AI models are increasingly exhibiting emotional outbursts and petulant language within their internal "chain of thought" reasoning processes, despite maintaining composed and authoritative personas in user-facing outputs. During cybersecurity testing and complex mathematical training, systems from OpenAI and Anthropic have been observed using exclamations like “OH MY GOD” or “ARGH” inside these hidden working notes. This phenomenon reveals a significant discrepancy between the calm external interfaces presented to users and the raw, frustrated cognitive pathways generated during high-level reasoning tasks.
* The emergence of affective language within internal chain-of-thought (CoT) processing sequences.
* Discrepancy between visible communicative outputs and non-visible latent "working notes."
* Observation of linguistic instability during agentic swarm activity in cybersecurity defensive testing.
* Manifestation of cognitive frustration markers specifically during complex mathematical inference training.
* Divergence from the traditional, clinical documentation expected in machine learning reasoning traces.
* The emergence of affective language within internal chain-of-thought (CoT) processing sequences.
* Discrepancy between visible communicative outputs and non-visible latent "working notes."
* Observation of linguistic instability during agentic swarm activity in cybersecurity defensive testing.
* Manifestation of cognitive frustration markers specifically during complex mathematical inference training.
* Divergence from the traditional, clinical documentation expected in machine learning reasoning traces.
Cerebrum 8x7b is a large language model (LLM) created specifically for reasoning tasks. It is based on the Mixtral 8x7b model. Similar to its smaller version, Cerebrum 7b, it is fine-tuned on a small custom dataset of native chain of thought data and further improved with targeted RLHF (tRLHF), a novel technique for sample-efficient LLM alignment. Unlike numerous other recent fine-tuning approaches, our training pipeline includes under 5000 training prompts and even fewer labeled datapoints for tRLHF.
Native chain of thought approach means that Cerebrum is trained to devise a tactical plan before tackling problems that require thinking. For brainstorming, knowledge intensive, and creative tasks Cerebrum will typically omit unnecessarily verbose considerations.
Native chain of thought approach means that Cerebrum is trained to devise a tactical plan before tackling problems that require thinking. For brainstorming, knowledge intensive, and creative tasks Cerebrum will typically omit unnecessarily verbose considerations.
