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HFEPX Hub

Web Browsing Papers (Last 30 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 14 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 27, 2026). 14 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequent quality control: Adjudication. Frequently cited benchmark: GSM8K. 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 31, 2026.

Papers: 14 Last published: Mar 31, 2026 Global RSS Tag RSS
Web BrowsingLast 30d

Researcher Quick Triage

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

All Sampled Papers (14) Replication-Ready Only (3)

High-Signal Coverage

100.0%

14 / 14 sampled papers are not low-signal flagged.

Replication-Ready Set

3

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

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

Currently showing only replication-ready papers in ranking and matrix sections (3 papers).

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Why This Matters For Eval Research

  • 37.5% of papers report explicit human-feedback signals, led by critique/edit feedback.
  • automatic metrics appears in 28.6% of papers in this hub.
  • GSM8K is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is adjudication (7.1% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.
  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.

Benchmark Interpretation

  • GSM8K appears in 12.5% of hub papers (1/14); use this cohort for benchmark-matched comparisons.
  • Interruptbench appears in 12.5% of hub papers (1/14); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 37.5% of hub papers (3/14); compare with a secondary metric before ranking methods.
  • cost is reported in 25% of hub papers (2/14); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (37.5% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (12.5% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

    Coverage is strong (62.5% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (62.5% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (12.5% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (25% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (62.5% benchmarks, 62.5% metrics).
  • Agentic evaluation appears in 100% of papers.

Known Gaps

  • Only 12.5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (12.5% coverage).

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Stratify by benchmark (GSM8K vs Interruptbench) 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

Human Eval Protocols Benchmark Slice: GSM8K Metric Slice: accuracy Recent High-Signal Papers

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
LUDOBENCH: Evaluating LLM Behavioural Decision-Making Through Spot-Based Board Game Scenarios in Ludo

Apr 7, 2026

 No
Not Reported
Simulation Env Ludobench Dice Not Reported
Don't Overthink It: Inter-Rollout Action Agreement as a Free Adaptive-Compute Signal for LLM Agents

Apr 9, 2026

 No
Not Reported
Automatic Metrics GSM8K Accuracy Not Reported
Memanto: Typed Semantic Memory with Information-Theoretic Retrieval for Long-Horizon Agents

Apr 23, 2026

 No
Not Reported
Automatic Metrics Longmemeval Accuracy Not Reported

Protocol Diff (Top Papers)

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

Signal LUDOBENCH: Evaluating LLM Behavioural Decision-Maki… Don't Overthink It: Inter-Rollout Action Agreement… Memanto: Typed Semantic Memory with Information-The…
Human Feedback Not reportedNot reportedNot reported
Evaluation Modes Simulation EnvAutomatic MetricsAutomatic Metrics
Benchmarks LudobenchGSM8KLongmemeval
Metrics DiceAccuracyAccuracy
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
Annotation Unit UnknownTrajectoryUnknown
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. Memanto: Typed Semantic Memory with Information-Theoretic Retrieval for Long-Horizon Agents

    High citation traction makes this a strong baseline for protocol comparison. Signals: automatic metrics. Focus: Longmemeval / accuracy. Abstract: The transition from stateless language model inference to persistent,.

  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. AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning

    High citation traction makes this a strong baseline for protocol comparison. Signals: automatic metrics. Focus: accuracy. Abstract: Large Language Models (LLMs) increasingly rely on agentic capabilities-iterative retrieval, tool.

  5. When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: simulation environments + critique/edit feedback. Focus: WebArena. Abstract: As LLM agents transition from short, static problem solving.

  6. LUDOBENCH: Evaluating LLM Behavioural Decision-Making Through Spot-Based Board Game Scenarios in Ludo

    Adds simulation environments for broader protocol coverage within this hub. Signals: simulation environments. Focus: Ludobench / dice. Abstract: We introduce LudoBench, a benchmark for evaluating LLM strategic reasoning.

  7. DongYuan: An LLM-Based Framework for Integrative Chinese and Western Medicine Spleen-Stomach Disorders Diagnosis

    Adds evaluation protocol evidence with pairwise preferences for broader protocol coverage within this hub. Signals: pairwise preferences. Focus: Ssdf-Bench. Abstract: tuning (SFT) and direct preference optimization (DPO), and.

  8. From Guessing to Placeholding: A Cost-Theoretic Framework for Uncertainty-Aware Code Completion

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: cost. Abstract: While Large Language Models (LLMs) have demonstrated exceptional proficiency in code completion,.

Known Limitations

Known Limitations

  • Only 12.5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (12.5% 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

  • Critique Edit (1)
  • Expert Verification (1)
  • Pairwise Preference (1)
  • Rubric Rating (1)

Evaluation Modes

  • Automatic Metrics (4)
  • Simulation Env (2)
  • Human Eval (1)

Top Benchmarks

  • GSM8K (1)
  • Interruptbench (1)
  • Longmemeval (1)
  • Ludobench (1)

Top Metrics

  • Accuracy (3)
  • Cost (2)
  • Agreement (1)
  • Dice (1)

Rater Population Mix

  • Domain Experts (1)

Quality Controls

  • Adjudication (1)
Coverage diagnostics (sample-based): human-feedback 21.4% · benchmarks 42.9% · metrics 35.7% · quality controls 7.1%.

Top Papers

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