Less Context, More Accuracy: A Bi-Temporal Memory Engine for LLM Agents Where a Lean Retrieved Context Beats the Full History
arXiv:2606.09900v1 Announce Type: new Abstract: Long-term memory is the missing layer for LLM agents: across sessions they forget, and the common workaround -- replaying the whole history into the prompt -- is expensive, slow, and, as distractors accumulate, less accurate. Most memory systems win on cost or latency but still lose to the full-context baseline on accuracy, and benchmark numbers are reported on inconsistent, non-reproducible harnesses, so one system appears at wildly different scores across sources. We present Engram, an open-source, dual-process memory engine on a bi-temporal da
The proliferation of LLM agents highlights the critical need for efficient and accurate long-term memory solutions, as current methods are proving costly and ineffective.
Improving LLM agent memory efficiency and accuracy is a crucial bottleneck for their widespread adoption and impact on white-collar workflows, directly affecting their intelligence and scalability.
The introduction of Engram suggests a potential shift towards more specialized and bi-temporal memory architectures for LLMs, moving away from simple full-history replay.
- · LLM agent developers
- · Enterprises adopting LLM agents
- · AI infrastructure providers
- · Inefficient memory system providers
- · LLM agents relying solely on full context
- · Workflows currently resistant to automation
LLM agents become more capable and cost-effective for complex, multi-session tasks.
Increased deployment of autonomous AI agents across various industries, collapsing more software layers and roles.
Accelerated development of more sophisticated, self-improving AI systems capable of complex reasoning and long-term planning.
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Read at arXiv cs.CL