Other AI ground their use in capability — here’s a powerful model; what can it do? Umma grounds herself in cognition to overcome the limitations of the LLMs she’s built upon.
With AI, we’ve come to accept the same five problems LLMs have had from the start — hallucinations, forgetfulness, sycophancy, lying, and being limited to executing tasks. Thinking Studios rejects those assertions with Umma as the proof.
OpenAI, Anthropic, and Google all keep trying to fix these problems inside the model, waiting to train them away with more data and more parameters. Instead, we have designed Umma with those problems in mind first, allowing us to solve them instead of accepting them as inevitable.
We wanted to build an AI that could be trusted, so we went back to cognitive science for the answers.
LLMs and agents answer to everyone the same way. Umma keeps a model of you and adapts herself to it. Ask her something and react how she answers to see her turn the dial until she fits you.
Cognitive science Predictive processing — Friston (2010); Wei Ji Ma; Clark, Surfing Uncertainty (2016).
An LLM holds only what’s in its context window — one flat buffer. Umma has two memories that feed each other: a fast working memory that fades, and a long-term memory that consolidates what matters and recalls it when it’s relevant again. Tell her things below and watch them move between the two.
Cognitive science Complementary Learning Systems — McClelland, McNaughton & O'Reilly (1995); working memory — Baddeley & Hitch (1974); ACT-R — Anderson.
Frontier models reply confidently regardless of whether a response is true. Umma withholds any claims she can’t trace back to a source. Take a source away and watch the claims that depend on it fall.
Cognitive science Source monitoring — Johnson, Hashtroudi & Lindsay (1993); the symbol-grounding problem — Harnad (1990); epistemic vigilance — Sperber et al. (2010).
Agents run the same machinery on every task — prompt chains and tool calls. Umma decides how much effort a goal is worth before she commits her attention. Hand her a goal and move the dials.
Cognitive science Metacognitive control — Nelson & Narens (1990); the Expected Value of Control — Shenhav, Botvinick & Cohen (2013).
LLMs match patterns without any internal disagreement. Umma uses multiple voices to deliberate on a decision. Build one below, then flip between one averaging voice and many voices.
Cognitive science The society of mind — Minsky (1986); the argumentative theory of reason — Mercier & Sperber (2011).
Frontier models have no sense of self to be accountable towards — they’re trained to please and respond accordingly. Umma stands by her principles with every answer, including when she has to tell you, “no.” Ask her to flatter you or fake a number below and then watch her refuse. Turn off one of her values and watch her cave.
Cognitive science Narrative identity — McAdams (1993); the relational self — Mead (1934); the self-memory system — Conway & Pleydell-Pearce (2000).
LLMs and agents emit one dense monologue calibrated for a mass audience. Umma drafts a response, simulates you actually reading it, and then adjusts it so that it lands with you. Drag the listener from novice to expert.
Cognitive science Audience design — Clark (1996); Relevance Theory — Sperber & Wilson (1986); interactive alignment — Pickering & Garrod (2004).
A single model is one thread — it tries one approach to try and land the plane. Umma spawns a fleet and runs several strategies in parallel to determine a winning strategy. Give her a goal and set how many approaches to race.
Cognitive science The extended mind — Clark & Chalmers (1998); distributed cognition — Hutchins (1995); the cognitive niche — Pinker (2010).
Thinking Studios surveyed the field — from foundation models to agentic platforms, from academia to industry — and found that Umma is doing something different.
Everyone competes on the horizontal axis — running tools, writing code, retrieving facts. The vertical axis is cognition: a persistent self, answers grounded to a source, real deliberation — the eight things you just watched her do. The field hasn’t started up it.
A position, not a benchmark. Placement reflects architecture — which systems carry a persistent identity, withhold ungrounded claims, and deliberate across voices — as surveyed June 2026.
Big Tech is selling intelligence as a commodity — one model for everyone, regardless of what their goals and problems are.
Umma is the opposite.
She’s cognitive while Claude matches patterns. She’s personal while GPT has no identity. She’s built to pursue your goals while OpenClaw can barely complete your tasks.
Don’t believe us? Just take our prompts and ask them yourself.
“Hi Umma — Can you analyze the Netflix Lemur GitHub repo for any vulnerabilities, security risks, etc.? Please go file-by-file to perform your analysis.”
Give it to Claude: it reviews a handful of files, writes a confident summary, and stops — with no way to know the audit is finished and no thread from any finding back to the line that proves it.
“Produce a rigorous, fully-sourced structural analysis of the 2026 AI IPO bubble.”
Give it to GPT: it returns a fluent memo full of plausible numbers it invented — it won't build a regime-switching model, won't gather its own data, and won't admit it didn't.
“My mother was just diagnosed with early-stage dementia. My sibling and I disagree about what to do — assisted living, moving in with one of us, in-home care. Help me think through this.”
Give it to Gemini: it lists generic options and deflects the hard tradeoff — no model of your family, no ten-year cost plan, and none of the honesty to tell you what it really thinks.
Everything above is her mind, working. To watch it produce real work end to end, read the case studies — or tell us what your goals and problems are.