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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
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- PossiblePossibly related (embedding) · 67%pzqpzq/LSF_MDia →
- PossiblePossibly related (embedding) · 66%Bridging the Gap Between Latent and Explicit Reasoning with Looped Transformers →
- PossiblePossibly related (embedding) · 63%Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering →
- PossiblePossibly related (embedding) · 60%DJC-GO-SOLO/Latent-SFT →
- PossiblePossibly related (embedding) · 60%Does Verbose Chain-of-Thought Really Help? In-Distribution Evidence that Content, Not Length, Matters →
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repopzqpzq/LSF_MDiapaperBridging the Gap Between Latent and Explicit Reasoning with Looped TransformerspaperCan We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation SteeringrepoDJC-GO-SOLO/Latent-SFTpaperDoes Verbose Chain-of-Thought Really Help? In-Distribution Evidence that Content, Not Length, Matters
Related across the graph
paperDoes Verbose Chain-of-Thought Really Help? In-Distribution Evidence that Content, Not Length, MattersrepoDJC-GO-SOLO/Latent-SFTpaperBridging the Gap Between Latent and Explicit Reasoning with Looped Transformersrepopzqpzq/LSF_MDiapaperCan We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering