FragToken: Amplifying LLM Inference Costs through Noncanonical Token Generation

arXiv:2609.31552 · cs.CR · Submitted 2026-09-25 · Read on arXiv

cs.CR

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Benchmark — 19× in this paper · explained in 2 episodes
• Distillation — 13× in this paper · explained in 2 episodes
• Self-distillation — 12× in this paper · explained in 6 episodes
• Tokenization — 7× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Accuracy — 6× in this paper · explained in 1 episode
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Ablation study — 5× in this paper · explained in 1 episode
• Quantization — 5× in this paper · explained in 12 episodes
• Trace — 5× in this paper · explained in 2 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• GRPO — 4× in this paper · explained in 6 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Token Inflation Ratio — 3× in this paper
• Adversarial Attacks — 2× in this paper · explained in 1 episode
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• LLM-as-a-Judge — 2× in this paper · explained in 3 episodes
• Large Language Model (LLM) — 2× in this paper · explained in 3 episodes
• Perplexity — 2× in this paper · explained in 3 episodes
• Scaling Laws — 2× in this paper · explained in 4 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Adversarial Prompting — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Core — 1× in this paper · explained in 2 episodes
• Frozen model — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Reasoning Traces — 1× in this paper · explained in 3 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• Training Verifiers to Solve Math Word Problems
• Denial-of-Service Poisoning Attacks against Large Language Models
• The Llama 3 Herd of Models
• Baseline Defenses for Adversarial Attacks Against Aligned Language Models
• Scaling Laws for Neural Language Models
• OverThink: Slowdown Attacks on Reasoning LLMs
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• 2 OLMo 2 Furious
• Yi: Open Foundation Models by 01.AI
• Breaking the Loop: Detecting and Mitigating Denial-of-Service Vulnerabilities in Large Language Models
• Universal and Transferable Adversarial Attacks on Aligned Language Models

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