[Paper] Characterizing Mamba's Selective Memory using Auto-Encoders

Source: arXiv - 2512.15653v1

Overview

The paper investigates what kinds of information the Mamba family of state‑space language models (SSMs) tends to forget as they process longer text streams. By training an auto‑encoder to reconstruct the original input from Mamba’s hidden state, the authors expose systematic biases—e.g., math symbols, organization names, and non‑standard dialects—that are more likely to be lost. Understanding these blind spots is crucial for developers who consider SSMs as a memory‑efficient alternative to Transformers in production systems.

Key Contributions

• Token‑level forgetting analysis: Quantifies forgetting rates for different parts of speech, named‑entity types, and linguistic varieties.
• Sequence‑type profiling: Shows that whole domains (mathematical expressions, code snippets, etc.) suffer higher information loss.
• Auto‑encoder probing framework: Introduces a simple, reproducible method to measure hidden‑state fidelity without modifying the original SSM.
• Empirical study on Mamba models: Evaluates models ranging from 130 M to 1.4 B parameters across 4–256 token windows.
• Link to pre‑training frequency: Demonstrates a strong correlation between token rarity in the pre‑training corpus and forgetting propensity.

Methodology

1. Data preparation: The authors sample a diverse set of sentences covering natural language, code, math problems, and dialectal variants.
2. Hidden‑state extraction: Each token sequence is fed through a frozen Mamba model; the final hidden state (the “memory vector”) is recorded.
3. Auto‑encoder training: A lightweight encoder‑decoder network learns to reconstruct the original token sequence solely from the hidden state. The reconstruction loss serves as a proxy for how much information the SSM retained.
4. Error analysis: Reconstruction errors are broken down by token type (POS tags, named‑entity categories, dialect markers) and by whole‑sequence domain.
5. Frequency correlation: Token frequencies in the original Mamba pre‑training corpus are computed, and statistical tests assess the relationship between rarity and forgetting rate.

The approach is deliberately model‑agnostic: any fixed‑memory LM can be probed with the same auto‑encoder pipeline.

Results & Findings

A strong inverse correlation (Pearson r ≈ ‑0.73) was found between token frequency in the pre‑training data and its forgetting rate. Larger models (1.4 B) exhibit lower overall loss but retain the same relative bias patterns.

Practical Implications

• Choosing the right model for domain‑specific apps: If your product processes equations, financial data, or niche jargon, a vanilla Mamba model may silently drop critical tokens. Consider augmenting the model with domain‑specific fine‑tuning or hybrid architectures (e.g., a small Transformer cache for high‑precision tokens).
• Designing memory‑efficient pipelines: The auto‑encoder probe can be integrated into CI tests to flag when a new SSM version starts forgetting a target token set, enabling early detection before deployment.
• Data collection strategy: The frequency‑forgetting link suggests that enriching pre‑training corpora with under‑represented tokens (math symbols, dialectal text) can directly improve retention, guiding data‑curation budgets.
• Hybrid inference systems: Developers could keep a lightweight “token‑watchdog” that monitors for high‑risk tokens and forces a re‑encoding step (e.g., re‑run the segment through a small Transformer) when they appear.
• Interpretability tools: The reconstruction‑error heatmaps produced by the auto‑encoder can serve as a debugging overlay for developers building LLM‑powered assistants, highlighting where the model’s memory may be insufficient.

Limitations & Future Work

• Fixed window size: Experiments stop at 256 tokens; behavior on truly long documents (thousands of tokens) remains untested.
• Auto‑encoder capacity: The probe itself may introduce bias; a more expressive decoder could mask forgetting rather than reveal it.
• Model scope: Only the Mamba family was examined; it is unclear whether the observed patterns generalize to other SSM variants (e.g., S4, Hyena).
• Mitigation strategies: The paper identifies the problem but does not propose concrete architectural changes or training objectives to reduce selective forgetting. Future work could explore memory‑augmentation techniques, curriculum‑based pre‑training, or token‑aware regularization.

Authors

• Tamanna Hossain
• Robert L. Logan
• Ganesh Jagadeesan
• Sameer Singh
• Joel Tetreault
• Alejandro Jaimes

Paper Information

• arXiv ID: 2512.15653v1
• Categories: cs.CL
• Published: December 17, 2025
• PDF: Download PDF