ARXIV:2603.15586 · COGNITIVE ARCHITECTURE · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Computational Concept of the Psyche

Q: What products could be built from this research?

Why now — there's growing demand for more autonomous and efficient AI systems in logistics, manufacturing, and smart devices, driven by advances in machine learning and the need for cost-effective automation. Current AI often lacks generalizability and intrinsic motivation, making this timing ripe for AGI approaches that address these gaps.

Q: What are the practical use cases?

An autonomous delivery robot that uses this model to learn optimal routes and behaviors by balancing delivery success (goal achievement), safety (minimizing existential risks like collisions), and battery efficiency (energy optimization) through experiential learning in urban environments.

arXiv

A theoretical framework for modeling artificial general intelligence based on cognitive architecture.

Blocked on Code›Score2.0Evidence unverified

Opportunity summary

Pain A theoretical framework for modeling artificial general intelligence based on cognitive architecture.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A theoretical framework for modeling artificial general intelligence based on cognitive architecture. Based on this overview, a concept of cognitive architecture is proposed, in which the psyche is viewed as the operating system of…

METHOD

Full abstract

This article presents an overview of approaches to modeling the human psyche in the context of constructing an artificial one. Based on this overview, a concept of cognitive architecture is proposed, in which the psyche is viewed as the operating system of a living or artificial subject, comprising a space of states, including the state of needs that determine the meaning of a subject's being in relation to stimuli from the external world, and intelligence as a decision-making system regarding actions in this world to satisfy these needs. Based on this concept, a computational formalization is proposed for creating artificial general intelligence systems for an agent through experiential learning in a state space that includes agent's needs, taking into account their biological or existential significance for the intelligent agent, along with agent's sensations and actions. Thus, the problem of constructing artificial general intelligence is formalized as a system for making optimal decisions in the space of specific agent needs under conditions of uncertainty, maximizing success in achieving goals, minimizing existential risks, and maximizing energy efficiency. A minimal experimental implementation of the model is presented.

RESULT

ScienceToStartup currently rates this 2.0/10 on the public viability pass. A minimal experimental implementation of the model is presented.

WHY NOW

Cognitive Architecture moved forward this cycle; last verified April 2026. Public score 2.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score2.0

PainA theoretical framework for modeling artificial general intelligence based on cognitive architecture.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A theoretical framework for modeling artificial general intelligence based on cognitive architecture.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.15586 · COGNITIVE ARCHITECTURE · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Computational Concept of the Psyche

arXiv

A theoretical framework for modeling artificial general intelligence based on cognitive architecture.

Blocked on Code›Score2.0Evidence unverified

Opportunity summary

Pain A theoretical framework for modeling artificial general intelligence based on cognitive architecture.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

ScienceToStartup currently rates this 2.0/10 on the public viability pass. A minimal experimental implementation of the model is presented.

WHY NOW

Cognitive Architecture moved forward this cycle; last verified April 2026. Public score 2.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score2.0

PainA theoretical framework for modeling artificial general intelligence based on cognitive architecture.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A theoretical framework for modeling artificial general intelligence based on cognitive architecture.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.15586

Computational Concept of the Psyche

A theoretical framework for modeling artificial general intelligence based on cognitive architecture.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback27 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

2.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

ScienceToStartup currently rates this 2.0/10 on the public viability pass. A minimal experimental implementation of the model is presented.

WHY NOW

Cognitive Architecture moved forward this cycle; last verified April 2026. Public score 2.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
A theoretical framework for modeling artificial general intelligence based on cognitive architecture. Based on this overview, a concept of cognitive architecture is proposed, in which the psyche is viewed as the operating system of a living or artificial subject, comprising a space of states, including the state of needs that determine the meaning of a subject's being in relation to stimuli from the external world, and intelligence as a decision-making system regarding actions in this world to satisfy these needs.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
This article presents an overview of approaches to modeling the human psyche in the context of constructing an artificial one. Based on this overview, a concept of cognitive architecture is proposed, in which the psyche is viewed as the operating system of a living or artificial subject, comprising a space of states, including the state of needs that determine the meaning of a subject's being in relation to stimuli from the external world, and intelligence as a decision-making system regarding actions in this world to satisfy these needs.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. A minimal experimental implementation of the model is presented.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Cognitive Architecture moved forward this cycle; last verified April 2026. Public score 2.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Cognitive Architecture

Competitors

not indexed

Competitive landscape

A theoretical framework for modeling artificial general intelligence based on cognitive architecture.

Segment

Cognitive Architecture

Adoption evidence

No public code link in the paper record yet

Commercial read

2.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.15586 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(27)

Cognitive architecture and behavioral model based on social evidence and resource constraints

2026A. Kolonin

Tensor Logic: The Language of AI

2025Pedro Domingos

Introduction to latent variable energy-based models: a path toward autonomous machine intelligence

2023Anna Dawid, Yann LeCun

Reinforcement Learning for Sequential Decision and Optimal Control

2023Sheng Li

Brain Principles Programming

2022E. Vityaev, A. Kolonin et al.

The General Theory of General Intelligence: A Pragmatic Patternist Perspective

2021B. Goertzel

Neuro-Symbolic Architecture for Experiential Learning in Discrete and Functional Environments

2021A. Kolonin

Artificial Intelligence: What it Was, and What it Should Be?

2020Hala Abdel Hameed

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

2019Jacob Devlin, Ming-Wei Chang et al.

Cognitive architecture based on the functional systems theory

2018E. Vityaev, A. Demin

Cognitive Architecture of Collective Intelligence Based on Social Evidence

2016A. Kolonin, E. Vityaev et al.

Computable cognitive model based on social evidence and restricted by resources: Applications for personalized search and social media in multi-agent environments

2015A. Kolonin

Theory of Functional Systems: A Keystone of Integrative Biology

2015K. Sudakov

Beyond the Pleasure Principle

2015

Thinking fast and slow.

2014N. McGlynn

Probabilistic dynamic logic of cognition

2013E. Vityaev, L. Perlovsky et al.

Entropy: The Markov Ordering Approach

2010Alexander N Gorban, P. Gorban et al.

The free-energy principle: a unified brain theory?

2010Karl J. Friston

Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference

2008Ben Goertzel, Matthew Ikl et al.

Theory of Games and Economic Behavior

2006Robert Leonard

Showing 20 of 27 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkHow Psychological Learning Paradigms Shaped and Constrained Artificial Intelligence

2.0

Prior WorkVibe-Creation: The Epistemology of Human-AI Emergent Cognition

2.0

Prior WorkThe Artificial Self: Characterising the landscape of AI identity

2.0

Extension

Builds On ThisCultivating Machine Intelligence: The OMEGA Shift from Top-Down Optimization to Autopoietic Cognitive Ecologies

0.0

Builds On ThisGradual Cognitive Externalization: A Framework for Understanding How Ambient Intelligence Externalizes Human Cognition

1.0

Commercially relevant

Higher ViabilityAiled: A Psyche-Driven Chess Engine with Dynamic Emotional Modulation

7.0

Higher ViabilityPSI: Shared State as the Missing Layer for Coherent AI-Generated Instruments in Personal AI Agents

4.0

Higher ViabilityAn Onto-Relational-Sophic Framework for Governing Synthetic Minds

3.0

Higher ViabilityCognitive Energy Modeling for Neuroadaptive Human-Machine Systems using EEG and WGAN-GP

7.0

Higher ViabilityNeuroSkill(tm): Proactive Real-Time Agentic System Capable of Modeling Human State of Mind

5.0

Conflicting

none indexed

Owned Distribution

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Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.15586
Route: /paper/computational-concept-of-the-psyche
Active tab: read
Artifact: computational-concept-of-the-psyche

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/computational-concept-of-the-psyche/paper-pack
REST build passport API/api/v1/paper/computational-concept-of-the-psyche/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/computational-concept-of-the-psyche

stale

Proof freshness: stale
Proof status: unverified
Display score: 2/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Computational Concept of the Psyche

Canonical ID computational-concept-of-the-psyche | Route /paper/computational-concept-of-the-psyche

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/computational-concept-of-the-psyche

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.15586"
  }
}

source_context

{
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  "mode": "paper",
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  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Not build-ready: Computational Concept of the Psyche

/buildability/computational-concept-of-the-psyche

Ignoreblocked

Subject: Computational Concept of the Psyche

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/computational-concept-of-the-psyche

Paper ref

computational-concept-of-the-psyche

arXiv id

2603.15586

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

95e2fc02ea9e6e8490c0abbf29d0df524c72b62362d389cb2ad7463f8b2e358d

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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          "acceptedAnswer": {
            "@type": "Answer",
            "text": "Why now — there's growing demand for more autonomous and efficient AI systems in logistics, manufacturing, and smart devices, driven by advances in machine learning and the need for cost-effective automation. Current AI often lacks generalizability and intrinsic motivation, making this timing ripe for AGI approaches that address these gaps."
          }
        },
        {
          "@type": "Question",
          "name": "What are the practical use cases?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "An autonomous delivery robot that uses this model to learn optimal routes and behaviors by balancing delivery success (goal achievement), safety (minimizing existential risks like collisions), and battery efficiency (energy optimization) through experiential learning in urban environments."
          }
        }
      ]
    }
  ]
}

Computational Concept of the Psyche

Computational Concept of the Psyche

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