aiqbench analysis
This research identifies a critical "latent-to-language transition gap" where activation steering in continuous thought spaces fails to effectively influence subsequent language generation, unlike its efficacy in explicit chain-of-thought reasoning. The study demonstrates that task information remains identifiable in latent representations but decouples from output control at the transition boundary, suggesting this interface is a key area for future methodological design. These findings provide essential insights for developers aiming to refine alignment and intervention techniques in next-generation large language models.
When Steering Fails in Latent Reasoning: A Latent-to-Language Transition Gap
arXiv:2609.21662v1 Announce Type: cross Abstract: Activation steering has become a widely used approach for controlling language models during explicit chain-of-thought (CoT) reasoning, motivating its extension to latent CoT. However, we find that steering continuous thoughts produces substantially weaker effects on subsequent language generation than steering explicit CoT, even when the hidden representations are moved by comparable amounts. We first show that task information remains identifiable in continuous thoughts. Hence, we hypothesize a \textbf{latent-to-language transition gap}, in which an intervention effect in latent space fails to transfer to language generation. Two further results support this hypothesis: the output distribution changes abruptly at the transition boundary, and task-related directions exert much weaker bidirectional control in latent CoT than in explicit CoT. These findings identify the transition interface as a central target for evaluating and designing future latent-steering methods.
Abstract:Activation steering has become a widely used approach for controlling language models during explicit chain-of-thought (CoT) reasoning, motivating its extension to latent CoT. However, we find that steering continuous thoughts produces substantially weaker effects on subsequent language generation than steering explicit CoT, even when the hidden representations are moved by comparable amounts. We first show that task information remains identifiable in continuous thoughts. Hence, we hypothesize a \textbf{latent-to-language transition gap}, in which an intervention effect in latent space fails to transfer to language generation. Two further results support this hypothesis: the output distribution changes abruptly at the transition boundary, and task-related directions exert much weaker bidirectional control in latent CoT than in explicit CoT. These findings identify the transition interface as a central target for evaluating and designing future latent-steering methods.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2609.21662 [cs.CL]
(or arXiv:2609.21662v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.21662 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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