we sometimes can infer some mental health parameter if we have access to the sleep and activity parameters to the posting behaviors.

[00:11:57]Does this po person post at 3:00 a.m. in the morning? What are the topics? What are here the groups? What are the search time that this person is looking for?

[00:12:08]What are the purchases for or in relation to anything about health or training? And what are the sentiment changes that sometimes we notice here a mood change here in the communication on the public social media platforms?

[00:12:25]Then we can build behavior traces. Huh?

[00:12:27]Examples are purchases, browsing, movement, app use, sleep schedule or social interaction. And you know what?

[00:12:32]Behavior is usually so much more predictive on mathematical models than when we ask this person know can you give us a self-escription of you and we have so many data and we have so many beautiful data centers. We don't need some self-escription of a human being and we can build on temporal patterns.

[00:12:50]No, because timing matters extremely.

[00:12:53]Are you shopping at night? What is here the habit the pattern that you show in your shopping experience weekdays or weekends? What are you shopping for? How much do you travel? What are the frequency as you travel? What are your credit card? What about the changes here during the season? Amazing. Yeah. And your emotional posting cycles analyze here exactly the mood whenever you post something on public media. No. And this allows you prediction now a mathematical to build a mathematical model predicting now your mood. If you have a certain pattern the routines that you have in your professional life you know if you have a job from I don't know from 9 to5 and you just perform always the same pattern in your professional career. My goodness it is so easy for an AI exactly to learn here this pattern. What about your stress resistance? What about impulsivity if you shown new offers? Hey minus 50%. No. And then we have the social network structures. No. Humans are strongly shaped by peer groups. No.

[00:13:55]Someone follows you. You follow somebody. No. Who follows whom in return? What are the cluster affiliation? Which influences you engage with? And this gives us here strong inference here on the ideology aspiration and of course lifestyle. The most important here for buying. So what we achieve here is something beautiful.

[00:14:16]No, we have a crowd of people and completely inhomogeneous. No, and now with data science and here data brokers that sell your personal data here and available and here also as you see an American company or university, they are now able to separate certain groups, target groups exactly for their products. And you can even go for the topology of these groups. No, it is easy if you have leader and follower groups.

[00:14:44]So you have a homogeneous group. So the gorgeous thing what you can do you can define a mathematical specific persona that are now representative for this group that has I don't know 10,000 or 100,000 person or for this group that has 3 million people. Yeah. So we have here the perfect mathematical abstraction the behavior all the characteristic that we can work with because remember what was it here? Yeah.

[00:15:10]Simulate group dynamic. But before we can go in a group dynamic, we have to start with a single human being because the topic of this paper today is user harness and user is a singular. So let's start.

[00:15:22]Let's say a user is you for user beautiful does not observe here a the environment directly at a particular time t. Instead the user receives only a part of the environment as a partial observation and this observation is o and you have this here at a particular time t.

[00:15:40]Now we have here an omega function where we have exactly here our if you want part of the environment and here the user and this omega describes exactly what is accessible to this particular user you at that particular moment in time t such as events in the same room visible object that changes or social media messages that are addressed here to the user. So somehow we have to get a feeling for this. Think about this. You have the complete reality plane and then we have something that is observable and epsilon around the single particular human. This is only his observable environment in this particular moment of time. Now beautiful omega models here the physical and the communicative constraints uh spatial co-presence line of sight receiving directed message social media communication whatever and then we have somehow to model the subjective belief space of the single human being. Yeah. B for belief space at a time. T for the user U. Now to reason about social interaction the user mind must track not only their own beliefs but also nested beliefs. No about other agents about other people about what about the the trend and the group what is coming what is in what is out no? So we define there by now a hierarchical belief state B for belief of a particular time t for a particular user here in this one. and what we have hierarchical. So we have a first order belief state, a second order belief state and a third order nested belief structure that we have to take care of.

[00:17:15]And then we have to have an update. We have to have a belief update because something changes in our environment.

[00:17:21]You receive a message, some new information, some new knowledge, your friends are going other places, whatever. So this belief update, let's make it easy and let's make it a ruleguided belief update. Gamma gamma of axums r are now the evolution of the belief state over the time t and it is governed by an update operator gamma of r constrained now by only a set of logical perspective axioms are and here you have the formula for this now what is r is a rule set and acts as an inductive bias ensuring if you have no observation of the single human we have no update if an event is unobserved yes it happened but it did not happen to the user you and therefore or the belief at the user u at the time t is identical to the belief of the user u at t minus 1 or t + 1. We have communication boundaries.

[00:18:10]No, a message update m affects here the belief of the user you at the time t if and only if you is a recipient of this message. No, and we have nested beliefs and core observation and yeah everything beautiful.

[00:18:26]And then in general we have to have an action model. when do we ignite an action here in a human being and then a world transition function. So let's have a look at this. The user's action are dictated now strictly by their let's say subjective individual mental state B for the belief state here of a user U at a time T and their goal what they want to achieve. Hey, I want to buy a new dress.

[00:18:49]I want to buy a new car. I want to buy a new computer or just want to go and have fun with my friends or whatever. your goal rather than the true environmental state. You're human. You can change the environment. You have set your goals, what you want to achieve, what you want to experience, what you want to get. So this is the action defined here by our policy pi. And as you see, we have here elements is the goal and the belief and not the environment itself. So this subjective actions now for the single human being subsequently feed back into the objective state. Since we have taken now an action we change now the environment around us in the epsilon around us through a transition function where e represents here the external story events here that is happening and then finally finally we can go into the inference time proving or the consistency maximization so let's say we have given a benchmark story s we have to work with benchmarks here a query Q a set of multiple choices why because yes we're in America and we only do everything in multiple choice so we have to construct a trace a reasoning trace.

[00:19:54]So the user harness now parses the story line s into a discrete sequence and constructs now a particular trace as you see here in this formula. Now this here is here our trace.

[00:20:08]Second we have to have an evolation of the consistency. So for each candidate option here y which represents a claim about a belief a goal or an action or whatever the framework computes now a consistency score that's inval from 0 to one zero is nothing one is absolutely believe in this no great this is the formula now you might ask the orus how they position out the core of the agent the llm itself and the or tell us hey our llm is now restricted to two specialized role in this we have is LLM as a kind of a translation helper. What it does, it translates here the raw story text. No, we have a benchmark story into a prover readable fact structure. So we are parsing here the environment at a particular time t and extracting now the characters, the goal, the events, the timelines, what is happening to whom at what event and what are the consequences, what are the dependencies and whatever. And then our LLM is also here a proof auditor. So the LLM is now inspecting here the f final candidate proofs to verify the semantic compatibility and resolve the edge cases where strict symbolic rules are too rigid. And at this moment I said hey wait a minute what do you mean to verify the semantic compatibility? I thought we going to compute here we have an operator a projection operator. Why are we suddenly back to the semantic compatibility issue? And what do you mean when symbolic uh rules are too rigid, we fall back to a neural network?

[00:21:43]H. Now it turns out if you read the study a second time that instead of treating it theory of mind as a linguistic pattern matching exercise or a free form reasoning the authors formalize it only for one particular idea because what they want is a state estimation problem over an agent's hidden cognitive state because the state we don't know what the human wants, what it needs, how can we manipulate, how can we influence, how can we make the user happier. Yeah, this is the right wording. How can we make them happy?

[00:22:18]What they have to buy to be happy?

[00:22:22]So yeah, they tell us by enforcing an explicit projection operator or omega and a ruleguided belief update our gamma r at inference time. They built this for a particular reason to construct now a barrier against the perspective leakage.

[00:22:39]Remember the perspective leakage in my last videos also I showed you we are not allowed to give here the boss AI or the orchestrating AI access to all the complete uh solutions because otherwise we would have different effect happening deep inside the LM in my last two videos. So therefore we have to restrict the access to the solution and make the LLM work make the LLM go step by step do the heavy sinking step by step and come up with a solution. Notice very same the identical topic here. So they formalize it now as a simple state estimation problem over the cognitive state.

[00:23:19]And I said wait a minute does it mean there's no new mathematical insight?

[00:23:25]There's no new brilliant mathematical formula.

[00:23:29]I mean there's no math at all in this algorithm.

[00:23:34]Yes. It turns out that the system just maintains a relational database. This is a state transition graph structure of what each character believes in. And the update rule is just a step of a discrete logic. But we do not activate the neural network of our LLM. We are really here in symbolic logic.

[00:23:54]And the truth is this is a pure discrete state machine tracking not anyway a non-discrete option. So it requires if you want zero advanced math it is just a series of boolean operation and dictionary assignments. I'm so sorry I feel with you.

[00:24:12]So what we have we just have a little bit of a discrete mathematics a little bit of formal logic a little bit of set theory and a little bit of automata theory but this is it. There's no mathematical breakthrough theory.

[00:24:24]There's new mathematical theorem how we can operationalize here our projection operators. how we can build new embedding spaces. Nothing of that because the truth is here the orus utilize the LLM just a semantic to symbolic translator using here it deep language understanding to convert it a raw massing narrative text into discrete fact is store here at a database and then feed those facts into a rigorous rulebased symbolic loop. My goodness to ensure the final reasoning does not suffer from perspective leakage.

[00:25:01]Okay, this was a disappointment but let's have a look at result. Is it is it at least working? Do we at least have some benefits of this here? Now, so let's let's move in.

[00:25:11]You see here elements here from a Q1 8 billion, 14 billion, 32 billion, then the two GPD open source here and a GPD 5.4 at least. Otherwise, yeah, forget all about the llama. Please never use Llama 3 again on a benchmark in 2026.

[00:25:30]This is such a nonsync anymore.

[00:25:34]Okay. And then they have some benchmark and they do their testing and yes and you have a direct prompting where the model reasons and answer directly and you have a paradox prompting where the model tests each answer option by assuming it is true and checking for contradiction. And then you have the ledger prompting where the model is asked to organize its reasoning around the agent environment, observation, beliefs, goals and actions and come up with a solution. But the real interesting point is this now.

[00:25:59]So we have now this user harness and they have here the open source model.

[00:26:02]Beautiful. And then the closed source model and at least we have here an Opus 4.7 and a Sonnet 4.6 and a Gemini 3.1 Pro and yeah. Okay. GBD 5.4 still. Okay.

[00:26:15]But look at this. Suddenly here the first the simplest benchmark we have 100%.

[00:26:21]Hey great nice. And look at the other benchmarks.

[00:26:25]Now this is nice. Look at this. We have an opensource model. Let go with a Q14 billion model, a local model. And we have a macro accuracy of 95%.

[00:26:39]And then we go with the same methodology here that works on a 14B model to a clawed ous 4.7.

[00:26:46]And we have here an overall macro uh accuracy of 95.94%.

[00:26:54]So you might say, hey, wait a minute, this is nice. So this separation that is happening here, as trivial, mathematically trivial as it is, it opens up a way that the 14 billion miles suddenly has it with micro accuracy of almost identical to a clo OPUS 4.7. Now this is a nice aspect. Let's talk about this. Why is this happening? And of course, you will agree with me when I tell you this demonstrate here. a decoupling of the epistemic structure of the knowledge structure from a raw model generation capacity that dramatically elevates and stabilizes here the reasoning accuracy especially look at for a 14 billion model. Yes, even better for a 32 billion model. But this is now something why show you the study. This opens now something where we say okay maybe you don't have to pay for the opus 4.7 but you can have this locally.

[00:27:54]This second image here shows you here the macro accuracy on the y-axis and the effective model output token on a log scale here on the x-axis here and you see here the stars our new user harness.

[00:28:07]So you see all the stars are exactly where we want them close to 100% and here only 150 output tokens. This kind of let's say visually confirms that structuring here the inference path is far more compute efficient than eliciting a long unstructured chain of sort explanation here in our inference run. So yes we have to invest a little bit of premputee here before we go here and we have to prepare the system but look at this performance. This is really nice close to 100% macro accuracy with only 150 token per example output token.

[00:28:44]This is nice. So suddenly although it is a very simple flat not really challenging not innovative mathematical system it provides results.

[00:29:00]Let's have now a personal reflection.

[00:29:02]The study ended and what can what have I taken away from this? Yes, the cherry is a little bit smaller but we still have the chocolate cake.

[00:29:14]So again under the standard direct prompting or chain of sort the eye model must track the complete complete and complex epistemic transition entirely in its multiple layers. No using self attention layer to guess who knows what at what time in while simultaneously generating here the text of the answer.

[00:29:35]And now let's put this here and let's view this from a different perspective.

[00:29:39]Let's reframe this. Let's reframe this from a perspective how weak our AI systems are in their performance because what does user harness actually do?

[00:29:49]Let's see behind the curtain. It decoupled it separates this. No, because it extract the epistemic structure and moves it simply to an external protected symbolic tracker or a database or whatever you have memory and the model is no longer responsible for remembering in internally or calculating who knows what at what particular time and what changes in what time elements. No. So this means that the external loop handles here the tracking programmatically rule-based leaving now deterministic almost leaving now the LLM probabilistic interpretation to only perform simple localized I would almost say symbolic translation or audit checks.

[00:30:35]I never thought that this would have such a massive effect on the performance of a system.

[00:30:41]I thought about this and I found a simpler example. So let's unpack its three key components here. So we have the raw model, the AI model here. Let's say Opus 4.7, the generation capacity.

[00:30:52]Then we do have the epistemic structure, the knowledge structure inherent to particular benchmark and the mathematical consequences of decoupling here the complexity. Let's think about a pencil and paper analogy. It is not the same mathematic. It's just the idea. You ask two different people to solve a complex multi-step mathematical problem such as dividing a number here by another number. Yeah. And you have two persons. Person A is a math wizard.

[00:31:18]Yeah. With a high working memory capacity. This is my Opus 4.7. No example. Then we have a person B. This is me. I have a lower than average memory. I'm a simple 3 billion AI model.

[00:31:31]No hardly anything.

[00:31:33]And if you force them both to calculate now the entire division or mathematical operation whatever you have strictly in their heads without writing anything down person A the genius wizard here might get the answer right 70% of the time while person B me I will almost fail completely all the time so the calculation depends entirely on their internal mental capacity to hold and update here those numbers in our example but now let's change this and now let's do what the study did. Now you give both of them a pencil and a piece of paper. This is it. You can separate this. Now once you can write down the intermediate steps on the paper, the structure of the calculation is offloaded. Let's say from my little brain to the paper. I can write it down.

[00:32:23]I can make here a beautiful calculation, a long calculation. And suddenly both the person A, this is here the wizard. This is here my Opus 4.7 and person B, this is here my little personality. Can we both solve the problem with 99% accuracy?

[00:32:41]So you here person B is this 3 billion or 8 billion Cuban model.

[00:32:47]It is amazing that this simple things like separating here just giving here the eye a pencil and a piece of paper can increase the accuracy the macro accuracy of those benchmarks so significantly.

[00:33:05]So this means now in the inside only system where with AI AI is still horrible.

[00:33:13]But by externalizing the structure of the mathematical problem itself, we have a chance to decouple the success of the task from the raw internal memory capacity and the number crunching. So this means this separation as simple as it seems to us is the path to success for an AI system. So the reasoning burden is shifted from a probabilistic memory reconstruction to a simple deterministic structure execution in a database or wherever you have it or a piece of paper.

[00:33:47]So again it seems so simple but look at the result again the same topic a QN34B 95% mark accuracy on the same task like a claude ous 4.7 with 95.9 percentage mark accuracy our small little models our local models with the right harness complexity with the right insight how this AI operates how it works works. We can tune our little 14B model to come damn close to a clo OPUS 4.7 model. I hope you enjoyed it. You hope you had a little bit of fun. It was a little bit of a dramatic roller coaster, but at least we had our chocolate cake. And I hope to see you in the next video.