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HFEPX Hub

Coding + Pairwise Preference (Last 45 Days)

Updated from current HFEPX corpus (Apr 17, 2026). 18 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 17, 2026). 18 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Mixed. Common annotation unit: Pairwise. Frequently cited benchmark: AIME. 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 25, 2026.

Papers: 18 Last published: Mar 25, 2026 Global RSS Tag RSS
CodingPairwise PreferenceLast 45d

Researcher Quick Triage

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

All Sampled Papers (18) Replication-Ready Only (4)

High-Signal Coverage

100.0%

18 / 18 sampled papers are not low-signal flagged.

Replication-Ready Set

4

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 4 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 0 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 (4 papers).

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Why This Matters For Eval Research

  • 100% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 50% of papers in this hub.
  • AIME is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Rater context is mostly mixed rater pools, and annotation is commonly pairwise annotation; use this to scope replication staffing.
  • Stratify by benchmark (AIME vs APPS) before comparing methods.

Benchmark Interpretation

  • AIME appears in 5.6% of hub papers (1/18); use this cohort for benchmark-matched comparisons.
  • APPS appears in 5.6% of hub papers (1/18); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 33.3% of hub papers (6/18); compare with a secondary metric before ranking methods.
  • cost is reported in 27.8% of hub papers (5/18); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (100% vs 45% target).

  • Gap: Papers reporting quality controls

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

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (27.8% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (61.1% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (5.6% vs 35% target).

  • Moderate: Papers with known annotation unit

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

Strengths

  • Strong human-feedback signal (100% of papers).

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (5.6% coverage).

Suggested Next Analyses

  • Stratify by benchmark (AIME vs APPS) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
Recommended Queries (Expanded)

Recommended Queries

Benchmark Slice: AIME 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
Do Phone-Use Agents Respect Your Privacy?

Apr 1, 2026

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

Mar 19, 2026

 Yes
Pairwise Preference
Automatic Metrics Harmbench Not Reported Not Reported
Modeling and Benchmarking Spoken Dialogue Rewards with Modality and Colloquialness

Mar 16, 2026

 Yes
Pairwise Preference
Automatic Metrics Esdr Bench Accuracy Not Reported
$V_1$: Unifying Generation and Self-Verification for Parallel Reasoners

Mar 4, 2026

 Yes
Pairwise Preference
Automatic Metrics SWE Bench , AIME Pass@1 Not Reported

Protocol Diff (Top Papers)

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

Signal Do Phone-Use Agents Respect Your Privacy? CausalRM: Causal-Theoretic Reward Modeling for RLHF… Modeling and Benchmarking Spoken Dialogue Rewards w…
Human Feedback Pairwise PreferencePairwise PreferencePairwise Preference
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks APPS, MyphonebenchHarmbenchEsdr Bench
Metrics Task successNot reportedAccuracy
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
Annotation Unit UnknownUnknownPairwise
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Do Phone-Use Agents Respect Your Privacy?

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + pairwise preferences. Focus: APPS / task success. Abstract: Across five frontier models on 10 mobile.

  2. FEAST: Fully Connected Expressive Attention for Spatial Transcriptomics

    Start here for detailed protocol reporting and quality-control evidence. Signals: pairwise preferences. Focus: cost. Abstract: To address this, we propose FEAST (Fully connected Expressive Attention for Spatial Transcriptomics),.

  3. Comparing Developer and LLM Biases in Code Evaluation

    Start here for detailed protocol reporting and quality-control evidence. Signals: pairwise preferences. Abstract: As LLMs are increasingly used as judges in code applications, they should be evaluated in.

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

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: simulation environments + pairwise preferences. Focus: Vehiclemembench. Abstract: This evolution requires agents to continuously model multi-user preferences.

  5. Vibe Code Bench: Evaluating AI Models on End-to-End Web Application Development

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + pairwise preferences. Focus: accuracy. Abstract: We identify self-testing during generation as a strong performance.

  6. Modeling and Benchmarking Spoken Dialogue Rewards with Modality and Colloquialness

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: Esdr-Bench / accuracy. Abstract: To address these challenges,.

  7. $V_1$: Unifying Generation and Self-Verification for Parallel Reasoners

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: SWE-bench / pass@1. Abstract: While existing approaches typically.

  8. Sabiá-4 Technical Report

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: accuracy. Abstract: The models were developed through a.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (5.6% 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 (18)
  • Expert Verification (1)
  • Rubric Rating (1)

Evaluation Modes

  • Automatic Metrics (9)
  • Simulation Env (1)

Top Benchmarks

  • AIME (1)
  • APPS (1)
  • CodeContests (1)
  • Esdr Bench (1)

Top Metrics

  • Accuracy (6)
  • Cost (5)
  • Agreement (1)
  • F1 (1)

Rater Population Mix

  • Mixed (1)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 27.8% · metrics 61.1% · quality controls 0.0%.

Top Papers

  • Modeling and Benchmarking Spoken Dialogue Rewards with Modality and Colloquialness

    Jingyu Lu, Yuhan Wang, Fan Zhuo, Xize Cheng, Changhao Pan · Mar 16, 2026 · Citations: 0

    Pairwise Preference Automatic Metrics

    To address these challenges, we introduce SDiaReward, an end-to-end multi-turn reward model trained on SDiaReward-Dataset, a novel collection of episode-level preference pairs explicitly targeting these gaps.

  • $V_1$: Unifying Generation and Self-Verification for Parallel Reasoners

    Harman Singh, Xiuyu Li, Kusha Sareen, Monishwaran Maheswaran, Sijun Tan · Mar 4, 2026 · Citations: 0

    Pairwise Preference Automatic Metrics

    On code generation (LiveCodeBench, CodeContests, SWE-Bench) and math reasoning (AIME, HMMT) benchmarks, V_1-Infer improves Pass@1 by up to 10% over pointwise verification and outperforms recent test-time scaling methods while being…

  • Do Phone-Use Agents Respect Your Privacy?

    Zhengyang Tang, Ke Ji, Xidong Wang, Zihan Ye, Xinyuan Wang · Apr 1, 2026 · Citations: 0

    Pairwise Preference Automatic Metrics

    We study whether phone-use agents respect privacy while completing benign mobile tasks.

  • CausalRM: Causal-Theoretic Reward Modeling for RLHF from Observational User Feedbacks

    Hao Wang, Licheng Pan, Zhichao Chen, Chunyuan Zheng, Zhixuan Chu · Mar 19, 2026 · Citations: 0

    Pairwise Preference Automatic Metrics

    Despite the success of reinforcement learning from human feedback (RLHF) in aligning language models, current reward modeling heavily relies on experimental feedback data collected from human annotators under controlled and costly…

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