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ARXIV:2604.21098 · LLM BEHAVIOR ANALYSIS · SUBMITTED 24 APR · 20:33 UTC · FRESHNESS STALE
ARXIV:2604.21098LLM BEHAVIOR ANALYSISSUBMITTED 24 APR · 20:33 UTCFRESHNESS STALEOlli Järviniemi · Oliver Makins · Jacob Merizian · Robert Kirk · Ben Millwood · arXiv
Develops and applies methods to measure LLM propensity for unsanctioned behavior by analyzing environmental factors, finding equal contributions from strategic and non-strategic influences.
Opportunity summary
Pain Develops and applies methods to measure LLM propensity for unsanctioned behavior by analyzing environmental factors, finding equal contributions from strategic and non-strategic influences.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Develops and applies methods to measure LLM propensity for unsanctioned behavior by analyzing environmental factors, finding equal contributions from strategic and non-strategic influences. We contribute three methodological improvements: analysing effects of changes to environmental…
Motivated by loss of control risks from misaligned AI systems, we develop and apply methods for measuring language models' propensity for unsanctioned behaviour. We contribute three methodological improvements: analysing effects of changes to environmental…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Across 23 language models and 11 evaluation environments, we find approximately equal contributions from strategic and non-strategic factors for explaining behaviour, do not find…
LLM Behavior Analysis moved forward this cycle; last verified April 2026. Public score 2.0/10.
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Score2.0Analysis summary
Develops and applies methods to measure LLM propensity for unsanctioned behavior by analyzing environmental factors, finding equal contributions from strategic and non-strategic influences.
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10.48550/arXiv.2604.21098Develops and applies methods to measure LLM propensity for unsanctioned behavior by analyzing environmental factors, finding equal contributions from strategic and non-strategic influences.
Abstract
Motivated by loss of control risks from misaligned AI systems, we develop and apply methods for measuring language models' propensity for unsanctioned behaviour. We contribute three methodological improvements: analysing effects of changes to environmental factors on behaviour, quantifying effect sizes via Bayesian generalised linear models, and taking explicit measures against circular analysis. We apply the methodology to measure the effects of 12 environmental factors (6 strategic in nature, 6 non-strategic) and thus the extent to which behaviour is explained by strategic aspects of the environment, a question relevant to risks from misalignment. Across 23 language models and 11 evaluation environments, we find approximately equal contributions from strategic and non-strategic factors for explaining behaviour, do not find strategic factors becoming more or less influential as capabilities improve, and find some evidence for a trend for increased sensitivity to goal conflicts. Finally, we highlight a key direction for future propensity research: the development of theoretical frameworks and cognitive models of AI decision-making into empirically testable forms.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
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PROBLEM
Develops and applies methods to measure LLM propensity for unsanctioned behavior by analyzing environmental factors, finding equal contributions from strategic and non-strategic influences. We contribute three methodological improvements: analysing effects of changes to environm...
METHOD
Motivated by loss of control risks from misaligned AI systems, we develop and apply methods for measuring language models' propensity for unsanctioned behaviour. We contribute three methodological improvements: analysing effects of changes to environmental factors on behaviour,...
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Across 23 language models and 11 evaluation environments, we find approximately equal contributions from strategic and non-strategic factors for explaining behaviour, do not find strategic factors becomin...
WHY NOW
LLM Behavior Analysis moved forward this cycle; last verified April 2026. Public score 2.0/10.
# Propensity Inference: Environmental Contributors to LLM Behaviour Effects of environment-specific factors on unsanctioned behaviour ## Explicit Eval Hint - 1:1 1.5:1 - 2:1 AM: Alert 1:1
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Develops and applies methods to measure LLM propensity for unsanctioned behavior by analyzing environmental factors, finding equal contributions from strategic and non-strategic influences.
Segment
LLM Behavior Analysis
Adoption evidence
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Commercial read
2.0/10 public viability
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missing
reason
passport_row_missing
proof status
unverified
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confidence low
next verification path
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Technical feasibility
partial
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0 references, 3 sources, 50% evidence coverage.
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Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Prototype owner missing.
Build Passport does not name an implementer.
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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