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HFEPX Hub

Multi Agent + Pairwise Preference Papers

Updated from current HFEPX corpus (Mar 10, 2026). 10 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Mar 10, 2026). 10 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Pairwise. Frequently cited benchmark: AlpacaEval. Common metric signal: elo. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Feb 14, 2026.

Papers: 10 Last published: Feb 14, 2026 Global RSS Tag RSS
Multi AgentPairwise Preference

Researcher Quick Triage

This hub is best used for protocol triage and replication planning from abstract-level evidence. Quality band: Developing .

All Sampled Papers (10) Replication-Ready Only (1)

High-Signal Coverage

100.0%

10 / 10 sampled papers are not low-signal flagged.

Replication-Ready Set

1

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 1 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 0 papers report explicit quality controls (calibration/adjudication/IAA).

Primary action: Use this page for scouting only; collect additional papers before attempting replication-critical comparisons.

Currently showing only replication-ready papers in ranking and matrix sections (1 papers).

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Why This Matters For Eval Research

  • 100% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 30% of papers in this hub.
  • AlpacaEval is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Rater context is mostly domain experts, and annotation is commonly pairwise annotation; use this to scope replication staffing.
  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

Benchmark Interpretation

  • AlpacaEval appears in 10% of hub papers (1/10); use this cohort for benchmark-matched comparisons.
  • AlpacaEval 2.0 appears in 10% of hub papers (1/10); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • elo is reported in 10% of hub papers (1/10); compare with a secondary metric before ranking methods.
  • helpfulness is reported in 10% of hub papers (1/10); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (100% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (10% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (30% vs 35% target).

  • Moderate: Papers with known rater population

    Coverage is usable but incomplete (30% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (70% vs 35% target).

Strengths

  • Strong human-feedback signal (100% of papers).
  • Agentic evaluation appears in 100% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Benchmark coverage is thin (10% of papers mention benchmarks/datasets).
  • LLM-as-judge appears without enough inter-annotator agreement reporting.

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (AlpacaEval vs AlpacaEval 2.0) before comparing methods.
  • Track metric sensitivity by reporting both elo and helpfulness.
Recommended Queries (Expanded)

Recommended Queries

LLM-as-Judge Protocols Benchmark Slice: AlpacaEval Metric Slice: elo Recent High-Signal Papers

Start with These 3

Use these when you need one protocol anchor, one benchmark anchor, and one recent comparison point before reading the wider hub.

Start Here (Best First 6)

Ranked for protocol completeness (human signal, benchmark + metric anchors, quality controls, and judge/human overlap).

Protocol Matrix (Top 12)

Use this to quickly compare protocol ingredients instead of scanning long prose.

Paper HF Signal Eval Modes Benchmarks Metrics QC
Elo-Evolve: A Co-evolutionary Framework for Language Model Alignment

Feb 14, 2026

 Yes
Pairwise Preference
Automatic Metrics MT Bench , AlpacaEval Elo Not Reported
Suggested Reading Order (Extended)

This section is intentionally expanded only when needed; use “Start Here” above for a faster pass.

Suggested Reading Order

  1. Build, Judge, Optimize: A Blueprint for Continuous Improvement of Multi-Agent Consumer Assistants

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge + pairwise preferences. Abstract: Grocery shopping further amplifies these difficulties, as user requests are often underspecified, highly.

  2. Exploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in Planning

    Start here for detailed protocol reporting and quality-control evidence. Signals: pairwise preferences. Abstract: When automating plan generation for a real-world sequential decision problem, the goal is often not.

  3. Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks

    Start here for detailed protocol reporting and quality-control evidence. Signals: pairwise preferences. Abstract: Crucially, further analysis of intermediate agent outputs suggests that alignment between analytical outputs and downstream.

  4. Elo-Evolve: A Co-evolutionary Framework for Language Model Alignment

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + pairwise preferences. Focus: MT-Bench / elo. Abstract: Current alignment methods for Large Language Models.

  5. Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: hit@5. Abstract: These domains typically involve fixed content.

  6. PrivAct: Internalizing Contextual Privacy Preservation via Multi-Agent Preference Training

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: helpfulness. Abstract: By embedding privacy preferences into each.

  7. Multi-Agent Comedy Club: Investigating Community Discussion Effects on LLM Humor Generation

    Adds evaluation protocol evidence with pairwise preferences for broader protocol coverage within this hub. Signals: pairwise preferences. Abstract: Across 50 rounds (250 paired monologues) judged by five expert.

  8. Decentralized Ranking Aggregation: Gossip Algorithms for Borda and Copeland Consensus

    Adds evaluation protocol evidence with pairwise preferences for broader protocol coverage within this hub. Signals: pairwise preferences. Abstract: The concept of ranking aggregation plays a central role in.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Benchmark coverage is thin (10% of papers mention benchmarks/datasets).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Research Utility Snapshot

Human Feedback Mix

  • Pairwise Preference (10)
  • Rubric Rating (2)
  • Expert Verification (1)

Evaluation Modes

  • Automatic Metrics (3)
  • Llm As Judge (1)
  • Simulation Env (1)

Top Benchmarks

  • AlpacaEval (1)
  • AlpacaEval 2.0 (1)
  • MT Bench (1)

Top Metrics

  • Elo (1)
  • Helpfulness (1)
  • Hit@5 (1)

Rater Population Mix

  • Domain Experts (3)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 10.0% · metrics 30.0% · quality controls 0.0%.

Top Papers

  • Elo-Evolve: A Co-evolutionary Framework for Language Model Alignment

    Jing Zhao, Ting Zhen, Junwei Bao, Hongfei Jiang, Yang Song · Feb 14, 2026 · Citations: 0

    Pairwise Preference Automatic Metrics Multi Agent

    Current alignment methods for Large Language Models (LLMs) rely on compressing vast amounts of human preference data into static, absolute reward functions, leading to data scarcity, noise sensitivity, and training instability.

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