LLMs Can Get "Brain Rot": A Pilot Study on Twitter/X

We propose and test the **LLM Brain Rot Hypothesis**: continual exposure to *junk web text* induces lasting cognitive decline in large language models (LLMs). To unveil junk effects, we designed a novel controlled experiment on real Twitter/X corpora, by constructing junk and reverse-controlled datasets via two orthogonal operationalizations: **M1** (engagement degree) and **M2** (semantic quality), with matched token scale and training operations across conditions. Compared to the control group, continual pre-training of 4 LLMs on the junk dataset causes non-trivial declines (Hedges' $g>0.3$) on reasoning, long-context understanding, safety, and inflating "dark traits" (e.g., psychopathy, narcissism). The gradual mixtures of junk and control datasets also yield dose-response cognition decay: for example, under M1, ARC-Challenge with Chain-of-Thought drops $72.1 \rightarrow 57.2$ and RULER-CWE $83.7 \rightarrow 52.3$ as junk ratio rises from $0$% to $100$%. Error forensics reveal several key insights. First, we identify *thought-skipping as the primary lesion in reasoning*: models increasingly truncate or skip chains. Second, partial but incomplete healing is observed: scaling instruction tuning and clean continual pre-training improve the declined cognition, yet cannot restore baseline capability, suggesting persistent representational drift rather than format mismatch. Finally, we discover that the popularity, a non-semantic metric, of a tweet is a better indicator of the Brain Rot effect than the length in M1. Together, the results provide significant, multi-perspective evidence that \textit{social effects of data could be a causal driver of LLM capability decay in continual pre-training}, thereby motivating routine "cognitive health checks" for deployed and evolving LLMs.