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HFEPX Hub

General + Red Team (Last 45 Days)

Updated from current HFEPX corpus (Apr 19, 2026). 14 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 19, 2026). 14 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequently cited benchmark: Semeval. 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: 14 Last published: Apr 8, 2026 Global RSS Tag RSS
GeneralRed TeamLast 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 (14) Replication-Ready Only (2)

High-Signal Coverage

100.0%

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

Replication-Ready Set

2

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 2 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: Use this page for scouting only; collect additional papers before attempting replication-critical comparisons.

Currently showing only replication-ready papers in ranking and matrix sections (2 papers).

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Why This Matters For Eval Research

  • 100% of papers report explicit human-feedback signals, led by red-team protocols.
  • automatic metrics appears in 42.9% of papers in this hub.
  • Semeval 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 domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

Benchmark Interpretation

  • Semeval appears in 7.1% of hub papers (1/14); use this cohort for benchmark-matched comparisons.
  • Tracesafe-Bench appears in 7.1% of hub papers (1/14); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 14.3% of hub papers (2/14); compare with a secondary metric before ranking methods.
  • helpfulness is reported in 14.3% of hub papers (2/14); 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).

  • Gap: Papers naming benchmarks/datasets

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

  • Strong: Papers naming evaluation metrics

    Coverage is strong (57.1% vs 35% target).

  • Gap: Papers with known rater population

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

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (7.1% 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 (14.3% coverage).
  • Annotation unit is under-specified (7.1% coverage).

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (Semeval vs Tracesafe-Bench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and helpfulness.
Recommended Queries (Expanded)

Recommended Queries

LLM-as-Judge Protocols Benchmark Slice: Semeval 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
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
SemEval-2026 Task 6: CLARITY -- Unmasking Political Question Evasions

Mar 14, 2026

 Yes
Red Team
Automatic Metrics Semeval F1 , F1 macro Not Reported

Protocol Diff (Top Papers)

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

Signal TraceSafe: A Systematic Assessment of LLM Guardrail… SemEval-2026 Task 6: CLARITY -- Unmasking Political…
Human Feedback Red TeamRed Team
Evaluation Modes Automatic MetricsAutomatic Metrics
Benchmarks Tracesafe BenchSemeval
Metrics AccuracyF1, F1 macro
Quality Controls Not reportedNot reported
Rater Population UnknownDomain Experts
Annotation Unit TrajectoryUnknown
Suggested Reading Order (Extended)

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

Suggested Reading Order

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

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + red-team protocols. Focus: Tracesafe-Bench / accuracy. Abstract: As large language models (LLMs) evolve from static.

  2. Trojan-Speak: Bypassing Constitutional Classifiers with No Jailbreak Tax via Adversarial Finetuning

    Start here for detailed protocol reporting and quality-control evidence. Signals: red-team protocols. Abstract: Fine-tuning APIs offered by major AI providers create new attack surfaces where adversaries can bypass.

  3. Prompt Attack Detection with LLM-as-a-Judge and Mixture-of-Models

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge + red-team protocols. Focus: latency. Abstract: In production, guardrails must mitigate these attacks under strict low-latency constraints,.

  4. SemEval-2026 Task 6: CLARITY -- Unmasking Political Question Evasions

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + red-team protocols. Focus: Semeval / f1. Abstract: Political speakers often avoid answering questions directly.

  5. Red-Teaming Vision-Language-Action Models via Quality Diversity Prompt Generation for Robust Robot Policies

    Adds simulation environments with red-team protocols for broader protocol coverage within this hub. Signals: simulation environments + red-team protocols. Focus: task success. Abstract: Furthermore, results from a user.

  6. WebWeaver: Breaking Topology Confidentiality in LLM Multi-Agent Systems with Stealthy Context-Based Inference

    Adds automatic metrics with red-team protocols for broader protocol coverage within this hub. Signals: automatic metrics + red-team protocols. Focus: accuracy. Abstract: Communication topology is a critical factor.

  7. Exposing Long-Tail Safety Failures in Large Language Models through Efficient Diverse Response Sampling

    Adds automatic metrics with red-team protocols for broader protocol coverage within this hub. Signals: automatic metrics + red-team protocols. Focus: cost. Abstract: Safety tuning through supervised fine-tuning and.

  8. IH-Challenge: A Training Dataset to Improve Instruction Hierarchy on Frontier LLMs

    Adds automatic metrics with red-team protocols for broader protocol coverage within this hub. Signals: automatic metrics + red-team protocols. Focus: helpfulness. Abstract: Instruction hierarchy (IH) defines how LLMs.

Known Limitations

Known Limitations

  • Only 0% 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

  • Red Team (14)
  • Pairwise Preference (1)

Evaluation Modes

  • Automatic Metrics (6)
  • Llm As Judge (1)
  • Simulation Env (1)

Top Benchmarks

  • Semeval (1)
  • Tracesafe Bench (1)

Top Metrics

  • Accuracy (2)
  • Helpfulness (2)
  • Cost (1)
  • F1 (1)

Rater Population Mix

  • Domain Experts (2)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 14.3% · metrics 57.1% · quality controls 0.0%.

Top Papers

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