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HFEPX Benchmark Hub

HotpotQA Or WebArena Benchmark Papers

Updated from current HFEPX corpus (Apr 27, 2026). 22 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Apr 27, 2026). 22 papers are grouped in this benchmark page. Common evaluation modes: Simulation Env, Automatic Metrics. Common annotation unit: Trajectory. Frequently cited benchmark: HotpotQA. 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: 22 Last published: Mar 22, 2026 Global RSS

Researcher Quick Triage

Use this page for benchmark-matched method comparisons and eval protocol selection. Quality band: Medium .

High-Signal Coverage

100.0%

22 / 22 sampled papers are not low-signal flagged.

Replication-Ready Set

7

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 12 papers explicitly name benchmark datasets in the sampled set.
  • 7 papers report at least one metric term in metadata extraction.
  • Start with the ranked shortlist below before reading all papers.

Primary action: Start with the top 2 benchmark-matched papers, then compare evaluation modes in the protocol matrix.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 33.3% of papers report explicit human-feedback signals, led by demonstration data.
  • simulation environments appears in 31.8% of papers in this hub.
  • HotpotQA is a recurring benchmark anchor for cross-paper comparisons in this page.
Protocol Notes (Expanded)

Protocol Takeaways

  • 1 sampled papers report both human evaluation and LLM-as-judge, supporting direct agreement checks.
  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Rater context is mostly unspecified rater pools, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.

Benchmark Interpretation

  • HotpotQA appears in 58.3% of hub papers (7/22); use this cohort for benchmark-matched comparisons.
  • WebArena appears in 41.7% of hub papers (5/22); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 33.3% of hub papers (4/22); compare with a secondary metric before ranking methods.
  • cost is reported in 25% of hub papers (3/22); compare with a secondary metric before ranking methods.

Start Here (Benchmark-Matched First 6)

Ranked by protocol completeness so you can quickly find papers suitable for comparison studies.

Protocol Matrix (Top 10)

Compare protocol ingredients quickly before deep-reading full papers.

Paper Eval Modes Human Feedback Metrics Quality Controls
AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

Mar 22, 2026

 Human Eval, Llm As Judge Demonstrations Precision, Pass@1 Not reported
MemMachine: A Ground-Truth-Preserving Memory System for Personalized AI Agents

Apr 6, 2026

 Automatic Metrics Not reported Accuracy, Recall Not reported
OSCAR: Orchestrated Self-verification and Cross-path Refinement

Apr 2, 2026

 Automatic Metrics Not reported Accuracy Not reported
When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation

Apr 1, 2026

 Simulation Env Critique Edit Not reported Not reported
Go-Browse: Training Web Agents with Structured Exploration

Jun 4, 2025

 Simulation Env Not reported Success rate Not reported
RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QA

Oct 23, 2025

 Automatic Metrics Not reported Accuracy, F1 Not reported
Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs

Oct 1, 2025

 Automatic Metrics Not reported F1 Not reported
CLAUSE: Agentic Neuro-Symbolic Knowledge Graph Reasoning via Dynamic Learnable Context Engineering

Sep 25, 2025

 Automatic Metrics Not reported Accuracy, Latency Not reported
DSPO: Stable and Efficient Policy Optimization for Agentic Search and Reasoning

Oct 10, 2025

 Simulation Env Demonstrations Not reported Not reported
Structured Agent Distillation for Large Language Model

May 20, 2025

 Simulation Env Demonstrations Not reported Not reported
Researcher Workflow (Detailed)

Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (33.3% vs 45% target).

  • Gap: Papers reporting quality controls

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

  • Strong: Papers naming benchmarks/datasets

    Coverage is strong (100% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (58.3% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (0% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (41.7% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (100% benchmarks, 58.3% metrics).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 83.3% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% 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 (HotpotQA vs WebArena) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.

Recommended Queries

Judge vs Human AgreementBenchmark Slice: HotpotQAMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Evaluation Modes

  • Simulation Env (7)
  • Automatic Metrics (5)
  • Human Eval (1)
  • Llm As Judge (1)

Human Feedback Mix

  • Demonstrations (3)
  • Critique Edit (1)

Top Benchmarks

  • HotpotQA (7)
  • WebArena (5)
  • OSWorld (2)
  • ALFWorld (1)

Top Metrics

  • Accuracy (4)
  • Cost (3)
  • F1 (2)
  • Latency (2)

Top Papers On This Benchmark

Related Benchmark Hubs

DROP Or HotpotQA Or WebArena Benchmark Papers HotpotQA Benchmark Papers HotpotQA Benchmark Papers (Last 365 Days) HotpotQA In CS.CL Papers HotpotQA Benchmark Papers (Last 300 Days) HotpotQA Benchmark Papers (Last 240 Days) GPQA Or BFCL Benchmark Papers (23) HumanEval+ Or BFCL Benchmark Papers (21) MATH-500 Or BFCL Benchmark Papers (25) GPQA Or HumanEval+ Benchmark Papers (22) MATH-500 Or GPQA Benchmark Papers (25) MATH-500 Or HumanEval+ Benchmark Papers (24) MATH-500 Benchmark Papers (15) MMLU Or AIME Or AlpacaEval Benchmark Papers (60) MMLU Or AIME Or BFCL Benchmark Papers (59) GSM8K Or MMLU Or AIME Benchmark Papers (72) MMLU Or AIME Or HotpotQA Benchmark Papers (62) MMLU Or AIME Or HumanEval+ Benchmark Papers (57) MMLU Or AIME Or LiveCodeBench Benchmark Papers (59) MMLU Or AIME Or LongBench Benchmark Papers (59) MMLU Or AIME Or MATH-500 Benchmark Papers (58) MMLU Or AIME Or SWE-bench Benchmark Papers (64) MMLU Or AIME Benchmark Papers (50) GSM8K Or MMLU Or AlpacaEval Benchmark Papers (65)
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