activity
20232026
most citedOptimizing Language Model's Reasoning Abilities with Weak Supervision

1 citations · 1 across the 13 of their papers we have counts for

collaborators

13 papers

cs.DB2026

From Interpretation to Compilation: A Compilation-Based Execution Engine for Semantic Operator Systems

Wenkai Dong, Yifan Wang

Semantic operators extend data processing with natural-language predicates. Existing semantic operator systems commonly execute these operators through interpretation-based executi…

cs.AI2026

PMMC: Prospective Multimodal Memory Compilation for Long-Term LVLM Agents

Jingyu Sun, Yan Lin, Yuyang Xue +10

Long-term memory is essential for LVLM agents to maintain consistency and integrate information across extended multimodal interactions. Existing agent memory systems, however, oft…

cs.CV2026

DECIS: Dual-Evidence Corrective Verification for Interpretable Strabismus Diagnostic Decision-Making

Xikai Tang, Yifan Wang, Jiafan Zhuang +12

Strabismus is a common ocular disorder that requires fine-grained subtype diagnosis for individualized treatment planning. However, existing deep learning methods mainly provide di…

cs.MM2026

Conan-embedding-v3: Fusing Modality-Specific Models for Omni-Modal Embedding

Shiyu Li, Zhiyuan Hu, Yifan Wang +3

Omni-modal retrieval promises a single embedding space for text, image, video, document, and audio inputs, but building such a unified retriever is difficult since these modalities…

cs.DB2026

From Interpretation to Compilation: Compilation-Based Execution of Semantic Operators [Vision]

Wenkai Dong, Yifan Wang

Semantic operator systems extend data processing with natural-language interfaces, supporting operations such as semantic filtering, mapping, and joining. Existing systems commonly…

cs.CV2026

Are Reasoning Vision-Language Models Robust to Semantic Visual Distractions?

Yizheng Sun, Mochuan Zhan, Yanan Ma +10

Reasoning Vision-Language Models (VLMs) achieve strong performance on complex multimodal tasks, but reliable real-world application requires handling visual inputs that are messier…

cs.HC2026

CANote: Empowering Fact-checking Note Writing Through Scaffolded and Provenance-based Human-AI Collaboration

Shuning Zhang, Jingruo Chen, Yuwei Chuai +4

Crowdsourced fact-checking mechanisms, such as X's Community Notes, play a critical role in mitigating the spread of misinformation. However, drafting high-quality, evidence-based…

cs.DB2026

Bridge the Last-Mile Gap to Semantic Analytics: Compiling Natural-Language Queries into Semantic Operator Pipelines

Wenkai Dong, Ruyu Li, Sairam Gurajada +1

Automated AI workflows increasingly rely on natural-language reasoning over heterogeneous data, but lack a practical way to execute it through optimized semantic data systems. Rece…

cs.CV2026

Can MLLMs Reason Beyond Language? VisReason: A Comprehensive Benchmark for Vision-Centric Reasoning

Longteng Guo, Yifan Wang, Pengkang Huo +4

Recent multimodal large language models (MLLMs) achieve strong performance on visual reasoning benchmarks, yet it remains unclear to what extent such performance reflects reasoning…

cs.CV2026

UnAC: Adaptive Visual Prompting with Abstraction and Stepwise Checking for Complex Multimodal Reasoning

Yifan Wang, Yun Fu

Although recent LMMs have become much stronger at visual perception, they remain unreliable on problems that require multi-step reasoning over visual evidence. In this paper, we pr…

cs.HC2025

Fact-Checking Comments Precede Community Notes to Misleading Posts on X

Shuning Zhang, Dai Shi, Yuwei Chuai +6

Community-based fact-checking systems, such as X's Community Notes program, offer a potentially scalable approach against online misinformation diffusion. However, their efficacy i…

cs.CL2024★ 1 cited

Optimizing Language Model's Reasoning Abilities with Weak Supervision

Yongqi Tong, Sizhe Wang, Dawei Li +6

While Large Language Models (LLMs) have demonstrated proficiency in handling complex queries, much of the past work has depended on extensively annotated datasets by human experts.…

cs.CL2023

Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinking

Yongqi Tong, Yifan Wang, Dawei Li +4

Chain-of-Thought(CoT) prompting and its variants explore equipping large language models (LLMs) with high-level reasoning abilities by emulating human-like linear cognition and log…