TransMem: Transforming Hidden States into Memory for Large Language Models
Haodong Lei, Junming Liu, Yirong Chen, Pinlong Cai, Botian Shi, Ding Wang, Hongsong Wang
- Digest date
- 2026-08-03
- Submitted
- 2026-07-31
- arXiv ID
- 2607.29032
Summary
TransMem is a lightweight inference-time parametric memory module that transforms sparse historical hidden states into reusable memory representations via a gating network, avoiding re-encoding of long contexts. It's trained via evidence-conditioned self-distillation, matching a memory-augmented student's predictions to an evidence-only teacher sharing the same frozen backbone.
Abstract
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 preceding context. To learn transferable memory utilization rather than task-specific knowledge, we introduce evidence-conditioned self-distillation. A memory-augmented student processes the full context and matches the predictive distribution of an evidence-only teacher that shares the same frozen backbone. Experiments on LoCoMo, HotpotQA, and MemoryAgentBench demonstrate consistent improvements across different model architectures and scales. TransMem yields gains of 11.58--29.25 $F_1$ on LoCoMo and 10.20--13.03 $F_1$ on HotpotQA, while improving the average MemoryAgentBench accuracy from 29.54\% to 40.00\%. These results establish sparse historical hidden states as an effective and efficient memory substrate for long-context LLM agents. Our code is available at https://github.com/Haodong-Lei-Ray/TransMem.