PRISM: PRIvileged Sibling-guided Multi-Agent Topology Design
Quick Answer
PRISM introduces a novel approach to multi-agent systems by utilizing privileged information to enhance topology design.
Quick Take
This method outperforms existing baselines across six benchmarks, addressing the limitations of traditional post-training methods that lack per-query preferences. The results indicate significant improvements in agent collaboration and task performance.
Key Points
- PRISM recovers per-query signals using privileged information from sibling graphs.
- The method improves topology design across six diverse benchmarks.
- Traditional methods often lead to suboptimal performance due to lack of query-specific preferences.
- Code for PRISM is publicly available for further research and application.
- Matched-teacher controls validate the effectiveness of the privileged query information.
DeepSignal Analysis
What happened
PRISM is a new approach to multi-agent systems that leverages privileged information to improve the design of collaboration topologies. It addresses limitations in existing methods by incorporating per-query preferences, leading to enhanced agent collaboration and task performance across six benchmarks.
Key evidence
- PRISM utilizes privileged information from a sibling graph to enhance the learning process for topology design in multi-agent systems.
- The method outperforms all evaluated topology-design baselines across six diverse benchmarks, indicating its effectiveness.
- Traditional post-training methods lack per-query preferences, which PRISM addresses by recovering this signal from the same corpus.
Why it matters
The introduction of PRISM could significantly advance the field of multi-agent systems by allowing for more tailored and effective collaboration strategies. By overcoming the limitations of existing methods, it may lead to improved performance in complex problem-solving scenarios, which is crucial for applications in various domains.
What to watch
Paper Resources
📖 Reader Mode
~2 min readAbstract:Multi-agent systems (MAS) based on large language models (LLMs) have become a powerful way to address complex problems, and their effectiveness depends critically on the collaboration topology that decides which agents participate and how they communicate. Recent work learns to generate this topology from the task query, fitting the generator to corpora of execution-verified graphs. The post-training method for such generators remains a limitation: a corpus lists the graphs that solved a query but never ranks them, leaving continued supervised fine-tuning (SFT) with no per-query preference to exploit. Without such a preference, the generator drifts toward the topologies the corpus contains most often rather than those that suit each query, so a gain on one task can come at a cost on another. We propose PRISM, which recovers per-query signal from the same corpus by learning with privileged information: a teacher reads, through a zero-initialized low-rank branch, a sibling graph that also solved the query and is unavailable at deployment, and the student matches its per-decision targets on trajectories the student samples itself. Across six diverse benchmarks PRISM improves over every topology-design baseline evaluated, and matched-teacher controls support the contribution of privileged query information. Code is available in this link.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.20628 [cs.CL] |
| (or arXiv:2610.20628v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20628 arXiv-issued DOI via DataCite |
Submission history
From: Tuan Dat Phuong [view email]
[v1]
Tue, 29 Sep 2026 17:22:16 UTC (284 KB)
— Originally published at arxiv.org
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