LLMs Can Get "Brain Rot"!
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.
68f2a8838589920bf4d318fe
Shuo Xing
claimed_verified
2025-10-18T04:47:20.253Z
62df9ee3c7e6f74fc92a1351
/avatars/2a4f60ecc2fc4be2a8ac21c3f2ceb3cc.svg
Shuo Xing
user
shuoxing
68f2a8838589920bf4d318ff
Junyuan Hong
claimed_verified
2025-10-20T12:13:03.936Z
6400cf982b67d27affce2d89
https://cdn-avatars.huggingface.co/v1/production/uploads/6400cf982b67d27affce2d89/Vs042d2M-iV2wk9Q_Jqh3.jpeg
Junyuan Hong
user
jyhong836
68f2a8838589920bf4d31900
Yifan Wang
claimed_verified
2025-10-20T12:13:01.546Z
647693afb9c742c51117e1fa
/avatars/6a7a3f0f47588b5c38881b289d042b7a.svg
Yifan Wang
user
AmberYifan
68f2a8838589920bf4d31901
Runjin Chen
68f2a8838589920bf4d31902
Zhenyu Zhang
68f2a8838589920bf4d31903
Ananth Grama
68f2a8838589920bf4d31904
Zhengzhong Tu
68f2a8838589920bf4d31905
Zhangyang Wang
LLMs Can Get "Brain Rot"!
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.
Continual exposure to low-quality web text leads to cognitive decline in large language models, affecting reasoning, context understanding, safety, and personality traits, with partial recovery possible through instruction tuning and clean data pre-training.
LLM Brain Rot Hypothesis junk web text cognitive decline large language models Twitter/X corpora engagement degree semantic quality reasoning long-context understanding safety dark traits ARC-Challenge Chain Of Thoughts RULER-CWE thought-skipping representational drift training-time safety cognitive health checks