HFEPX · Eval paper review
Yiming Lu, Xun Wang, Simin Ma, Shujian Liu +5 more
Published
Oct 22, 2025
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
Mar 18, 2026
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.
Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.
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.
Treat as adjacent context, not a core eval-method reference.
If you are doing eval pipeline work, start here
Multi-agent LLM systems have demonstrated impressive capabilities in complex collaborative tasks, yet most frameworks treat communication as instantaneous and free, overlooking a fundamental constraint in real world teamwork, collaboration cost. We propose a scalable framework implemented via Communication to Completion (C2C), which explicitly models communication as a constrained resource with realistic temporal costs. We introduce the Alignment Factor (AF), a dynamic metric inspired by Shared Mental Models, to quantify the link between task understanding and work efficiency. Through experiments on 15 software engineering workflows spanning three complexity tiers and team sizes from 5 to 17 agents, we demonstrate that cost-aware strategies achieve over 40% higher efficiency compared to unconstrained interaction. Our analysis reveals emergent coordination patterns: agents naturally adopt manager centric hub-and-spoke topologies, strategically escalate from asynchronous to synchronous channels based on complexity, and prioritize high value help requests. These patterns remain consistent across multiple frontier models (GPT-5.2, Claude Sonnet 4.5, Gemini 2.5 Pro). This study moves beyond simple agent construction, offering a theoretical foundation for quantifying and optimizing the dynamics of collaboration in future digital workplaces.
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.
None explicit
No explicit feedback protocol extracted.
"Multi-agent LLM systems have demonstrated impressive capabilities in complex collaborative tasks, yet most frameworks treat communication as instantaneous and free, overlooking a fundamental constraint in real world teamwork, collaboration cost."
Automatic Metrics
Includes extracted eval setup.
"Multi-agent LLM systems have demonstrated impressive capabilities in complex collaborative tasks, yet most frameworks treat communication as instantaneous and free, overlooking a fundamental constraint in real world teamwork, collaboration cost."
Not reported
No explicit QC controls found.
"Multi-agent LLM systems have demonstrated impressive capabilities in complex collaborative tasks, yet most frameworks treat communication as instantaneous and free, overlooking a fundamental constraint in real world teamwork, collaboration cost."
Not extracted
No benchmark anchors detected.
"Multi-agent LLM systems have demonstrated impressive capabilities in complex collaborative tasks, yet most frameworks treat communication as instantaneous and free, overlooking a fundamental constraint in real world teamwork, collaboration cost."
Not extracted
No metric anchors detected.
"Multi-agent LLM systems have demonstrated impressive capabilities in complex collaborative tasks, yet most frameworks treat communication as instantaneous and free, overlooking a fundamental constraint in real world teamwork, collaboration cost."
No benchmark or dataset names were extracted from the available abstract.
No metric terms were extracted from the available abstract.
Multi-agent LLM systems have demonstrated impressive capabilities in complex collaborative tasks, yet most frameworks treat communication as instantaneous and free, overlooking a fundamental constraint in real world teamwork, collaboration cost.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
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.
