HFEPX Archive Slice

HFEPX Daily Archive: 2026-02-06

Updated from current HFEPX corpus (Mar 10, 2026). 7 papers are grouped in this daily page.

Read Full Context

Updated from current HFEPX corpus (Mar 10, 2026). 7 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Frequently cited benchmark: Chemcotbench. Common metric signal: cost. 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 Feb 6, 2026.

Papers: 7 Last published: Feb 6, 2026 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: Developing .

High-Signal Coverage

100.0%

7 / 7 papers are not low-signal flagged.

Benchmark Anchors

57.1%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

71.4%

Papers with reported metric mentions in extraction output.

0 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.

Get this digest every Friday →

Why This Time Slice Matters

14.3% of papers report explicit human-feedback signals, led by expert verification.
automatic metrics appears in 42.9% of papers in this hub.
Chemcotbench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways For This Period

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 mixed annotation units; use this to scope replication staffing.
Track metric sensitivity by reporting both cost and task success.

Start Here (Highest-Signal Papers In This Slice)

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

How Well Can LLM Agents Simulate End-User Security and Privacy Attitudes and Behaviors?
Feb 6, 2026 · Citations: 0 · Score: 6.0

Eval: Simulation Env · Metrics: Coherence
Fine-Tuning and Evaluating Conversational AI for Agricultural Advisory
Feb 6, 2026 · Citations: 0 · Score: 6.0

Eval: Automatic Metrics · Metrics: Accuracy, F1
LatentChem: From Textual CoT to Latent Thinking in Chemical Reasoning
Feb 6, 2026 · Citations: 0 · Score: 6.0

Eval: Automatic Metrics · Metrics: Win rate, Task success
Measuring Complexity at the Requirements Stage: Spectral Metrics as Development Effort Predictors
Feb 6, 2026 · Citations: 0 · Score: 5.5

Eval: Automatic Metrics · Metrics: Cost
RoPE-LIME: RoPE-Space Locality + Sparse-K Sampling for Efficient LLM Attribution
Feb 6, 2026 · Citations: 0 · Score: 5.0

Eval: Not reported · Metrics: Nll
Personality as Relational Infrastructure: User Perceptions of Personality-Trait-Infused LLM Messaging
Feb 6, 2026 · Citations: 0 · Score: 0.0

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
How Well Can LLM Agents Simulate End-User Security and Privacy Attitudes and Behaviors? Feb 6, 2026	Simulation Env	Sp Abcbench	Coherence	Not reported
Fine-Tuning and Evaluating Conversational AI for Agricultural Advisory Feb 6, 2026	Automatic Metrics	Dg Eval	Accuracy, F1	Not reported
LatentChem: From Textual CoT to Latent Thinking in Chemical Reasoning Feb 6, 2026	Automatic Metrics	Chemcotbench	Win rate, Task success	Not reported
Measuring Complexity at the Requirements Stage: Spectral Metrics as Development Effort Predictors Feb 6, 2026	Automatic Metrics	Not reported	Cost	Not reported
RoPE-LIME: RoPE-Space Locality + Sparse-K Sampling for Efficient LLM Attribution Feb 6, 2026	Not reported	MMLU, HotpotQA	Nll	Not reported
Personality as Relational Infrastructure: User Perceptions of Personality-Trait-Infused LLM Messaging Feb 6, 2026	Not reported	Not reported	Not reported	Not reported
Stopping Computation for Converged Tokens in Masked Diffusion-LM Decoding Feb 6, 2026	Not reported	Not reported	Not reported	Not reported

Researcher Workflow (Detailed)

Checklist

Gap: Papers with explicit human feedback

Coverage is a replication risk (14.3% 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 (42.9% vs 35% target).
Moderate: Papers with known rater population

Coverage is usable but incomplete (28.6% vs 35% target).
Gap: Papers with known annotation unit

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

Strengths

Agentic evaluation appears in 28.6% of papers.

Known Gaps

Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
Annotation unit is under-specified (0% coverage).
Benchmark coverage is thin (14.3% of papers mention benchmarks/datasets).

Suggested Next Analyses

Track metric sensitivity by reporting both cost and task success.

Recommended Queries

Benchmark Slice: Chemcotbench Metric Slice: cost Recent High-Signal Papers

Known Limitations

Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
Annotation unit is under-specified (0% 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 (3)
Simulation Env (1)

Top Metrics

Cost (2)
Task success (1)
Win rate (1)

Top Benchmarks

Chemcotbench (1)

Quality Controls

Papers In This Archive Slice

How Well Can LLM Agents Simulate End-User Security and Privacy Attitudes and Behaviors?
Yuxuan Li, Leyang Li, Hao-Ping Lee, Sauvik Das · Feb 6, 2026 · Citations: 0

A growing body of research assumes that large language model (LLM) agents can serve as proxies for how people form attitudes toward and behave in response to security and privacy (S&P) threats.
Measuring Complexity at the Requirements Stage: Spectral Metrics as Development Effort Predictors
Maximilian Vierlboeck, Antonio Pugliese, Roshanak Nilchian, Paul Grogan, Rashika Sugganahalli Natesh Babu · Feb 6, 2026 · Citations: 0

Expert Verification

Complexity in engineered systems presents one of the most persistent challenges in modern development since it is driving cost overruns, schedule delays, and outright project failures.
Personality as Relational Infrastructure: User Perceptions of Personality-Trait-Infused LLM Messaging
Dominik P. Hofer, David Haag, Rania Islambouli, Jan D. Smeddinck · Feb 6, 2026 · Citations: 0
Fine-Tuning and Evaluating Conversational AI for Agricultural Advisory
Sanyam Singh, Naga Ganesh, Vineet Singh, Lakshmi Pedapudi, Ritesh Kumar · Feb 6, 2026 · Citations: 0

We present a hybrid LLM architecture that decouples factual retrieval from conversational delivery: supervised fine-tuning with LoRA on expert-curated GOLDEN FACTS (atomic, verified units of agricultural knowledge) optimizes fact recall,…
Stopping Computation for Converged Tokens in Masked Diffusion-LM Decoding
Daisuke Oba, Danushka Bollegala, Masahiro Kaneko, Naoaki Okazaki · Feb 6, 2026 · Citations: 0
LatentChem: From Textual CoT to Latent Thinking in Chemical Reasoning
Xinwu Ye, Yicheng Mao, Jia Zhang, Yimeng Liu, Li Hao · Feb 6, 2026 · Citations: 0

Long Horizon

Across diverse chemical reasoning benchmarks, LatentChem achieves a 59.88\% non-tie win rate over strong CoT-based baselines on ChemCoTBench, while delivering a 10.84\times average inference speedup.
RoPE-LIME: RoPE-Space Locality + Sparse-K Sampling for Efficient LLM Attribution
Isaac Picov, Ritesh Goru · Feb 6, 2026 · Citations: 0

Tool Use

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

Need human evaluators for your AI research? Scale annotation with expert AI Trainers.

Post a Job Get a Quote