3 citations · 3 across the 7 of their papers we have counts for
10 papers
Inherited, Not Invented: Tracing the Provenance of Wait in Reasoning Models
Zhizhuo Kou, Sizai Hou, Long Chen +6
Reinforcement learning (RL) is credited with teaching reasoning models to write {wait} before reconsidering a step, yet large RL accuracy gains do not require the word. This view c…
Entertain, then Cancel: How Thinking Makes Reasoning Models Read Some and Or Literally
Zhizhuo Kou, Jian Yang, Pengfei Hao +4
When a speaker says ``some students passed'', listeners infer that not all of them did. Reasoning models now let users decide how long a model thinks before it answers, but we do n…
GNFormer: Gaussian Neural Field Transformer for Efficient Camera-based Occupancy Prediction
Guangcheng Chen, Li He, Hong Zhang
Camera-based occupancy prediction provides geometric and semantic scene understanding for robot perception and has been adopted in autonomous vehicle and UAV perception systems. Ga…
Beyond Static Rules: Automated Discovery of Latent Vulnerabilities in Text-to-SQL
Hanqing Wang, Yongdong Chi, Jian Yang +4
While Large Language Models (LLMs) have achieved remarkable success in Text-to-SQL tasks, their deployment in real-world environments is hindered by latent reliability issues. Iden…
MARDoc: A Memory-Aware Refinement Agent Framework for Multimodal Long Document QA
Kaifeng Chen, Hongtao Liu, Qiyao Peng +4
Iterative retrieval-reasoning agents have recently shown promise for multimodal long-document question answering. However, most existing systems maintain a single growing context t…
MIRA: Mid-training Rubric Anchoring for Source-Aware Data Selection
Haowen Wang, Yaxin Du, Jian Yang +9
Mid-training has become an important stage in modern LLM development, using large-scale curated mixtures to strengthen capabilities before final post-training. Its data selection p…
Rethinking Stepwise Model Routing: A Cost-Efficient Table Reasoning Perspective
Shenghao Ye, Yuxiang Wang, Yu Guo +3
Large Reasoning Models (LRMs) achieve strong performance on table reasoning tasks but incur substantial inference cost due to long reasoning traces. Stepwise model routing mitigate…
Rubric-Guided Process Reward for Stepwise Model Routing
Shenghao Ye, Yu Guo, Zhengheng Li +2
Stepwise model routing improves the efficiency of Large Reasoning Models (LRMs) by assigning each reasoning step to a suitable model. Recent methods formulate routing as a sequenti…
Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languages
Yongdong chi, Hanqing Wang, Zonghan Yang +4
Text-to-SQL transforms the user queries from natural language to executable SQL programs, enabling non-experts to interact with complex databases. Existing prompt-based methods cra…
SQLForge: Synthesizing Reliable and Diverse Data to Enhance Text-to-SQL Reasoning in LLMs
Yu Guo, Dong Jin, Shenghao Ye +3
Large Language models (LLMs) have demonstrated significant potential in text-to-SQL reasoning tasks, yet a substantial performance gap persists between existing open-source models…