Repetition in Language Generation

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25 papers6 papers in the last four weeks, up 100% on the four weeks before. 0.0% of all new papers.

Character-Name Concentration in LLM Fiction: Measuring Displacement and Mitigating Repetition
Oct 31, 2026Alejandro Mozo Quesada, Sergio Gálvez Rojas, Francisco López ValverdeRepetition in Language Generation Controllable Text Generation
Universidad de Málaga, Spain
Why Full-Duplex Speech Models Collapse in Long Conversations
Oct 23, 2026Yeonju Kim, Bella Godiva, Se Jin Park +1Full-Duplex Spoken Dialogue Systems Long-Context Modeling
Integrated Vision and Language Lab, KAIST, South Korea · Kyung Hee University, South Korea
Copying Under Repetition: From Prefix Matching to Position Hashing
Oct 19, 2026Anxin Guo, Tianlong Huang, Chenxiao Yang +1Transformer Repetition in Language Generation
Northwestern University · Duke University · Toyota Technological Institute at Chicago
When Do Repetition Loops Lock? Truncation Sampling, Bounded Penalties, and a Blind Spot in DRY
Oct 17, 2026Yichuan Chen, Xin WangRepetition in Language Generation
School of Art and Design, Zhejiang Sci-Tech University, Hangzhou, China
To Reason but Not to Stop: Understanding and Recovering Pruned Large Reasoning Models
Oct 14, 2026Minh-Anh Nguyen, Nguyen T. Ha Lan, Xiyan Fu +2Repetition in Language Generation LLM Reasoning
Nanyang Technological University, Singapore · VinUniversity, Vietnam
Information-Theoretic Foundations of Online Temperature Sampling
Oct 10, 2026Jin Sima, Nikolaos Papagiannis, Ananth Grama +1Repetition in Language Generation Language Model Decoding
Purdue University
Uncovering Uncontrolled Repetition through Residual Stream Dynamics
Sep 30, 2026Yuanhe Zhang, Xinyao Zhou, Haoran Gao +5VLM Robustness Repetition in Language Generation
Beijing University of Posts and Telecommunications · JIUTIAN Research · Nanyang Technological University +1
Repetition, Not Length: Isolating the Counting Failure in Neural Text-to-Speech
Sep 29, 2026Kirill Borodin, Vasilii Kudryavtsev, Maxim Maslov +1TTS Synthesis TTS Evaluation
BitmanagerAI, Dubai, UAE · lab260, Yerevan, Armenia · MTUCI, Moscow, Russia
SlopBench: How Well Can We Rank Language Models by Slop? A Multi-Domain Benchmark of Repetitive AI Writing
Sep 27, 2026Dhruv Roongta, Harsha Gaddipati, Anh Tuan HuynhLLM Evaluation Repetition in Language Generation
Slashy, San Francisco, California, USA
FOCUS & RePAIR: Mitigating Text Degeneration via Token-Level Guidance for Pruned Large Language Models
Aug 27, 2026Junyoung Lee, Sehyeon Park, Shinhyoung Jang +5Repetition in Language Generation LLM Pruning
Department of Electrical Engineering and Computer Science, POSTECH, Pohang, Republic of Korea · Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of Korea · Hanyang University, Seoul, Republic of Korea
What a Cross-Model Fixed-Point Census Can and Cannot Arbitrate About Repetition
Aug 24, 2026Nicolás Vera ZúñigaRepetition in Language Generation Data Repetition in Language Model Training
Independent Researcher, Chile
Don't Repeat Yourself: Stopping Verbatim Loops at Sampling Time
Aug 24, 2026Philipp Emanuel Weidmann, Allen Roush, Judah Goldfeder +2Repetition in Language Generation
Thoughtworks · New York University
XTC: Head-Aware Sampling by Excluding Top Choices
Aug 24, 2026Philipp Emanuel Weidmann, Allen Roush, Judah Goldfeder +2Language Model Decoding Diversity-Aware Sampling
Independent Researcher · Thoughtworks · New York University
Interrupting the Loop: Periodic Subject Changes Raise Judged Surprise and Connection in Base Language Models
Aug 20, 2026Roberto I. Ono FilhoText Generation Causal Interventions in Language Models
Independent researcher
Sample More, Reflect Less: Self-Refine and Reflexion Lose to Repeated Sampling at Equal Token Cost, from 1.5B to 7B
Jul 30, 2026Iliya MirzaeiLanguage Model Self-Assessment LLM Self-Refinement
Department of Computer Science Stony Brook University
Bayesian Repetition Penalty: A Principled Adjacent-Conditional Framework for Reversing Attention Collapse in Autoregressive Language Models
Jul 17, 2026Wenjie Fan, Bin Ma, Dong LiLanguage Model Decoding Autoregressive Language Modeling
Yotta Labs
Telescope: Improving Zero Shot Detection of LLM Generated Content By Measuring Token Repetition Probability
Jul 5, 2026Christopher Nassif, Josh F. CooperAI-Generated Text Detection Language Model Generation Evaluation
Virginia Tech Department of Electrical and Computer Engineering · Virginia Tech Department of Industrial and Systems Engineering
PARTREP: Learning What to Repeat for Decoder-only LLMs
Jul 2, 2026Andikawati P Widjaja, Yongjun Kim, Hyounghun Kim +1Decoder-Only Language Models LLM Prompting
♡Bandung Institute of Technology · ♠Pohang University of Science and Technology
Low Perplexity is Repetition: A One-Dimensional Self-Conditioning Attractor in Continuous Diffusion LMs
Jul 1, 2026Shuai Zhang, Zijie Chen, Hongliang He +2Repetition in Language Generation Language Model Generation Evaluation
Zhejiang University · Westlake University · Ant Group
Can Editing 1 Neuron Fix Repetition Loops in LLMs?
Jun 9, 2026Aristotelis Lazaridis, Aman Sharma, Dylan Bates +3Model Editing Data Repetition in Language Model Training
Edgerunner AI
Extreme Low-Bit Inference in Reasoning Models: Failure Modes and Targeted Recovery
Jun 1, 2026Ekaterina Alimaskina, Darya Rudas, Denis Shveykin +3LLM Quantization Repetition in Language Generation
BRAIn Lab, Moscow, Russia
Self-Attention as a Covariance Readout: A Unified View of In-Context Learning and Repetition
May 11, 2026Haoren Xu, Guanhua FangSoftmax Attention Repetition in Language Generation
Fudan University
LoopGuard: Breaking Self-Reinforcing Attention Loops via Dynamic KV Cache Intervention
Apr 11, 2026Dongjie Xu, Hao Wu, Weijie Shi +7Repetition in Language Generation KV-Cache Management
Soochow University · Tongji University · The Hong Kong University of Science and Technology +2
Demystifying OPD: Length Inflation and Stabilization Strategies for Large Language Models
Apr 9, 2026Feng Luo, Yu-Neng Chuang, Guanchu Wang +4Repetition in Language Generation Language Model Distillation
Department of Computer Science, Rice University, Houston, USA · Department of Computer Science, University of North Carolina at Charlotte, Charlotte, USA · Department of Computer Science, Johns Hopkins University, Baltimore, USA +1
Dynamic Context Evolution for Scalable Synthetic Data Generation
Apr 8, 2026Ryan Lingo, Rajeev ChhajerRepetition in Language Generation Output Diversity in Language Models
Honda Research Institute, USA, Inc.

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Character-Name Concentration in LLM Fiction: Measuring Displacement and Mitigating Repetition

Alejandro Mozo Quesada, Sergio Gálvez Rojas, Francisco López Valverde

Universidad de Málaga, Spain

Repetition in Language Generation Controllable Text Generation Text Generation Output Diversity in Language Models Language Model Generation Evaluation

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Character names in large language model (LLM) fiction concentrate on a handful of recurrent names such as 'Elara' and 'Elias', a measurable face of AI slop. Existing fixes either require logit access or retraining, or operate per request and cannot control concentration across a corpus. How much prompt-level strategies reduce concentration, and at what cost to name naturalness, has not been systematically measured. We present an empirical study of name-concentration control across eight strategies and introduce speculative naming, a fine-tuning-free method that adapts the draft-verify-reject paradigm of speculative decoding from tokens to names. The method requests candidate name-sets through verbalized sampling, scores them against a SlopTracker that combines a per-model fingerprint with a runtime counter, and selects through a temperature-controlled rule or its strict variant (HARD). We use 20 LLMs to generate 44,782 stories in four languages from text and visual stimuli (88,919 named characters), and measure concentration with a list-free collision metric (Slop Ratio, SR) decomposed into known and emergent components. List-based strategies do not remove slop: static banlists displace concentration to emergent names and more than halve the share of attested real names. Speculative naming removes it: the sampling scaffold alone cuts SR by 89% on average, the dynamic tracker brings the total reduction to 95%, mean collision probability falls from 7.3% to 0.4%, and known-name collisions hold at zero while naturalness stays close to baseline (42-69% attested by language) and within-story collisions drop from 4.3% to 0.2%. The strategy ranking is stable across languages, model families, and name-counting conventions. Name concentration is thus controllable at the corpus level without logit access or retraining; broader creative diversity beyond naming remains open.