Challenge: Existing approaches generate workflows either at task level or query level, but their relative costs and benefits remain unclear.
Approach: They propose a query-level workflow generation framework that generates tasks at task level and query level.
Outcome: The proposed framework reduces token usage by up to 83% compared to existing approaches . it maintains competitive performance with an average degradation of just 0.61% compared with existing approaches across multiple datasets .

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FusionFlow: Enabling Deep Structural Exploration for Automated Agentic Workflow Generation (2026.acl-long)

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Challenge: Existing workflow generation methods rely on incremental refinement or tree-based search over a single evolving workflow.
Approach: They propose a framework centered on workflow fusion that synthesizes multiple independently evolved workflows and allows exploration of deeper regions of the workflow space within a finite budget.
Outcome: Experiments show that FusionFlow outperforms existing workflow generation methods on six reasoning benchmarks.
Evolving Agentic Workflow Driven by Human-Agent Collaboration (2026.findings-acl)

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Challenge: Existing approaches to generate agentic workflows using large language models are limited by high manual design costs, inefficient agentic search, and poor dynamic adaptability to new tasks and human preferences.
Approach: They propose an evolutionary framework for generating agentic workflows through human-agent collaboration using evolutionary algorithms that mutate and cross over their structures, prompts, and LLM backbones.
Outcome: The proposed framework surpasses other automated baselines by 27.34% while achieving comparable performance to o1-preview at only one-fourth of the cost.
AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems (2026.acl-long)

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Challenge: Automated expansion methods often result in bloated structures with redundant agents, leading to excessive token consumption.
Approach: They propose a plug-and-play compression framework for graph-structured multi-agent workflows . they estimate the importance score of each agent and remove redundant agents .
Outcome: Experiments show that AgentSlimming reduces average token cost by 78.9% with negligible performance degradation.
Superficial Success vs. Internal Breakdown: An Empirical Study of Generalization in Adaptive Multi-Agent Systems (2026.findings-acl)

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Challenge: Adaptive multi-agent systems (MAS) are increasingly adopted as solutions to complex problems.
Approach: They conduct extensive empirical study on adaptive multi-agent systems . they find they are prone to topological overfitting and exhibit illusory coordination . authors urge prioritization of generalization in MAS development and evaluation .
Outcome: a new study shows adaptive multi-agent systems are prone to overfitting and lack coordination . the findings highlight the need to prioritize generalization in MAS development .
EvoAgentX: An Automated Framework for Evolving Agentic Workflows (2025.emnlp-demos)

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Challenge: Existing MAS frameworks often require manual workflow configuration and lack native support for dynamic evolution and performance optimization.
Approach: They propose an open-source platform that automates generation, execution, and evolutionary optimization of multi-agent workflows.
Outcome: The proposed platform automates generation, execution, and evolutionary optimization of multi-agent workflows.
MasRouter: Learning to Route LLMs for Multi-Agent Systems (2025.acl-long)

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Challenge: Multi-agent systems (MAS) powered by Large Language Models (LLMs) have been demonstrated to push the boundaries of LLM capabilities, yet they often face significant costs and challenges in dynamic LLM selection.
Approach: They propose a multi-agent system routing solution that integrates all components of MAS into a unified routing framework.
Outcome: The proposed solution is high-performing, cost-effective, and efficient . it reduces overhead by up to 52.07 compared to current methods on HumanEval .
GTA: Generating Long-horizon Tasks for Web Agents at Scale (2026.acl-long)

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Challenge: Existing benchmarks provide only coarse start–goal annotations without intermediate trajectories . Existing frameworks provide no supervision over the agent's latent decision process .
Approach: They propose a framework that integrates crawling, retrieval-based seeding, in-context generation and automated quality control to produce realistic tasks paired with executable trajectories.
Outcome: The proposed framework decouples crawling from generation for greater efficiency and ensures dense supervision through deterministic replays and systematic validation.
AdaptFlow: Adaptive Workflow Optimization via Meta-Learning (2025.findings-emnlp)

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Challenge: Existing approaches to large language models rely on static templates or manual workflows.
Approach: AdaptFlow is a language-based meta-learning framework inspired by model-agnostic meta- learning.
Outcome: AdaptFlow outperforms manual and automated workflows on question answering, code generation and mathematical reasoning benchmarks.
SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence (2025.emnlp-main)

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Challenge: Existing agentic system generation frameworks lack autonomy, autonomy, and functionality . current frameworks are too rigid, limiting adaptability and scalability.
Approach: They propose a framework that fully automates agentic system generation, optimization, and collaboration . they construct agents from scratch and jointly refine functionality and coordination .
Outcome: The proposed framework outperforms ADAS on six real-world, open-ended, and exploratory tasks on the TravelPlanner benchmark.
Scaling Collaborative Effort with Agents (2026.findings-acl)

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Challenge: Current evaluations of agents focus on producing high-quality, final outputs in one shot, failing to account for the inherently iterative nature of many real-world problems.
Approach: They propose a framework that captures how an agent’s utility grows with increasing user involvement.
Outcome: The proposed framework captures how an agent’s utility grows with increasing user involvement, revealing a missing ingredient in agent design: the ability to sustain engagement and scaffold user understanding.

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