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ARXIV:2603.02630 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.02630AGENTSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Optimize prompt efficiency in Multi-Agent Systems using bandit algorithms and graph neural networks for improved performance.
Opportunity summary
Pain Optimize prompt efficiency in Multi-Agent Systems using bandit algorithms and graph neural networks for improved performance.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Optimize prompt efficiency in Multi-Agent Systems using bandit algorithms and graph neural networks for improved performance. Since many deployment scenarios preclude MAS workflow modifications and its performance is highly sensitive to the input prompts,…
Large Language Models (LLMs) have achieved great success in many real-world applications, especially the one serving as the cognitive backbone of Multi-Agent Systems (MAS) to orchestrate complex workflows in practice. Since many deployment scenarios…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Since many deployment scenarios preclude MAS workflow modifications and its performance is highly sensitive to the input prompts, prompt optimization emerges as a more…
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10.
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Optimize prompt efficiency in Multi-Agent Systems using bandit algorithms and graph neural networks for improved performance.
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Paper Pack
10.48550/arXiv.2603.02630Optimize prompt efficiency in Multi-Agent Systems using bandit algorithms and graph neural networks for improved performance.
Abstract
Large Language Models (LLMs) have achieved great success in many real-world applications, especially the one serving as the cognitive backbone of Multi-Agent Systems (MAS) to orchestrate complex workflows in practice. Since many deployment scenarios preclude MAS workflow modifications and its performance is highly sensitive to the input prompts, prompt optimization emerges as a more natural approach to improve its performance. However, real-world prompt optimization for MAS is impeded by three key challenges: (1) the need of sample efficiency due to prohibitive evaluation costs, (2) topology-induced coupling among prompts, and (3) the combinatorial explosion of the search space. To address these challenges, we introduce MASPOB (Multi-Agent System Prompt Optimization via Bandits), a novel sample-efficient framework based on bandits. By leveraging Upper Confidence Bound (UCB) to quantify uncertainty, the bandit framework balances exploration and exploitation, maximizing gains within a strictly limited budget. To handle topology-induced coupling, MASPOB integrates Graph Neural Networks (GNNs) to capture structural priors, learning topology-aware representations of prompt semantics. Furthermore, it employs coordinate ascent to decompose the optimization into univariate sub-problems, reducing search complexity from exponential to linear. Extensive experiments across diverse benchmarks demonstrate that MASPOB achieves state-of-the-art performance, consistently outperforming existing baselines.
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Extraction status
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
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Viability
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Commercial
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Dimensions overall score 7.0
PROBLEM
Optimize prompt efficiency in Multi-Agent Systems using bandit algorithms and graph neural networks for improved performance. Since many deployment scenarios preclude MAS workflow modifications and its performance is highly sensitive to the input prompts, prompt optimization eme...
METHOD
Large Language Models (LLMs) have achieved great success in many real-world applications, especially the one serving as the cognitive backbone of Multi-Agent Systems (MAS) to orchestrate complex workflows in practice. Since many deployment scenarios preclude MAS workflow modific...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Since many deployment scenarios preclude MAS workflow modifications and its performance is highly sensitive to the input prompts, prompt optimization emerges as a more natural approach to improve its perf...
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Optimize prompt efficiency in Multi-Agent Systems using bandit algorithms and graph neural networks for improved performance. Since many deployment scenarios preclude MAS workflow modifications and its performance is highly sensitive to the input prompts, prompt optimization emerges as a more natural approach to improve its performance.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large Language Models (LLMs) have achieved great success in many real-world applications, especially the one serving as the cognitive backbone of Multi-Agent Systems (MAS) to orchestrate complex workflows in practice. Since many deployment scenarios preclude MAS workflow modifications and its performance is highly sensitive to the input prompts, prompt optimization emerges as a more natural approach to improve its performance.
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. Since many deployment scenarios preclude MAS workflow modifications and its performance is highly sensitive to the input prompts, prompt optimization emerges as a more natural approach to improve its performance.
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Optimize prompt efficiency in Multi-Agent Systems using bandit algorithms and graph neural networks for improved performance.
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Commercial read
7.0/10 public viability
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proof status
unverified
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