11 papers
CURIO: Curiosity-Driven Test-Time Learning for Open-Ended Discovery
Tao Feng, Fangxu Yu, Zijie Lei +5
Open-ended discovery requires learning from repeated attempts while continuing to explore directions whose value is not yet apparent. Search with a frozen large language model (LLM…
UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models
Lei Xin, Bin Gu, Peize Li +8
Sparse mixture-of-experts (MoE) layers expand recommendation capacity through conditional computation, yet a trained checkpoint still stores and routes over its full expert bank. W…
Evaluating Deep-Search Agents under Hierarchical Web Evidence Poisoning
Zhongan Bi, Qiwen Wang, Jianrong Jiang +11
Search-augmented LLM agents are increasingly used for consumer decisions, making them vulnerable to Generative Engine Optimization (GEO) poisoning. Existing benchmarks largely meas…
Think with Structured Grounding: Perceptual Reinforcement Learning for Chart and Visual-Tabular Understanding
Changjiang Jiang, Qiannian Zhao, Lei Xin +3
Multimodal Large Language Models (MLLMs) capable of thinking with images often rely on external tools for fine-grained perception. However, this reliance introduces significant inf…
JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency
Aichen Cai, Anmeng Zhang, Anyu Li +66
We introduce JoyAI-LLM Flash, an efficient Mixture-of-Experts (MoE) language model designed to redefine the trade-off between strong performance and token efficiency in the sub-50B…
HyTRec: A Hybrid Temporal-Aware Attention Architecture for Long Behavior Sequential Recommendation
Lei Xin, Yuhao Zheng, Ke Cheng +3
Modeling long sequences of user behaviors has emerged as a critical frontier in generative recommendation. However, existing solutions face a dilemma: linear attention mechanisms a…
Fake-HR1: Rethinking Reasoning of Vision Language Model for Synthetic Image Detection
Changjiang Jiang, Xinkuan Sha, Fengchang Yu +5
Recent studies have demonstrated that incorporating Chain-of-Thought (CoT) reasoning into the detection process can enhance a model's ability to detect synthetic images. However, e…
Beyond Human Annotation: Recent Advances in Data Generation Methods for Document Intelligence
Dehao Ying, Fengchang Yu, Haihua Chen +3
The advancement of Document Intelligence (DI) demands large-scale, high-quality training data, yet manual annotation remains a critical bottleneck. While data generation methods ar…
TabDSR: Decompose, Sanitize, and Reason for Complex Numerical Reasoning in Tabular Data
Changjiang Jiang, Fengchang Yu, Haihua Chen +2
Complex reasoning over tabular data is crucial in real-world data analysis, yet large language models (LLMs) often underperform due to complex queries, noisy data, and limited nume…
SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the SpineMed-450k Corpus
Ming Zhao, Wenhui Dong, Yang Zhang +23
Spine disorders affect 619 million people globally and are a leading cause of disability, yet AI-assisted diagnosis remains limited by the lack of level-aware, multimodal datasets.…
Ivy-Fake: A Unified Explainable Framework and Benchmark for Image and Video AIGC Detection
Changjiang Jiang, Wenhui Dong, Zhonghao Zhang +8
The rapid development of Artificial Intelligence Generated Content (AIGC) techniques has enabled the creation of high-quality synthetic content, but it also raises significant secu…