Open challenges in long-horizon reasoning, retention stability, and memory quality under uncontrolled memory accumulation

Determine approaches to address the open challenges of long-horizon reasoning performance, temporal retention stability, and memory quality in autonomous conversational agents when memory retention is uncontrolled and accumulates over extended dialogues.

Background

The paper studies memory management for autonomous conversational agents operating over long-horizon dialogues, where uncontrolled memory growth leads to retrieval noise, computational overhead, and false memory propagation. Benchmarks such as LOCOMO and LOCCO demonstrate performance degradation as dialogue length increases, and MultiWOZ shows notable false memory rates when historical context is retained without regulation.

Within the Results and Analysis, the authors explicitly note that maintaining strong reasoning performance, stable temporal retention, and high memory quality remains an unresolved area when memory is allowed to accumulate without control. This motivates their adaptive budgeted forgetting framework, which seeks to regulate memory through relevance-guided scoring and constrained optimization.