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HFEPX Hub

Tool Use Or Web Browsing Papers

Updated from current HFEPX corpus (Apr 12, 2026). 137 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 12, 2026). 137 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: 137 Last published: Mar 22, 2026 Global RSS Tag RSS
Tool UseWeb 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 137 total papers in this hub).

High-Signal Coverage

100.0%

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

Replication-Ready Set

23

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

1

Papers containing both `human_eval` and `llm_as_judge`.

  • 23 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 1 papers support judge-vs-human agreement analysis.
  • 3 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

  • 34.8% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 46% 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 (2.2% 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 4.5% of hub papers (4/137); use this cohort for benchmark-matched comparisons.
  • BIRD appears in 2.2% of hub papers (2/137); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 40.4% of hub papers (36/137); compare with a secondary metric before ranking methods.
  • cost is reported in 19.1% of hub papers (17/137); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (34.8% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (3.4% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

    Coverage is strong (36% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (74.2% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (10.1% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (28.1% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (36% benchmarks, 74.2% metrics).
  • 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 3.4% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (10.1% coverage).
  • LLM-as-judge appears without enough inter-annotator agreement reporting.

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 BIRD) 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
\$OneMillion-Bench: How Far are Language Agents from Human Experts?

Mar 9, 2026

Yes Automatic Metrics Onemillion Bench Accuracy , Coherence 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
Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters

Feb 11, 2026

Yes Not Reported LiveCodeBench , BrowseComp Latency , Cost Not Reported
MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks

Feb 18, 2026

Yes Automatic Metrics Memoryarena Recall Not Reported
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
VehicleMemBench: An Executable Benchmark for Multi-User Long-Term Memory in In-Vehicle Agents

Mar 25, 2026

Yes Simulation Env Vehiclemembench 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
DongYuan: An LLM-Based Framework for Integrative Chinese and Western Medicine Spleen-Stomach Disorders Diagnosis

Mar 30, 2026

Yes Not Reported Ssdf Bench Not Reported 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 \$OneMillion-Bench: How Far are Language Agents fro…
Human Feedback DemonstrationsExpert VerificationRubric Rating
Evaluation Modes Human Eval, Llm As JudgeAutomatic MetricsAutomatic Metrics
Benchmarks WebArena, ToolBenchSodium BenchOnemillion Bench
Metrics Precision, Pass@1AccuracyAccuracy, Coherence
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownDomain ExpertsDomain Experts
Annotation Unit TrajectoryUnknownMulti Dim Rubric
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. Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models

    High citation traction makes this a strong baseline for protocol comparison. Signals: automatic metrics. Focus: accuracy. Abstract: The advent of agentic multimodal models has empowered systems to actively.

  3. Don't Overthink It: Inter-Rollout Action Agreement as a Free Adaptive-Compute Signal for LLM Agents

    High citation traction makes this a strong baseline for protocol comparison. Signals: automatic metrics. Focus: GSM8K / accuracy. Abstract: Inference-time compute scaling has emerged as a powerful technique.

  4. 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.

  5. InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge. Focus: Innoeval. Abstract: However, existing idea evaluation methods often suffer from narrow knowledge horizons, flattened.

  6. 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.

  7. \$OneMillion-Bench: How Far are Language Agents from Human Experts?

    Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: Onemillion-Bench / accuracy. Abstract: We adopt a rubric-based.

Known Limitations

Known Limitations

  • Only 3.4% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (10.1% 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 (13)
  • Demonstrations (6)
  • Expert Verification (4)
  • Rubric Rating (4)

Evaluation Modes

  • Automatic Metrics (63)
  • Simulation Env (18)
  • Llm As Judge (3)
  • Human Eval (2)

Top Benchmarks

  • WebArena (4)
  • BIRD (2)
  • BrowseComp (2)
  • OSWorld (2)

Top Metrics

  • Accuracy (36)
  • Cost (17)
  • Latency (9)
  • Success rate (7)

Rater Population Mix

  • Domain Experts (9)

Quality Controls

  • Adjudication (3)
Coverage diagnostics (sample-based): human-feedback 51.7% · benchmarks 51.7% · metrics 70.0% · quality controls 5.0%.

Top Papers

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