Don't Ask the LLM to Track Freshness: A Deterministic Recipe for Memory Conflict Resolution

Researchers demonstrate that deterministic post-retrieval aggregation using serial numbers outperforms LLM-based conflict resolution in memory systems by 10-28 percentage points. The study reveals that the bottleneck in fact-consolidation tasks is assembly logic rather than storage, with implications for building more reliable AI agents that track evolving information.

This research addresses a critical failure mode in LLM-based memory systems: when facts contradict, current approaches delegate resolution to the language model itself, which performs poorly despite explicit instructions about temporal ordering. The MemoryAgentBench evaluation reveals stark performance gaps—HippoRAG-v2 achieves only 54% accuracy on single-hop conflict resolution, while multi-hop scenarios remain near-unsolved across 22 systems.

The core insight is architectural: rather than asking LLMs to judge which fact is "fresher," the researchers replace that judgment step with deterministic Python logic (max serial number). This simple swap yields 10.8-point improvements on single-hop tasks, scaling to 21-point gains at larger dataset sizes. The approach reaches 78% accuracy with gpt-4o-mini and 94.8% with gpt-4o, substantially outperforming published baselines.

This finding reshapes how developers should think about memory system design. The bottleneck isn't retrieval quality or storage mechanisms—it's the aggregation layer where candidate facts compete. This suggests that expensive retrieval improvements may yield diminishing returns if assembly logic remains LLM-mediated. The deterministic recipe extends to multi-hop reasoning through a per-hop Self-Ask variant, though performance drops to 30-51% on more complex queries.

For the AI systems industry, this represents corrective guidance away from end-to-end LLM judgment toward hybrid architectures combining retrieval with deterministic conflict resolution. The mechanism's portability to timestamp-based ordering indicates broader applicability across memory update scenarios.

• →Replacing LLM judgment with deterministic max(serial) aggregation improves fact-consolidation accuracy by 10-28 percentage points across benchmarks.
• →The assembly step, not retrieval or storage, is the primary bottleneck in conflict resolution for evolving facts in memory systems.
• →Deterministic aggregation reaches 78-94.8% single-hop accuracy depending on model size, substantially beating published baselines like HippoRAG-v2.
• →Multi-hop conflict resolution remains challenging at 30-51% accuracy, requiring question-type-aware handling beyond simple deterministic primitives.
• →The approach generalizes from serial numbers to timestamps, suggesting broader applicability for memory systems handling temporal data.