Monitoring latent reasoning in looped language models
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arXiv:2609.24196v1 Announce Type: new Abstract: Looped Language Models (LoopLMs) perform "latent reasoning" by recursively refining internal latent representations with shared weights, offering a mor...
arXiv:2610. 02741v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring provides a promising approach for detecting undesirable model behavior.
Looped Language Models (LoopLMs) perform "latent reasoning" by recursively refining internal latent representations with shared weights, offering a more effective alternative to explicit verbal reason...
arXiv:2607. 18100v1 Announce Type: new Abstract: Extended reasoning has become standard for frontier Large Language Models (LLMs), yet the trajectories these models produce remain largely uncontrollable.
arXiv:2606. 31779v1 Announce Type: new Abstract: Language models typically reason via explicit chain-of-thought (CoT), generating intermediate steps token-by-token.
The paper investigates the use of looped language models for compositional tool calling, where models must coordinate multiple API calls and maintain state across interactions. Experiments on API-Bank, BFCL, and NESTful show that recurrent computation generally improves compositional and dependency-aware tool use, with accuracy increasing as recurrent depth grows. Adaptive inference offers a better compute‑performance trade‑off by allocating extra computation only when necessary.