Oblivion: Self-Adaptive Agentic Memory Control through Decay-Driven Activation
arXiv:2604.00131v1 Announce Type: new Abstract: Human memory adapts through selective forgetting: experiences become less accessible over time but can be reactivated by reinforcement or contextual cues. In contrast, memory-augmented LLM agents rely on "always-on" retrieval and "flat" memory storage, causing high interference and latency as histories grow. We introduce Oblivion, a memory control framework that casts forgetting as decay-driven reductions in accessibility, not explicit deletion. Oblivion decouples memory control into read and write paths. The read path decides when to consult memory, based on agent uncertainty and memory buffer sufficiency, avoiding redundant always-on access. The write path decides what to strengthen, by reinforcing memories contributing to forming the response. Together, this enables hierarchical memory organization that maintains persistent high-level strategies while dynamically loading details as needed. We evaluate on both static and dynamic long-h
arXiv:2604.00131v1 Announce Type: new Abstract: Human memory adapts through selective forgetting: experiences become less accessible over time but can be reactivated by reinforcement or contextual cues. In contrast, memory-augmented LLM agents rely on "always-on" retrieval and "flat" memory storage, causing high interference and latency as histories grow. We introduce Oblivion, a memory control framework that casts forgetting as decay-driven reductions in accessibility, not explicit deletion. Oblivion decouples memory control into read and write paths. The read path decides when to consult memory, based on agent uncertainty and memory buffer sufficiency, avoiding redundant always-on access. The write path decides what to strengthen, by reinforcing memories contributing to forming the response. Together, this enables hierarchical memory organization that maintains persistent high-level strategies while dynamically loading details as needed. We evaluate on both static and dynamic long-horizon interaction benchmarks. Results show that Oblivion dynamically adapts memory access and reinforcement, balancing learning and forgetting under shifting contexts, highlighting that memory control is essential for effective LLM-agentic reasoning. The source code is available at https://github.com/nec-research/oblivion.
Executive Summary
This article presents Oblivion, a novel memory control framework for memory-augmented Large Language Model (LLM) agents. The framework decouples memory control into read and write paths, enabling hierarchical memory organization and dynamic adaptation of memory access and reinforcement. This approach addresses the limitations of traditional 'always-on' retrieval and 'flat' memory storage, characterized by high interference and latency. The authors evaluate Oblivion on static and dynamic long-horizon interaction benchmarks, demonstrating its effectiveness in balancing learning and forgetting under shifting contexts. The framework's ability to maintain persistent high-level strategies while dynamically loading details as needed highlights the importance of memory control in LLM-agentic reasoning.
Key Points
- ▸ Oblivion decouples memory control into read and write paths, enabling hierarchical memory organization
- ▸ The framework adapts memory access and reinforcement dynamically, balancing learning and forgetting
- ▸ Oblivion addresses the limitations of traditional 'always-on' retrieval and 'flat' memory storage
- ▸ The authors evaluate Oblivion on both static and dynamic long-horizon interaction benchmarks
Merits
Strength in Hierarchical Memory Organization
Oblivion's decoupled memory control enables the creation of hierarchical memory structures, allowing for efficient storage and retrieval of information. This is particularly beneficial for LLM-agentic reasoning, where high-level strategies need to be maintained while details are loaded dynamically.
Dynamic Adaptation of Memory Access and Reinforcement
Oblivion's ability to adapt memory access and reinforcement dynamically is a significant improvement over traditional 'always-on' retrieval and 'flat' memory storage. This allows the agent to balance learning and forgetting under shifting contexts, leading to more effective LLM-agentic reasoning.
Demerits
Limited Evaluation on Real-World Scenarios
While Oblivion is evaluated on both static and dynamic long-horizon interaction benchmarks, its performance on real-world scenarios has not been extensively tested. Further evaluation is needed to ensure the framework's effectiveness in practical applications.
Complexity in Implementation
Oblivion's decoupled memory control may introduce complexity in implementation, particularly for developers without prior experience in memory management and LLM-agentic reasoning. This may limit the framework's adoption and deployment in real-world scenarios.
Expert Commentary
Oblivion's contribution to memory management in AI systems is significant, addressing the limitations of traditional memory storage and retrieval methods. The framework's ability to adapt memory access and reinforcement dynamically, balancing learning and forgetting under shifting contexts, highlights the importance of memory control in LLM-agentic reasoning. While further evaluation on real-world scenarios is needed, Oblivion's potential applications in areas such as decision-making and problem-solving make it a promising area of research. As AI systems become increasingly prevalent in our daily lives, the development of more efficient and effective memory management frameworks like Oblivion is essential for ensuring the success of these systems.
Recommendations
- ✓ Further evaluation of Oblivion on real-world scenarios is recommended to ensure its effectiveness in practical applications.
- ✓ The development of Oblivion highlights the need for further research into memory management in AI systems, with implications for the development of more efficient and effective AI policies.
Sources
Original: arXiv - cs.CL
