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HFEPX Hub

Multi Agent Or Web Browsing Papers

Updated from current HFEPX corpus (Apr 12, 2026). 257 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 12, 2026). 257 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: WebArena. 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 22, 2026.

Papers: 257 Last published: Mar 22, 2026 Global RSS Tag RSS
Multi AgentWeb Browsing

Researcher Quick Triage

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

Analysis blocks below are computed from the currently loaded sample (60 of 257 total papers in this hub).

High-Signal Coverage

100.0%

60 / 60 sampled papers are not low-signal flagged.

Replication-Ready Set

12

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

1

Papers containing both `human_eval` and `llm_as_judge`.

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

Primary action: Start with the top 2 papers in “Start Here”, then validate assumptions in the protocol matrix.

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Why This Matters For Eval Research

  • 32% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 42.8% of papers in this hub.
  • WebArena is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • 1 sampled papers report both human evaluation and LLM-as-judge, supporting direct agreement checks.
  • Most common quality-control signal is adjudication (2.3% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.

Benchmark Interpretation

  • WebArena appears in 2.3% of hub papers (4/257); use this cohort for benchmark-matched comparisons.
  • OSWorld appears in 1.7% of hub papers (3/257); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 36% of hub papers (63/257); compare with a secondary metric before ranking methods.
  • cost is reported in 13.7% of hub papers (24/257); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (32% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (5.1% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (21.1% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (66.9% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (17.7% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (18.9% vs 35% target).

Strengths

  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 100% of papers.

Known Gaps

  • Only 5.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (17.7% coverage).
  • Annotation unit is under-specified (18.9% coverage).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (WebArena vs OSWorld) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
  • 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
AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

Mar 22, 2026

Yes Human Eval , Llm As Judge WebArena , ToolBench Precision , Pass@1 Not Reported
SODIUM: From Open Web Data to Queryable Databases

Mar 19, 2026

Yes Automatic Metrics Sodium Bench Accuracy Not Reported
Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking

Feb 27, 2026

Yes Llm As Judge AdvBench , Jbf Eval Success rate , Jailbreak success rate Not Reported
Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models

Feb 2, 2026

Yes Automatic Metrics Vdr Bench Not Reported Adjudication
StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning

Mar 3, 2026

Yes Automatic Metrics Kernelbench Success rate Not Reported
Elo-Evolve: A Co-evolutionary Framework for Language Model Alignment

Feb 14, 2026

Yes Automatic Metrics MT Bench , AlpacaEval Elo Not Reported
MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks

Feb 18, 2026

Yes Automatic Metrics Memoryarena Recall Not Reported
Team of Thoughts: Efficient Test-time Scaling of Agentic Systems through Orchestrated Tool Calling

Feb 18, 2026

No
Not Reported
Automatic Metrics LiveCodeBench Accuracy Calibration
Let's Think in Two Steps: Mitigating Agreement Bias in MLLMs with Self-Grounded Verification

Jul 15, 2025

Yes Automatic Metrics , Simulation Env VisualWebArena , OSWorld Accuracy Not Reported
When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation

Apr 1, 2026

Yes Simulation Env WebArena , Interruptbench Not Reported Not Reported
Meanings and Measurements: Multi-Agent Probabilistic Grounding for Vision-Language Navigation

Mar 19, 2026

Yes Simulation Env Mapg Bench Not Reported Not Reported
RedTeamCUA: Realistic Adversarial Testing of Computer-Use Agents in Hybrid Web-OS Environments

May 28, 2025

Yes Automatic Metrics Rtc Bench Jailbreak success rate Not Reported

Protocol Diff (Top Papers)

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

Signal AgentHER: Hindsight Experience Replay for LLM Agent… SODIUM: From Open Web Data to Queryable Databases Jailbreak Foundry: From Papers to Runnable Attacks…
Human Feedback DemonstrationsExpert VerificationRed Team
Evaluation Modes Human Eval, Llm As JudgeAutomatic MetricsLlm As Judge
Benchmarks WebArena, ToolBenchSodium BenchAdvBench, Jbf Eval
Metrics Precision, Pass@1AccuracySuccess rate, Jailbreak success rate
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownDomain ExpertsUnknown
Annotation Unit TrajectoryUnknownUnknown
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. CounselReflect: A Toolkit for Auditing Mental-Health Dialogues

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation + rubric ratings. Abstract: The system integrates two families of evaluation signals: (i) 12 model-based metrics.

  2. An Agentic System for Rare Disease Diagnosis with Traceable Reasoning

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + expert verification. Focus: recall. Abstract: Rare diseases affect over 300 million individuals worldwide, yet timely.

  3. AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + demonstration data. Focus: WebArena / precision. Abstract: AgentHER realises this idea through a four-stage.

  4. Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge + red-team protocols. Focus: AdvBench / success rate. Abstract: This system enables a standardized AdvBench.

  5. Let's Think in Two Steps: Mitigating Agreement Bias in MLLMs with Self-Grounded Verification

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: VisualWebArena / accuracy. Abstract: Multimodal LLMs (MLLMs) offer.

Known Limitations

Known Limitations

  • Only 5.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (17.7% 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 (21)
  • Expert Verification (13)
  • Demonstrations (10)
  • Critique Edit (6)

Evaluation Modes

  • Automatic Metrics (110)
  • Simulation Env (46)
  • Llm As Judge (12)
  • Human Eval (6)

Top Benchmarks

  • WebArena (4)
  • OSWorld (3)
  • BIRD (2)
  • Paperbench (2)

Top Metrics

  • Accuracy (63)
  • Cost (24)
  • Success rate (9)
  • Precision (8)

Rater Population Mix

  • Domain Experts (31)

Quality Controls

  • Adjudication (6)
  • Calibration (3)
Coverage diagnostics (sample-based): human-feedback 80.0% · benchmarks 31.7% · metrics 63.3% · quality controls 8.3%.

Top Papers

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