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HFEPX Hub

CS.LG Papers (Last 30 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 476 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 27, 2026). 476 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Calibration. Frequently cited benchmark: MMLU. 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 Apr 8, 2026.

Papers: 476 Last published: Apr 8, 2026 Global RSS
Cs.LGLast 30d

Researcher Quick Triage

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

Analysis blocks below are computed from the currently loaded sample (60 of 476 total papers in this hub).

All Sampled Papers (60) Replication-Ready Only (10)

High-Signal Coverage

100.0%

60 / 60 sampled papers are not low-signal flagged.

Replication-Ready Set

10

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 10 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 (10 papers).

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Why This Matters For Eval Research

  • 3.6% of papers report explicit human-feedback signals, led by demonstration data.
  • automatic metrics appears in 14.9% of papers in this hub.
  • MMLU is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is rater calibration (0.8% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; 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

  • MMLU appears in 1.5% of hub papers (7/476); use this cohort for benchmark-matched comparisons.
  • DROP appears in 1.3% of hub papers (6/476); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 14.7% of hub papers (70/476); compare with a secondary metric before ranking methods.
  • cost is reported in 7.1% of hub papers (34/476); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (3.6% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0.8% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (9.5% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (33% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (2.7% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (6.9% vs 35% target).

Strengths

  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 0.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (2.7% coverage).
  • Annotation unit is under-specified (6.9% coverage).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (MMLU vs DROP) 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

Judge vs Human Agreement Benchmark Slice: MMLU 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
Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

Apr 8, 2026

 Yes
Pairwise Preference , Rubric Rating
Human Eval , Automatic Metrics Rewardbench Accuracy , Helpfulness Not Reported
TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectories

Apr 8, 2026

 Yes
Red Team
Automatic Metrics Tracesafe Bench Accuracy Not Reported
Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers

Apr 1, 2026

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

Apr 1, 2026

 Yes
Pairwise Preference
Automatic Metrics APPS , Myphonebench Task success Not Reported
ReDAct: Uncertainty-Aware Deferral for LLM Agents

Apr 8, 2026

 No
Not Reported
Simulation Env ALFWorld Token cost 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
FlowForge: A Staged Local Rollout Engine for Flow-Field Prediction

Apr 21, 2026

 No
Not Reported
Automatic Metrics Pdebench , Cfdbench Accuracy 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
Weakly Supervised Distillation of Hallucination Signals into Transformer Representations

Apr 7, 2026

 No
Not Reported
Llm As Judge , Automatic Metrics SQuAD F1 Not Reported
SkillX: Automatically Constructing Skill Knowledge Bases for Agents

Apr 6, 2026

 No
Not Reported
Automatic Metrics BFCL Task success Not Reported

Protocol Diff (Top Papers)

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

Signal Personalized RewardBench: Evaluating Reward Models… TraceSafe: A Systematic Assessment of LLM Guardrail… Paper Reconstruction Evaluation: Evaluating Present…
Human Feedback Pairwise Preference, Rubric RatingRed TeamRubric Rating
Evaluation Modes Human Eval, Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks RewardbenchTracesafe BenchPaperwrite Bench
Metrics Accuracy, HelpfulnessAccuracyNot reported
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
Annotation Unit PairwiseTrajectoryMulti 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. 33fd51e6-b301-4b95-bbd1-d67e2381a5bf

    Start here for detailed protocol reporting and quality-control evidence.

  2. 59e00b99-c25b-4de8-95be-ef4f7ec656eb

    Start here for detailed protocol reporting and quality-control evidence.

  3. 681c998d-9840-48f9-b312-ccb4cb7bbd27

    Start here for detailed protocol reporting and quality-control evidence.

  4. Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + pairwise preferences. Focus: Rewardbench / accuracy. Abstract: While benchmarks for general response quality are.

  5. TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectories

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + red-team protocols. Focus: Tracesafe-Bench / accuracy. Abstract: As large language models (LLMs) evolve from.

  6. RuleForge: Automated Generation and Validation for Web Vulnerability Detection at Scale

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge + expert verification. Focus: auroc. Abstract: This paper focuses on RuleForge's architecture and operational deployment.

  7. Reliable Self-Harm Risk Screening via Adaptive Multi-Agent LLM Systems

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge. Focus: precision. Abstract: However, common evaluation approaches, like LLM-as-a-judge, do not indicate when a decision.

  8. ReDAct: Uncertainty-Aware Deferral for LLM Agents

    Adds simulation environments for broader protocol coverage within this hub. Signals: simulation environments. Focus: ALFWorld / cost. Abstract: Recently, LLM-based agents have become increasingly popular across many applications,.

Known Limitations

Known Limitations

  • Only 0.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (2.7% 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

  • Demonstrations (5)
  • Expert Verification (5)
  • Pairwise Preference (4)
  • Rubric Rating (3)

Evaluation Modes

  • Automatic Metrics (71)
  • Simulation Env (7)
  • Llm As Judge (4)
  • Human Eval (1)

Top Benchmarks

  • MMLU (7)
  • DROP (6)
  • GSM8K (3)
  • LongBench (3)

Top Metrics

  • Accuracy (70)
  • Cost (34)
  • Precision (14)
  • F1 (12)

Rater Population Mix

  • Domain Experts (13)

Quality Controls

  • Calibration (4)
Coverage diagnostics (sample-based): human-feedback 28.3% · benchmarks 18.3% · metrics 36.7% · quality controls 0.0%.

Top Papers

  • Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

    Qiyao Ma, Dechen Gao, Rui Cai, Boqi Zhao, Hanchu Zhou · Apr 8, 2026 · Citations: 0

    Pairwise PreferenceRubric Rating Human EvalAutomatic Metrics

    Pluralistic alignment has emerged as a critical frontier in the development of Large Language Models (LLMs), with reward models (RMs) serving as a central mechanism for capturing diverse human values.

  • TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectories

    Yen-Shan Chen, Sian-Yao Huang, Cheng-Lin Yang, Yun-Nung Chen · Apr 8, 2026 · Citations: 0

    Red Team Automatic Metrics Long Horizon

    As large language models (LLMs) evolve from static chatbots into autonomous agents, the primary vulnerability surface shifts from final outputs to intermediate execution traces.

  • 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…

  • ReDAct: Uncertainty-Aware Deferral for LLM Agents

    Dzianis Piatrashyn, Nikita Kotelevskii, Kirill Grishchenkov, Nikita Glazkov, Ivan Nasonov · Apr 8, 2026 · Citations: 0

    Simulation Env Long Horizon

    Recently, LLM-based agents have become increasingly popular across many applications, including complex sequential decision-making problems.

  • 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.

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

    Ojas Jain, Dhruv Kumar · Apr 7, 2026 · Citations: 0

    Simulation Env Multi Agent

    We introduce LudoBench, a benchmark for evaluating LLM strategic reasoning in Ludo, a stochastic multi-agent board game whose dice mechanics, piece capture, safe-square navigation, and home-path progression introduce meaningful planning…

  • FlowForge: A Staged Local Rollout Engine for Flow-Field Prediction

    Xiaowen Zhang, Ziming Zhou, Fengnian Zhao, David L. S. Hung · Apr 21, 2026 · Citations: 0

    Automatic Metrics Long Horizon

    We introduce FlowForge, a staged local rollout engine that predicts future flow fields by compiling a locality-preserving update schedule and executing it with a shared lightweight local predictor.

  • S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models

    Jack Young · Apr 1, 2026 · Citations: 0

    Automatic Metrics Long Horizon

    Using roughly 48 execution-verified HumanEval training solutions, tuning a single initial state matrix per recurrent layer, with zero inference overhead, outperforms LoRA by +10.8 pp (p < 0.001) on HumanEval.

  • Weakly Supervised Distillation of Hallucination Signals into Transformer Representations

    Shoaib Sadiq Salehmohamed, Jinal Prashant Thakkar, Hansika Aredla, Shaik Mohammed Omar, Shalmali Ayachit · Apr 7, 2026 · Citations: 0

    Llm As JudgeAutomatic Metrics

    We introduce a weak supervision framework that combines three complementary grounding signals: substring matching, sentence embedding similarity, and an LLM as a judge verdict to label generated responses as grounded or hallucinated without…

  • SkillX: Automatically Constructing Skill Knowledge Bases for Agents

    Chenxi Wang, Zhuoyun Yu, Xin Xie, Wuguannan Yao, Runnan Fang · Apr 6, 2026 · Citations: 0

    Automatic Metrics Long Horizon

    Learning from experience is critical for building capable large language model (LLM) agents, yet prevailing self-evolving paradigms remain inefficient: agents learn in isolation, repeatedly rediscover similar behaviors from limited…

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