ConsistencyGate Prevents Memory Contamination in LLM Agents
A recent study published on arXiv introduces ConsistencyGate, a mechanism designed for write-time admission control in LLM agents to mitigate memory contamination. Over multiple interactions, LLM agents gather information in external memory, where a single erroneous fact can lead to ongoing incorrect reasoning, known as memory contamination. While current memory management solutions focus on retrieval and capacity, they overlook the accuracy of write-time processes. ConsistencyGate evaluates the LLM K times to obtain a soft support score before finalizing a candidate fact, allowing admission only if the average score surpasses a set threshold. This approach is model-agnostic, does not require fine-tuning, and includes a log-probability variant for applications sensitive to latency. The study assesses its impact on real-world data.
Key facts
- arXiv:2607.22962v1 introduces ConsistencyGate.
- ConsistencyGate is a write-time admission gate for LLM agents.
- Memory contamination occurs when a hallucinated fact persists as a false premise.
- Existing memory management does not address write-time correctness.
- ConsistencyGate queries the LLM K times for a soft support score.
- A candidate fact is admitted only when the average support score exceeds a threshold.
- The mechanism is model-agnostic and requires no fine-tuning.
- A log-probability variant exists for latency-sensitive deployments.
Entities
Institutions
- arXiv