Daily Archive
Updated from current HFEPX corpus (Feb 27, 2026). 17 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Simulation Env. Common annotation unit: Trajectory. 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 Dec 11, 2025.
Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 17 papers for HFEPX Fortnight Archive: 2025-F25. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on multiple benchmark families and metric focus on accuracy, cost. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.
11.8% of papers report explicit human-feedback signals, led by demonstration data.
Evidence: AITutor-EvalKit: Exploring the Capabilities of AI Tutors , Explanation Bias is a Product: Revealing the Hidden Lexical and Position Preferences in Post-Hoc Feature Attribution , Interpreto: An Explainability Library for Transformers , KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification
automatic metrics appears in 82.4% of papers in this hub.
Evidence: Explanation Bias is a Product: Revealing the Hidden Lexical and Position Preferences in Post-Hoc Feature Attribution , Interpreto: An Explainability Library for Transformers , KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification , QSTN: A Modular Framework for Robust Questionnaire Inference with Large Language Models
long-horizon tasks appears in 11.8% of papers, indicating agentic evaluation demand.
Evidence: Aerial Vision-Language Navigation with a Unified Framework for Spatial, Temporal and Embodied Reasoning , Conflict-Aware Fusion: Resolving Logic Inertia in Large Language Models via Structured Cognitive Priors , Explanation Bias is a Product: Revealing the Hidden Lexical and Position Preferences in Post-Hoc Feature Attribution , Interpreto: An Explainability Library for Transformers
Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
Evidence: Explanation Bias is a Product: Revealing the Hidden Lexical and Position Preferences in Post-Hoc Feature Attribution , Interpreto: An Explainability Library for Transformers , KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification , QSTN: A Modular Framework for Robust Questionnaire Inference with Large Language Models
Rater context is mostly unspecified rater pools, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
Evidence: Explanation Bias is a Product: Revealing the Hidden Lexical and Position Preferences in Post-Hoc Feature Attribution , Interpreto: An Explainability Library for Transformers , KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification , QSTN: A Modular Framework for Robust Questionnaire Inference with Large Language Models
Track metric sensitivity by reporting both accuracy and cost.
Evidence: Explanation Bias is a Product: Revealing the Hidden Lexical and Position Preferences in Post-Hoc Feature Attribution , Interpreto: An Explainability Library for Transformers , KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification , QSTN: A Modular Framework for Robust Questionnaire Inference with Large Language Models
Papers with explicit human feedback
Coverage is a replication risk (11.8% vs 45% target).
Papers reporting quality controls
Coverage is a replication risk (0% vs 30% target).
Papers naming benchmarks/datasets
Coverage is a replication risk (0% vs 35% target).
Papers naming evaluation metrics
Coverage is strong (52.9% vs 35% target).
Papers with known rater population
Coverage is a replication risk (0% vs 35% target).
Papers with known annotation unit
Coverage is a replication risk (5.9% vs 35% target).
Start here for detailed protocol reporting, including rater and quality-control evidence.
2. Interpreto: An Explainability Library for Transformers
Start here for detailed protocol reporting, including rater and quality-control evidence.
3. KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification
Start here for detailed protocol reporting, including rater and quality-control evidence.
4. QSTN: A Modular Framework for Robust Questionnaire Inference with Large Language Models
Adds automatic metrics for broader coverage within this hub.
Adds simulation environments for broader coverage within this hub.
6. Group Representational Position Encoding
Adds automatic metrics for broader coverage within this hub.
7. STaRR: Spatial-Temporal Token-Dynamics-Aware Responsive Remasking for Diffusion Language Models
Adds automatic metrics for broader coverage within this hub.
Adds automatic metrics for broader coverage within this hub.
automatic_metrics vs simulation_env
both=0, left_only=14, right_only=3
0 papers use both Automatic Metrics and Simulation Env.
Metric Brief
accuracy
Coverage: 4 papers (23.5%)
4 papers (23.5%) mention accuracy.
Examples: KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification , STaRR: Spatial-Temporal Token-Dynamics-Aware Responsive Remasking for Diffusion Language Models , Conflict-Aware Fusion: Resolving Logic Inertia in Large Language Models via Structured Cognitive Priors
Metric Brief
cost
Coverage: 4 papers (23.5%)
4 papers (23.5%) mention cost.
Examples: QSTN: A Modular Framework for Robust Questionnaire Inference with Large Language Models , Aerial Vision-Language Navigation with a Unified Framework for Spatial, Temporal and Embodied Reasoning , Group Representational Position Encoding
Metric Brief
perplexity
Coverage: 2 papers (11.8%)
2 papers (11.8%) mention perplexity.
Examples: Reconstructing KV Caches with Cross-layer Fusion For Enhanced Transformers , Randomized Masked Finetuning: An Efficient Way to Mitigate Memorization of PIIs in LLMs
Most Cited In This Hub
Fast path to methods with the strongest citation traction in this scope.
Papers: Explanation Bias is a Product: Revealing the Hidden Lexical and Position Preferences in Post-Hoc Feature Attribution , Interpreto: An Explainability Library for Transformers , KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification
Most Recent
Fast path to latest protocol changes and newly published evaluation setups.
Papers: Explanation Bias is a Product: Revealing the Hidden Lexical and Position Preferences in Post-Hoc Feature Attribution , Interpreto: An Explainability Library for Transformers , KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification
Best Protocol Detail
Papers with explicit rater/unit metadata and quality-control signals for reproducibility.
Papers: Explanation Bias is a Product: Revealing the Hidden Lexical and Position Preferences in Post-Hoc Feature Attribution , Interpreto: An Explainability Library for Transformers , KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification
Jonathan Kamp, Roos Bakker, Dominique Blok · Dec 11, 2025
In this work, we delve beyond the superficial inconsistencies between attribution methods, structuring their biases through a model- and method-agnostic framework of three evaluation metrics.
Antonin Poché, Thomas Mullor, Gabriele Sarti, Frédéric Boisnard, Corentin Friedrich · Dec 10, 2025
Interpreto is an open-source Python library for interpreting HuggingFace language models, from early BERT variants to LLMs.
Erfan Nourbakhsh, Nasrin Sanjari, Ali Nourbakhsh · Dec 9, 2025
Age-related macular degeneration (AMD) and choroidal neovascularization (CNV)-related conditions are leading causes of vision loss worldwide, with optical coherence tomography (OCT) serving as a cornerstone for early detection and managemen
Maximilian Kreutner, Jens Rupprecht, Georg Ahnert, Ahmed Salem, Markus Strohmaier · Dec 9, 2025
QSTN enables robust evaluation of questionnaire presentation, prompt perturbations, and response generation methods.
Huilin Xu, Zhuoyang Liu, Yixiang Luomei, Feng Xu · Dec 9, 2025
Extensive experiments on the AerialVLN and OpenFly benchmark validate the effectiveness of our method.
Yifan Zhang, Zixiang Chen, Yifeng Liu, Zhen Qin, Huizhuo Yuan · Dec 8, 2025
We present GRAPE (Group Representational Position Encoding), a unified framework for positional encoding based on group actions.
Xinhao Sun, Huaijin Zhao, Maoliang Li, Zihao Zheng, Jiayu Chen · Dec 7, 2025
Diffusion Language Models (DLMs) enable parallel decoding via iterative denoising, where remasking strategies play a critical role in balancing inference speed and output quality.
Qiming Bao, Xiaoxuan Fu, Michael Witbrock · Dec 6, 2025
We present a controlled evaluation framework consisting of four stress tests: (1) rule deletion (redundant vs.
Hongzhan Lin, Zhiqi Bai, Xinmiao Zhang, Sen Yang, Xiang Li · Dec 3, 2025
Transformer decoders have achieved strong results across tasks, but the memory required for the KV cache becomes prohibitive at long sequence lengths.
Numaan Naeem, Kaushal Kumar Maurya, Kseniia Petukhova, Ekaterina Kochmar · Dec 3, 2025
We present AITutor-EvalKit, an application that uses language technology to evaluate the pedagogical quality of AI tutors, provides software for demonstration and evaluation, as well as model inspection and data visualization.
Kunj Joshi, David A. Smith · Dec 2, 2025
We present MaxTER, a Pareto-optimal evaluation framework for assessing privacy-utility tradeoffs, and show the performance of RMFT vs Deduplication by Area Under The Response Curve (AURC) metric.
Songwen Zhao, Danqing Wang, Kexun Zhang, Jiaxuan Luo, Zhuo Li · Dec 2, 2025
Vibe coding is a new programming paradigm in which human engineers instruct large language model (LLM) agents to complete complex coding tasks with little supervision.
Dawei Li, Abdullah Alnaibari, Arslan Bisharat, Manny Sandoval, Deborah Hall · Dec 2, 2025
To assess mediation quality, we construct a large Reddit-based dataset and propose a multi-stage evaluation pipeline combining principle-based scoring, user simulation, and human comparison.
Tom Zehle, Timo Heiß, Moritz Schlager, Matthias Aßenmacher, Matthias Feurer · Dec 2, 2025
It integrates multiple contemporary discrete prompt optimizers, supports systematic and reproducible benchmarking, and returns framework-agnostic prompt strings, enabling seamless integration into existing LLM pipelines while remaining agno
Sai Koneru, Fabian Retkowski, Christian Huber, Lukas Hilgert, Seymanur Akti · Dec 2, 2025
The globalization of education and rapid growth of online learning have made localizing educational content a critical challenge.
Robert Belanec, Ivan Srba, Maria Bielikova · Dec 2, 2025
While its modular design supports extensibility, it natively provides a representative set of 19 PEFT methods, 27 classification and text generation datasets addressing 12 tasks, and both standard and PEFT-specific evaluation metrics.
Adel Moumen, Guangzhi Sun, Philip C. Woodland · Dec 1, 2025
However, progress has been largely English-centric due to scarce spoken evaluation benchmarks and training data, making cross-lingual learning difficult.
