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HFEPX Hub

Coding Papers (Last 30 Days)

Updated from current HFEPX corpus (Apr 17, 2026). 42 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 17, 2026). 42 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: APPS. 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: 42 Last published: Mar 31, 2026 Global RSS Tag RSS
CodingLast 30d

Researcher Quick Triage

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

High-Signal Coverage

100.0%

42 / 42 sampled papers are not low-signal flagged.

Replication-Ready Set

10

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

1

Papers containing both `human_eval` and `llm_as_judge`.

  • 10 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

  • 52.4% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 61.9% of papers in this hub.
  • APPS 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 (2.4% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.

Benchmark Interpretation

  • APPS appears in 2.4% of hub papers (1/42); use this cohort for benchmark-matched comparisons.
  • BFCL appears in 2.4% of hub papers (1/42); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 31% of hub papers (13/42); compare with a secondary metric before ranking methods.
  • cost is reported in 26.2% of hub papers (11/42); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (52.4% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (7.1% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (28.6% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (71.4% vs 35% target).

  • Gap: Papers with known rater population

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

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (33.3% vs 35% target).

Strengths

  • Strong human-feedback signal (52.4% of papers).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 52.4% of papers.

Known Gaps

  • Only 7.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (14.3% coverage).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (APPS vs BFCL) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
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
Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers

Apr 1, 2026

Yes Automatic Metrics Paperwrite Bench Not Reported Not Reported
Do Phone-Use Agents Respect Your Privacy?

Apr 1, 2026

Yes Automatic Metrics APPS , Myphonebench Task success Not Reported
CausalRM: Causal-Theoretic Reward Modeling for RLHF from Observational User Feedbacks

Mar 19, 2026

Yes Automatic Metrics Harmbench Not Reported Not Reported
Grounding Arabic LLMs in the Doha Historical Dictionary: Retrieval-Augmented Understanding of Quran and Hadith

Mar 25, 2026

No
Not Reported
Human Eval , Llm As Judge Not Reported Accuracy , Kappa Inter Annotator Agreement 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
CounselReflect: A Toolkit for Auditing Mental-Health Dialogues

Mar 31, 2026

Yes Human Eval Not Reported Not Reported Adjudication
PRBench: End-to-end Paper Reproduction in Physics Research

Mar 29, 2026

Yes Automatic Metrics , Simulation Env Not Reported Accuracy , Success rate Not Reported
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
QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

Apr 6, 2026

Yes Automatic Metrics Not Reported Inference cost Not Reported
LLM-Powered Workflow Optimization for Multidisciplinary Software Development: An Automotive Industry Case Study

Mar 22, 2026

Yes Automatic Metrics Not Reported F1 Not Reported
S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models

Apr 1, 2026

No
Not Reported
Automatic Metrics MATH 500 , GSM8K Pass@1 , Inference cost Not Reported

Protocol Diff (Top Papers)

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

Signal Paper Reconstruction Evaluation: Evaluating Present… Do Phone-Use Agents Respect Your Privacy? CausalRM: Causal-Theoretic Reward Modeling for RLHF…
Human Feedback Rubric RatingPairwise PreferencePairwise Preference
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks Paperwrite BenchAPPS, MyphonebenchHarmbench
Metrics Not reportedTask successNot reported
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
Annotation Unit Multi Dim RubricUnknownUnknown
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. The Detection-Extraction Gap: Models Know the Answer Before They Can Say It

    High citation traction makes this a strong baseline for protocol comparison. Signals: automatic metrics. Focus: accuracy. Abstract: Modern reasoning models continue generating long after the answer is already.

  3. Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework

    High citation traction makes this a strong baseline for protocol comparison. Signals: automatic metrics. Focus: recall. Abstract: The rapid growth of scientific literature has made it increasingly difficult.

  4. From Hallucination to Structure Snowballing: The Alignment Tax of Constrained Decoding in LLM Reflection

    High citation traction makes this a strong baseline for protocol comparison. Signals: critique/edit feedback. Abstract: Intrinsic self-correction in Large Language Models (LLMs) frequently fails in open-ended reasoning tasks.

  5. Grounding Arabic LLMs in the Doha Historical Dictionary: Retrieval-Augmented Understanding of Quran and Hadith

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation. Focus: accuracy. Abstract: Gemini also serves as an LLM-as-a-judge system for automatic evaluation in our.

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

    Adds simulation environments with critique/edit feedback for broader protocol coverage within this hub. Signals: simulation environments + critique/edit feedback. Focus: WebArena. Abstract: As LLM agents transition from short,.

  7. VehicleMemBench: An Executable Benchmark for Multi-User Long-Term Memory in In-Vehicle Agents

    Adds simulation environments with pairwise preferences for broader protocol coverage within this hub. Signals: simulation environments + pairwise preferences. Focus: Vehiclemembench. Abstract: This evolution requires agents to continuously.

  8. PRBench: End-to-end Paper Reproduction in Physics Research

    Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: accuracy. Abstract: All tasks are contributed by domain.

Known Limitations

Known Limitations

  • Only 7.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (14.3% 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 (8)
  • Critique Edit (6)
  • Rubric Rating (6)
  • Expert Verification (4)

Evaluation Modes

  • Automatic Metrics (26)
  • Simulation Env (4)
  • Human Eval (3)
  • Llm As Judge (1)

Top Benchmarks

  • APPS (1)
  • BFCL (1)
  • BIRD (1)
  • GSM8K (1)

Top Metrics

  • Accuracy (13)
  • Cost (11)
  • Inference cost (2)
  • Latency (2)

Rater Population Mix

  • Domain Experts (6)

Quality Controls

  • Adjudication (1)
  • Calibration (1)
  • Inter Annotator Agreement Reported (1)
Coverage diagnostics (sample-based): human-feedback 52.4% · benchmarks 28.6% · metrics 71.4% · quality controls 7.1%.

Top Papers

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