---
title: "Scaling External Knowledge Input Beyond Context Windows of"
description: "The authors also build ∞Bench+, a multi-hop QA set derived from ∞Bench that fixes biases in the original. 71 references."
authors: ["Zijun Liu", "Zhennan Wan", "Peng Li", "Ming Yan", "Fei Huang", "Yang Liu"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.468/"
doi: "10.18653/v1/2026.acl-long.468"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/liu-2026-scaling-external-knowledge-input-beyond/"
---
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# Scaling External Knowledge Input Beyond Context Windows of LLMs via Multi-Agent Collaboration

**Authors:** [Zijun Liu](https://md.research.elamur.ai/free/en/authors/zijun-liu/), Zhennan Wan, [Peng Li](https://md.research.elamur.ai/free/en/authors/peng-li/), [Ming Yan](https://md.research.elamur.ai/free/en/authors/ming-yan/), [Fei Huang](https://md.research.elamur.ai/free/en/authors/fei-huang/) and [Yang Liu](https://md.research.elamur.ai/free/en/authors/yang-liu/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.468/) · [PDF](https://aclanthology.org/2026.acl-long.468.pdf) · [DOI 10.18653/v1/2026.acl-long.468](https://doi.org/10.18653/v1/2026.acl-long.468) · [OpenAlex](https://openalex.org/W4415037627)

**Citations:** 1 citations (OpenAlex, 2026-10-14)

## Summary
Large language models can use more retrieved evidence to solve harder tasks, but their context window caps how much they can take in. Truncation, RAG and compression all lose information, and longer-context training is expensive. The authors study whether performance can keep improving as external knowledge grows past the window, without any extra training. They identify two weaknesses in existing multi-agent pipelines. First, each agent can only exchange information with a few neighbours. Second, the reasoning step gets flooded with redundant content.

Their framework, ExtAgents, splits the input into small chunks handled by Seeking Agents. These agents share and update only their most salient findings across the whole group (global knowledge synchronization). A single Reasoning Agent then adds this knowledge gradually over several rounds (knowledge-accumulating reasoning). The authors also build ∞Bench+, a multi-hop QA set derived from ∞Bench that fixes biases in the original. ExtAgents beats non-training baselines on ∞Bench+, HotpotQA and long survey generation, whether the input fits the window or exceeds it. It also stays efficient because the agents run in parallel. This matters for web-scale QA, enterprise knowledge bases and literature-heavy writing.

## Key points
- The paper introduces **∞Bench+**, built from ∞Bench with an automated pipeline. It targets cases where a query could otherwise be answered by scanning one small window of the document. The benchmark includes En.QA and Zh.QA subsets.
- Compared baselines include LLM×MapReduce, Chain of Agents and LongAgent. In early tests, LLM×MapReduce could score worse with more input than with truncated input. ExtAgents instead kept improving beyond the 128k window.
- Ablations show that both components matter. Removing knowledge-accumulating reasoning hurts as input grows, and removing global synchronization lowers scores at several input lengths.
- The method was tested with Llama-3.1-8B-Instruct, gpt-4o-mini and gpt-4o, and stronger models gain more. Llama performed noticeably worse on Chinese QA than on English QA.
- On HotpotQA, the authors used Llama-3.2-3B-Instruct as the Seeking Agents and Llama-3.1-8B-Instruct as the Reasoning Agent. This setup was about 3.1× faster than using the 8B model for both roles, and it scored higher than the 3B-only configuration. Code and data: github.com/THUNLP-MT/ExtAgents.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Zijun Liu, Zhennan Wan, Peng Li, Ming Yan, Fei Huang and Yang Liu. 2026. Scaling External Knowledge Input Beyond Context Windows of LLMs via Multi-Agent Collaboration. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{liu2026scaling,
  title = {{Scaling External Knowledge Input Beyond Context Windows of LLMs via Multi-Agent Collaboration}},
  author = {Zijun Liu, Zhennan Wan, Peng Li, Ming Yan, Fei Huang and Yang Liu},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.468},
  url = {https://doi.org/10.18653/v1/2026.acl-long.468}
}
```

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*Updated 31 October 2026*