LLM Memory

Related topics:

• Agent Workflow Memory
  Despite the potential of language model-based agents to solve real-world tasks such as web navigation, current methods still struggle with long-horizon tasks with complex action trajectories. In contr…
• AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
  In this paper, we introduce a novel learning paradigm for adaptive Large Language Model (LLM) agents that eliminates the need for fine-tuning the underlying LLMs. Existing approaches are often either …
• Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
  Large language model (LLM) applications such as agents and domain-specific reasoning increasingly rely on context adaptation—modifying inputs with instructions, strategies, or evidence, rather than we…
• Artifacts as Memory Beyond the Agent Boundary
  The situated view of cognition holds that intelligent behavior depends not only on internal memory, but on an agent’s active use of environmental resources. Here, we begin formalizing this intuition w…
• Beyond Context Limits: Subconscious Threads for Long-Horizon Reasoning
  To break the context limits of large language models (LLMs) that bottleneck reasoning accuracy and efficiency, we propose the Thread Inference Model (TIM1), a family of LLMs trained for recursive and …
• CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization
  Language agents have shown some ability to interact with an external environment, e.g., a virtual world such as ScienceWorld, to perform complex tasks, e.g., growing a plant, without the startup costs…
• ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning
  Narrative comprehension on long stories and novels has been a challenging domain attributed to their intricate plotlines and entangled, often evolving relations among characters and entities. Given th…
• Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations
  Maintaining long-term conversations has always been a long-term pursuit in current open-domain dialogue systems (Liu et al., 2016; Zhang et al., 2018; Kann et al., 2022; Song et al., 2023), commonly k…
• Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations
  In the field of natural language processing, open-domain chatbots have emerged as an important research topic. However, a major limitation of existing open-domain chatbot research is its singular focu…
• Diagnosing Memorization in Chain-of-Thought Reasoning, One Token at a Time
  Large Language Models (LLMs) perform well on reasoning benchmarks but often fail when inputs alter slightly, raising concerns about the extent to which their success relies on memorization. This issue…
• Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources
  https:// CGMI: Configurable General Multi-Agent Interaction Framework [https://arxiv.org/abs/2308.12503](https://arxiv.org/abs/2308.12503) [[Memory]] [[Role Play]] “With the capabilities of large…
• Efficient Nearest Neighbor Language Models
  In this paper, we take the recently proposed k-nearest neighbors language model (Khandelwal et al., 2020) as an example, exploring methods to improve its efficiency along various dimensions. Experimen…
• Evaluating Very Long-Term Conversational Memory of LLM Agents
  Existing works on long-term open-domain dialogues focus on evaluating model responses within contexts spanning no more than five chat sessions. Despite advancements in long-context large language mode…
• Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering
  Large language model (LLM) agents are increasingly built less by changing model weights than by reorganizing the runtime around them. Capabilities that earlier systems expected the model to recover in…
• From LLM to Conversational Agent: A Memory Enhanced Architecture with Fine-Tuning of Large Language Models
  RAISE, an enhancement of the ReAct framework, incorporates a dual-component memory system, mirroring human short-term and long-term memory, to maintain context and continuity in conversations. It enta…
• Generalization through Memorization: Nearest Neighbor Language Models
  We introduce kNN-LMs, which extend a pre-trained neural language model (LM) by linearly interpolating it with a k-nearest neighbors (kNN) model. The nearest neighbors are computed according to distanc…
• Generative Agents: Interactive Simulacra of Human Behavior
  “Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, …
• Hierarchical Reasoning Model
  Reasoning, the process of devising and executing complex goal-oriented action sequences, remains a critical challenge in AI. Current large language models (LLMs) primarily employ Chain-of-Thought (CoT…
• Hogwild! Inference: Parallel LLM Generation via Concurrent Attention
  Large Language Models (LLMs) have demonstrated the ability to tackle increasingly complex tasks through advanced reasoning, long-form content generation, and tool use. Solving these tasks often involv…
• How much do language models memorize?
  We propose a new method for estimating how much a model “knows” about a datapoint and use it to measure the capacity of modern language models. We formally separate memorization into two components: u…
• It’s All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization
  Designing efficient and effective architectural backbones has been in the core of research efforts to enhance the capability of foundation models. Inspired by the human cognitive phenomenon of attenti…
• Learning to Relate to Previous Turns in Conversational Search
  As in any conversation in natural language, queries in conversational search may involve omissions, references to previous turns, and ambiguities [32]. Thus, a primary challenge for effective conversa…
• Learning to Select the Relevant History Turns in Conversational Question Answering
  “The increasing demand for web-based digital assistants has given a rapid rise in the interest of the Information Retrieval (IR) community towards the field of conversational question answering (ConvQ…
• Making Sense of Memory in AI Agents
  However, there’s also another approach to categorizing memory types for AI agents from a design pattern perspective. Sarah Wooders from Letta argues that an LLM is a tokens-in-tokens-out function, not…
• Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads
  Large Language Models (LLMs) employ autoregressive decoding that requires sequential computation, with each step reliant on the previous one’s output. This creates a bottleneck as each step necessitat…
• MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation
  “We propose MemoChat, a pipeline for refining instructions that enables large language models (LLMs) to effectively employ self-composed memos for maintaining consistent long-range open-domain convers…
• Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models
  Current method like Domain Adaptive Pretraining (DAPT) requires costly full-parameter training and suffers from catastrophic forgetting. Meanwhile, Retrieval-Augmented Generation (RAG) introduces subs…
• Multi-Token Attention
  Soft attention is a critical mechanism powering LLMs to locate relevant parts within a given context. However, individual attention weights are determined by the similarity of only a single query and …
• Nested Learning: The Illusion of Deep Learning Architecture Expanded
  In the previous sections, we discussed the concept of nested learning and how existing well-known components of neural networks such as popular optimizers and architectures fall under the NL paradigm.…
• Nested Learning: The Illusion of Deep Learning Architectures
  Over the last decades, developing more powerful neural architectures and simultaneously designing optimization algorithms to effectively train them have been the core of research efforts to enhance th…
• On the Limits of Innate Planning in Large Language Models
  Large language models (LLMs) achieve impressive results on many benchmarks, yet their capacity for planning and stateful reasoning remains unclear. We study these abilities directly, without code exec…
• PRIME: Large Language Model Personalization with Cognitive Memory and Thought Processes
  Large language model (LLM) personalization aims to align model outputs with individuals’ unique preferences and opinions. While recent efforts have implemented various personalization methods, a unifi…
• Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models
  Our findings indicate that the approach to reasoning the models use is unlike retrieval, and more like a generalisable strategy that synthesises procedural knowledge from documents doing a similar for…
• Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data Contamination
  To obtain trustworthy evaluation signals, we introduce a generator that creates fully synthetic arithmetic problems of arbitrary length and difficulty, yielding clean datasets we call RandomCalculatio…
• See you soon again, chatbot? A design taxonomy to characterize user-chatbot relationships with different time horizons
  Users interact with chatbots for various purposes and motivations – and for different periods of time. However, since chatbots are considered social actors and given that time is an essential componen…
• Seeing, Listening, Remembering, and Reasoning: A Multimodal Agent with Long-Term Memory
  Imagine that in the future a household robot can autonomously carry out household tasks without your explicit instructions; it must have learned the operational rules of your home through daily experi…
• The Emotion-Memory Link: Do Memorability Annotations Matter for Intelligent Systems?
  Abstract—Humans have a selective memory, remembering relevant episodes and forgetting the less relevant information. Possessing awareness of event memorability for a user could help intelligent system…
• Think-in-Memory: Recalling and Post-thinking Enable LLMs with Long-Term Memory
  Memory-augmented Large Language Models (LLMs) have demonstrated remarkable performance in long-term human-machine interactions, which basically relies on iterative recalling and reasoning of history t…
• Titans: Learning to Memorize at Test Time
  Over more than a decade there has been an extensive research effort of how effectively utilize recurrent models and attentions. While recurrent models aim to compress the data into a fixed-size memory…
• Toward Conversational Agents with Context and Time Sensitive Long-term Memory
  There has recently been growing interest in conversational agents with long-term memory which has led to the rapid development of language models that use retrieval-augmented generation (RAG). Until r…