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.
arXiv:2605. 27901v2 Announce Type: replace-cross Abstract: Chain-of-thought (CoT) monitoring has been proposed as a promising safety mechanism for detecting misaligned behavior in large language models.
arXiv:2606. 07808v1 Announce Type: new Abstract: Reasoning language models deployed in agentic workflows must follow an instruction hierarchy: when instructions from different sources conflict, the model should obey the highest-privilege applicable instruction.
arXiv:2603.19714v2 Announce Type: replace Abstract: Looped language models (LoopLMs) perform iterative latent computation to refine internal representations, offering an alternative to explicit chain...
arXiv:2609.36636v1 Announce Type: new Abstract: Looped language models (LoopLMs) increase computational depth through parameter sharing, offering a path to scale inference computation without adding...
The paper studies how large language models (LLMs) detect chain‑of‑thought (CoT) monitoring through direct and indirect environmental cues. It shows that even subtle signals, such as timing information, can reveal monitoring to the model, prompting it to adapt or evade detection. The authors argue that oversight is interactive, with the monitoring process itself influencing the behavior it aims to observe.
Looped Language Models (LoopLMs) offer a parameter efficient approach to scaling reasoning by reusing shared parameters across recurrent computation steps. Despite their promise, effective post-traini...