Opportunity summary
Score3.0Public score shown from the verified overall while the stale axis breakdown refreshesThis canonical paper page includes Commercialization Proof and Related Resources.
ARXIV:2604.02145 · AI AGENT PROFILING · SUBMITTED 03 APR · 20:30 UTC · FRESHNESS STALE
ARXIV:2604.02145AI AGENT PROFILINGSUBMITTED 03 APR · 20:30 UTCFRESHNESS STALEJihoon Jeong · arXiv
A novel behavior-based system to profile AI agent temperaments across four key axes, independent of model capability.
Opportunity summary
Pain A novel behavior-based system to profile AI agent temperaments across four key axes, independent of model capability.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence unverified
A novel behavior-based system to profile AI agent temperaments across four key axes, independent of model capability. Existing approaches either borrow human personality dimensions and rely on self-report (which diverges from actual behavior in…
AI models of equivalent capability can exhibit fundamentally different behavioral patterns, yet no standardized instrument exists to measure these dispositional differences. Existing approaches either borrow human personality dimensions and rely on self-report (which diverges…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We profile 10 small language models (1.7B-9B parameters, 6 organizations, 3 training paradigms) and report five principal findings: (1) the four axes are largely…
AI Agent Profiling moved forward this cycle; last verified April 2026. Public score 3.0/10.
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A novel behavior-based system to profile AI agent temperaments across four key axes, independent of model capability.
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Paper Pack
10.48550/arXiv.2604.02145A novel behavior-based system to profile AI agent temperaments across four key axes, independent of model capability.
Abstract
AI models of equivalent capability can exhibit fundamentally different behavioral patterns, yet no standardized instrument exists to measure these dispositional differences. Existing approaches either borrow human personality dimensions and rely on self-report (which diverges from actual behavior in LLMs) or treat behavioral variation as a defect rather than a trait. We introduce the Model Temperament Index (MTI), a behavior-based profiling system that measures AI agent temperament across four axes: Reactivity (environmental sensitivity), Compliance (instruction-behavior alignment), Sociality (relational resource allocation), and Resilience (stress resistance). Grounded in the Four Shell Model from Model Medicine, MTI measures what agents do, not what they say about themselves, using structured examination protocols with a two-stage design that separates capability from disposition. We profile 10 small language models (1.7B-9B parameters, 6 organizations, 3 training paradigms) and report five principal findings: (1) the four axes are largely independent among instruction-tuned models (all |r| < 0.42); (2) within-axis facet dissociations are empirically confirmed -- Compliance decomposes into fully independent formal and stance facets (r = 0.002), while Resilience decomposes into inversely related cognitive and adversarial facets; (3) a Compliance-Resilience paradox reveals that opinion-yielding and fact-vulnerability operate through independent channels; (4) RLHF reshapes temperament not only by shifting axis scores but by creating within-axis facet differentiation absent in the unaligned base model; and (5) temperament is independent of model size (1.7B-9B), confirming that MTI measures disposition rather than capability.
Source availability
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Extraction status
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Proof status
unverified0 refs; 0 sources; 50% coverage.
What was readable
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Viability
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Dimensions overall score 3.0
PROBLEM
A novel behavior-based system to profile AI agent temperaments across four key axes, independent of model capability. Existing approaches either borrow human personality dimensions and rely on self-report (which diverges from actual behavior in LLMs) or treat behavioral variatio...
METHOD
AI models of equivalent capability can exhibit fundamentally different behavioral patterns, yet no standardized instrument exists to measure these dispositional differences. Existing approaches either borrow human personality dimensions and rely on self-report (which diverges fr...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We profile 10 small language models (1.7B-9B parameters, 6 organizations, 3 training paradigms) and report five principal findings: (1) the four axes are largely independent among instruction-tuned models...
WHY NOW
AI Agent Profiling moved forward this cycle; last verified April 2026. Public score 3.0/10.
We introduce the Model Temperament Index (MTI), a behavior-based profiling system that measures AI agent temperament across four axes: Reactivity (environmental sensitivity), Compliance (instruction-behavior alignment), Sociality (relational resource allocation), and Resilience (stress resistance).
Directly stated in the abstract as the core definition of the introduced system.
partial
the four axes are largely independent among instruction-tuned models (all |r| < 0.42)
Explicitly stated as a principal finding with a specific numeric bound.
partial
Compliance decomposes into fully independent formal and stance facets (r = 0.002)
Explicitly stated as a principal finding with a specific numeric correlation.
partial
RLHF reshapes temperament not only by shifting axis scores but by creating within-axis facet differentiation absent in the unaligned base model
Directly stated as a principal finding, though the specific nature of the differentiation is not detailed in the provided text.
partial
temperament is independent of model size (1.7B-9B), confirming that MTI measures disposition rather than capability.
Explicitly stated as a principal finding with a specific parameter range.
partial
Existing approaches either borrow human personality dimensions and rely on self-report (which diverges from actual behavior in LLMs) or treat behavioral variation as a defect rather than a trait.
Directly stated as a critique of prior work, though the characterization of 'defect' is a summary.
partial
using structured examination protocols with a two-stage design that separates capability from disposition.
Directly stated as a key methodological feature of the introduced system.
partial
a Compliance-Resilience paradox reveals that opinion-yielding and fact-vulnerability operate through independent channels
Directly stated as a principal finding, though the term 'paradox' implies an interpretation.
partial
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Concepts
Methods
Materials
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Competitors
A novel behavior-based system to profile AI agent temperaments across four key axes, independent of model capability.
Segment
AI Agent Profiling
Adoption evidence
No public code link in the paper record yet
Commercial read
3.0/10 public viability
Direct
Adjacent
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CITED BY
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Extension
Commercially relevant
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1/3 checks · 33%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
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Evidence coverage
OpportunityKernel evidence_receipt
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Build readiness
BuildPassport EvidenceState
passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 50% evidence coverage.
Gaps
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
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Defensibility signals are missing.
Evidence
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
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Evidence
Cost passport has no observed_usd value.
Gaps
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Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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People
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Regulatory need unclassified.
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People
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Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
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