13 papers
Grid-Bench: Can LLMs Predict How Discrete Worlds Change?
Zixiang Xu, Zirui Song, Wanrong Zheng +16
Spatial instructions describe how to move objects, transform regions, or modify their surroundings. When several operations are applied in sequence, each acts on a configuration sh…
Exploring the Design Space of Permutation Diffusion LLMs
Zhe Xu, Mingfu Liang, Kavosh Asadi +16
Permutation generation with language models requires both text-conditioned ranking and a global one-to-one assignment between candidates and output positions. We study this problem…
The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers
Zhe Xu, Prachi Agrawal, Kavosh Asadi +17
Large Language Models (LLMs) have emerged as powerful assets for recommender systems. However, deploying them as generative recommenders or zero-shot rankers at web-scale remains b…
Diffusion-GR2: Diffusion Generative Reasoning Re-ranker
Zhuoxuan Zhang, Kangqi Ni, Yuhang Chen +12
Generative reasoning re-rankers achieve strong recommendation accuracy by emitting a chain-of-thought before re-ordering a candidate list, but they are slow at inference: an autore…
Self-Guided Test-Time Training for Long-Context LLMs
Xinyu Zhu, Zhe Xu, Xiaohan Wei +10
Long-context processing has become increasingly important for large language models (LLMs), but simply extending the context window does not guarantee effective utilization of long…
SCOReD: Student-Aware CoT Optimization for Recommendation Distillation
Haz Sameen Shahgir, Yufei Li, Xiaohan Wei +8
Chain-of-thought (CoT) distillation in the recommendation domain is a necessary precursor to RL training, but raw teacher traces are ill-suited to this task. Large teachers approac…
GR2 Technical Report
Yufei Li, Zaiwei Zhang, Mingfu Liang +67
Industrial recommendation systems serve billions of users through a multi-stage funnel -- retrieval, early-stage ranking, and re-ranking -- where the final re-ranking step dispropo…
End-to-End Dynamic Sparsity for Resource-Adaptive LLM Inference
Yuhang Chen, Jinhao Duan, Ruichen Zhang +11
Large Language Models (LLMs) inference is typically deployed under a static resource assumption, where models execute a fixed computational graph regardless of the runtime environm…
Bifocal Diffusion Language Models: Asymmetric Bidirectional Context for Parallel Generation
Yuhang Chen, Xianfeng Wu, Jinhao Duan +11
Discrete diffusion language models (dLLMs) recover masked tokens in parallel, offering significant speedups over autoregressive (AR) generation. However, such promising frameworks…
ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation
Yihua Zhang, Mingfu Liang, Jiyan Yang +11
Recent advances in multimodal recommenders excel at feature fusion but remain opaque and inefficient decision-makers, lacking explicit reasoning and self-awareness of uncertainty.…
Learning Evidence Highlighting for Frozen LLMs
Shaoang Li, Yanhang Shi, Yufei Li +10
Large Language Models (LLMs) can reason well, yet often miss decisive evidence when it is buried in long, noisy contexts. We introduce HiLight, an Evidence Emphasis framework that…
GR2: Generative Reasoning Re-ranker
Mingfu Liang, Yufei Li, Jay Xu +20
Recent studies increasingly explore Large Language Models (LLMs) as a new paradigm for recommendation systems due to their scalability and world knowledge. However, existing work h…
Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification
Bo-Ru Lu, Frank Shyu, Yun-Nung Chen +2
Connectionist temporal classification (CTC) is a powerful approach for sequence-to-sequence learning, and has been popularly used in speech recognition. The central ideas of CTC in…