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HFEPX Hub

CS.CR + General Papers

Updated from current HFEPX corpus (Mar 8, 2026). 12 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Mar 8, 2026). 12 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequently cited benchmark: APPS. Common metric signal: jailbreak success rate. 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 Jan 7, 2026.

Papers: 12 Last published: Jan 7, 2026 Global RSS Tag RSS
Cs.CRGeneral

Researcher Quick Triage

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

All Sampled Papers (12) Replication-Ready Only (1)

High-Signal Coverage

100.0%

12 / 12 sampled papers are not low-signal flagged.

Replication-Ready Set

1

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 1 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 (1 papers).

Why This Matters For Eval Research

  • 75% of papers report explicit human-feedback signals, led by red-team protocols.
  • automatic metrics appears in 50% of papers in this hub.
  • APPS 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.
  • Track metric sensitivity by reporting both jailbreak success rate and helpfulness.

Benchmark Interpretation

  • APPS appears in 8.3% of hub papers (1/12); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • jailbreak success rate is reported in 25% of hub papers (3/12); compare with a secondary metric before ranking methods.
  • helpfulness is reported in 16.7% of hub papers (2/12); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (75% 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 (8.3% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (50% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (8.3% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (8.3% vs 35% target).

Strengths

  • Strong human-feedback signal (75% of papers).
  • Agentic evaluation appears in 33.3% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (8.3% coverage).
  • Annotation unit is under-specified (8.3% coverage).

Suggested Next Analyses

  • Track metric sensitivity by reporting both jailbreak success rate and helpfulness.
Recommended Queries (Expanded)

Recommended Queries

Benchmark Slice: APPS Metric Slice: jailbreak success rate 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
RLShield: Practical Multi-Agent RL for Financial Cyber Defense with Attack-Surface MDPs and Real-Time Response Orchestration

Feb 26, 2026

 No
Not Reported
Automatic Metrics APPS Cost Not Reported
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Contextualized Privacy Defense for LLM Agents

    Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments. Focus: helpfulness. Abstract: LLM agents increasingly act on users' personal information, yet existing privacy defenses remain.

  2. RLShield: Practical Multi-Agent RL for Financial Cyber Defense with Attack-Surface MDPs and Real-Time Response Orchestration

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: APPS / cost. Abstract: Financial systems run nonstop and must stay reliable even during cyber.

  3. "Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + expert verification. Abstract: Large language model (LLM) agents are rapidly becoming trusted copilots in high-stakes.

  4. What Matters For Safety Alignment?

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + red-team protocols. Focus: success rate. Abstract: This paper presents a comprehensive empirical study on.

  5. MANATEE: Inference-Time Lightweight Diffusion Based Safety Defense for LLMs

    Adds automatic metrics with red-team protocols for broader protocol coverage within this hub. Signals: automatic metrics + red-team protocols. Focus: success rate. Abstract: Defending LLMs against adversarial jailbreak.

  6. Steering Dialogue Dynamics for Robustness against Multi-turn Jailbreaking Attacks

    Adds evaluation protocol evidence with red-team protocols for broader protocol coverage within this hub. Signals: red-team protocols. Focus: helpfulness. Abstract: Large language models (LLMs) are shown to be.

  7. A Systematic Review of Algorithmic Red Teaming Methodologies for Assurance and Security of AI Applications

    Adds automatic metrics with red-team protocols for broader protocol coverage within this hub. Signals: automatic metrics + red-team protocols. Abstract: Cybersecurity threats are becoming increasingly sophisticated, making traditional.

  8. Intent Laundering: AI Safety Datasets Are Not What They Seem

    Adds evaluation protocol evidence with red-team protocols for broader protocol coverage within this hub. Signals: red-team protocols. Abstract: We systematically evaluate the quality of widely used AI safety.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (8.3% 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 (8)
  • Expert Verification (1)

Evaluation Modes

  • Automatic Metrics (6)
  • Simulation Env (1)

Top Benchmarks

  • APPS (1)

Top Metrics

  • Jailbreak success rate (3)
  • Helpfulness (2)
  • Success rate (2)
  • Cost (1)

Rater Population Mix

  • Domain Experts (1)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 75.0% · benchmarks 8.3% · metrics 41.7% · quality controls 0.0%.

Top Papers

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