Opening — Why this matters now
Large language models have become remarkably fluent. They explain, summarize, reason, and occasionally even surprise us. But fluency is not the same as adaptability. As AI systems are pushed out of chat windows and into open, messy, real-world environments, a quiet limitation is becoming impossible to ignore: language alone does not teach an agent how to live.
The paper Human Simulation Computation by Hong Su confronts this limitation head-on. Its central claim is simple, almost blunt: if an AI system cannot think, act, learn, and reflect as a continuous loop, it will never adapt the way humans do. And without adaptation, intelligence plateaus quickly—no matter how many parameters you add.
Background — Context and prior art
Most modern AI reasoning stacks are language-centric. Even advanced techniques—chain-of-thought prompting, reflection, self-consistency—operate within a single inference episode. They improve answers, not agents.
On the other end of the spectrum, embodied AI and reinforcement learning systems do interact with environments, but they tend to be:
- Narrowly task-defined
- Reward-function dependent
- Opaque in their internal reasoning
Cognitive architectures tried to bridge this gap decades ago, but lacked today’s scalable language models. What Su proposes is not a rejection of LLMs, but a repositioning: LLMs as components inside a broader human-like simulation loop, not the loop itself.
Analysis — What the paper actually proposes
The core contribution is Human Simulation Computation (HSC): a closed-loop framework that treats intelligence as an ongoing process rather than a sequence of tasks.
At its core, HSC cycles through five tightly coupled stages:
- Thinking – Goal-oriented, difference-driven reasoning that decides whether, how, and when to act.
- Action – Not just task execution, but deliberate probing of the environment to obtain feedback and reduce uncertainty.
- Reflection – Retrospective evaluation of thinking paths, actions, and outcomes.
- Learning – On-time accumulation of experience from everything: thoughts, actions, failures, and feedback.
- Activity Scheduling – A control layer that decides what deserves attention now, later, or during idle time.
Formally, the system evolves as a state transition loop:
$$ s_{t+1} = L\big(s_t, R(s_t, A(s_t, T(s_t, f_t)))\big) $$
The equation matters less than the philosophy behind it: results do not come directly from inputs, but from repeated cognition–action cycles.
Human thinking strategies, operationalized
The paper’s most underappreciated strength is its systematic catalog of human thinking strategies—and its insistence that these cannot be learned reliably from text alone.
Some of the key strategies embedded into HSC include:
| Strategy | Why it matters for AI |
|---|---|
| Main-feature-oriented reasoning | Reduces combinatorial explosion by focusing on what actually changed |
| Scope expansion via action | Forces the system to acquire missing context instead of hallucinating |
| Oppositional & holistic thinking | Prevents premature convergence on a single explanation |
| Risk avoidance & positive reframing | Supports long-term stability under repeated failure |
| Candidate action planning | Enables reversible, testable decisions instead of brittle commitments |
Crucially, action is treated as part of thinking, not something that happens afterward. Acting is how the system tests its own beliefs.
Findings — Why language-only learning breaks
The paper’s theoretical argument is clear: human-like reasoning strategies emerge from interaction, not narration.
Language data reflects descriptions of thinking, not the feedback loops that shaped it. Without environmental interaction:
- Incorrect beliefs cannot be falsified
- Prediction errors cannot be measured
- Learning collapses into stylistic consistency
By contrast, HSC introduces explicit verification signals—discrepancies between predicted and observed outcomes—that drive genuine adaptation.
Difference-based reasoning further reduces search complexity by filtering candidate actions to those that deviate meaningfully from the norm:
$$ C’ = {c \in C \mid \Delta(c) > \tau} $$
This is not just efficient—it is human.
Implications — What this means for real AI systems
HSC quietly reframes the goal of AI development. The objective is no longer:
“Solve more tasks.”
It becomes:
“Maintain and improve the ability to operate in an open environment.”
For practitioners, this has uncomfortable implications:
- Prompt engineering alone will not scale
- Autonomous agents need memory, scheduling, and reflection—not just better decoding
- Evaluation must include behavior over time, not single-shot accuracy
For business and system designers, HSC suggests that durable AI advantage will come from agents that learn from use, not models that merely respond.
Conclusion — AI that lives, not just talks
Human Simulation Computation does not promise flashy benchmarks or immediate product gains. What it offers instead is something more foundational: a coherent answer to why today’s AI feels smart but fragile.
By embedding thinking, action, learning, reflection, and scheduling into a single adaptive loop, HSC reframes intelligence as ongoing survival and improvement, not linguistic performance.
If language models are the mouth of modern AI, HSC is an argument that we finally need a nervous system.
Cognaptus: Automate the Present, Incubate the Future.