7 citations · 32 across the 52 of their papers we have counts for
57 papers
Mixture-of-Memories: Low-Overhead Language Modeling with Modular Knowledge Injection
Yifan Lu, Rubin Wei, Ziyun Zou +8
Large language models store substantial factual knowledge in their parameters, yet this knowledge is difficult to control, update, and reliably elicit at inference time. Existing m…
Counterfactual Anatomy-guided Spatial-Temporal Decoding for Annotation-Free Hallucination Mitigation in Medical VLMs
Yifan Lu, Adinath Dukre, Abhijit Das +4
Medical vision-language models (Med-VLMs) have demonstrated strong performance on medical visual question answering, yet they remain prone to hallucination, generating clinically u…
Beyond Asking: A Pipeline for Personalized Game Generation that Reads Players from Behavior
Yifan Lu, Xiaopeng Yuan, Haohan Wang
Personalized game generation requires inferring a player's abilities and behavioral style from how they play. Large language models have made this inference more attainable than ev…
DREAM Technical Report
Bin Zhang, Bowen Zheng, Chao Yi +74
Industrial recommender systems commonly use cascaded retrieval, ranking, and re-ranking pipelines. Although efficient, these pipelines fragment information and objectives across mo…
RA-ClipScore: Making Generative Model Evaluation More Interpretable
Yifan Lu, Taras Kucherenko, Hedvig Kjellström +1
Generative models can produce images nearly indistinguishable from real data, yet rigorous and interpretable evaluation remains challenging. Conventional metrics such as FID provid…
LongCat Sparse Attention: Taming the Lightning via Streaming-aware Hierarchical Cross-Layer Indexing
Wen Zan, Jiaqi Zhang, Jianchao Tan +11
DeepSeek Sparse Attention (DSA) enables efficient long-context modeling through its Lightning Indexer. However, practical deployment remains constrained by the indexer's expensive…
RecGPT-V3 Technical Report
Bowen Zheng, Chao Yi, Dian Chen +26
Large language models (LLMs) are transforming recommender systems from matching co-occurrence patterns in historical behavior toward reasoning about the intent that drives it. RecG…
NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation
NVIDIA, :, Aarti Basant +33
As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck. In closed-loop simulation, the driving pol…
Gaussian Mixture Modeling for Event-Aware Visual Allocation in Long Video Understanding
Yifan Lu, Ziqi Zhang, Chunfeng Yuan +3
Large Vision-Language Models (LVLMs) face significant challenges in long video understanding due to the excessive computational cost and information loss associated with uniform sa…
PROTON: Prototype-Based Test-Time Online OOD Detection for Medical VLMs
Abhijit Das, Nichula Wasalathilaka, Yifan Lu +4
Medical vision-language models (VLMs) enable zero-shot clinical image classification, yet reliably detecting out-of-distribution (OOD) inputs at deployment remains an open problem.…
MONA: Muon Optimizer with Nesterov Acceleration for Scalable Language Model Training
Jiacheng Li, Jianchao Tan, Hongtao Xu +5
The Muon optimizer has recently offered a promising alternative to AdamW for large language model training, leveraging matrix orthogonalization to produce geometry-aware updates. H…
PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion
Yifan Lu, Qi Wu, Jay Zhangjie Wu +4
Most practical high-resolution text-to-image systems, including latent diffusion and autoregressive models, perform generation in a compact latent space, and a decoder maps the gen…
Mitigating Context-Memory Conflicts in LLMs through Dynamic Cognitive Reconciliation Decoding
Yigeng Zhou, Wu Li, Yifan Lu +6
Large language models accumulate extensive parametric knowledge through pre-training. However, knowledge conflicts occur when outdated or incorrect parametric knowledge conflicts w…
Enhancing Cross-Problem Vehicle Routing via Federated Learning
Xiangchi Meng, Jianan Zhou, Jie Gao +4
Vehicle routing problems (VRPs) constitute a core optimization challenge in modern logistics and supply chain management. The recent neural combinatorial optimization (NCO) has dem…
AsyncTLS: Efficient Generative LLM Inference with Asynchronous Two-level Sparse Attention
Yuxuan Hu, Jianchao Tan, Jiaqi Zhang +7
Long-context inference in LLMs faces the dual challenges of quadratic attention complexity and prohibitive KV cache memory. While token-level sparse attention offers superior accur…
FG-GDN: Enhancing Long-Context Gated Delta Networks with Doubly Fine-Grained Control
Pingwei Sun, Yuxuan Hu, Jianchao Tan +6
Linear attention mechanisms have emerged as promising alternatives to softmax attention, offering linear-time complexity during inference. Recent advances such as Gated DeltaNet (G…
GRASP: Gradient Realignment via Active Shared Perception for Multi-Agent Collaborative Optimization
Sihan Zhou, Tiantian He, Yifan Lu +2
Non-stationarity arises from concurrent policy updates and leads to persistent environmental fluctuations. Existing approaches like Centralized Training with Decentralized Executio…
LongCat-Next: Lexicalizing Modalities as Discrete Tokens
Meituan LongCat Team, Bin Xiao, Chao Wang +86
The prevailing Next-Token Prediction (NTP) paradigm has driven the success of large language models through discrete autoregressive modeling. However, contemporary multimodal syste…
LongCat-Flash-Thinking-2601 Technical Report
Meituan LongCat Team, Anchun Gui, Bei Li +162
We introduce LongCat-Flash-Thinking-2601, a 560-billion-parameter open-source Mixture-of-Experts (MoE) reasoning model with superior agentic reasoning capability. LongCat-Flash-Thi…
Efficient Context Scaling with LongCat ZigZag Attention
Chen Zhang, Yang Bai, Jiahuan Li +19
We introduce LongCat ZigZag Attention (LoZA), which is a sparse attention scheme designed to transform any existing full-attention models into sparse versions with rather limited c…
Evidence-Grounded Multi-Agent Planning Support for Urban Carbon Governance via RAG
Yuyan Huang, Haoran Li, Yifan Lu +3
Urban carbon governance requires planners to integrate heterogeneous evidence -- emission inventories, statistical yearbooks, policy texts, technical measures, and academic finding…
A Preliminary Study on the Promises and Challenges of Native Top- Sparse Attention
Di Xiu, Hongyin Tang, Bolin Rong +4
Large Language Models (LLMs) are increasingly prevalent in the field of long-context modeling, however, their inference computational costs have become a critical bottleneck hinder…
Optimizing Native Sparse Attention with Latent Attention and Local Global Alternating Strategies
Yuxuan Hu, Jianchao Tan, Jiaqi Zhang +7
In this work, we conduct a systematic analysis of Native Sparse Attention (NSA) and propose targeted improvements that enhance long-context modeling. A key insight is that alternat…
CHIPS: Efficient CLIP Adaptation via Curvature-aware Hybrid Influence-based Data Selection
Xinlin Zhuang, Yichen Li, Xiwei Liu +11
Adapting CLIP to vertical domains is typically approached by novel fine-tuning strategies or by continual pre-training (CPT) on large domain-specific datasets. Yet, data itself rem…