The paper argues that the architecture of multi‑agent large language model (LLM) frameworks, rather than just the intelligence of the underlying models, largely determines system performance. It introduces a taxonomy of architectural dimensions—such as orchestration, memory, planning interfaces, specialization, and communication topology—and presents MAFBench, a unified evaluation suite. An empirical study across nine frameworks, keeping the LLM constant, reveals six design principles and shows that choices like orchestration and communication topology can dramatically affect latency, accuracy, and coordination success.
By Abdelghny Orogat, Ana Rostam, Essam Mansour
The paper introduces POrchestra, a proactive agentic orchestration framework that lets sub‑agents request harness adjustments during execution, enhancing adaptability to dynamic environments. It also proposes On‑Policy Orchestration Distillation (OPOD), which uses hindsight feedback from sub‑agents’ requests to provide targeted improvement signals for training the orchestration policy. Experiments on three long‑horizon benchmarks show that POrchestra boosts task success by about 9.1% over the best baseline, and with OPOD the improvement rises to 10.0%, while also improving cost‑performance trade‑offs.
By Xueyang Feng, Chaozhuo Li, Xiaohe Bo, Jiakai Tang, Chenxi Li, Qiwei Ye, Xu Chen
Naive Prompt Optimization (NPO) is a lightweight, single‑lineage method that iteratively refines prompts using a teacher model’s rollout feedback. It matches or surpasses the performance of more complex optimizers like GEPA while requiring fewer rollouts, and its advantage grows with stronger teacher models. In interactive games, NPO remains competitive, and prompts optimized by NPO transfer well to other student models within the same family.
By Yuan Chang, Xiaoqi Chen
AT-AT (Automatic Tool Optimization from Agent Traces) is an agentic tool optimizer that improves shared toolsets offline using historical agent executions while keeping agents unchanged. It employs a lightweight coding agent to read agent trajectories and evaluation results, selectively editing tool descriptions and implementations when allowed. In benchmarks, AT-AT achieves the highest task completion rates in Airline and Telecom domains at lower cost than strong baselines, and it also improves performance on TerminalBench-2 and TBLite, while reducing cost or error rates on production MCP servers.
By Maya Ravichandran, Pragya Tripathi, Dharamendra Kumar, Adith Swaminathan, Nathan Kallus
RobustSGPO extends semantic‑gradient‑based prompt optimization (SGPO) by explicitly defining the edit scope, constructing and validating patches, and allowing search to resume from either the current or retained snapshots. In the AgentX brainstorming workflow, experiments on 120 tasks and 95 runs show that periodic permission scheduling improves test scores, while RobustSGPO raises completion rates on held‑out tasks from 60.0% to 80.0% and test quality from 3.77 to 4.14 within a 20‑million‑token budget. The approach also demonstrates that category‑based retention mitigates source‑task degradation after a shift, and that search‑space control enhances quality through executable edits and alternative starting points.
By Zibo Zhao, Jijun Shi, Mo Zhou, Zhongyuan Wang, Shifu Bie, Yunfei Zhang, Xuanting Zhou, Xiangyu Wu, Bin Liu, Ruiming Tang, Wenwu Ou, Kun Gai
arXiv:2608. 06714v1 Announce Type: new Abstract: Recent systems for optimizing prompts, programs, and ML workflows typically rely on explicit outer-loop controllers such as evolutionary search, bandits, or textual-gradient methods.
By Junbo Li, Boyi Liu, Canwen Xu, Yite Wang, Yuxiong He, Zhangyang Wang, Qiang Liu, Zhewei Yao