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HFEPX Hub

CS.CL + Multi Agent Papers

Updated from current HFEPX corpus (Feb 27, 2026). 28 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Ranking. Frequent quality control: Adjudication. Frequently cited benchmark: Retrieval. Common metric signal: accuracy. 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 26, 2026.

Papers: 28 Last published: Feb 26, 2026 Global RSS Tag RSS
Cs.CLMulti Agent

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 28 papers for CS.CL + Multi Agent Papers. Dominant protocol signals include automatic metrics, simulation environments, human evaluation, with frequent benchmark focus on Retrieval, Lawbench and metric focus on accuracy, calibration. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.

Why This Matters For Eval Research

Protocol Takeaways

Benchmark Interpretation

  • Retrieval appears in 10.7% of hub papers (3/28); use this cohort for benchmark-matched comparisons.
  • Lawbench appears in 3.6% of hub papers (1/28); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 28.6% of hub papers (8/28); compare with a secondary metric before ranking methods.
  • calibration is reported in 3.6% of hub papers (1/28); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Close gap on Papers with explicit human feedback. Coverage is a replication risk (25% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (7.1% vs 30% target).
  • Tighten coverage on Papers naming benchmarks/datasets. Coverage is usable but incomplete (21.4% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (39.3% vs 35% target).
  • Close gap on Papers with known rater population. Coverage is a replication risk (17.9% vs 35% target).
  • Tighten coverage on Papers with known annotation unit. Coverage is usable but incomplete (28.6% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (25% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (7.1% vs 30% target).

Papers naming benchmarks/datasets

Coverage is usable but incomplete (21.4% vs 35% target).

Papers naming evaluation metrics

Coverage is strong (39.3% vs 35% target).

Papers with known rater population

Coverage is a replication risk (17.9% vs 35% target).

Papers with known annotation unit

Coverage is usable but incomplete (28.6% vs 35% target).

Suggested Reading Order

  1. 1. AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  2. 2. TherapyProbe: Generating Design Knowledge for Relational Safety in Mental Health Chatbots Through Adversarial Simulation

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  3. 3. Inner Speech as Behavior Guides: Steerable Imitation of Diverse Behaviors for Human-AI coordination

    Start here for detailed protocol reporting, including rater and quality-control evidence.

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

    Include a human-eval paper to anchor calibration against automated judge settings.

  5. 5. World-Model-Augmented Web Agents with Action Correction

    Include an LLM-as-judge paper to assess judge design and agreement assumptions.

  6. 6. SAMAS: A Spectrum-Guided Multi-Agent System for Achieving Style Fidelity in Literary Translation

    Adds automatic metrics for broader coverage within this hub.

  7. 7. Whisper: Courtside Edition Enhancing ASR Performance Through LLM-Driven Context Generation

    Adds automatic metrics for broader coverage within this hub.

  8. 8. Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 7.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (17.9% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

human_eval vs llm_as_judge

both=0, left_only=1, right_only=1

0 papers use both Human Eval and Llm As Judge.

human_eval vs automatic_metrics

both=0, left_only=1, right_only=21

0 papers use both Human Eval and Automatic Metrics.

llm_as_judge vs automatic_metrics

both=0, left_only=1, right_only=21

0 papers use both Llm As Judge and Automatic Metrics.

Benchmark Brief

Lawbench

Coverage: 1 papers (3.6%)

1 papers (3.6%) mention Lawbench.

Examples: Multimodal Multi-Agent Empowered Legal Judgment Prediction

Benchmark Brief

LiveCodeBench

Coverage: 1 papers (3.6%)

1 papers (3.6%) mention LiveCodeBench.

Examples: Team of Thoughts: Efficient Test-time Scaling of Agentic Systems through Orchestrated Tool Calling

Metric Brief

calibration

Coverage: 1 papers (3.6%)

1 papers (3.6%) mention calibration.

Examples: Team of Thoughts: Efficient Test-time Scaling of Agentic Systems through Orchestrated Tool Calling

Metric Brief

error rate

Coverage: 1 papers (3.6%)

1 papers (3.6%) mention error rate.

Examples: Whisper: Courtside Edition Enhancing ASR Performance Through LLM-Driven Context Generation

Top Papers

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