Language Model Robustness

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37 papers in the last four weeks, up 54% on the four weeks before. 0.4% of all new papers.

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The First Drop of Ink: Nonlinear Impact of Misleading Information in Long-Context Reasoning
May 11, 2026Muhan Gao, Zih-Ching Chen, Kuan-Hao HuangLong-Context Inference Language Model Robustness
Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA · NVIDIA AI Technology Center, NVIDIA Corporation, Santa Clara, CA, USA
How Should LLMs Listen While Speaking? A Study of User-Stream Routing in Full-Duplex Spoken Dialogue
May 11, 2026Hui Lu, Xueyuan Chen, Huimeng Wang +4Language Model Robustness Full-Duplex Spoken Dialogue Systems
The Chinese University of Hong Kong · Tsinghua University · SenseTime Research
Synthetic Pre-Pre-Training Improves Language Model Robustness to Noisy Pre-Training Data
May 11, 2026Xu Guo, Runyu Peng, Jian Tong +4Language Model Pretraining Synthetic Data Pretraining
Shanghai AI Laboratory · Fudan University · Shanghai Innovation Institute
EchoDistill: Robust Large Audio Language Models via Noisy-to-Clean Self-Distillation
May 11, 2026Kaiwen Luo, Chunxi Luo, Liang Lin +5Audio Understanding Audio-Language Models
NTU · SHU · ICT, CAS +4
Absurd World: A Simple Yet Powerful Method to Absurdify the Real-world for Probing LLM Reasoning Capabilities
May 10, 2026Ryan Albright, Golam Md Muktadir, Zarif Ikram +3LLM Evaluation Language Model Robustness
The Nueva School, San Mateo, USA · University of Southern California · Notre Dame College, Dhaka, Bangladesh +2
Can We Trust LLMs for Mental Health Screening? Consistency, ASR Robustness, and Evidence Faithfulness
May 10, 2026Erfan Loweimi, Sofia de la Fuente Garcia, Samira Loveymi +2Healthcare Mental Health
Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK · Department of Computer Engineering, Ahvaz Campus, Islamic Azad University, Ahvaz, Iran · School of Health and Social Care, Edinburgh Napier University, Edinburgh, UK
How LLMs Are Persuaded: A Few Attention Heads, Rerouted
May 10, 2026Xiangkun Sun, Lingkai Kong, Aoqi Zhang +2Self-Attention LLM Interpretability
Northeastern University · Harvard University · Tsinghua University +1
WASIL: In-the-Wild Arabic Spoken Interactions with LLMs
May 9, 2026Zien Sheikh Ali, Hamdy Mubarak, Soon-Gyo Jung +3Voice Agent Evaluation Arabic NLP
Qatar Computing Research Institute, Qatar
FragileFlow: Spectral Control of Correct-but-Fragile Predictions for Foundation Model Robustness
May 9, 2026Zhuoyun Li, Boxuan Wang, Jinwei Hu +2VLM Robustness Spectral Regularization
School of Computer Science and Informatics, University of Liverpool, UK
DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rules
May 9, 2026Devin Yasith De Silva, Dhaval Patel, Christodoulos Constantinides +7Logical Reasoning LLM Evaluation
2Rensselaer Polytechnic Institute · 1IBM · 3Independent Contributor
Coordinates of Capability: A Unified MTMM-Geometric Framework for LLM Evaluation
May 8, 2026Adib Sakhawat, Tahsin Islam, Takia Farhin +3LLM Evaluation Language Model Robustness
Systems and Software Lab (SSL) Department of Computer Science and Engineering Islamic University of Technology, Dhaka, Bangladesh
Can Language Models Identify Side Effects of Breast Cancer Radiation Treatments?
May 8, 2026Natalie Seah, Danielle S. Bitterman, Daphna Spiegel +1Breast Cancer Healthcare
University of Virginia · Mass General Brigham, Dana-Farber Cancer Institute, Harvard Medical School
The Text Uncanny Valley: Non-Monotonic Performance Degradation in LLM Information Retrieval
May 8, 2026Zekai Tong, Ruiyao Xu, Aryan Shrivastava +2LLM Evaluation LLM Reliability
University of Chicago · 2Northwestern University
GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations
May 8, 2026Jyotika Singh, Fang Tu, Aziza Mirsaidova +11Mathematical Reasoning Benchmarks Benchmark Design
Oracle AI
Can LLMs Take Retrieved Information with a Grain of Salt?
May 7, 2026Behzad Shayegh, Mohamed Osama Ahmed, Fred Tung +1Retrieval-Augmented Generation LLM Evaluation
RBC Borealis
Preference Instability in Reward Models: Detection and Mitigation via Sparse Autoencoders
May 7, 2026Shunchang Liu, Xin Chen, Belen Martin Urcelay +1Reward Modeling Sparse Autoencoders
ETH Zürich · Georgia Institute of Technology · ELLIS Institute Finland +1
Towards Emotion Consistency Analysis of Large Language Models in Emotional Conversational Contexts
May 7, 2026Sneha Oram, Ojaswita Bhushan, Pushpak BhattacharyyaLLM Evaluation Language Model Robustness
Indian Institute of Technology Bombay, India
Information Theoretic Adversarial Training of Large Language Models
May 6, 2026Yiwei Zhang, Jeremiah Birrell, Reza Ebrahimi +3Adversarial Training LLM Alignment
Purdue University · West Lafayette, IN 47907, USA · Texas State University +5
Paraphrase-Induced Output-Mode Collapse: When LLMs Break Character Under Semantically Equivalent Inputs
May 6, 2026Aofan Liu, Jingxiang MengPrompt Sensitivity LLM Evaluation
1Peking University. · University of Chicago.
NoisyCausal: A Benchmark for Evaluating Causal Reasoning Under Structured Noise
May 5, 2026Zhi Xu, Yun FuStructural Causal Models Causal Reasoning in Language Models
Northeastern University
TriBench-Ko: Evaluating LLM Risks in Judicial Workflows
May 5, 2026Haesung Lee, Gyubin Choi, Eun-Ju Lee +5LLM Evaluation Language Model Safety Evaluation
Center for Trustworthy AI, Seoul National University · Graduate School of Data Science, Seoul National University · Responsible AI Team, KT +1
Stop Automating Peer Review Without Rigorous Evaluation
May 4, 2026Joachim Baumann, Jiaxin Pei, Sanmi Koyejo +1LLM-as-a-Judge Scholarly Peer Review
Stanford University · Bocconi University
Evaluating Reasoning Models for Queries with Presuppositions
May 4, 2026Rose Sathyanathan, Kinshuk Vasisht, Danish PruthiLLM Evaluation Language Model Robustness
Indian Institute of Science Bengaluru, KA, India
Perturbation Dose Responses in Recursive LLM Loops: Raw Switching, Stochastic Floors, and Persistent Escape under Append, Replace, and Dialog Updates
May 4, 2026Pawel KaplanskiLanguage Model Robustness LLM Context Management
Kaplanski AI Lab · Kaplanski Ai Lab
What Single-Prompt Accuracy Misses: A Multi-Variant Reliability Audit of Language Models
May 3, 2026Ranit Karmakar, Jayita ChatterjeeLLM Evaluation Language Model Calibration
Harvard University, Boston, MA 02115 · Boston, MA 02134
Format Sensitivity Index: Token-Controlled Prompt Wrapper Robustness and Schema Compliance in LLM Benchmarking
May 2, 2026Deep Pankajbhai MehtaPrompt Sensitivity LLM Evaluation
Adobe Inc.
Where Do Prompt Perturbations Break Generation? A Segment-Level View of Robustness in LoRA-Tuned Language Models
May 2, 2026Zhuoyun Li, Boxuan Wang, Jinwei Hu +6Neural Network Robustness Noise Robustness
School of Computer Science and Informatics, University of Liverpool, UK
Lost in the Tower of Babel: The Adverse Effects of Incidental Multilingualism in LLMs
May 2, 2026Anjishnu Mukherjee, Chutong Meng, Antonios AnastasopoulosMultilingual Language Models Multilingual Language Model Evaluation
Department of Computer Science George Mason University
When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems
May 1, 2026Lingxi Zhang, Guangtao Zheng, Hanjie ChenAdversarial Attacks Language Model Robustness
1Rice University · University of Virginia
CLEAR: Revealing How Noise and Ambiguity Degrade Reliability in LLMs for Medicine
May 1, 2026Kevin H. Guo, Chao Yan, Avinash Baidya +5Healthcare LLM Reliability
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA · Intuit AI Research, Mountain View, CA, USA · Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA +2

May 11, 2026, cs.AIJ/K move · Enter open · S save

The First Drop of Ink: Nonlinear Impact of Misleading Information in Long-Context Reasoning

Muhan Gao, Zih-Ching Chen, Kuan-Hao Huang

Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA · NVIDIA AI Technology Center, NVIDIA Corporation, Santa Clara, CA, USA

Long-Context Inference Language Model Robustness

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As large language models are increasingly deployed in retrieval-augmented generation and agentic systems that accumulate extensive context, understanding how distracting information affects long-context performance becomes critical. Prior work shows that semantically relevant yet misleading documents degrade performance, but the quantitative relationship between the proportion of distractors and performance remains unstudied. In this work, we systematically vary the hard-distractor proportion in fixed-length contexts, revealing a striking nonlinear pattern: as the proportion of hard distractors increases, performance drops sharply within the first small fraction, while the remainder of the range yields only marginal additional decline. We term this ''The First Drop of Ink'' effect, analogous to how a single drop of ink contaminates water. Our theoretical and empirical analyses grounded in attention mechanics show that hard distractors capture disproportionate attention even at small proportions, with diminishing marginal impact as their proportion grows. Controlled experiments further show that filtering gains mainly come from context-length reduction rather than distractor removal; substantial recovery requires reducing the hard-distractor proportion to near zero, highlighting the importance of upstream retrieval precision.

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Latest papers 338