Graceful forgetting: Memory as a process
- URL: http://arxiv.org/abs/2502.11105v3
- Date: Mon, 15 Sep 2025 16:48:55 GMT
- Title: Graceful forgetting: Memory as a process
- Authors: Alain de Cheveigné,
- Abstract summary: A rational framework is proposed to explain how we accommodate sensory input within bounded memory.<n>The framework is intended as an aid to make sense of our extensive knowledge of memory, and bring us closer to an understanding of memory in functional and mechanistic terms.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A rational framework is proposed to explain how we accommodate unbounded sensory input within bounded memory. Memory is stored as statistics organized into structures that are repeatedly summarized and compressed to make room for new input. Repeated summarization requires an intensive ongoing process guided by heuristics that help optimize the memory for future needs. Sensory input is rapidly encoded as simple statistics that are progressively elaborated into more abstract constructs. This framework differs from previous accounts of memory by its emphasis on a process that is intensive, complex, and expensive, its reliance on statistics as a representation of memory, and the use of heuristics to guide the choice of statistics at each summarization step. The framework is intended as an aid to make sense of our extensive knowledge of memory, and bring us closer to an understanding of memory in functional and mechanistic terms.
Related papers
- Graph-based Agent Memory: Taxonomy, Techniques, and Applications [63.70340159016138]
Memory emerges as the core module in the Large Language Model (LLM)-based agents for long-horizon complex tasks.<n>Among diverse paradigms, graph stands out as a powerful structure for agent memory due to the intrinsic capabilities to model relational dependencies.<n>This survey presents a comprehensive review of agent memory from the graph-based perspective.
arXiv Detail & Related papers (2026-02-05T13:49:05Z) - Memora: A Harmonic Memory Representation Balancing Abstraction and Specificity [26.512226057571947]
Memora is a harmonic memory representation that structurally balances abstraction and specificity.<n>We show that Memora establishes a new state-of-the-art on the LoCoMo and LongMemEval benchmarks, demonstrating better retrieval relevance and reasoning effectiveness as memory scales.
arXiv Detail & Related papers (2026-02-03T09:44:43Z) - The AI Hippocampus: How Far are We From Human Memory? [77.04745635827278]
Implicit memory refers to the knowledge embedded within the internal parameters of pre-trained transformers.<n>Explicit memory involves external storage and retrieval components designed to augment model outputs with dynamic, queryable knowledge representations.<n>Agentic memory introduces persistent, temporally extended memory structures within autonomous agents.
arXiv Detail & Related papers (2026-01-14T03:24:08Z) - Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning [55.251697395358285]
Large language models (LLMs) are increasingly deployed as intelligent agents that reason, plan, and interact with their environments.<n>To effectively scale to long-horizon scenarios, a key capability for such agents is a memory mechanism that can retain, organize, and retrieve past experiences.<n>We propose CompassMem, an event-centric memory framework inspired by Event Theory.
arXiv Detail & Related papers (2026-01-08T08:44:07Z) - Memory in the Age of AI Agents [217.9368190980982]
This work aims to provide an up-to-date landscape of current agent memory research.<n>We identify three dominant realizations of agent memory, namely token-level, parametric, and latent memory.<n>To support practical development, we compile a comprehensive summary of memory benchmarks and open-source frameworks.
arXiv Detail & Related papers (2025-12-15T17:22:34Z) - Evaluating Long-Term Memory for Long-Context Question Answering [100.1267054069757]
We present a systematic evaluation of memory-augmented methods using LoCoMo, a benchmark of synthetic long-context dialogues annotated for question-answering tasks.<n>Our findings show that memory-augmented approaches reduce token usage by over 90% while maintaining competitive accuracy.
arXiv Detail & Related papers (2025-10-27T18:03:50Z) - Mem-α: Learning Memory Construction via Reinforcement Learning [20.916677456417464]
Large language model (LLM) agents are constrained by limited context windows.<n>Current memory-augmented agents depend on pre-defined instructions and tools for memory updates.<n>Mem-alpha is a reinforcement learning framework that trains agents to effectively manage complex memory systems.
arXiv Detail & Related papers (2025-09-30T08:02:34Z) - MemGen: Weaving Generative Latent Memory for Self-Evolving Agents [57.1835920227202]
We propose MemGen, a dynamic generative memory framework that equips agents with a human-esque cognitive faculty.<n>MemGen enables agents to recall and augment latent memory throughout reasoning, producing a tightly interwoven cycle of memory and cognition.
arXiv Detail & Related papers (2025-09-29T12:33:13Z) - Cognitive Memory in Large Language Models [8.059261857307881]
This paper examines memory mechanisms in Large Language Models (LLMs), emphasizing their importance for context-rich responses, reduced hallucinations, and improved efficiency.
It categorizes memory into sensory, short-term, and long-term, with sensory memory corresponding to input prompts, short-term memory processing immediate context, and long-term memory implemented via external databases or structures.
arXiv Detail & Related papers (2025-04-03T09:58:19Z) - LightThinker: Thinking Step-by-Step Compression [53.8069487638972]
We propose LightThinker, a method that enables large language models to dynamically compress intermediate thoughts during reasoning.
Inspired by human cognitive processes, LightThinker compresses thought steps into compact representations and discards the original reasoning chains.
Experiments show that LightThinker reduces peak memory usage and inference time, while maintaining competitive accuracy.
arXiv Detail & Related papers (2025-02-21T16:57:22Z) - Hierarchical Working Memory and a New Magic Number [1.024113475677323]
We propose a recurrent neural network model for chunking within the framework of the synaptic theory of working memory.
Our work provides a novel conceptual and analytical framework for understanding the on-the-fly organization of information in the brain that is crucial for cognition.
arXiv Detail & Related papers (2024-08-14T16:03:47Z) - Spatially-Aware Transformer for Embodied Agents [20.498778205143477]
This paper explores the use of Spatially-Aware Transformer models that incorporate spatial information.
We demonstrate that memory utilization efficiency can be improved, leading to enhanced accuracy in various place-centric downstream tasks.
We also propose the Adaptive Memory Allocator, a memory management method based on reinforcement learning.
arXiv Detail & Related papers (2024-02-23T07:46:30Z) - Memory Efficient Neural Processes via Constant Memory Attention Block [55.82269384896986]
Constant Memory Attentive Neural Processes (CMANPs) are an NP variant that only requires constant memory.
We show CMANPs achieve state-of-the-art results on popular NP benchmarks while being significantly more memory efficient than prior methods.
arXiv Detail & Related papers (2023-05-23T23:10:19Z) - Sequential Memory with Temporal Predictive Coding [6.228559238589584]
We propose a PC-based model for emphsequential memory, called emphtemporal predictive coding (tPC)
We show that our tPC models can memorize and retrieve sequential inputs accurately with a biologically plausible neural implementation.
arXiv Detail & Related papers (2023-05-19T20:03:31Z) - A Cognitive Architecture for Machine Consciousness and Artificial Superintelligence: Thought Is Structured by the Iterative Updating of Working Memory [0.0]
This article provides an analytical framework for how to simulate human-like thought processes within a computer.
It describes how attention and memory should be structured, updated, and utilized to search for associative additions to the stream of thought.
arXiv Detail & Related papers (2022-03-29T22:28:30Z) - ABC: Attention with Bounded-memory Control [67.40631793251997]
We show that bounded-memory control (ABC) can be subsumed into one abstraction, attention with bounded-memory control (ABC)
ABC reveals new, unexplored possibilities. First, it connects several efficient attention variants that would otherwise seem apart.
Last, we present a new instance of ABC, which draws inspiration from existing ABC approaches, but replaces their memory-organizing functions with a learned, contextualized one.
arXiv Detail & Related papers (2021-10-06T03:53:25Z) - Memory and attention in deep learning [19.70919701635945]
Memory construction for machine is inevitable.
Recent progresses on modeling memory in deep learning have revolved around external memory constructions.
The aim of this thesis is to advance the understanding on memory and attention in deep learning.
arXiv Detail & Related papers (2021-07-03T09:21:13Z) - Learning to Rehearse in Long Sequence Memorization [107.14601197043308]
Existing reasoning tasks often have an important assumption that the input contents can be always accessed while reasoning.
Memory augmented neural networks introduce a human-like write-read memory to compress and memorize the long input sequence in one pass.
But they have two serious drawbacks: 1) they continually update the memory from current information and inevitably forget the early contents; 2) they do not distinguish what information is important and treat all contents equally.
We propose the Rehearsal Memory to enhance long-sequence memorization by self-supervised rehearsal with a history sampler.
arXiv Detail & Related papers (2021-06-02T11:58:30Z) - Kanerva++: extending The Kanerva Machine with differentiable, locally
block allocated latent memory [75.65949969000596]
Episodic and semantic memory are critical components of the human memory model.
We develop a new principled Bayesian memory allocation scheme that bridges the gap between episodic and semantic memory.
We demonstrate that this allocation scheme improves performance in memory conditional image generation.
arXiv Detail & Related papers (2021-02-20T18:40:40Z) - Memformer: A Memory-Augmented Transformer for Sequence Modeling [55.780849185884996]
We present Memformer, an efficient neural network for sequence modeling.
Our model achieves linear time complexity and constant memory space complexity when processing long sequences.
arXiv Detail & Related papers (2020-10-14T09:03:36Z) - Self-Attentive Associative Memory [69.40038844695917]
We propose to separate the storage of individual experiences (item memory) and their occurring relationships (relational memory)
We achieve competitive results with our proposed two-memory model in a diversity of machine learning tasks.
arXiv Detail & Related papers (2020-02-10T03:27:48Z)
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.