The paper introduces a two‑phase pipeline that mines reproducible design principles for large language model multi‑agent systems by reusing all intermediate search trajectories from an MCTS optimizer. Using six composable operators and 26 Enigmata tasks, the authors generate a corpus of 28,568 executable workflows and apply an AutoResearch loop to discover and validate orchestration patterns. Key findings include that Star‑first workflows underperform, SharedMemory outperforms debate, and prompt specificity and placeholder‑based routing provide comparable gains to communication‑structure selection.
arXiv:2608. 10039v1 Announce Type: new Abstract: Agentic workflows have become an important abstraction for building reliable LLM-based automation systems by organizing large language models (LLMs), tools, and control logic into explicit execution structures.
The paper introduces InFlowOp, a label‑free optimization framework that assigns costs to each decision in a multi‑agent workflow, balancing agent competence against execution time. It determines task granularity and agent assignment before execution and corrects faults during execution using the same cost metric. The authors also present Braid, a benchmark for multi‑agent coordination, and show that InFlowOp outperforms single‑agent baselines by up to 11.97% across various domains.
arXiv:2606. 12674v1 Announce Type: new Abstract: Compact language models (LMs) reduce cost, latency, and deployment risk for tool agents.
The paper introduces BenchAgent, a framework for evaluating multi‑agent large language model (LLM) workflows against a single‑agent baseline, BenchAgent Core. Using GPT‑4.1 across ten reasoning, coding, and tool‑use benchmarks, the study compares six multi‑agent configurations, finding that EvoAgent achieves the highest mean pooled accuracy (79.70%) while other agents lag by 1.71–8.76 percentage points. The analysis also highlights trade‑offs in token consumption and accuracy, offering guidance for selecting and optimizing LLM multi‑agent workflows.
arXiv:2609.01045v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities as powerful components in agentic systems, enabling sophisticated reasoning and...
The paper investigates how different components of test-time inference—prompting, post-processing, and selection—affect performance in structured workflow planning on DisasterBench. By breaking inference into discovery, validation, and selection stages, the authors compare independent sampling, nominal tree-search, and multi-operator search across two model families. Their findings show that while tree-search and independent sampling achieve similar workflow discovery, structural validation rarely discards correct workflows, yet majority voting often fails to select them, revealing significant tool-choice or information-flow errors. A tool-specification-aware verifier can recover some losses but also overturns more correct decisions.
arXiv:2610.14966v1 Announce Type: new Abstract: Long-horizon agents must complete workflows in which later activities depend on earlier outputs and changes to shared state. A key challenge for evalua...
The paper introduces BRIA (BRanch-Inspect-Adopt), an agent that isolates tool‑candidate investigations into separate branches, executing each candidate independently and then inspecting the execution evidence to adopt only useful results. BRIA dynamically retrieves additional tools as new needs arise during task execution and is evaluated on PlanBench‑XL and MCP‑Atlas with a library of 1,897 tools, outperforming or matching the strongest retrieval baselines. On MCP‑Atlas, BRIA achieves an average claim score of 89.77 versus 75.40 for MCP‑Zero, and it lies on the Pareto frontier for task performance and input‑token cost.
STRIATUM-CTF is a modular agentic framework that uses the Model Context Protocol to mediate between large language model reasoning and the execution of offensive‑security tools. By exposing tools as strictly typed, schema‑validated functions, the protocol rejects invalid tool calls and returns structured observations that the agent can use to self‑correct. In controlled experiments the system achieved 77.8–86.7% success across 15 CTF challenges, and in a live multi‑team CTF at Florida State University it placed first among more than 30 teams.
RSIFlow is a recursive self‑improvement framework that combines adaptive workflow orchestration with a meta‑agent that evolves its own harness. It diagnoses performance bottlenecks from task‑agent trajectories and environmental feedback, then dynamically selects component‑specific improvement workflows. Experiments on six benchmarks and two backbone models show that RSIFlow achieves state‑of‑the‑art average performance, with ablation studies confirming the benefits of adaptive orchestration and meta‑harness evolution.
arXiv:2608. 00107v1 Announce Type: new Abstract: Agentic systems must repeatedly decide whether to answer directly, decompose a task, invoke a tool, execute code, delegate to a specialist, verify an intermediate result, or recover from failure.
arXiv:2610.32441v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly capable of supporting scientific workflows, but their capabilities across diverse research activitie...