arXiv AI By Jaewon Chu, Jinwoo Seo, Jaewon Cho, Jeehye Na, Yunyang Xiong, Youngdae Kim, Hyunwoo J. Kim

AgentGrad: Intervention-guided Prompt Optimization for Multi Agent Systems

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv Computation and Language
Oct 8

Goldsmith: Gold-Loss-Guided Definition Optimization with an Agentic Annotation Harness

Goldsmith is an agentic pipeline that transforms a small set of expert-annotated calibration examples into a reusable structured annotation definition treated as a trainable textual object. Candidate definitions are evaluated on the same gold examples using an executable structured loss, and a large language model editor revises high-loss failures only when loss decreases. The approach outperforms direct rewriting, OPRO, APE, and PromptBreeder in prompt-optimization tests and enhances downstream annotation across typed span, pair-level relation, and fixed-trigger event-argument tasks when combined with retrieval, score-based routing, and human review.

By Yihan Li, Hanyi Zhang, Xiaoxi Jiang, Man Guo
arXiv AI
Sep 18

Rethinking Multi-Agent Collaboration: When More Is Less

The paper examines when multi‑agent collaboration is beneficial versus single‑agent approaches. It finds that collaboration yields systematic advantages mainly in long‑horizon tasks with sparse dependencies, while single agents perform better in tightly coupled, sequential workflows. The authors introduce SAIGE, a lightweight multi‑agent mechanism that models collaboration as a dynamically evolving graph, and show that it balances context efficiency and task performance without always improving outcomes as more agents are added.

By Yishuo Yuan, Yibo Wu, Yihan Zhang, Minyuan Sun, Shenliang Li, Xinkai Ma, Yifan Li, Jiaheng Liu
arXiv AI
Oct 27

Recursive Semantic Divergence for LLM Agent Consistency

The paper introduces Recursive Semantic Divergence (RSD), an unsupervised, trajectory‑level consistency metric for large language model agents that use tools. RSD recursively measures expected cumulative semantic disagreement between two independent rollouts from the same state, using natural language inference to detect logical inconsistency. When applied as a reward signal during fine‑tuning, RSD significantly reduces variability in task progress across benchmarks and model scales while matching supervised fine‑tuning performance without needing ground‑truth labels.

By Harshavardhan Abichandani, Penny Chong, Atin Ghosh, Daniel Dahlmeier