LLMs Can Get "Brain Rot"!
Authors: name: Shuo Xing; name: Junyuan Hong; name: Yifan Wang; name: Runjin Chen; name: Zhenyu Zhang; name: Ananth Grama; name: Zhengzhong Tu; name: Zhangyang Wang
Published: 2025-10-15 13:28:49

Abstract: 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
causally isolate data quality, we run controlled experiments on real Twitter/X
corpora, constructing junk and reversely controlled datasets via two orthogonal
operationalizations: M1 (engagement degree) and M2 (semantic quality), with
matched token scale and training operations across conditions. Contrary 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 Thoughts drops 74.9 rightarrow 57.2 and RULER-CWE 84.4 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: models increasingly truncate or skip
reasoning chains, explaining most of the error growth. Second, partial but
incomplete healing is observed: scaling instruction tuning and clean data
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 data
quality is a causal driver of LLM capability decay, reframing curation for
continual pretraining as a training-time safety problem and motivating
routine "cognitive health checks" for deployed LLMs.