Three Roles, One Model: Role Orchestration at Inference Time to Close the Performance Gap Between Small and Large Agents explores A novel inference-time scaffolding technique significantly boosts the performance of small LLM agents on complex tasks, making them competitive with much larger models without additional training.. Commercial viability score: 7/10 in LLM Agents.
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Agent Handoff
Canonical ID three-roles-one-model-role-orchestration-at-inference-time-to-close-the-performance-gap-between-small-and-large-agents | Route /paper/three-roles-one-model-role-orchestration-at-inference-time-to-close-the-performance-gap-between-small-and-large-agents
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/three-roles-one-model-role-orchestration-at-inference-time-to-close-the-performance-gap-between-small-and-large-agentsMCP example
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/buildability/three-roles-one-model-role-orchestration-at-inference-time-to-close-the-performance-gap-between-small-and-large-agents
Subject: Three Roles, One Model: Role Orchestration at Inference Time to Close the Performance Gap Between Small and Large Agents
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Dimensions overall score 7.0
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This equation captures one of the core mathematical components of the system. the benchmark size (N = 168). The broader implication is not that small models match frontier systems, but that a meanin
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Receipt path
/buildability/three-roles-one-model-role-orchestration-at-inference-time-to-close-the-performance-gap-between-small-and-large-agents
Paper ref
three-roles-one-model-role-orchestration-at-inference-time-to-close-the-performance-gap-between-small-and-large-agents
arXiv id
2604.11465
Generated at
2026-04-14T16:50:51.311Z
Evidence freshness
stale
Last verification
2026-04-14T16:50:51.311Z
Sources
3
References
0
Coverage
50%
Lineage hash
30a65fba097df804f28b8260bf1b26859f63b5ca67fc23198120cc7a9df6ef3a
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unsigned_external
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Pending verification refs / 3 sources / Verification pending
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references
This equation captures one of the core mathematical components of the system. Both configurations use greedy decoding (temperature=0), a fixed random seed of 100, and a maximum of 3,000 completion
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This equation captures one of the core mathematical components of the system. We formalize the AppWorld agent task as a partially observable Markov decision process (POMDP) M = (S,A,T ,R,O,Ω)
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