All AI memory papers

General Agentic Memory (GAM) combines a Memorizer, Researcher, page-store, and memory to keep full trajectories while constructing lightweight guidance for deep research. On RULER 128K retrieval, GAM achieves 97.70% accuracy compared to 94.25% for RAG using GPT-4o-mini, while also reaching 64.07 F1 on HotpotQA-56K.

H-MEM organizes long-term dialogue history into a four-level hierarchy using the Domain Layer, Category Layer, Memory Trace Layer, and Episode Layer, with positional index encoding and a dynamic memory update mechanism. On the LoCoMo dataset, H-MEM raises average F1 by 14.98 and BLEU-1 by 12.77 points over baselines like MemoryBank and MemGPT while reducing retrieval compute from 7.34×10^9 to 4.38×10^7 operations on adversarial tasks.

HINDSIGHT organizes agent memory into four networks via TEMPR and layers CARA on top to retain, recall, and reflect with explicit opinions and behavioral profiles. On LongMemEval, HINDSIGHT with Gemini-3 Pro scores 91.4% overall versus 60.2% for full-context GPT-4o, while HINDSIGHT with OSS-20B jumps from 39.0% to 83.6% over a full-context OSS-20B baseline.

IMDMR combines a Memory Storage Layer, Multi-Dimensional Search Engine, Intelligent Query Processor, and Response Generation Module to retrieve conversational memories across semantic, entity, category, intent, context, and temporal dimensions. On the synthetic 1,000 conversation benchmark, IMDMR-Prod achieves an overall score of 0.792 compared to 0.207 for spaCy + RAG, a 3.8x improvement.

HGMEM represents working memory as a hypergraph with Hypergraph-based Memory Storage, Adaptive Memory-based Evidence Retrieval, and Dynamic Memory Evolving to build high-order correlations across entities and facts. On Prelude long narrative understanding, HGMEM with GPT-4o achieves 73.81% accuracy compared to 72.22% for HippoRAG v2, while also reaching 69.74 comprehensiveness on Longbench generative sense-making QA.

Reflective Memory Management (RMM) uses a memory bank, retriever, reranker, and LLM to implement Prospective Reflection and Retrospective Reflection for topic-based storage and RL-based retrieval refinement. On LongMemEval, RMM with GTE achieves 69.8% Recall@5 and 70.4% accuracy, compared to 62.4% Recall@5 and 63.6% accuracy for GTE RAG.

KV-DISTILL combines a learnable state selection scorer, a LoRA-based adapted LMθ, and a KL-type objective function to compress KV caches into shorter, reusable representations. On SQuAD, KV-DISTILL with 25% KV retention achieves 86.6% accuracy on LLAMA-3 versus 87.6% for the uncompressed baseline, while H2I drops to 56.6%.

Latent learning uses oracle episodic retrieval, within-experience in-context learning, parametric learning, and latent learning benchmarks to study how reinstating full past episodes into context changes generalization. Latent learning shows that oracle retrieval solves latent tests like reversals and codebooks where parametric-only transformers stay near 0% despite strong performance on forward and in-context variants.

Learning from Supervision with Semantic and Episodic Memory combines a performance agent, critic agent, semantic memory, episodic memory, and memory retriever to turn label-grounded critiques into reusable supervision without parameter updates. On the Multi-Condition Ranking dataset with Mixtral 8x22B and o4-mini as critic, Learning from Supervision with Semantic and Episodic Memory reaches 85.6% accuracy, a 24.8% gain over the EP_LABEL baseline at 60.8%.

LEGOMem builds a procedural memory bank, full task memories, subtask memories, LEGOMem-Dynamic, and LEGOMem-QueryRewrite to store successful trajectories and allocate them to orchestrators and task agents. On OfficeBench, LEGOMem reaches 58.44% overall success for the LLM team versus 45.83% with no memory, and lifts the SLM team from 24.78% to 38.16%.

LiCoMemory organizes long term dialogue with CogniGraph, Query Processing and Integrated Rerank, and Real Time Interactions to keep session summaries, triples, and chunks linked. On LongMemEval with GPT-4o-mini, LiCoMemory reaches 73.80% accuracy and 76.63% recall, beating Mem0g by 9.0 and 7.1 points.

LightMem pipelines a Cognitive-Inspired Sensory Memory, Topic Segmentation Submodule, Topic-Aware Short-Term Memory, and Long-Term Memory with Sleep-Time Update to filter, group, summarize, and asynchronously consolidate dialogue history. On LongMemEval-S with Qwen3-30B-A3B-Instruct-2507, LightMem reaches 70.20% ACC vs 65.20% for A-MEM (+5.00 points) while reducing total token usage by up to 21.8× and API calls by up to 17.1×.

LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household Robotics coordinates a routing agent, task planning agent, and knowledge base agent over RAG and ChromaDB to translate household commands into grounded robot actions. In three tabletop scenarios, Qwen2.5-32B in LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household Robotics achieves 84.3% total lenient task planning accuracy versus 68.7% for Gemma2-27B and 61.1% for LLaMa3.1-8B.

MEM1 trains an RL agent that updates a compact Internal State (IS) using Masked Trajectory for Policy Optimization, 2D Attention Mask, and Multi-Objective Task Design to jointly consolidate memory and reason. On 16-objective multi-hop QA, MEM1-7B reaches EM 1.97 vs 0.567 for Qwen2.5-14B-Instruct while using 10.4×10² peak tokens vs 38.4×10².

MemBench evaluates LLM-based agents with multi-scenario datasets, multi-level memory content, and a time-aware benchmark using components like Multi-scenario Dataset, Multi-level Memory Content, and Multi-metric Evaluation. MemBench shows that mechanisms such as GenerativeAgent, MemGPT, MemoryBank, and SCMemory can drop from accuracies around 0.7 on 10k-token settings to roughly 0.3–0.4 at 100k tokens, exposing clear capacity limits.

Memento combines a Planner, Executor, Case Memory, Subtask Memory, and Tool Memory inside a memory-based MDP, learning a neural case-selection policy over a growing Case Bank instead of updating LLM weights. On GAIA, Memento achieves 87.88% Pass@3 on validation and 79.40% on the test set, surpassing DeepResearcher’s 51.8% F1 and 60.5% PM on DeepResearcher benchmarks by +14.8 F1 and +19.9 PM.

MemEvolve decomposes agent memory into Encode, Store, Retrieve, and Manage modules and meta evolves these components via a dual evolution process over candidate architectures. On xBench DeepSearch, MemEvolve with GPT 5 mini raises Flash Searcher pass@1 from 69.0 to 74.0 and WebWalkerQA accuracy from 58.82 to 61.18 while keeping API cost near 0.141 per query.

MemGuide combines Intent-Aligned Retrieval, Missing-Slot Guided Filtering, a CoT Slot Reasoner, a LLaMA-8B filter, and an LLM Reader over QA-style memory banks to select goal-consistent memories. On the MS-TOD benchmark, MemGuide reaches a 0.70 Joint Goal Accuracy and 0.99 Success Rate with GPT-4o-mini, improving Success Rate by 0.15 over AutoTOD and reducing Dialogue Turn Efficiency from 7.80 to 3.19.

MemInsight augments agent memory using Attribute Mining, Annotation and Attribute Prioritization, and Memory Retrieval modules that generate and exploit structured attributes over past interactions. On the LoCoMo question answering benchmark, MemInsight with Claude-3-Sonnet priority augmentation achieves 60.5% Recall@5 versus 26.5% for DPR, a 34.0-point improvement.

Memoria augments LLM chats with structured conversation logging, dynamic user persona via KG, session level memory for real time context, and seamless retrieval for context aware responses to provide persistent, interpretable memory. On LongMemEvals single-session-user and knowledge-update subsets, Memoria reaches 87.1% and 80.8% accuracy respectively, surpassing A-Mem (OpenAI) while using much shorter prompts.

MemoriesDB stores each Memory Record, Edges and Relations, and the Temporal Semantic Stack inside PostgreSQL with pgvector, exposing unified temporal–semantic–relational queries. MemoriesDB’s main result is a working implementation that demonstrates scalable time-bounded recall and hybrid semantic–structural queries on commodity SQL infrastructure without specialized vector or graph engines.

Memorization to Generalization recasts diffusion training and sampling as Dense Associative Memory dynamics, analyzing memorized, spurious, and generalized states via energy basins and curvature. Memorization to Generalization shows that as training size grows on MNIST, CIFAR10, FASHION-MNIST, LSUN-CHURCH, and Stable Diffusion, spurious states peak at the memorization–generalization boundary and have distinct basin volume and curvature signatures.