Daily Archive
Updated from current HFEPX corpus (Feb 27, 2026). 5 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequently cited benchmark: APPS. Common metric signal: accuracy. Newest paper in this set is from Feb 9, 2026.
Updated from current HFEPX corpus (Feb 27, 2026). This page covers 5 papers centered on HFEPX Daily Archive: 2026-02-09. Common evaluation modes include Automatic Metrics, Simulation Env, with benchmark emphasis on APPS, LongBench. Use the anchored takeaways below to compare protocol choices and identify papers with stronger evidence depth.
Evaluation emphasis: Automatic Metrics and Simulation Env appear frequently in this slice.
Evidence: UI-Venus-1.5 Technical Report , Prototype-Based Disentanglement for Controllable Dysarthric Speech Synthesis , Large Language Models and Impossible Language Acquisition: "False Promise" or an Overturn of our Current Perspective towards AI
Benchmark concentration: APPS, LongBench helps control cross-paper variance.
Evidence: Prototype-Based Disentanglement for Controllable Dysarthric Speech Synthesis , Large Language Models and Impossible Language Acquisition: "False Promise" or an Overturn of our Current Perspective towards AI , Language Modeling and Understanding Through Paraphrase Generation and Detection
Metric concentration: accuracy, coherence is repeatedly reported in this group.
Evidence: Large Language Models and Impossible Language Acquisition: "False Promise" or an Overturn of our Current Perspective towards AI , Language Modeling and Understanding Through Paraphrase Generation and Detection , Document Reconstruction Unlocks Scalable Long-Context RLVR
Stratify by benchmark (APPS vs LongBench) before comparing methods.
Evidence: Language Modeling and Understanding Through Paraphrase Generation and Detection , Document Reconstruction Unlocks Scalable Long-Context RLVR , UI-Venus-1.5 Technical Report
Track metric sensitivity by reporting both accuracy and coherence.
Evidence: Document Reconstruction Unlocks Scalable Long-Context RLVR , UI-Venus-1.5 Technical Report , Prototype-Based Disentanglement for Controllable Dysarthric Speech Synthesis
Papers with explicit human feedback is visible in approximately 40% of papers in this set.
Evidence: UI-Venus-1.5 Technical Report , Prototype-Based Disentanglement for Controllable Dysarthric Speech Synthesis , Large Language Models and Impossible Language Acquisition: "False Promise" or an Overturn of our Current Perspective towards AI
Papers with explicit human feedback
Coverage is usable but incomplete (40% vs 45% target).
Papers reporting quality controls
Coverage is a replication risk (0% vs 30% target).
Papers naming benchmarks/datasets
Coverage is strong (40% vs 35% target).
Papers naming evaluation metrics
Coverage is strong (60% vs 35% target).
Papers with known rater population
Coverage is strong (40% vs 35% target).
Papers with known annotation unit
Coverage is strong (40% vs 35% target).
1. UI-Venus-1.5 Technical Report
Start with this anchor paper for scope and protocol framing. Covers Simulation Env.
2. Prototype-Based Disentanglement for Controllable Dysarthric Speech Synthesis
Covers Automatic Metrics.
Covers Automatic Metrics. Includes human-feedback signal: Critique Edit.
4. Language Modeling and Understanding Through Paraphrase Generation and Detection
Covers Automatic Metrics.
5. Document Reconstruction Unlocks Scalable Long-Context RLVR
Covers Automatic Metrics. Includes human-feedback signal: Rubric Rating.
automatic_metrics vs simulation_env
both=0, left_only=4, right_only=1
0 papers use both Automatic Metrics and Simulation Env.
Venus Team, Changlong Gao, Zhangxuan Gu, Yulin Liu, Xinyu Qiu · Feb 9, 2026
GUI agents have emerged as a powerful paradigm for automating interactions in digital environments, yet achieving both broad generality and consistently strong task performance remains challenging.
Haoshen Wang, Xueli Zhong, Bingbing Lin, Jia Huang, Xingduo Pan · Feb 9, 2026
Dysarthric speech exhibits high variability and limited labeled data, posing major challenges for both automatic speech recognition (ASR) and assistive speech technologies.
Ziyan Wang, Longlong Ma · Feb 9, 2026
In Chomsky's provocative critique "The False Promise of CHATGPT," Large Language Models (LLMs) are characterized as mere pattern predictors that do not acquire languages via intrinsic causal and self-correction structures like humans, there
Jan Philip Wahle · Feb 9, 2026
Language enables humans to share knowledge, reason about the world, and pass on strategies for survival and innovation across generations.
Yao Xiao, Lei Wang, Yue Deng, Guanzheng Chen, Ziqi Jin · Feb 9, 2026
However, it often relies on gold-standard answers or explicit evaluation rubrics provided by powerful teacher models or human experts, which are costly and time-consuming.
