Today we're releasing Celt 4, the most advanced creative intelligence model yet for complex, real-world applications. Celt 4 is engineered to bridge the gap between raw computational power and human-like creative intuition, implementing a novel Recursive Thought Cloning alignment stage that forces the model to engage in hidden, multi-step reasoning before generation.
As models continue to advance, we've observed that improvements in benchmarks often translate poorly to specialized creative domains such as viral content generation, "impossible" simulation coding, and intent-aware reasoning.
Abstract
We introduce Celt 4, a new family of frontier language models engineered to bridge the gap between raw computational power and human-like creative intuition. While recent advancements in Large Language Models (LLMs) have focused on general-purpose benchmarks, significant regression has been observed in stylistic adaptability, lateral thinking, and "one-shot" complex coding.
The Celt 4 family—comprising the frontier-class Celt 4 and the high-efficiency Celt 4 Lite—addresses this by implementing a novel Recursive Thought Cloning alignment stage. This methodology forces the model to engage in a hidden, multi-step reasoning process ("System 2" thinking) to critique, plan, and refine its output before generation.
Qualitative and internal quantitative evaluations on our Creativity Bench 1 (CB-1) suite demonstrate that Celt 4 achieves state-of-the-art (SOTA) performance in viral content generation, "impossible" simulation coding, and intent-aware reasoning, significantly outperforming existing models in tasks requiring high nuance and low hallucination rates.
1. Introduction
The prevailing paradigm in AI training—Reinforcement Learning from Human Feedback (RLHF)—has successfully aligned models to be safe and helpful. However, this often results in "Mode Collapse" regarding creativity, leading to models that speak in a generic, hedged, and repetitive voice (often termed "AI Slop").
Celtic Labs hypothesizes that true intelligence is not just about the output, but the process.
Celt 4 was developed to validate the "Process-First" Hypothesis: that by training a model to explicitly verbalize its internal reasoning, intent analysis, and creative direction in a hidden state, we can unlock capabilities that are suppressed in standard instruction-tuned models.
2. The Celt 4 Model Family
We are releasing two distinct models, each optimized for specific cognitive loads.
2.1 Celt 4 (The Frontier)
Celt 4 is our most capable model, designed for deep reasoning, complex synthesis, and high-stakes creative direction.
2.2 Celt 4 Lite (The Velocity Engine)
Celt 4 Lite is distilled for extreme efficiency and speed, designed to be the "Always-On" intelligence for daily tasks.
3. Training Methodology: Thought Cloning
Unlike standard models trained to predict the next token immediately, Celt 4 is trained on a proprietary dataset of Reasoning Traces.
3.1 The Hidden Monologue
Celt 4 employs a "Silent Thought" protocol. Before generating a response, the model enters a latent reasoning state where it:
- Analyzes Intent: Decodes what the user actually wants, rather than just what they typed.
- Audits for Quality: Explicitly checks its own draft for generic tropes and removes them.
- Self-Corrects: In coding tasks, it predicts potential syntax errors or logic bugs and corrects them before final output.
3.2 Identity & Epistemic Alignment
A key focus of our post-training was Epistemic Humility. We utilized a "Truth Engine" dataset where the model was penalized for hallucinating libraries, citations, or facts. The model is trained to identify the limits of its own knowledge within its thought block and refuse logically impossible requests rather than fabricating a solution.
4. Evaluation: Creativity Bench 1 (CB-1)
To measure progress in areas ignored by standard benchmarks (like MMLU), Celtic Labs developed Creativity Bench 1 (CB-1), a suite of 400 adversarial prompts designed to test lateral thinking, wit, and coding aesthetics.
Key Findings:
- Viral Retention: Celt 4 demonstrated a superior ability to generate "Hooks" under 20 words that contained high psychological tension, whereas competitor models averaged 40+ words with generic intros.
- One-Shot Visualization: In tests requesting raw HTML/JS artifacts, Celt 4 produced working, bug-free code in 96% of trials, compared to the industry average of 72%.
- Style Mimicry: Celt 4 successfully adopted complex personas without breaking character or reverting to "Helpful Assistant" voice.
5. Conclusion & Availability
Celt 4 represents a significant step forward in Creative AI. By shifting the focus from generic helpfulness to rigorous, process-based reasoning, we have created a model that serves not just as an assistant, but as a Creative Engine.
We believe that intelligence should be abundant, fast, and distinctly capable of human-like nuance.