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HFEPX · Eval paper review

Learning to Interrupt in Language-based Multi-agent Communication

Danqing Wang, Da Yin, Ruta Desai, Lei Li +2 more

Published

Apr 7, 2026

Citations

0

Trust level

Low

Usefulness score

15/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Apr 7, 2026

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Tags

Automatic Metrics General Multi Agent

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Main weakness

The available metadata is too thin to trust this as a primary source.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
15/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Multi-agent systems using large language models (LLMs) have demonstrated impressive capabilities across various domains. However, current agent communication suffers from verbose output that overload context and increase computational costs. Although existing approaches focus on compressing the message from the speaker side, they struggle to adapt to different listeners and identify relevant information. An effective way in human communication is to allow the listener to interrupt and express their opinion or ask for clarification. Motivated by this, we propose an interruptible communication framework that allows the agent who is listening to interrupt the current speaker. Through prompting experiments, we find that current LLMs are often overconfident and interrupt before receiving enough information. Therefore, we propose a learning method that predicts the appropriate interruption points based on the estimated future reward and cost. We evaluate our framework across various multi-agent scenarios, including 2-agent text pictionary games, 3-agent meeting scheduling, and 3-agent debate. The results of the experiment show that our HANDRAISER can reduce the communication cost by 32.2% compared to the baseline with comparable or superior task performance. This learned interruption behavior can also be generalized to different agents and tasks.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Multi-agent systems using large language models (LLMs) have demonstrated impressive capabilities across various domains."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"Multi-agent systems using large language models (LLMs) have demonstrated impressive capabilities across various domains."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Multi-agent systems using large language models (LLMs) have demonstrated impressive capabilities across various domains."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Multi-agent systems using large language models (LLMs) have demonstrated impressive capabilities across various domains."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Multi-agent systems using large language models (LLMs) have demonstrated impressive capabilities across various domains."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Not reported
Expertise required
General
Evaluation details
Evaluation modes
Automatic Metrics
Agentic eval
Multi Agent
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Multi-agent systems using large language models (LLMs) have demonstrated impressive capabilities across various domains.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Multi-agent systems using large language models (LLMs) have demonstrated impressive capabilities across various domains.
  • However, current agent communication suffers from verbose output that overload context and increase computational costs.
  • Although existing approaches focus on compressing the message from the speaker side, they struggle to adapt to different listeners and identify relevant information.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Contribution summary

  • Motivated by this, we propose an interruptible communication framework that allows the agent who is listening to interrupt the current speaker.
  • Therefore, we propose a learning method that predicts the appropriate interruption points based on the estimated future reward and cost.
  • We evaluate our framework across various multi-agent scenarios, including 2-agent text pictionary games, 3-agent meeting scheduling, and 3-agent debate.

Why it matters for eval

  • Motivated by this, we propose an interruptible communication framework that allows the agent who is listening to interrupt the current speaker.
  • We evaluate our framework across various multi-agent scenarios, including 2-agent text pictionary games, 3-agent meeting scheduling, and 3-agent debate.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    Detected: Automatic Metrics

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    No metric terms extracted.