TransMem: Transforming Hidden States into Memory for Large Language Models
Large language model (LLM) agents increasingly operate over long interaction histories, where effective reasoning requires identifying and exploiting task-relevant evidence distributed across past observations and actions. However, useful information encoded in previously computed representations is often underutilized during subsequent generation. We propose \textbf{TransMem}, a lightweight inference-time parametric memory module that transforms sparse historical hidden states from a frozen LLM backbone into reusable memory representations. TransMem uses a lightweight gating network to dynamically apply the latent intervention to the current hidden states, without repeatedly encoding the...
| Authors | Haodong Lei, Junming Liu, Yirong Chen et al. |
|---|---|
| Primary category | cs.MA |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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