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HFEPX Hub

CS.MA + General Papers

Updated from current HFEPX corpus (Apr 12, 2026). 21 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 12, 2026). 21 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Adjudication. Frequently cited benchmark: ALFWorld. 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 Mar 19, 2026.

Papers: 21 Last published: Mar 19, 2026 Global RSS Tag RSS
Cs.MAGeneral

Researcher Quick Triage

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

High-Signal Coverage

100.0%

21 / 21 sampled papers are not low-signal flagged.

Replication-Ready Set

2

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

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

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

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Why This Matters For Eval Research

  • 23.8% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 52.4% of papers in this hub.
  • ALFWorld is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is adjudication (9.5% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.

Benchmark Interpretation

  • ALFWorld appears in 4.8% of hub papers (1/21); use this cohort for benchmark-matched comparisons.
  • Furina-Bench appears in 4.8% of hub papers (1/21); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 28.6% of hub papers (6/21); compare with a secondary metric before ranking methods.
  • precision is reported in 14.3% of hub papers (3/21); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (23.8% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (9.5% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (14.3% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (57.1% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (9.5% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (19% vs 35% target).

Strengths

  • Agentic evaluation appears in 95.2% of papers.

Known Gaps

  • Only 9.5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.5% coverage).
  • Annotation unit is under-specified (19% coverage).

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Stratify by benchmark (ALFWorld vs Furina-Bench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and precision.
  • Add inter-annotator agreement checks when reproducing these protocols.
Recommended Queries (Expanded)

Recommended Queries

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
ReDAct: Uncertainty-Aware Deferral for LLM Agents

Apr 8, 2026

No
Not Reported
Simulation Env ALFWorld Cost , Token cost Not Reported
PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory

Apr 9, 2026

No
Not Reported
Automatic Metrics Latentneeds Bench Precision , Latency Not Reported
The Geometry of Dialogue: Graphing Language Models to Reveal Synergistic Teams for Multi-Agent Collaboration

Oct 30, 2025

Yes Automatic Metrics Not Reported Accuracy , Coherence Not Reported
I Can't Believe It's Corrupt: Evaluating Corruption in Multi-Agent Governance Systems

Mar 19, 2026

Yes Simulation Env Not Reported Not Reported Not Reported
From Competition to Coordination: Market Making as a Scalable Framework for Safe and Aligned Multi-Agent LLM Systems

Nov 18, 2025

No
Not Reported
Automatic Metrics Not Reported Accuracy Adjudication
Social Dynamics as Critical Vulnerabilities that Undermine Objective Decision-Making in LLM Collectives

Apr 7, 2026

No
Not Reported
Automatic Metrics , Simulation Env Not Reported Accuracy Not Reported
Exploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in Planning

Mar 2, 2026

Yes Not Reported Not Reported Not Reported Not Reported
SEVADE: Self-Evolving Multi-Agent Analysis with Decoupled Evaluation for Hallucination-Resistant Irony Detection

Aug 9, 2025

No
Not Reported
Automatic Metrics Not Reported Accuracy , F1 Adjudication
AgentSwing: Adaptive Parallel Context Management Routing for Long-Horizon Web Agents

Mar 29, 2026

No
Not Reported
Automatic Metrics Not Reported Precision Not Reported
Governed Memory: A Production Architecture for Multi-Agent Workflows

Mar 18, 2026

No
Not Reported
Automatic Metrics Not Reported Accuracy , Precision Not Reported
COMIC: Agentic Sketch Comedy Generation

Mar 11, 2026

Yes Not Reported Not Reported Not Reported Not Reported
MA-SAPO: Multi-Agent Reasoning for Score-Aware Prompt Optimization

Oct 18, 2025

Yes Not Reported Not Reported Not Reported Not Reported

Protocol Diff (Top Papers)

Fast side-by-side comparison for the highest-ranked papers in this hub.

Signal ReDAct: Uncertainty-Aware Deferral for LLM Agents PASK: Toward Intent-Aware Proactive Agents with Lon… The Geometry of Dialogue: Graphing Language Models…
Human Feedback Not reportedNot reportedPairwise Preference
Evaluation Modes Simulation EnvAutomatic MetricsAutomatic Metrics
Benchmarks ALFWorldLatentneeds BenchNot reported
Metrics Cost, Token costPrecision, LatencyAccuracy, Coherence
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
Annotation Unit TrajectoryUnknownPairwise
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: Latentneeds-Bench / precision. Abstract: Proactivity is a core expectation for AGI.

  2. ReDAct: Uncertainty-Aware Deferral for LLM Agents

    Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments. Focus: ALFWorld / cost. Abstract: Recently, LLM-based agents have become increasingly popular across many applications, including.

  3. Social Dynamics as Critical Vulnerabilities that Undermine Objective Decision-Making in LLM Collectives

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: Large language model (LLM) agents are increasingly acting as human delegates in multi-agent.

  4. FURINA: A Fully Customizable Role-Playing Benchmark via Scalable Multi-Agent Collaboration Pipeline

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation. Focus: Furina-Bench. Abstract: FURINA-Builder simulates dialogues between a test character and other characters drawn from.

  5. I Can't Believe It's Corrupt: Evaluating Corruption in Multi-Agent Governance Systems

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: simulation environments + rubric ratings. Abstract: We evaluate multi-agent governance simulations in which agents occupy formal governmental.

  6. The Geometry of Dialogue: Graphing Language Models to Reveal Synergistic Teams for Multi-Agent Collaboration

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: automatic metrics + pairwise preferences. Focus: accuracy. Abstract: Our method constructs a "language model graph" that.

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

    Adds evaluation protocol evidence with pairwise preferences for broader protocol coverage within this hub. Signals: pairwise preferences. Abstract: When automating plan generation for a real-world sequential decision problem,.

  8. StoryBox: Collaborative Multi-Agent Simulation for Hybrid Bottom-Up Long-Form Story Generation Using Large Language Models

    Adds simulation environments for broader protocol coverage within this hub. Signals: simulation environments. Focus: coherence. Abstract: Human writers often begin their stories with an overarching mental scene, where.

Known Limitations

Known Limitations

  • Only 9.5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.5% coverage).
  • 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 (3)
  • Critique Edit (1)
  • Rubric Rating (1)

Evaluation Modes

  • Automatic Metrics (11)
  • Simulation Env (7)
  • Human Eval (1)

Top Benchmarks

  • ALFWorld (1)
  • Furina Bench (1)
  • Latentneeds Bench (1)

Top Metrics

  • Accuracy (6)
  • Precision (3)
  • Coherence (2)
  • Cost (2)

Rater Population Mix

  • Domain Experts (2)

Quality Controls

  • Adjudication (2)
Coverage diagnostics (sample-based): human-feedback 23.8% · benchmarks 14.3% · metrics 57.1% · quality controls 9.5%.

Top Papers

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