AgentGrad: Intervention-guided Prompt Optimization for Multi Agent Systems
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arXiv:2607. 20668v1 Announce Type: cross Abstract: TextGrad improves language-model systems by revising text from feedback.
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.
arXiv:2507. 09839v2 Announce Type: replace Abstract: An increasing number of NLP applications interact with large language models (LLMs) through black-box APIs, making prompt engineering critical for controlling model behavior.
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.
arXiv:2606.18902v3 Announce Type: replace Abstract: Context engineering has emerged as a primary lever for improving AI systems without parameter updates. Recent work showing that textual gradients d...
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.