Latent Reasoning Landscape in 2026: Mapping BDH-CQ, HRM/TRM, Coconut [D]
After following various arXiv papers and researcher discussions on X/bluesky about latent reasoning and continual learning, one idea which resonates strongly is that path forward (towards AGI) may depend less on generating ever-longer chains of thought and more on finding architectures that can reason beyond the token stream. LLMs routinely reach correct answers through flawed or fabricated CoT steps, and produce perfectly logical steps that end in wrong answers (Kambhampati, 2025). The trace doesn't track the computation which clarifies that verbalized CoT is an imitation of reasoning and not the mechanism itself. The alternative mechanism which gets the most attention is latent reasoning: instead of verbalizing every intermediate result, the model repeatedly transforms its continuous hidden state and decodes only the answer. I’m breaking latent reasoning down into at least five distinct families: Continuous thoughts in autoregressive LMs: Coconut (Hao et al., 2024) feeds the model's own final hidden state back in as the next input embedding while Soft Thinking (Zhang et al., 2025) reasons in a continuous concept space. Theory here argues a single continuous state can hold several search frontiers at once and expand them in parallel (Zhu et al., 2025) . Compressed discrete non-linguistic tokens: Abstract-CoT (Ramji et al., 2026) swaps verbal rationales for a short sequence from a learned vocabulary. It is non-linguistic, but still serial and externally decoded, the discrete end of the spectrum . Recurrent depth and looped models: recurrent-depth LMs (Geiping et al., 2025) and looped Transformers (Saunshi et al., 2025; Zhu et al., 2026) reapply a shared block to a latent state. Mostly framed as parameter efficiency and test-time-compute scaling, not as a new reasoning interface . Task-trained recursive solvers: HRM (Wang et al., 2025) and TRM (Jolicoeur-Martineau, 2025) recursively refine latent and candidate-answer states. Their ARC pipelines are transductive: eval