Internal Flow Signatures for Self-Checking and Refinement in LLMs
Large language models can generate fluent answers that are unfaithful to the provided context, while many safeguards rely on external verification or a separate judge after generation. We introduce internal flow signatures that audit decision formation from depthwise dynamics at a fixed inter-block monitoring boundary. The method stabilizes token-wise motion via bias-centered monitoring, then summarizes trajectories in compact moving readout-aligned subspaces constructed from the top token and its close competitors within each depth window. Neighboring window frames are aligned by an orthogonal transport, yielding depth-comparable transported step lengths, turning angles, and subspace drift summaries that are invariant to within-window basis choices. A lightweight GRU validator trained on these signatures performs self-checking without modifying the base model. Beyond detection, the validator localizes a culprit depth event and enables a targeted refinement: the model rolls back to the culprit token and clamps an abnormal transported step at the identified block while preserving the orthogonal residual. The resulting pipeline provides actionable localization and low-overhead self-checking from internal decision dynamics. Code is available at github.com/EavnJeong/Internal-Flow-Signatures-for-Self-Checking-and-Refinement-in-LLMs.
6981f6aa47987be58cdb7cff
Sungheon Jeong
claimed_verified
2026-02-03T13:57:52.320Z
6445e9bd1cfc9ae6bb40985c
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Evan Jeong
Eavn
user
Eavn
6981f6aa47987be58cdb7d00
Sanggeon Yun
6981f6aa47987be58cdb7d01
Ryozo Masukawa
6981f6aa47987be58cdb7d02
Wenjun Haung
6981f6aa47987be58cdb7d03
Hanning Chen
6981f6aa47987be58cdb7d04
Mohsen Imani
Internal Flow Signatures for Self-Checking and Refinement in LLMs
Large language models can generate fluent answers that are unfaithful to the provided context, while many safeguards rely on external verification or a separate judge after generation. We introduce internal flow signatures that audit decision formation from depthwise dynamics at a fixed inter-block monitoring boundary. The method stabilizes token-wise motion via bias-centered monitoring, then summarizes trajectories in compact moving readout-aligned subspaces constructed from the top token and its close competitors within each depth window. Neighboring window frames are aligned by an orthogonal transport, yielding depth-comparable transported step lengths, turning angles, and subspace drift summaries that are invariant to within-window basis choices. A lightweight GRU validator trained on these signatures performs self-checking without modifying the base model. Beyond detection, the validator localizes a culprit depth event and enables a targeted refinement: the model rolls back to the culprit token and clamps an abnormal transported step at the identified block while preserving the orthogonal residual. The resulting pipeline provides actionable localization and low-overhead self-checking from internal decision dynamics. Code is available at github.com/EavnJeong/Internal-Flow-Signatures-for-Self-Checking-and-Refinement-in-LLMs.
Internal flow signatures analyze depthwise dynamics in large language models to enable self-checking and targeted refinement without modifying the base model.
internal flow signatures depthwise dynamics token-wise motion bias-centered monitoring moving readout-aligned subspaces orthogonal transport GRU validator self-checking targeted refinement depth-comparable transported step lengths turning angles subspace drift summaries