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Showing papers from Zhipu AI (China) Show all papers

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arXivHefei University of TechnologyTsinghuaZhipu AIDiffusion models

GFD-OPD: Guidance-Folded On-Policy Distillation of Diffusion Models Across Scales

GFD-OPD fixes diffusion on-policy distillation by reducing student-teacher gaps and preventing classifier-free guidance error amplification, achieving state-of-the-art compression results.

Zhenxing Zhang, Jiayan Teng, Wenxu Wu, Zhuoyi Yang and 5 more

Published Sep 30, 2026 · 0 citations

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10/20 AI panelreviewers recommend it

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AI panel: 10 of 20 reviewers recommend it
lenient 2/5
medium 8/10
strict 0/5
78%Highly rated
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arXivTsinghuaZhipu AIInstruction tuning

ScopeIF: Improving Scope-Aware Precise Instruction-Following in Large Language Models via Graded Reward Modeling

ScopeIF improves LLM instruction-following via graded reward modeling and scope-aware constraints, enabling small models to match frontier performance.

Bosi Wen, Yilin Niu, Xiaoying Ning, Ying Zhang and 2 more

Published Sep 26, 2026 · 0 citations

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 0/5
78%Highly rated
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arXivBeijing Institute of TechnologyZhipu AITsinghuaLLM agents & planning

From Anomalies to Failures: Constructing Causal Error Graphs for Agentic Trace Diagnosis

CEG-Agent introduces causal error graphs and a taxonomy separating anomalies, errors, and failures to diagnose agentic traces, achieving state-of-the-art results on the CEG-Bench benchmark.

Shu-Xun Yang, Yidong Wang, Zhuoer Feng, Bosi Wen and 6 more

Published Sep 26, 2026 · 0 citations

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AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 0/5
80%Must read
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arXivTsinghuaZhipu AIU Electronic Science and TechnolAgent benchmarks & environments

MTAC-IFBench: Benchmarking Instruction-Following in Multi-Turn Agentic Coding

MTAC-IFBench benchmarks multi-turn instruction-following in agentic coding via progressive constraints, revealing rapid performance degradation in current code agents as sessions lengthen.

Bosi Wen, Cunxiang Wang, Jiayi Gui, Haoke Zhang and 5 more

Published Sep 14, 2026 · 0 citations

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 0/5
70%Highly rated
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ACL 2025TsinghuaU Chinese Academy of SciencesZhipu AILong-context modeling

LongReward: Improving Long-context Large Language Models with AI Feedback

LongReward improves long-context LLMs by applying AI feedback to long-text instruction data via a multi-granularity reward model that evaluates both global coherence and local accuracy.

Jiajie Zhang, Zhongni Hou, Xin Lv, Shulin Cao and 6 more

Published 2025 · 4 citations

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4/20 AI panelreviewers recommend it

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AI panel: 4 of 20 reviewers recommend it
lenient 2/5
medium 2/10
strict 0/5