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HFEPX Hub

Coding + Rubric Rating Papers

Updated from current HFEPX corpus (Apr 27, 2026). 20 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 27, 2026). 20 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Human Eval. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequent quality control: Calibration. 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 31, 2026.

Papers: 20 Last published: Mar 31, 2026 Global RSS Tag RSS
CodingRubric Rating

Researcher Quick Triage

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

High-Signal Coverage

100.0%

20 / 20 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.
  • 4 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

  • 100% of papers report explicit human-feedback signals, led by rubric ratings.
  • automatic metrics appears in 40% of papers in this hub.
  • AIME is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is rater calibration (10% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.
  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.

Benchmark Interpretation

  • AIME appears in 5% of hub papers (1/20); use this cohort for benchmark-matched comparisons.
  • Kernelbench appears in 5% of hub papers (1/20); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 15% of hub papers (3/20); compare with a secondary metric before ranking methods.
  • cost is reported in 15% of hub papers (3/20); 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).

  • Moderate: Papers reporting quality controls

    Coverage is usable but incomplete (20% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

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

  • Strong: Papers naming evaluation metrics

    Coverage is strong (40% vs 35% target).

  • Strong: Papers with known rater population

    Coverage is strong (40% vs 35% target).

  • Strong: Papers with known annotation unit

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

Strengths

  • Strong human-feedback signal (100% of papers).
  • Most papers provide measurable evaluation context (35% benchmarks, 40% metrics).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • 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 (AIME vs Kernelbench) 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
Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers

Apr 1, 2026

Yes Automatic Metrics Paperwrite Bench Not Reported Not Reported
Is this Idea Novel? An Automated Benchmark for Judgment of Research Ideas

Mar 11, 2026

Yes Human Eval Rinobench Not Reported Gold Questions
StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning

Mar 3, 2026

Yes Automatic Metrics Kernelbench Success rate Not Reported
Document Reconstruction Unlocks Scalable Long-Context RLVR

Feb 9, 2026

Yes Automatic Metrics LongBench Coherence Not Reported
Beyond the Resumé: A Rubric-Aware Automatic Interview System for Information Elicitation

Mar 2, 2026

Yes Automatic Metrics Not Reported Not Reported Calibration
Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation

Oct 5, 2025

Yes Automatic Metrics , Simulation Env AIME Accuracy , Pass@k 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
KLong: Training LLM Agent for Extremely Long-horizon Tasks

Feb 19, 2026

Yes Not Reported SWE Bench , MLE Bench Not Reported Not Reported
QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

Apr 6, 2026

Yes Automatic Metrics Not Reported Inference cost Not Reported
Cross-Cultural Expert-Level Art Critique Evaluation with Vision-Language Models

Jan 12, 2026

Yes Not Reported Not Reported Not Reported Calibration
ScholarEval: Research Idea Evaluation Grounded in Literature

Oct 17, 2025

Yes Not Reported Scholareval Not Reported 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… Is this Idea Novel? An Automated Benchmark for Judg… StitchCUDA: An Automated Multi-Agents End-to-End GP…
Human Feedback Rubric RatingRubric RatingRubric Rating
Evaluation Modes Automatic MetricsHuman EvalAutomatic Metrics
Benchmarks Paperwrite BenchRinobenchKernelbench
Metrics Not reportedNot reportedSuccess rate
Quality Controls Not reportedGold QuestionsNot reported
Rater Population UnknownDomain ExpertsUnknown
Annotation Unit Multi Dim RubricMulti Dim RubricMulti 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. Is this Idea Novel? An Automated Benchmark for Judgment of Research Ideas

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation + rubric ratings. Focus: Rinobench. Abstract: Yet, evaluation of these approaches remains largely inconsistent and is.

  3. Beyond the Resumé: A Rubric-Aware Automatic Interview System for Information Elicitation

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + rubric ratings. Focus: cost. Abstract: We present a system that leverages an LLM interviewer to.

  4. QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

    High citation traction makes this a strong baseline for protocol comparison. Signals: automatic metrics + rubric ratings. Focus: cost. Abstract: Our training recipe has three stages: (1) supervised.

  5. Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers

    High citation traction makes this a strong baseline for protocol comparison. Signals: automatic metrics + rubric ratings. Focus: Paperwrite-Bench / cost. Abstract: PaperRecon disentangles the evaluation of the.

  6. Small Reward Models via Backward Inference

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge + rubric ratings. Abstract: However, the dominant LLM-as-a-Judge paradigm relies on the strong reasoning capabilities.

  7. Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation

    Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: AIME / accuracy. Abstract: Evaluation outcomes are modeled.

  8. StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning

    Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: Kernelbench / success rate. Abstract: To fundamentally improve.

Known Limitations

Known Limitations

  • LLM-as-judge appears without enough inter-annotator agreement reporting.
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
  • Cross-page comparisons should be benchmark- and metric-matched to avoid protocol confounding.
Research Utility Snapshot (Detailed)

Research Utility Snapshot

Human Feedback Mix

  • Rubric Rating (20)
  • Expert Verification (2)
  • Pairwise Preference (2)
  • Critique Edit (1)

Evaluation Modes

  • Automatic Metrics (8)
  • Human Eval (3)
  • Simulation Env (2)
  • Llm As Judge (1)

Top Benchmarks

  • AIME (1)
  • Kernelbench (1)
  • LongBench (1)
  • MLE Bench (1)

Top Metrics

  • Accuracy (3)
  • Cost (3)
  • Success rate (2)
  • Coherence (1)

Rater Population Mix

  • Domain Experts (8)

Quality Controls

  • Calibration (2)
  • Adjudication (1)
  • Gold Questions (1)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 35.0% · metrics 40.0% · quality controls 20.0%.

Top Papers

  • CounselReflect: A Toolkit for Auditing Mental-Health Dialogues

    Yahan Li, Chaohao Du, Zeyang Li, Christopher Chun Kuizon, Shupeng Cheng · Mar 31, 2026 · Citations: 0

    Rubric RatingExpert Verification Human Eval Web Browsing

    The system integrates two families of evaluation signals: (i) 12 model-based metrics produced by task-specific predictors, and (ii) rubric-based metrics that extend coverage via a literature-derived library (69 metrics) and user-defined…

  • Is this Idea Novel? An Automated Benchmark for Judgment of Research Ideas

    Tim Schopf, Michael Färber · Mar 11, 2026 · Citations: 0

    Rubric Rating Human Eval

    To address this, we introduce RINoBench, the first comprehensive benchmark for large-scale evaluation of research idea novelty judgments.

  • Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation

    Mohsen Hariri, Amirhossein Samandar, Michael Hinczewski, Vipin Chaudhary · Oct 5, 2025 · Citations: 0

    Rubric Rating Automatic MetricsSimulation Env

    We present a principled Bayesian evaluation framework that replaces Pass@k and average accuracy over N trials (avg@N) with posterior estimates of a model's underlying success probability and credible intervals, yielding stable rankings and…

  • StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning

    Shiyang Li, Zijian Zhang, Winson Chen, Yuebo Luo, Mingyi Hong · Mar 3, 2026 · Citations: 0

    Rubric Rating Automatic Metrics Multi Agent

    To address the challenge, in this work, we propose StitchCUDA, a multi-agent framework for end-to-end GPU program generation, with three specialized agents: a Planner to orchestrate whole system design, a Coder dedicated to implementing it…

  • Beyond the Resumé: A Rubric-Aware Automatic Interview System for Information Elicitation

    Harry Stuart, Masahiro Kaneko, Timothy Baldwin · Mar 2, 2026 · Citations: 0

    Rubric Rating Automatic Metrics

    Effective hiring is integral to the success of an organisation, but it is very challenging to find the most suitable candidates because expert evaluation (e.g.\ interviews conducted by a technical manager) are expensive to deploy at scale.

  • PRBench: End-to-end Paper Reproduction in Physics Research

    Shi Qiu, Junyi Deng, Yiwei Deng, Haoran Dong, Jieyu Fu · Mar 29, 2026 · Citations: 0

    Rubric RatingExpert Verification Automatic MetricsSimulation Env

    We introduce PRBench, a benchmark of 30 expert-curated tasks spanning 11 subfields of physics.

  • Document Reconstruction Unlocks Scalable Long-Context RLVR

    Yao Xiao, Lei Wang, Yue Deng, Guanzheng Chen, Ziqi Jin · Feb 9, 2026 · Citations: 0

    Rubric Rating Automatic Metrics

    However, it often relies on gold-standard answers or explicit evaluation rubrics provided by powerful teacher models or human experts, which are costly and time-consuming.

  • Automated Coding of Communication Data Using ChatGPT: Consistency Across Subgroups

    Jiangang Hao, Wenju Cui, Patrick Kyllonen, Emily Kerzabi · Oct 23, 2025 · Citations: 0

    Rubric Rating Human EvalAutomatic Metrics

    Prior research has established that ChatGPT can be directly instructed with coding rubrics to code the communication data and achieves accuracy comparable to human raters.

  • Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers

    Atsuyuki Miyai, Mashiro Toyooka, Zaiying Zhao, Kenta Watanabe, Toshihiko Yamasaki · Apr 1, 2026 · Citations: 0

    Rubric Rating Automatic Metrics

    We introduce Paper Reconstruction Evaluation (PaperRecon), an evaluation framework in which an overview (overview.md) is created from an existing paper, after which an agent generates a full paper based on the overview and minimal…

  • KLong: Training LLM Agent for Extremely Long-horizon Tasks

    Yue Liu, Yingwei Ma, Yibo Miao, Yanhao Li, Yuchong Xie · Feb 19, 2026 · Citations: 0

    Rubric Rating Long Horizon

    Then, we introduce Research-Factory, an automated pipeline that generates high-quality training data by collecting research papers and constructing evaluation rubrics.

  • Cross-Cultural Expert-Level Art Critique Evaluation with Vision-Language Models

    Haorui Yu, Xuehang Wen, Fengrui Zhang, Qiufeng Yi · Jan 12, 2026 · Citations: 0

    Rubric RatingCritique Edit

    Existing benchmarks assess perception without interpretation, and common evaluation proxies, such as automated metrics and LLM-judge averaging, are unreliable for culturally sensitive generative tasks.

  • Small Reward Models via Backward Inference

    Yike Wang, Faeze Brahman, Shangbin Feng, Teng Xiao, Hannaneh Hajishirzi · Feb 14, 2026 · Citations: 0

    Rubric Rating Llm As Judge

    However, the dominant LLM-as-a-Judge paradigm relies on the strong reasoning capabilities of large models, while alternative approaches require reference responses or explicit rubrics, limiting flexibility and broader accessibility.

  • ScholarEval: Research Idea Evaluation Grounded in Literature

    Hanane Nour Moussa, Patrick Queiroz Da Silva, Daniel Adu-Ampratwum, Alyson East, Zitong Lu · Oct 17, 2025 · Citations: 0

    Rubric Rating

    As AI tools become increasingly common for research ideation, robust evaluation is critical to ensure the validity and usefulness of generated ideas.

  • QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

    LM-Provers, Yuxiao Qu, Amrith Setlur, Jasper Dekoninck, Edward Beeching · Apr 6, 2026 · Citations: 0

    Rubric Rating Automatic Metrics

    To support further research on open mathematical reasoning, we release the full QED-Nano pipeline, including the QED-Nano and QED-Nano-SFT models, the FineProofs-SFT and FineProofs-RL datasets, and the training and evaluation code.

  • Ice Cream Doesn't Cause Drowning: Benchmarking LLMs Against Statistical Pitfalls in Causal Inference

    Jin Du, Li Chen, Xun Xian, An Luo, Fangqiao Tian · May 19, 2025 · Citations: 0

    Rubric Rating

    Current benchmarks usually involve simplified tasks.

  • Comparing Developer and LLM Biases in Code Evaluation

    Aditya Mittal, Ryan Shar, Zichu Wu, Shyam Agarwal, Tongshuang Wu · Mar 25, 2026 · Citations: 0

    Pairwise PreferenceRubric Rating

    We present TRACE (Tool for Rubric Analysis in Code Evaluation), a framework that evaluates LLM judges' ability to predict human preferences and automatically extracts rubric items to reveal systematic biases in how humans and models weigh…

  • When Names Change Verdicts: Intervention Consistency Reveals Systematic Bias in LLM Decision-Making

    Abhinaba Basu, Pavan Chakraborty · Mar 19, 2026 · Citations: 0

    Rubric Rating

    Validation against real COMPAS recidivism data shows COMPAS-derived flip rates exceed pooled synthetic rates, suggesting our benchmark provides a conservative estimate of real-world bias.

  • Extending Minimal Pairs with Ordinal Surprisal Curves and Entropy Across Applied Domains

    Andrew Katz · Mar 15, 2026 · Citations: 0

    Rubric Rating

    Additionally, standard prompting-based evaluation requires expensive text generation, may elicit post-hoc rationalizations rather than model judgments, and discards information about model uncertainty.

  • Toward LLM-Supported Automated Assessment of Critical Thinking Subskills

    Marisa C. Peczuh, Nischal Ashok Kumar, Ryan Baker, Blair Lehman, Danielle Eisenberg · Oct 14, 2025 · Citations: 0

    Rubric Rating

    As the world becomes increasingly saturated with AI-generated content, disinformation, and algorithmic persuasion, critical thinking - the capacity to evaluate evidence, detect unreliable claims, and exercise independent judgment - is…

  • RM-R1: Reward Modeling as Reasoning

    Xiusi Chen, Gaotang Li, Ziqi Wang, Bowen Jin, Cheng Qian · May 5, 2025 · Citations: 0

    Pairwise PreferenceRubric Rating

    Reward modeling is essential for aligning large language models with human preferences through reinforcement learning.

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