HFEPX Archive Slice

HFEPX Weekly Archive: 2025-W38

Updated from current HFEPX corpus (Mar 1, 2026). 8 papers are grouped in this daily page.

Read Full Context

Updated from current HFEPX corpus (Mar 1, 2026). 8 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics. Most common rater population: Domain Experts. Frequent quality control: Calibration. Frequently cited benchmark: AdvBench. Common metric signal: helpfulness. 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 Sep 20, 2025.

Papers: 8 Last published: Sep 20, 2025 Global RSS

Researcher Quick Triage

Use this archive page for time-slice monitoring (what changed in evaluation methods, metrics, and protocol quality this period). Quality band: Medium .

High-Signal Coverage

100.0%

8 / 8 papers are not low-signal flagged.

Benchmark Anchors

37.5%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

50.0%

Papers with reported metric mentions in extraction output.

1 papers report explicit quality controls for this archive period.
Prioritize papers with both benchmark and metric anchors for reliable longitudinal comparisons.

Primary action: Use this slice as early signal only; benchmark/metric anchoring is limited for rigorous period-over-period claims.

Why This Slice Matters (Expanded)

Why This Time Slice Matters

25% of papers report explicit human-feedback signals, led by critique/edit feedback.
automatic metrics appears in 62.5% of papers in this hub.
AdvBench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Notes (Expanded)

Protocol Takeaways For This Period

Most common quality-control signal is rater calibration (12.5% of papers).
Rater context is mostly domain experts, and annotation is commonly mixed annotation units; use this to scope replication staffing.
Add inter-annotator agreement checks when reproducing these protocols.

Start Here (Highest-Signal Papers In This Slice)

Ranked by protocol completeness and evidence density for faster period-over-period review.

ATTS: Asynchronous Test-Time Scaling via Conformal Prediction
Sep 18, 2025 · Citations: 0 · Score: 6.5

Eval: Automatic Metrics · Metrics: Accuracy, Latency
A Simple and Efficient Jailbreak Method Exploiting LLMs' Helpfulness
Sep 17, 2025 · Citations: 0 · Score: 6.5

Eval: Automatic Metrics · Metrics: Helpfulness
Evolving Language Models without Labels: Majority Drives Selection, Novelty Promotes Variation
Sep 18, 2025 · Citations: 0 · Score: 5.5

Eval: Automatic Metrics · Metrics: Pass@1, Pass@16
PeruMedQA: Benchmarking Large Language Models (LLMs) on Peruvian Medical Exams -- Dataset Construction and Evaluation
Sep 15, 2025 · Citations: 0 · Score: 4.0

Eval: Automatic Metrics · Metrics: Accuracy
ClearFairy: Capturing Creative Workflows through Decision Structuring, In-Situ Questioning, and Rationale Inference
Sep 18, 2025 · Citations: 0 · Score: 3.5

Eval: Automatic Metrics · Metrics: Not reported
KANO: Kolmogorov-Arnold Neural Operator
Sep 20, 2025 · Citations: 0 · Score: 1.5

Eval: Not reported · Metrics: Not reported

Protocol Matrix (Top 10)

Quickly compare method ingredients across this archive slice.

Paper	Eval Modes	Benchmarks	Metrics	Quality Controls
ATTS: Asynchronous Test-Time Scaling via Conformal Prediction Sep 18, 2025	Automatic Metrics	AIME	Accuracy, Latency	Calibration
A Simple and Efficient Jailbreak Method Exploiting LLMs' Helpfulness Sep 17, 2025	Automatic Metrics	AdvBench	Helpfulness	Not reported
Evolving Language Models without Labels: Majority Drives Selection, Novelty Promotes Variation Sep 18, 2025	Automatic Metrics	MMLU, MMLU Pro	Pass@1, Pass@16	Not reported
PeruMedQA: Benchmarking Large Language Models (LLMs) on Peruvian Medical Exams -- Dataset Construction and Evaluation Sep 15, 2025	Automatic Metrics	Not reported	Accuracy	Not reported
ClearFairy: Capturing Creative Workflows through Decision Structuring, In-Situ Questioning, and Rationale Inference Sep 18, 2025	Automatic Metrics	Not reported	Not reported	Not reported
KANO: Kolmogorov-Arnold Neural Operator Sep 20, 2025	Not reported	Not reported	Not reported	Not reported
The AI Memory Gap: Users Misremember What They Created With AI or Without Sep 15, 2025	Not reported	Not reported	Not reported	Not reported
Collaborative Document Editing with Multiple Users and AI Agents Sep 15, 2025	Not reported	Not reported	Not reported	Not reported

Researcher Workflow (Detailed)

Checklist

Gap: Papers with explicit human feedback

Coverage is a replication risk (25% vs 45% target).
Gap: Papers reporting quality controls

Coverage is a replication risk (12.5% vs 30% target).
Gap: Papers naming benchmarks/datasets

Coverage is a replication risk (12.5% vs 35% target).
Gap: Papers naming evaluation metrics

Coverage is a replication risk (12.5% vs 35% target).
Gap: Papers with known rater population

Coverage is a replication risk (12.5% vs 35% target).
Gap: Papers with known annotation unit

Coverage is a replication risk (0% vs 35% target).

Strengths

This hub still surfaces a concentrated paper set for protocol triage and replication planning.

Known Gaps

Only 12.5% of papers report quality controls; prioritize calibration/adjudication evidence.
Rater population is under-specified (12.5% coverage).
Annotation unit is under-specified (0% coverage).

Suggested Next Analyses

Add inter-annotator agreement checks when reproducing these protocols.

Recommended Queries

Benchmark Slice: AdvBench Metric Slice: helpfulness Recent High-Signal Papers

Known Limitations

Only 12.5% of papers report quality controls; prioritize calibration/adjudication evidence.
Rater population is under-specified (12.5% coverage).
Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Snapshot (Detailed)

Evaluation Modes

Automatic Metrics (5)

Top Metrics

Helpfulness (1)

Top Benchmarks

AdvBench (1)

Quality Controls

Calibration (1)

Papers In This Archive Slice

KANO: Kolmogorov-Arnold Neural Operator
Jin Lee, Ziming Liu, Xinling Yu, Yixuan Wang, Haewon Jeong · Sep 20, 2025 · Citations: 0

In the quantum Hamiltonian learning benchmark, KANO reconstructs ground-truth Hamiltonians in closed-form symbolic representations accurate to the fourth decimal place in coefficients and attains \approx 6\times10^{-6} state infidelity from…
Evolving Language Models without Labels: Majority Drives Selection, Novelty Promotes Variation
Yujun Zhou, Zhenwen Liang, Haolin Liu, Wenhao Yu, Kishan Panaganti · Sep 18, 2025 · Citations: 0

Large language models (LLMs) are increasingly trained with reinforcement learning from verifiable rewards (RLVR), yet real-world deployment demands models that can self-improve without labels or external judges.
ATTS: Asynchronous Test-Time Scaling via Conformal Prediction
Jing Xiong, Qiujiang Chen, Fanghua Ye, Zhongwei Wan, Chuanyang Zheng · Sep 18, 2025 · Citations: 0

Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
ClearFairy: Capturing Creative Workflows through Decision Structuring, In-Situ Questioning, and Rationale Inference
Kihoon Son, DaEun Choi, Tae Soo Kim, Young-Ho Kim, Sangdoo Yun · Sep 18, 2025 · Citations: 0

Critique Edit

Furthermore, exploratory applications demonstrate that captured steps can enhance generative AI agents in Figma, yielding predictions better aligned with professionals and producing coherent outcomes.
A Simple and Efficient Jailbreak Method Exploiting LLMs' Helpfulness
Xuan Luo, Yue Wang, Zefeng He, Geng Tu, Jing Li · Sep 17, 2025 · Citations: 0

Red Team

This study reveals a critical safety blind spot in modern LLMs: learning-style queries, which closely resemble ordinary educational questions, can reliably elicit harmful responses.
The AI Memory Gap: Users Misremember What They Created With AI or Without
Tim Zindulka, Sven Goller, Daniela Fernandes, Robin Welsch, Daniel Buschek · Sep 15, 2025 · Citations: 0

Our findings reveal a significant gap in memory: After AI use, the odds of correct attribution dropped, with the steepest decline in mixed human-AI workflows, where either the idea or elaboration was created with AI.
Collaborative Document Editing with Multiple Users and AI Agents
Florian Lehmann, Krystsina Shauchenka, Daniel Buschek · Sep 15, 2025 · Citations: 0

We propose integrating AI agents directly into collaborative writing environments.
PeruMedQA: Benchmarking Large Language Models (LLMs) on Peruvian Medical Exams -- Dataset Construction and Evaluation
Rodrigo M. Carrillo-Larco, Jesus Lovón Melgarejo, Manuel Castillo-Cara, Gusseppe Bravo-Rocca · Sep 15, 2025 · Citations: 0

Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

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