# Paper: Multi-Agent Routing as Set-Valued Prediction: A WildChat Benchmark and Cost-Aware Evaluation

---
type: paper
title: "Multi-Agent Routing as Set-Valued Prediction: A WildChat Benchmark and Cost-Aware Evaluation"
authors: Ananto Nayan Bala, Faisal Muhammad Shah
year: 2026
venue: arXiv
url: https://arxiv.org/abs/2606.28925
code_url:
source: arxiv
collected_at: 2026-07-08
published_at: 2026-06-27
updated_at: 2026-06-27
status: queued
relevance: high
topics:
  - agent-evaluation
  - multi-agent
  - rag
  - tool-use
  - world-model
methods:
  -
benchmarks:
  -
models:
  -
datasets:
  - cs.LG
  - cs.AI
  - cs.IR
  - cs.MA
related_concepts:
  -
related_jobs:
  -
related_experiments:
  -
related_projects:
  -
collection_score: 21
collection_queries: multi-agent-llm
---

## One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

## Why Collected

- matched queries: multi-agent-llm
- inferred topics: agent-evaluation, multi-agent, rag, tool-use, world-model
- arXiv categories: cs.LG, cs.AI, cs.IR, cs.MA
- collection score: 21

## Review Checklist

- Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
- Does it include a benchmark, dataset, code, or reproducible experimental setup?
- Should it be promoted from `queued` to `skimmed` or `summarized`?

## Links

- arXiv: https://arxiv.org/abs/2606.28925