AI agents should treat memory as a changing web of useful connections, not static storage.

Most agent memory systems retrieve old facts as if the past were a filing cabinet.

The paper proposes FluxMem, a memory system that stores facts, past task episodes, and reusable skills as connected pieces in a graph.

When the agent works on a task, FluxMem first gathers likely useful memories, then uses feedback from the task to fix the memory connections by adding missing links, removing bad ones, or rewriting memories at the right level of detail.

Over time, it also turns repeated successful task paths into reusable skills, so the agent does not need to rebuild the same reasoning pattern again and again.

The authors tested FluxMem on long conversation memory, web navigation, and general assistant tasks, which checks whether the idea works across very different agent problems.

FluxMem got stronger results than the compared memory systems, including 95.06 average accuracy on LoCoMo and a 12.73-point gain on GAIA with Kimi K2.

The big deal is that the paper shifts agent memory from “store and retrieve” toward “keep repairing and strengthening the connections that actually help the agent act.”

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Link – arxiv. org/abs/2605.28773

Title: "Rethinking Memory as Continuously Evolving Connectivity"