most citedSeed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning

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cs.CL2026

On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability

Zihan Qiu, Zekun Wang, Xiao Li +33

We describe the architecture and ablations of Qwen3.8-Flash-Next, a sparse mixture-of-experts model with 125B parameters, 6B activated per token, and additional 51B parameters of n…

cs.CL2026

Erase-then-Delta Attention: Decoupling Erase and Write Addresses in Delta-Rule Linear Attention

Xiao Li, Chengruidong Zhang, Hao Luo +15

Delta-rule linear attention improves recurrent memory updates by correcting what is already stored at the current write address before writing new content. However, the active corr…

cs.CL2026

Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory

Haozhen Zhang, Haodong Yue, Tao Feng +8

Memory is increasingly central to Large Language Model (LLM) agents operating beyond a single context window, yet most existing systems rely on offline, query-agnostic memory const…

cs.CL2026

A Unified View of Attention and Residual Sinks: Outlier-Driven Rescaling is Essential for Transformer Training

Zihan Qiu, Zeyu Huang, Kaiyue Wen +16

We investigate the functional role of emergent outliers in large language models, specifically attention sinks (a few tokens that consistently receive large attention logits) and r…

cs.CL2025

When Meaning Stays the Same, but Models Drift: Evaluating Quality of Service under Token-Level Behavioral Instability in LLMs

Xiao Li, Joel Kreuzwieser, Alan Peters

We investigate how large language models respond to prompts that differ only in their token-level realization but preserve the same semantic intent, a phenomenon we call prompt var…

cs.CL2025

Proactive Guidance of Multi-Turn Conversation in Industrial Search

Xiaoyu Li, Xiao Li, Li Gao +5

The evolution of Large Language Models (LLMs) has significantly advanced multi-turn conversation systems, emphasizing the need for proactive guidance to enhance users' interactions…