Huginn-3.5B: Depth-Recurrent Language Model

• Huginn-3.5B is a depth-recurrent language model that scales inference by reusing a core transformer block for iterative refinement in latent space.
• It leverages latent chain-of-thought reasoning, enabling internal multi-step computation for tasks like math, logic, and programming.
• Empirical results show improved performance on reasoning benchmarks while highlighting challenges in latent interpretability and stability.

Huginn-3.5B is a large-scale depth-recurrent LLM architecture designed to scale test-time computation via iterative refinement in latent space rather than explicit expansion of the model or token sequence. Named and scaled analogously to other multi-billion parameter models, Huginn-3.5B stands as a central example of a recurrent-depth approach to LLMing, in which computational depth can be increased at inference time by reusing model blocks for additional iterative reasoning steps. This architecture is particularly notable for its focus on latent chain-of-thought reasoning, enabling the model to perform internal reasoning processes that are not directly manifest as explicit natural language outputs.

1. Architectural Principles and Model Design

Huginn-3.5B is founded on the depth-recurrent transformer paradigm, where a core recurrent block can be unrolled for an arbitrary number of steps at test time, enabling the model to scale its computation flexibly without increasing parameter count (2502.05171). The architecture is divided into three principal components:

• Prelude Block: Embeds input tokens into a high-dimensional latent space through several transformer layers.
• Core Recurrent Block: Applies a shared module iteratively to incrementally refine a latent state, denoted as $s_i = R(e, s_{i-1})$, where $e$ is the embedded input and $R$ is the recurrent block.
• Coda Block: Maps the final latent state to output token probabilities via a projection.

This pipeline allows Huginn-3.5B to decouple parameter scaling from compute scaling at inference: the model can “ponder” longer by increasing recurrence depth, thus approximating the representational capacity of much larger static models (for example, at 132 unrolls, an 8-layer physical model can behave like a virtual 132-layer model) (2502.05171).

2. Latent Reasoning and Chain-of-Thought Mechanisms

Huginn-3.5B’s central innovation is latent reasoning, whereby computation happens in the model’s continuous latent space rather than via explicit natural language chain-of-thought (CoT) tokens. In each recurrent block iteration, Huginn-3.5B refines hidden representations, effectively "thinking" internally through several steps before emitting a final prediction. This enables reasoning processes—such as arithmetic, symbolic transformation, or logic—that are challenging to externalize as strings of text (2505.16782, 2502.05171).

Contrary to traditional CoT prompting, which improves interpretability but is inefficient, Huginn-3.5B’s latent CoT is potentially more efficient and can capture forms of reasoning not easily verbalized. The latent iterative approach is stochastic during training, so the model is encouraged to operate under variable compute budgets (2502.05171). Key training equations are:

$e = P(x)$

$s_0 \sim \mathcal{N}(0, \sigma^2 I)$

$s_{i} = R(e, s_{i-1})$

$p = C(s_{r})$

where $r$ (the number of unrolls) can vary per example.

3. Empirical Performance and Benchmarking

Huginn-3.5B demonstrates that increasing recurrent depth during inference yields improvements on a spectrum of reasoning-heavy benchmarks. Empirical results on ARC, HellaSwag, MMLU, and GSM8K indicate that, as the number of latent recurrent steps increases (from 4 to 32 or more), performance on math and logic tasks consistently improves, sometimes rivaling models an order of magnitude larger in parameter count (2502.05171).

Nevertheless, in direct comparative analysis, especially on arithmetic-heavy benchmarks, the improvements from increasing recurrence are modest and do not match the gains from explicit externalized chain-of-thought methodologies. For GSM8K, for instance, Huginn-3.5B increases in accuracy as recurrence rises, but lags behind the best CoT-augmented models (2507.02199). This suggests limitations in the efficiency of current latent reasoning induction.

4. Probing Latent State Dynamics and Interpretability

The internal reasoning processes of Huginn-3.5B have been the subject of probing studies using methods such as the Logit Lens and Coda Lens (2507.02199). The Logit Lens projects normalized hidden states onto the vocabulary to examine which tokens are most likely at each recurrent step, while the Coda Lens passes states through additional coda transformer blocks before projection. Findings indicate that:

• There is no clear evidence of structured, interpretable latent chain-of-thought; token rank trajectories do not reveal phase-wise separation aligned with human-stepwise reasoning.
• Probing outcomes are highly inconsistent by recurrent block position and decoding strategy; earlier blocks may produce content-like numerals while later blocks become uninterpretable, and vice versa depending on the probing method.

This highlights the opacity of the model’s latent reasoning process and points to a tradeoff between computational efficiency and interpretability.

5. Training Methodology and Scalability

Huginn-3.5B is trained using ~800 billion tokens, with data weighted toward code and mathematical reasoning. The recurrent nature of the model necessitates careful normalization and initialization (e.g., pre-norm architectures, parameter-free normalization), as the unrolled recurrent process is prone to representation collapse without such strategies (2502.05171). Stochastic recurrent unrolling during training (sampling $r$ from a log-normal Poisson distribution) teaches robustness across varied compute regimes.

A notable aspect is the efficiency of the architecture: test-time compute can be scaled up dynamically per token, with inference cost determined by the number of recurrences rather than parameter size. This allows the model to expend computation selectively – an adaptive compute paradigm.

6. Applications, Limitations, and Open Challenges

Huginn-3.5B is particularly suited for tasks involving complex reasoning, including mathematics, program synthesis, and logical inference, where it leverages latent, repeatable computation to refine answers. Architecturally, features such as per-token adaptive compute, key-value cache sharing, and self-speculative decoding are natively supported (2502.05171).

However, the approach presents several limitations:

• Lack of interpretability: Internal reasoning is not externally visible or easily probed for diagnostic or alignment purposes (2507.02199).
• Incomplete latent CoT emergence: Mere recurrence is insufficient to induce structured multi-step latent reasoning; performance on CoT-requiring tasks lags models prompting explicit rationales.
• Convergence and stability: Recurrence magnitude and training regime must be carefully tuned to prevent latent collapse or instability.

Continued research is needed to bridge the gap between latent and explicit reasoning—possible avenues include hybrid architectures, auxiliary latent-state supervision, or recurrent block specialization for reasoning tasks (2505.16782, 2507.02199).

7. Comparative Context and Future Directions

Huginn-3.5B is situated within a broader trend toward greater computational depth and latent reasoning in LLMs. Compared to mixture-of-experts (MoE) models, such as LLaMA-MoE-3.5B (2406.16554), Huginn-3.5B’s depth-recurrent approach is parameter-efficient, with scaling in computational depth but not width. The model contrasts with contemporary externally supervised CoT architectures, emphasizing internal and flexible computation over human-readable rationales.

Ongoing research points to several directions:

• More interpretable and stable induction of latent CoT via architectural and training innovations.
• Integration with other token-efficient attention mechanisms (e.g., 2-simplicial attention (2507.02754)) to enhance reasoning without requiring exponential data.
• Hybrid approaches blending latent and explicit reasoning, or alternating between the two depending on task requirements.

This continued exploration will determine the practicality and limits of latent reasoning frameworks like Huginn-3.5B in both research and applied settings.