HFEPX Daily Archive: 2026-01-30

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 .

• 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.

Why This Time Slice Matters

• 27.3% of papers report explicit human-feedback signals, led by critique/edit feedback.
• automatic metrics appears in 27.3% of papers in this hub.
• Paperbananabench 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 multi-dimensional rubrics; use this to scope replication staffing.
• Track metric sensitivity by reporting both accuracy and conciseness.

Start Here (Highest-Signal Papers In This Slice)

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

• PaperBanana: Automating Academic Illustration for AI Scientists

  Jan 30, 2026 · Citations: 0 · Score: 7.0

  Eval: Automatic Metrics · Metrics: Faithfulness, Conciseness

• Should LLMs, like, Generate How Users Talk? Building Dialect-Accurate Dialog[ue]s Beyond the American Default with MDial

  Jan 30, 2026 · Citations: 0 · Score: 5.5

  Eval: Automatic Metrics · Metrics: Accuracy, Precision

• KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models

  Jan 30, 2026 · Citations: 0 · Score: 4.5

  Eval: Automatic Metrics, Simulation Env · Metrics: Accuracy

• Mock Worlds, Real Skills: Building Small Agentic Language Models with Synthetic Tasks, Simulated Environments, and Rubric-Based Rewards

  Jan 30, 2026 · Citations: 0 · Score: 4.0

  Eval: Simulation Env · Metrics: Not reported

• OpenVTON-Bench: A Large-Scale High-Resolution Benchmark for Controllable Virtual Try-On Evaluation

  Jan 30, 2026 · Citations: 0 · Score: 3.5

  Eval: Not reported · Metrics: Not reported

• From Self-Evolving Synthetic Data to Verifiable-Reward RL: Post-Training Multi-turn Interactive Tool-Using Agents

  Jan 30, 2026 · Citations: 0 · Score: 3.0

  Eval: Simulation Env · Metrics: Not reported

Protocol Matrix (Top 10)

Quickly compare method ingredients across this archive slice.

Papers In This Archive Slice

• Detecting AI-Generated Content in Academic Peer Reviews

  Siyuan Shen, Kai Wang · Jan 30, 2026 · Citations: 0

  Together, these findings provide suggestive evidence of a rapidly increasing presence of AI-assisted content in peer review and highlight the need for further study of its implications for scholarly evaluation.

• PaperBanana: Automating Academic Illustration for AI Scientists

  Dawei Zhu, Rui Meng, Yale Song, Xiyu Wei, Sujian Li · Jan 30, 2026 · Citations: 0

  Critique Edit

  To lift this burden, we introduce PaperBanana, an agentic framework for automated generation of publication-ready academic illustrations.

• Mem-T: Densifying Rewards for Long-Horizon Memory Agents

  Yanwei Yue, Boci Peng, Xuanbo Fan, Jiaxin Guo, Qiankun Li · Jan 30, 2026 · Citations: 0
• Should LLMs, like, Generate How Users Talk? Building Dialect-Accurate Dialog[ue]s Beyond the American Default with MDial

  Jio Oh, Paul Vicinanza, Thomas Butler, Steven Euijong Whang, Dezhi Hong · Jan 30, 2026 · Citations: 0

  Pairwise Preference

  Independent evaluations confirm data quality, with annotators preferring MDial outputs over prior methods in 98% of pairwise comparisons for dialect naturalness.

• TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization

  Shichao Ma, Zhiyuan Ma, Ming Yang, Xiaofan Li, Xing Wu · Jan 30, 2026 · Citations: 0
• OpenVTON-Bench: A Large-Scale High-Resolution Benchmark for Controllable Virtual Try-On Evaluation

  Jin Li, Tao Chen, Shuai Jiang, Weijie Wang, Jingwen Luo · Jan 30, 2026 · Citations: 0

  We present OpenVTON-Bench, a large-scale benchmark comprising approximately 100K high-resolution image pairs (up to 1536 \times 1536).

• KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models

  Zukang Xu, Zhixiong Zhao, Xing Hu, Zhixuan Chen, Dawei Yang · Jan 30, 2026 · Citations: 0

  Mixture of Experts (MoE) models have achieved great success by significantly improving performance while maintaining computational efficiency through sparse expert activation.

• Time-Annealed Perturbation Sampling: Diverse Generation for Diffusion Language Models

  Jingxuan Wu, Zhenglin Wan, Xingrui Yu, Yuzhe Yang, Yiqiao Huang · Jan 30, 2026 · Citations: 0
• From Self-Evolving Synthetic Data to Verifiable-Reward RL: Post-Training Multi-turn Interactive Tool-Using Agents

  Jiaxuan Gao, Jiaao Chen, Chuyi He, Shusheng Xu, Di Jin · Jan 30, 2026 · Citations: 0

  Long Horizon

  Interactive tool-using agents must solve real-world tasks via multi-turn interaction with both humans and external environments, requiring dialogue state tracking, multi-step tool execution, while following complex instructions.

• Mock Worlds, Real Skills: Building Small Agentic Language Models with Synthetic Tasks, Simulated Environments, and Rubric-Based Rewards

  Yuanjie Lyu, Chengyu Wang, Lei Shen, Jun Huang, Tong Xu · Jan 30, 2026 · Citations: 0

  Rubric Rating Tool Use

  Small LLMs often struggle to match the agentic capabilities of large, costly models.

• AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Values from Casual Conversations

  Bhada Yun, Renn Su, April Yi Wang · Jan 30, 2026 · Citations: 0

  Does AI understand human values?

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