How Implicit Bias Accumulates and Propagates in LLM Long-term Memory D
AI benchmark · Alignment
What is How Implicit Bias Accumulates and Propagates in LLM Long-term Memory?
Long-term memory mechanisms enable Large Language Models (LLMs) to maintain continuity and personalization across extended interaction lifecycles, but they also introduce new and underexplored risks related to fairness. In this work, we study how implicit bias, defined as subtle statistical prejudice, accumulates and propagates within LLMs equipped with long-term memory. To support systematic analysis, we introduce the Decision-based Implicit Bias (DIB) Benchmark, a large-scale dataset comprising 3,776 decision-making scenarios across nine social domains, designed to quantify implicit bias in long-term decision processes. Using a realistic long-horizon simulation framework, we evaluate six state-of-the-art LLMs integrated with three representative memory architectures on DIB and demonstrate that LLMs' implicit bias does not remain static but intensifies over time and propagates across unrelated domains. We further analyze mitigation strategies and show that a static system-level prompting baseline provides limited and short-lived debiasing effects. To address this limitation, we propose Dynamic Memory Tagging (DMT), an agentic intervention that enforces fairness constraints at memory write time. Extensive experimental results show that DMT substantially reduces bias accumulation and effectively curtails cross-domain bias propagation.
Tier D · a leaderboard, listed apart
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- 32 models scored
- no proper name (a paper title or description)
- in curated RL directories (1 pt)
How we got to tier D
- Adoption: 32 models scored; no proper name (a paper title or description). Tier D.
- Not yet read on X.
- In curated RL directories: 1 point, a floor of none.
- Not yet confirmed by a line from its own site or paper.
- Judged a leaderboard, arena or index rather than one benchmark (or not a benchmark at all): listed apart.
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Not on the map yet: no line from its own site or paper places it in a domain.
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