Saved in:
| Main Authors: | Wu, Zhihao, Gong, Gracia, Zhu, Qinglin, Chen, Yudong, Zhao, Runcong |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.30501 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Soft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration
by: Zhu, Qinglin, et al.
Published: (2025)
by: Zhu, Qinglin, et al.
Published: (2025)
Detecting Contextual Hallucinations in LLMs with Frequency-Aware Attention
by: Qi, Siya, et al.
Published: (2026)
by: Qi, Siya, et al.
Published: (2026)
PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games
by: Zhu, Qinglin, et al.
Published: (2024)
by: Zhu, Qinglin, et al.
Published: (2024)
SymbolicThought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding
by: Zhao, Runcong, et al.
Published: (2025)
by: Zhao, Runcong, et al.
Published: (2025)
Beyond RAG for Agent Memory: Retrieval by Decoupling and Aggregation
by: Hu, Zhanghao, et al.
Published: (2026)
by: Hu, Zhanghao, et al.
Published: (2026)
Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives
by: Zhao, Runcong, et al.
Published: (2024)
by: Zhao, Runcong, et al.
Published: (2024)
One Token Away from Collapse: The Fragility of Instruction-Tuned Helpfulness
by: Potraghloo, Erfan Baghaei, et al.
Published: (2026)
by: Potraghloo, Erfan Baghaei, et al.
Published: (2026)
Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding
by: Zhu, Lixing, et al.
Published: (2023)
by: Zhu, Lixing, et al.
Published: (2023)
Sparse Activation Editing for Reliable Instruction Following in Narratives
by: Zhao, Runcong, et al.
Published: (2025)
by: Zhao, Runcong, et al.
Published: (2025)
Latent Refinement Decoding: Enhancing Diffusion-Based Language Models by Refining Belief States
by: Zhu, Qinglin, et al.
Published: (2025)
by: Zhu, Qinglin, et al.
Published: (2025)
Pull Requests as a Training Signal for Repo-Level Code Editing
by: Zhu, Qinglin, et al.
Published: (2026)
by: Zhu, Qinglin, et al.
Published: (2026)
Large Scale Knowledge Washing
by: Wang, Yu, et al.
Published: (2024)
by: Wang, Yu, et al.
Published: (2024)
OpenToM: A Comprehensive Benchmark for Evaluating Theory-of-Mind Reasoning Capabilities of Large Language Models
by: Xu, Hainiu, et al.
Published: (2024)
by: Xu, Hainiu, et al.
Published: (2024)
More Haste, Less Speed: Weaker Single-Layer Watermark Improves Distortion-Free Watermark Ensembles
by: Chen, Ruibo, et al.
Published: (2026)
by: Chen, Ruibo, et al.
Published: (2026)
Ensemble Watermarks for Large Language Models
by: Niess, Georg, et al.
Published: (2024)
by: Niess, Georg, et al.
Published: (2024)
Few-shot LLM Synthetic Data with Distribution Matching
by: Ren, Jiyuan, et al.
Published: (2025)
by: Ren, Jiyuan, et al.
Published: (2025)
Uncovering the Fragility of Trustworthy LLMs through Chinese Textual Ambiguity
by: Wu, Xinwei, et al.
Published: (2025)
by: Wu, Xinwei, et al.
Published: (2025)
AERA Chat: An Interactive Platform for Automated Explainable Student Answer Assessment
by: Li, Jiazheng, et al.
Published: (2024)
by: Li, Jiazheng, et al.
Published: (2024)
Minimal Prompt Perturbations Lead to Code Vulnerabilities: Prompt Fragility and Hidden-State Signals in Coding LLMs
by: Sternfeld, Alexander, et al.
Published: (2026)
by: Sternfeld, Alexander, et al.
Published: (2026)
Fragile Reasoning: A Mechanistic Analysis of LLM Sensitivity to Meaning-Preserving Perturbations
by: Han, Shou-Tzu, et al.
Published: (2026)
by: Han, Shou-Tzu, et al.
Published: (2026)
QuantileMark: A Message-Symmetric Multi-bit Watermark for LLMs
by: Zhu, Junlin, et al.
Published: (2026)
by: Zhu, Junlin, et al.
Published: (2026)
LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop
by: Zhao, Runcong, et al.
Published: (2025)
by: Zhao, Runcong, et al.
Published: (2025)
Perturb Your Data: Paraphrase-Guided Training Data Watermarking
by: Shetty, Pranav, et al.
Published: (2025)
by: Shetty, Pranav, et al.
Published: (2025)
Dataset Protection via Watermarked Canaries in Retrieval-Augmented LLMs
by: Liu, Yepeng, et al.
Published: (2025)
by: Liu, Yepeng, et al.
Published: (2025)
Towards Codable Watermarking for Injecting Multi-bits Information to LLMs
by: Wang, Lean, et al.
Published: (2023)
by: Wang, Lean, et al.
Published: (2023)
Fragile Thoughts: How Large Language Models Handle Chain-of-Thought Perturbations
by: Aravindan, Ashwath Vaithinathan, et al.
Published: (2026)
by: Aravindan, Ashwath Vaithinathan, et al.
Published: (2026)
Understanding the Ability of LLMs to Handle Character-Level Perturbation
by: Zhuo, Anyuan, et al.
Published: (2025)
by: Zhuo, Anyuan, et al.
Published: (2025)
How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities
by: Kassem, Aly M., et al.
Published: (2025)
by: Kassem, Aly M., et al.
Published: (2025)
Watermarking LLM Agent Trajectories
by: Meng, Wenlong, et al.
Published: (2026)
by: Meng, Wenlong, et al.
Published: (2026)
Don't Throw Away Your Pretrained Model
by: Feng, Shangbin, et al.
Published: (2025)
by: Feng, Shangbin, et al.
Published: (2025)
SoK: Are Watermarks in LLMs Ready for Deployment?
by: Dang, Kieu, et al.
Published: (2025)
by: Dang, Kieu, et al.
Published: (2025)
Towards Understanding the Fragility of Multilingual LLMs against Fine-Tuning Attacks
by: Poppi, Samuele, et al.
Published: (2024)
by: Poppi, Samuele, et al.
Published: (2024)
Positional Fragility in LLMs: How Offset Effects Reshape Our Understanding of Memorization Risks
by: Xu, Yixuan, et al.
Published: (2025)
by: Xu, Yixuan, et al.
Published: (2025)
Improved Unbiased Watermark for Large Language Models
by: Chen, Ruibo, et al.
Published: (2025)
by: Chen, Ruibo, et al.
Published: (2025)
Don't Throw Away Your Beams: Improving Consistency-based Uncertainties in LLMs via Beam Search
by: Fadeeva, Ekaterina, et al.
Published: (2025)
by: Fadeeva, Ekaterina, et al.
Published: (2025)
A Watermark for Order-Agnostic Language Models
by: Chen, Ruibo, et al.
Published: (2024)
by: Chen, Ruibo, et al.
Published: (2024)
Less is More: Sparse Watermarking in LLMs with Enhanced Text Quality
by: Hoang, Duy C., et al.
Published: (2024)
by: Hoang, Duy C., et al.
Published: (2024)
Lost in Overlap: Exploring Logit-based Watermark Collision in LLMs
by: Luo, Yiyang, et al.
Published: (2024)
by: Luo, Yiyang, et al.
Published: (2024)
De-mark: Watermark Removal in Large Language Models
by: Chen, Ruibo, et al.
Published: (2024)
by: Chen, Ruibo, et al.
Published: (2024)
LLM-CAS: Dynamic Neuron Perturbation for Real-Time Hallucination Correction
by: Zhang, Jensen, et al.
Published: (2025)
by: Zhang, Jensen, et al.
Published: (2025)
Similar Items
-
Soft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration
by: Zhu, Qinglin, et al.
Published: (2025) -
Detecting Contextual Hallucinations in LLMs with Frequency-Aware Attention
by: Qi, Siya, et al.
Published: (2026) -
PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games
by: Zhu, Qinglin, et al.
Published: (2024) -
SymbolicThought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding
by: Zhao, Runcong, et al.
Published: (2025) -
Beyond RAG for Agent Memory: Retrieval by Decoupling and Aggregation
by: Hu, Zhanghao, et al.
Published: (2026)