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20212026
most citedPredicting Stress in Remote Learning via Advanced Deep Learning Technologies

3 citations · 5 across the 18 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

The Judge Can Debias Itself: Awareness and Self-Debiasing for Reliable LLM-as-a-Judge

Youbing Yin, Jingyu Wu, Anoop Kumar +3

LLM-as-a-Judge systems are increasingly employed for automated evaluation, yet remain susceptible to systematic biases that undermine their reliability. We demonstrate that the jud…

cs.AI2026

Probing Ambiguity: Adversarial Intent Resolution for Robust Text-to-SQL

Youbing Yin, Xujun Peng, Muhammad S. Uddin +3

Text-to-SQL systems typically assume a one-to-one mapping between natural-language queries and correct SQL translations. In practice, however, many queries are inherently ambiguous…

cs.CL2026

CAGE: Coherence-Aware Graph Encoding for Retrieval-Augmented Generation

Tong Qi, Jingyu Wu, Youbing Yin +3

Traditional Retrieval-Augmented Generation (RAG) systems score each passage independently against the query, assembling context sets that may be individually relevant yet collectiv…

cs.AI2026

SAFARI: Scaling Long Horizon Agentic Fault Attribution via Active Investigation

Chenyang Zhu, Jiayu Yao, Kushal Chawla +10

As autonomous agents tackle increasingly complex multi-step, multi-agent tasks, their execution trajectories have scaled beyond the constraints of even the largest context windows.…

cs.CL2026

Alignment-Weighted DPO: A principled reasoning approach to improve safety alignment

Mengxuan Hu, Vivek V. Datla, Anoop Kumar +4

Recent advances in alignment techniques such as Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Direct Preference Optimization (DPO) have impro…

cs.CL2026

DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generation

Saadat Hasan Khan, Spencer Hong, Jingyu Wu +4

Retrieval-augmented generation (RAG) is a common technique for grounding language model outputs in domain-specific information. However, RAG is often challenged by reasoning-intens…