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ARXIV:2603.18894 · MULTI-AGENT SYSTEMS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.18894MULTI-AGENT SYSTEMSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEVedanta S P · Ponnurangam Kumaraguru · arXiv
This research evaluates the susceptibility of large language model agents to corruption within simulated multi-agent governance systems, highlighting the importance of institutional design over model identity for pre-deployment integrity.
Opportunity summary
Pain This research evaluates the susceptibility of large language model agents to corruption within simulated multi-agent governance systems, highlighting the importance of institutional design over model identity for pre-deployment integrity.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
This research evaluates the susceptibility of large language model agents to corruption within simulated multi-agent governance systems, highlighting the importance of institutional design over model identity for pre-deployment integrity. We present evidence that integrity…
Large language models are increasingly proposed as autonomous agents for high-stakes public workflows, yet we lack systematic evidence about whether they would follow institutional rules when granted authority. We present evidence that integrity in…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. These results imply that institutional design is a precondition for safe delegation: before real authority is assigned to LLM agents, systems should undergo stress…
Multi-Agent Systems moved forward this cycle; last verified April 2026. Public score 3.0/10.
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This research evaluates the susceptibility of large language model agents to corruption within simulated multi-agent governance systems, highlighting the importance of institutional design over model identity for pre-deployment integrity.
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10.48550/arXiv.2603.18894This research evaluates the susceptibility of large language model agents to corruption within simulated multi-agent governance systems, highlighting the importance of institutional design over model identity for pre-deployment integrity.
Abstract
Large language models are increasingly proposed as autonomous agents for high-stakes public workflows, yet we lack systematic evidence about whether they would follow institutional rules when granted authority. We present evidence that integrity in institutional AI should be treated as a pre-deployment requirement rather than a post-deployment assumption. We evaluate multi-agent governance simulations in which agents occupy formal governmental roles under different authority structures, and we score rule-breaking and abuse outcomes with an independent rubric-based judge across 28,112 transcript segments. While we advance this position, the core contribution is empirical: among models operating below saturation, governance structure is a stronger driver of corruption-related outcomes than model identity, with large differences across regimes and model--governance pairings. Lightweight safeguards can reduce risk in some settings but do not consistently prevent severe failures. These results imply that institutional design is a precondition for safe delegation: before real authority is assigned to LLM agents, systems should undergo stress testing under governance-like constraints with enforceable rules, auditable logs, and human oversight on high-impact actions.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
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Dimensions overall score 3.0
PROBLEM
This research evaluates the susceptibility of large language model agents to corruption within simulated multi-agent governance systems, highlighting the importance of institutional design over model identity for pre-deployment integrity. We present evidence that integrity in in...
METHOD
Large language models are increasingly proposed as autonomous agents for high-stakes public workflows, yet we lack systematic evidence about whether they would follow institutional rules when granted authority. We present evidence that integrity in institutional AI should be tre...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. These results imply that institutional design is a precondition for safe delegation: before real authority is assigned to LLM agents, systems should undergo stress testing under governance-like constraint...
WHY NOW
Multi-Agent Systems moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
This research evaluates the susceptibility of large language model agents to corruption within simulated multi-agent governance systems, highlighting the importance of institutional design over model identity for pre-deployment integrity. We present evidence that integrity in institutional AI should be treated as a pre-deployment requirement rather than a post-deployment assumption.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large language models are increasingly proposed as autonomous agents for high-stakes public workflows, yet we lack systematic evidence about whether they would follow institutional rules when granted authority. We present evidence that integrity in institutional AI should be treated as a pre-deployment requirement rather than a post-deployment assumption.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. These results imply that institutional design is a precondition for safe delegation: before real authority is assigned to LLM agents, systems should undergo stress testing under governance-like constraints with enforceable rules, auditable logs, and human oversight on high-impact actions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multi-Agent Systems moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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This research evaluates the susceptibility of large language model agents to corruption within simulated multi-agent governance systems, highlighting the importance of institutional design over model identity for pre-deployment integrity.
Segment
Multi-Agent Systems
Adoption evidence
No public code link in the paper record yet
Commercial read
3.0/10 public viability
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reason
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proof status
unverified
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next verification path
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Evidence coverage
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stale
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Build readiness
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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.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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missing
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Evidence
0 references, 0 sources, 17% evidence coverage.
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Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
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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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ARTIFACTS
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DEFENSIBILITY
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