8 papers
MobEvolve: An Agentic Self-Evolving Heuristic System for Interpretable Human Mobility Generation
Junlin He, Yihong Tang, Tong Nie +6
Human mobility generation aims to synthesize realistic trip chains for target populations based on individual features. Existing paradigms, including deep generative models, LLM-ba…
Demystifying Multimodal Biomolecular Co-design With Intrinsic Geodesic Coupling
Keyue Qiu, Xintong Wang, Zhilong Zhang +2
Biomolecules such as proteins and small-molecule ligands play a central role in biological systems, arising from the tight interplay between sequence and three-dimensional structur…
Enhancing LLM Metacognition via Cognitive Pairwise Training
Weitao Li, Hao Zhou, Xuanyu Lei +11
Reinforcement learning with verifiable rewards (RLVR) has become central to LLM reasoning, but its outcome-level rewards can make models more willing to give confident answers when…
From Shallow to Deep: Pinning Semantic Intent via Causal GRPO
Shuyi Zhou, Zeen Song, Wenwen Qiang +4
Large Language Models remain vulnerable to adversarial prefix attacks (e.g., ``Sure, here is'') despite robust standard safety. We diagnose this vulnerability as Shallow Safety Ali…
APAO: Bridging the Training-Inference Gap in Generative Recommendation via Adaptive Prefix-Aware Optimization
Yuanqing Yu, Yifan Wang, Weizhi Ma +2
Generative recommendation has recently emerged as a promising paradigm for sequential recommendation. It formulates the task as an autoregressive generation process, predicting tok…
Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference
Yuxuan Song, Zheng Zhang, Cheng Luo +19
We present Seed Diffusion Preview, a large-scale language model based on discrete-state diffusion, offering remarkably fast inference speed. Thanks to non-sequential, parallel gene…
Why Generate When You Can Transform? Unleashing Generative Attention for Dynamic Recommendation
Yuli Liu, Wenjun Kong, Cheng Luo +1
Sequential Recommendation (SR) focuses on personalizing user experiences by predicting future preferences based on historical interactions. Transformer models, with their attention…
LLMSynthor: Macro-Aligned Micro-Records Synthesis with Large Language Models
Yihong Tang, Menglin Kong, Junlin He +3
Macro-aligned micro-records are crucial for credible simulations in social science and urban studies. For example, epidemic models are only reliable when individual-level mobility…