Signals 4 · free daily AI digest

Multi-agent Scaling Across Disjunctive and Compensatory Tasks

Paper recorded by Signals 4 on 2026-09-25 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-25 on arXiv · recorded by Signals 4 on 2026-09-28

Category: cs.AI · 人工智能 · first seen 2026-09-28

Abstract

Multi-agent LLM systems are often expected to improve as team size increases, yet the scaling behavior may depend on task structure. Our central contribution is to introduce Steiner's taxonomy of group tasks as a framework for analyzing multi-agent LLM scaling and focusing the analysis on disjunctive and compensatory tasks. We model independently sampled agents as conditionally independent given t

Read on arXiv →

#7 most recent of 420 cs.AI papers we have recorded · ↑ newer: DeepEdu-v1: Efficient and Scalable Agentic LLMs for Vietnamese Educati · ↓ older: A Flow Matching Framework for Neural Representational Dissimilarity
Cite this page: Multi-agent Scaling Across Disjunctive and Compensatory Tasks: the #7 most recent of 420 cs.AI papers we have recorded (as of 2026-09-25). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/multi-agent-scaling-across-disjunctive-and-compensatory-tasks.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.AI papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions