agent/papers/items/2026-2606-25514-unlocking-model-potentials-through-adaptive-multi-agent-scaffolding-for-efficien.md

Paper: Unlocking Model Potentials Through Adaptive Multi-Agent Scaffolding for Efficient Issue Resolution

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type: paper title: Unlocking Model Potentials Through Adaptive Multi-Agent Scaffolding for Efficient Issue Resolution authors: Yang Chen, Aliya Ahmad, Yiheng Zhou, Reyhaneh Jabbarvand year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.25514 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-24 updated_at: 2026-06-24 status: queued relevance: high topics:

• agent-evaluation
• coding-agent
• multi-agent
• planning
• rag
• tool-use
• workflow-agent methods:

benchmarks:

models:

datasets:

• cs.SE related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 17 collection_queries: coding-agent

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: coding-agent
• inferred topics: agent-evaluation, coding-agent, multi-agent, planning, rag, tool-use, workflow-agent
• arXiv categories: cs.SE
• collection score: 17

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2606.25514