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Daojian Zeng

ACL 2026×4效率与系统评测与基准安全智能体与工具推理
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0 一作 · 4 篇 · ACL 2026

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2026 顶会论文

4
作者位次 #4/6ACL 2026Poster· Natural Language Processing

Hetero-Designer: Automated Design of Multi-Agent Systems with Heterogeneous LLM s

Zhiheng Zhang · Yuanzhe Zhang · Bohan Yu · Daojian Zeng · Kang Liu · Jun Zhao

摘要

LLM-based Multi-agent systems (MAS) have shown strong capabilities across a wide range of domains. Their success largely hinges on the collaboration topology design, which has emerged as a central research focus in the automated MAS design.However, existing approaches are fundamentally constraine...

智能体与工具效率与系统评测与基准OpenReview 项目页
作者位次 #3/8ACL 2026Findings· Findings of ACL

LANTERN in the Event Stream: Training-Free Temporal Knowledge Graph Forecasting by Balancing Inertia and Shifts

Chengyuan Jin · Ao Chang · Daojian Zeng · Wenhao Teng · Xiangwen Liao · Kang Liu · Jun Zhao · Yubo Chen

摘要

Temporal knowledge graph forecasting(TKGF) asks a model to rank the mostplausible future entity for a query such as(s, r, ?, t) from historical events. Recenttraining-free methods use large languagemodels (LLMs) for this task, but their accuracydepends heavily on which past events areshown in the...

推理检索与知识OpenReview 项目页
作者位次 #3/5ACL 2026Findings· Findings of ACL

Preventing Safety Drift in Large Language Models via Coupled Weight and Activation Constraints

Songping Peng · Zhiheng Zhang · Daojian Zeng · Lincheng Jiang · Xieping Gao

摘要

Safety alignment in Large Language Models (LLMs) remains highly fragile during fine-tuning, where even benign adaptation can degrade pre-trained refusal behaviors and enable harmful responses. Existing defenses typically constrain either weights or activations in isolation, without considering th...

安全对齐与偏好可解释性OpenReview 项目页
作者位次 #6/7ACL 2026Findings· Findings of ACL

RS hield: A User-level Traceable Backdoor Watermark for LLM s in Embedding-as-a-Service

Lingyun Xiang · Yufan Zhong · Chengfu Ou · Zhihua Xia · Chunfang Yang · Daojian Zeng · Zhangjie Fu

摘要

Embedding-as-a-Service (EaaS) has emerged as a critical paradigm for commercializing large language models (LLMs). However, existing backdoor watermarking techniques are fundamentally limited to “zero-bit” detection, which prevents user-level traceability in multi-user EaaS scenarios. To address...

安全效率与系统评测与基准OpenReview 项目页

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