Novel Memory Forgetting Techniques for Autonomous AI Agents: Balancing Relevance and Efficiency

• URL: http://arxiv.org/abs/2604.02280v1
• Date: Thu, 02 Apr 2026 17:14:53 GMT
• Title: Novel Memory Forgetting Techniques for Autonomous AI Agents: Balancing Relevance and Efficiency
• Abstract summary: Long-horizon conversational agents require persistent memory for coherent reasoning.<n> uncontrolled accumulation causes temporal decay and false memory propagation.<n>This work introduces an adaptive budgeted forgetting framework that regulates memory through relevanceguided scoring and bounded optimization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-horizon conversational agents require persistent memory for coherent reasoning, yet uncontrolled accumulation causes temporal decay and false memory propagation. Benchmarks such as LOCOMO and LOCCO report performance degradation from 0.455 to 0.05 across stages, while MultiWOZ shows 78.2% accuracy with 6.8% false memory rate under persistent retention. This work introduces an adaptive budgeted forgetting framework that regulates memory through relevanceguided scoring and bounded optimization. The approach integrates recency, frequency, and semantic alignment to maintain stability under constrained context. Comparative analysis demonstrates improved long-horizon F1 beyond 0.583 baseline levels, higher retention consistency, and reduced false memory behavior without increasing context usage. These findings confirm that structured forgetting preserves reasoning performance while preventing unbounded memory growth in extended conversational settings.

Related papers

• Retrieval-Driven Memory Reconsolidation for Long-Term LLM Agents [39.55670043123564]
  Existing memory systems update memory when new information arrives.<n>Retrieval feedback is rarely exploited to reorganize memory for future access continuously.<n>We propose textbfREALM, a framework for continually evolving long-term memory in LLM agents.
  arXiv  Detail & Related papers  (2026-09-13T05:17:37Z)
• UniMem: Complementary Episodic-to-Parametric Memory for Boundary-Agnostic Task Streams [77.52140119830241]
  We propose UniMem, a self-routing framework for autonomous memory management.<n>UniMem uses learnable routing tokens as memory controllers, enabling adaptive coordination between complementary memory pathways.<n>Experiments on long-horizon streaming task sequences show that UniMem consistently outperforms baselines while maintaining execution fidelity.
  arXiv  Detail & Related papers  (2026-07-28T17:28:21Z)
• MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations [50.24315431387575]
  We introduce MemOps, a benchmark that reformulates conversational memory as a sequence of lifecycle operations.<n>MemOps represents each memory event with a structured trace specifying its trigger, target, scope, state transition, and supporting evidence.<n>Across long-context, retrieval-based, parametric and managed-memory systems, MemOps disentangles failure modes that final-answer accuracy alone conceals.
  arXiv  Detail & Related papers  (2026-07-14T15:33:44Z)
• Meta-Cognitive Memory Policy Optimization for Long-Horizon LLM Agents [13.846550145896314]
  Memory-augmented LLM agents tackle complex long-horizon tasks by summarizing interaction trajectories into compact memory.<n>Existing approaches typically train these memory policies using outcome-based reinforcement learning, failing to localize where intermediate memory quality degrades.<n>We argue that memory optimization should focus not merely on trajectory-level success, but on the clarity of the belief induced by intermediate summaries.
  arXiv  Detail & Related papers  (2026-05-28T16:17:19Z)
• MemGuard: Preventing Memory Contamination in Long-Term Memory-Augmented Large Language Models [56.31411457917676]
  We introduce MemGuard, a type-aware memory framework that preserves functional memory boundaries during memory construction and retrieval.<n>Across hallucination and long-horizon conversation benchmarks, MemGuard improves memory reliability by up to 28.27% while retrieving up to 5.8x fewer memory tokens than prior methods.
  arXiv  Detail & Related papers  (2026-05-27T06:04:19Z)
• A Heterogeneous Temporal Memory Governance Framework for Long-Term LLM Persona Consistency [2.447989276487424]
  Large language models often suffer from fact loss, timeline confusion, persona drift, and reduced stability during long-range interaction.<n>We introduce ARPM, an external temporal memory governance framework for long-term dialogue.<n>We show that ARPM treats continuity as a traceable, auditable, and transferable governance problem.
  arXiv  Detail & Related papers  (2026-05-14T13:12:49Z)
• MemMachine: A Ground-Truth-Preserving Memory System for Personalized AI Agents [2.541923091180284]
  Large Language Model (LLM) agents require persistent memory to maintain personalization, factual continuity, and long-horizon reasoning.<n>We present MemMachine, an open-source memory system that integrates short-term, long-term episodic, and profile memory.<n>MemMachine uses contextualized retrieval that expands nucleus matches with surrounding context, improving recall when relevant evidence spans multiple dialogue turns.
  arXiv  Detail & Related papers  (2026-04-06T16:57:06Z)
• Multi-Layered Memory Architectures for LLM Agents: An Experimental Evaluation of Long-Term Context Retention [0.0]
  Long-horizon dialogue systems suffer from semanticdrift and unstable memory retention across extended sessions.<n>This paper presents a Multi-Layer Memory Framework that decomposes dialogue history into working, episodic, and semantic layers with adaptive retrieval gating and retention regularization.
  arXiv  Detail & Related papers  (2026-03-31T02:58:26Z)
• Developing Adaptive Context Compression Techniques for Large Language Models (LLMs) in Long-Running Interactions [0.0]
  Large Language Models (LLMs) often experience performance degradation during long-running interactions due to increasing context length, memory saturation, and computational overhead.<n>This paper presents an adaptive context compression framework that integrates importance-aware memory selection, coherence-sensitive filtering, and dynamic budget allocation to retain essential conversational information while controlling context growth.
  arXiv  Detail & Related papers  (2026-03-31T02:57:48Z)
• Relax Forcing: Relaxed KV-Memory for Consistent Long Video Generation [73.84423888025171]
  Autoregressive (AR) video diffusion has recently emerged as a promising paradigm for long video generation.<n>We show that extending generation to minute-scale horizons remains challenging due to progressive temporal degradation.<n>We introduce Relax Forcing, a structured temporal memory mechanism for AR diffusion.
  arXiv  Detail & Related papers  (2026-03-22T18:59:24Z)
• MemPO: Self-Memory Policy Optimization for Long-Horizon Agents [52.00646524941419]
  Existing methods typically introduce the external memory module and look up the relevant information from the stored memory.<n>We propose the self-memory policy optimization algorithm (MemPO), which enables the agent to autonomously summarize and manage their memory.<n>MemPO achieves absolute F1 score gains of 25.98% over the base model and 7.1% over the previous SOTA baseline, while reducing token usage by 67.58% and 73.12%.
  arXiv  Detail & Related papers  (2026-02-28T14:43:02Z)
• NextMem: Towards Latent Factual Memory for LLM-based Agents [58.35585202907478]
  NextMem is a latent factual memory framework that utilizes an autoregressive autoencoder to efficiently construct latent memory.<n>Extensive experiments demonstrate that NextMem achieves superior performance.
  arXiv  Detail & Related papers  (2026-02-26T14:35:27Z)
• Training Large Reasoning Models Efficiently via Progressive Thought Encoding [63.254758972725654]
  Large reasoning models (LRMs) excel on complex problems but face a critical barrier to efficiency.<n>We introduce Progressive Thought, a parameter-efficient fine-tuning method that enables LRMs to reason effectively under fixed-size caches.
  arXiv  Detail & Related papers  (2026-02-18T20:03:38Z)
• Mem-T: Densifying Rewards for Long-Horizon Memory Agents [23.19373149519922]
  We introduce Mem-T, an autonomous memory agent that interfaces with a lightweight hierarchical memory database to perform dynamic updates and multi-turn retrieval over streaming inputs.<n>We also propose MoT-GRPO, a tree-guided reinforcement learning framework that transforms sparse terminal feedback into dense, step-wise supervision via memory operation tree backpropagation and hindsight credit assignment.
  arXiv  Detail & Related papers  (2026-01-30T14:23:33Z)
• Gated Differentiable Working Memory for Long-Context Language Modeling [80.27483324685434]
  We propose Gdwm (Gated Differentiable Working Memory), a framework that introduces a write controller to gate the consolidation process.<n>Experiments on ZeroSCROLLS and LongBench v2 demonstrate that Gdwm achieves comparable or superior performance with 4$times$ fewer gradient steps than uniform baselines.
  arXiv  Detail & Related papers  (2026-01-19T10:00:33Z)
• FlashMem: Distilling Intrinsic Latent Memory via Computation Reuse [4.210760734549566]
  FlashMem is a framework that distills intrinsic memory directly from transient reasoning states via computation reuse.<n>Experiments demonstrate that FlashMem matches the performance of heavy baselines while reducing inference latency by 5 times.
  arXiv  Detail & Related papers  (2026-01-09T03:27:43Z)
• Stable Hadamard Memory: Revitalizing Memory-Augmented Agents for Reinforcement Learning [64.93848182403116]
  Current deep-learning memory models struggle in reinforcement learning environments that are partially observable and long-term. We introduce the Stable Hadamard Memory, a novel memory model for reinforcement learning agents. Our approach significantly outperforms state-of-the-art memory-based methods on challenging partially observable benchmarks.
  arXiv  Detail & Related papers  (2024-10-14T03:50:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.