Zero-Mem: Zero-Token Memory Operations for LLM Agents
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Merged summary
TL;DR - Zero-Mem is an agent memory system that removes LLM calls from all memory operations, keeping raw interaction traces as the record and invoking a model only for final question answering. It matters because it cuts the recurring token and latency overhead that generation-based memory pipelines impose while avoiding lossy intermediate summaries.
- Memory is organized in two complementary views: an entity–context graph for cross-interaction connections, and a temporal hierarchy that preserves conversational locality and session state; per query, the two views are weighted and retrieved from jointly.
- "Zero-token" means no step outside final QA consumes LLM input or output tokens; encoder computation is accounted for separately.
- Deterministic calibration discards conflicting evidence and grounds the reader's answer in retrieved original traces rather than generated records.
- On long-memory and long-context QA benchmarks, it reports competitive accuracy with a 57.6% reduction in memory-operation time versus the fastest compared baseline (same reader and context budget); ablations support both views and their query-dependent coordination.
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Zero-Mem: Zero-Token Memory Operations for LLM Agents
TL;DR - Zero-Mem is an agent memory system that removes LLM calls from all memory operations, keeping raw interaction traces as the record and invoking a model only for final question answering. It matters because it cuts the recurring token and latency overhead that generation-based memory pipelines impose while avoiding lossy intermediate summaries.
- Memory is organized in two complementary views: an entity–context graph for cross-interaction connections, and a temporal hierarchy that preserves conversational locality and session state; per query, the two views are weighted and retrieved from jointly.
- "Zero-token" means no step outside final QA consumes LLM input or output tokens; encoder computation is accounted for separately.
- Deterministic calibration discards conflicting evidence and grounds the reader's answer in retrieved original traces rather than generated records.
- On long-memory and long-context QA benchmarks, it reports competitive accuracy with a 57.6% reduction in memory-operation time versus the fastest compared baseline (same reader and context budget); ablations support both views and their query-dependent coordination.