most citedSphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry

1 citations · 1 across the 3 of their papers we have counts for

collaborators

7 papers

cs.AI2026

Do LLMs Build World Models From Text? A Multilingual Diagnostic of Spatial Reasoning

Zhikai Pan, Chih-Ting Liao, Chunrui Liu +5

Whether large language models (LLMs) construct internal spatial world models from pure-text descriptions remains contested, and whether such capabilities transfer across languages…

cs.LG20261 cited

SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry

Rong Fu, Chunlei Meng, Jinshuo Liu +8

Reliable decision-making in complex multi-agent systems requires calibrated predictions and interpretable uncertainty. We introduce SphUnc, a unified framework combining hyperspher…

cs.CV2026

NeuroSymb-MRG: Differentiable Abductive Reasoning with Active Uncertainty Minimization for Radiology Report Generation

Rong Fu, Yiqing Lyu, Chunlei Meng +9

Automatic generation of radiology reports seeks to reduce clinician workload while improving documentation consistency. Existing methods that adopt encoder-decoder or retrieval-aug…

cs.IR2026

ADS-POI: Agentic Spatiotemporal State Decomposition for Next Point-of-Interest Recommendation

Zhenyu Yu, Chunlei Meng, Yangchen Zeng +2

Next point-of-interest (POI) recommendation requires modeling user mobility as a spatiotemporal sequence, where different behavioral factors may evolve at different temporal and sp…

cs.IR2026

CaST-POI: Candidate-Conditioned Spatiotemporal Modeling for Next POI Recommendation

Zhenyu Yu, Chunlei Meng, Yangchen Zeng +2

Next Point-of-Interest (POI) recommendation plays a crucial role in location-based services by predicting users' future mobility patterns. Existing methods typically compute a sing…

cs.CL2026

How Order-Sensitive Are LLMs? OrderProbe for Deterministic Structural Reconstruction

Yingjie He, Zhaolu Kang, Kehan Jiang +19

Large language models (LLMs) excel at semantic understanding, yet their ability to reconstruct internal structure from scrambled inputs remains underexplored. Sentence-level restor…