activity
20222026
collaborators
Showing 2026Show all

7 papers · 1 filter

cs.CL2026

Morphing into Hybrid Attention Models

Disen Lan, Jianbin Zheng, Yuxi Ren +5

Hybrid attention models improve long-context efficiency by retaining only a subset of full-attention layers and replacing the remaining layers with linear attention. However, the e…

cs.LG2026

TEMPO: Scaling Test-time Training for Large Reasoning Models

Qingyang Zhang, Xinke Kong, Haitao Wu +7

Test-time training (TTT) adapts model parameters on unlabeled test instances during inference time, which continuously extends capabilities beyond the reach of offline training. De…

cs.CR2026

ProjLens: Unveiling the Role of Projectors in Multimodal Model Safety

Kun Wang, Cheng Qian, Miao Yu +6

Multimodal Large Language Models (MLLMs) have achieved remarkable success in cross-modal understanding and generation, yet their deployment is threatened by critical safety vulnera…

cs.AI2026

CoTEvol: Self-Evolving Chain-of-Thoughts for Data Synthesis in Mathematical Reasoning

Zhuo Wang, Zhuo Zhang, Yafu Li +3

Large Language Models (LLMs) exhibit strong mathematical reasoning when trained on high-quality Chain-of-Thought (CoT) that articulates intermediate steps, yet costly CoT curation…

cs.LG2026

DiPO: Disentangled Perplexity Policy Optimization for Fine-grained Exploration-Exploitation Trade-Off

Xiaofan Li, Ming Yang, Zhiyuan Ma +9

Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed significant advances in the reasoning capabilities of Large Language Models (LLMs). However, effectively managin…

cs.LG2026

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR)

Chenhao Fang, Jordi Mola, Mark Harman +10

Although LLMs drive automation, it is critical to ensure immense consideration for high-stakes enterprise workflows such as those involving legal matters, risk management, and priv…

cs.LG2026

HiFloat4 Format for Language Model Pre-training on Ascend NPUs

Mehran Taghian, Yunke Peng, Xing Huang +22

Training large foundation models at low numerical precision is one of the most promising directions for reducing the compute and memory cost of modern AI. Recent 4-bit floating-poi…