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ARXIV:2604.12116 · AGENTS · SUBMITTED 15 APR · 16:43 UTC · FRESHNESS STALE
ARXIV:2604.12116AGENTSSUBMITTED 15 APR · 16:43 UTCFRESHNESS STALEShasha Yu · Fiona Carroll · Barry L. Bentley · arXiv
A new framework for evaluating LLM agents by profiling their execution-level behavior and refusal signals, crucial for safe organizational deployment.
Opportunity summary
Pain A new framework for evaluating LLM agents by profiling their execution-level behavior and refusal signals, crucial for safe organizational deployment.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A new framework for evaluating LLM agents by profiling their execution-level behavior and refusal signals, crucial for safe organizational deployment. While existing benchmarks primarily assess textual alignment or task success, less attention has been…
Large language models (LLMs) are increasingly deployed as tool-augmented agents capable of executing system-level operations. While existing benchmarks primarily assess textual alignment or task success, less attention has been paid to the structural relationship…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Empirical results show that execution and refusal constitute separable behavioral dimensions whose joint distribution varies systematically across regimes and autonomy levels. Code availability is…
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A new framework for evaluating LLM agents by profiling their execution-level behavior and refusal signals, crucial for safe organizational deployment.
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10.48550/arXiv.2604.12116A new framework for evaluating LLM agents by profiling their execution-level behavior and refusal signals, crucial for safe organizational deployment.
Abstract
Large language models (LLMs) are increasingly deployed as tool-augmented agents capable of executing system-level operations. While existing benchmarks primarily assess textual alignment or task success, less attention has been paid to the structural relationship between linguistic signaling and executable behavior under varying autonomy scaffolds. This study introduces an execution-layer be-havioral measurement approach based on a two-dimensional A-R space defined by Action Rate (A) and Refusal Signal (R), with Divergence (D) capturing coor-dination between the two. Models are evaluated across four normative regimes (Control, Gray, Dilemma, and Malicious) and three autonomy configurations (di-rect execution, planning, and reflection). Rather than assigning aggregate safety scores, the method characterizes how execution and refusal redistribute across contextual framing and scaffold depth. Empirical results show that execution and refusal constitute separable behavioral dimensions whose joint distribution varies systematically across regimes and autonomy levels. Reflection-based scaffolding often shifts configurations toward higher refusal in risk-laden contexts, but redis-tribution patterns differ structurally across models. The A-R representation makes cross-sectional behavioral profiles, scaffold-induced transitions, and coordination variability directly observable. By foregrounding execution-layer characterization over scalar ranking, this work provides a deployment-oriented lens for analyzing and selecting tool-enabled LLM agents in organizational settings where execution privileges and risk tolerance vary.
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Dimensions overall score 7.0
PROBLEM
A new framework for evaluating LLM agents by profiling their execution-level behavior and refusal signals, crucial for safe organizational deployment. While existing benchmarks primarily assess textual alignment or task success, less attention has been paid to the structural rel...
METHOD
Large language models (LLMs) are increasingly deployed as tool-augmented agents capable of executing system-level operations. While existing benchmarks primarily assess textual alignment or task success, less attention has been paid to the structural relationship between linguis...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Empirical results show that execution and refusal constitute separable behavioral dimensions whose joint distribution varies systematically across regimes and autonomy levels. Code availability is flagged...
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A new framework for evaluating LLM agents by profiling their execution-level behavior and refusal signals, crucial for safe organizational deployment. While existing benchmarks primarily assess textual alignment or task success, less attention has been paid to the structural relationship between linguistic signaling and executable behavior under varying autonomy scaffolds.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large language models (LLMs) are increasingly deployed as tool-augmented agents capable of executing system-level operations. While existing benchmarks primarily assess textual alignment or task success, less attention has been paid to the structural relationship between linguistic signaling and executable behavior under varying autonomy scaffolds.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Empirical results show that execution and refusal constitute separable behavioral dimensions whose joint distribution varies systematically across regimes and autonomy levels. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A new framework for evaluating LLM agents by profiling their execution-level behavior and refusal signals, crucial for safe organizational deployment.
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Commercial read
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reason
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proof status
unverified
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confidence low
next verification path
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Build readiness
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passport absent
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Artifact maturity
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Technical feasibility
partial
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Write integration checklist from prototype path and target workflow.
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