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arXiv:2605. 09239v2 Announce Type: replace-cross Abstract: Large language models fail at counting how many times a word repeats in a list, even though they perform well on far harder reasoning tasks.
The study investigates how textual neural models degrade when inputs contain noise such as typos, OCR errors, or dropped words. It finds that model performance decline is largely consistent across architectures under word‑level noise but diverges under character‑level noise, a difference attributed to tokenization rather than architecture. By applying a short contrastive training recipe, diverse encoders converge to a common robustness curve, enabling prediction of a model’s noise resilience and the ability to enhance robustness at specific noise scales through targeted training.
arXiv:2606. 18323v1 Announce Type: cross Abstract: Open autoregressive neural-codec text-to-speech (TTS) models sound excellent on typical inputs yet suffer stochastic catastrophic failures: on a meaningful fraction of utterances they emit silence, terminate early, or collapse into repetitive or hallucinated content.
The paper investigates how pruning large language models can lead to text degeneration, particularly repetition loops, even when perplexity and task accuracy stay stable. By treating decoding as a dynamical process, the authors separate degeneration into loop entry risk and loop persistence, showing that persistence depends on the escape mass given to plausible alternatives. They introduce two token‑level guidance objectives—FOCUS, which reweights distillation toward high‑confidence teacher regions, and RePAIR, which uses onset‑centered positive/negative continuation pairs with a margin loss—to reduce repetition and improve generation quality in pruned models.
arXiv:2606. 24998v1 Announce Type: new Abstract: Language models are running out of high-quality training data, and even aggressively deduplicated corpora retain some amount of repetition.
arXiv:2607. 22694v1 Announce Type: new Abstract: Attention collapse in autoregressive language models -- manifested as repetitive token loops where the model becomes trapped in self-reinforcing attractors -- is a persistent pathology that existing decoding-time heuristics fail to address at its root cause.