All AI memory papers

Dual Memory Networks combines a Dynamic Memory Network, Static Memory Network, a shared ReadOut module, Projection Layers ω, and a Memory Interactive Strategy to build sample-adaptive classifiers on top of frozen CLIP encoders. On zero-shot ImageNet with ViT-B/16, Dual Memory Networks achieves 72.25% accuracy vs 66.73% for CLIP and 68.98% for TPT, a +5.52 and +3.27 point gain respectively.

DynaMem maintains a Dynamic 3D Voxel Map, supports Embedded Vision Language Features and Multimodal Large Language Models querying, and exposes Exploration Primitives and an Obstacle map for navigation and manipulation. On real Stretch SE3 experiments, DynaMem achieves a 70% success rate on dynamic pick-and-drop tasks compared to 30% for the static OK-Robot baseline.

EMU combines a semantic memory embedding via deterministic conditional autoencoder and an episodic incentive built from desirability in the episodic buffer to guide cooperative MARL. EMU improves learning speed and final win-rates on StarCraft II SMAC and Google Research Football compared to QPLEX, CDS, and EMC, especially on super hard maps.

Embodied-RAG builds a multimodal Topological Map and a hierarchical Semantic Forest and then runs Top-down Retrieval with LLM-based selection and hybrid re-ranking to drive Generation of waypoints and explanations. On the E-multimodal Embodied-Experiences dataset, Embodied-RAG reaches P(Q|A)=0.67 for implicit queries (Q only), compared to 0.13 for LightRAG, while building graph memory 9.76× faster than LightRAG.

LOCOMO builds very long-term multi-modal dialogues by combining Persona, Temporal Event Graph, Virtual Agent Architecture, Image Sharing and Image Reaction, and Human Verification and Editing. On the LOCOMO QA benchmark, GPT-3.5-turbo-16k with 16K context achieves 37.8 F1 overall, 50.9 F1 with RAG over dialogs, while humans reach 87.9 F1.

MemoAnalyzer analyzes past inputs and long-term memories using prompt-based privacy inference, confidence and sensitivity visualization, and source tracking with an editing proxy. In a 5-day study on work, life, and academic tasks, MemoAnalyzer reduced total inferred private information by 22.3% compared to GPT memory while keeping completion time comparable to GPT and Manual baselines.

HiAgent manages working memory using Subgoal based Hierarchical Working Memory, Observation Summarization, and a Trajectory Retrieval module to chunk and selectively expand action observation histories. On five AgentBoard long horizon tasks, HiAgent reaches 42.00% overall success rate versus 21.00% for STANDARD, with 3.80 fewer average steps.

HippoRAG combines Offline Indexing, Online Retrieval, a schemaless knowledge graph, retrieval encoders, and node specificity to mimic hippocampal indexing for long-term memory. On 2WikiMultiHopQA, HippoRAG with Contriever achieves 89.5 R@5 vs 68.2 for ColBERTv2, a +21.3 point gain in single-step retrieval.

EM-LLM organises long token streams into episodic events via surprise-based segmentation, boundary refinement, and a two-stage memory retrieval with similarity and contiguity buffers. On LongBench with LLaMA-3.1-8B, EM-LLM reaches 51.58% average score versus 39.3% for full-context processing and 36.44% for RAG.

Ewe augments Llama-3.1 with an explicit working memory, real-time feedback, fact-checking outcomes, and relevant knowledge memories that are refreshed via FIFO KV-cache updates during decoding. On the Biography dataset, Ewe reaches 49.7 VeriScore F1 versus 37.1 for Llama-3.1 70B (+12.6), while keeping AlpacaEval win rate around 50% against the same baseline.

Larimar couples a BERT large encoder, a deterministic hierarchical memory module, a scope detector, and a GPT2-large or GPTJ-6B decoder via learned projection WM to perform memory-conditioned generation. On CounterFact and ZsRE, Larimar attains up to 100.0% edit success and 0.97 edit retention rate while being 4–10× faster than ROME and GRACE.

Activation Beacon progressively compresses long contexts by interleaving beacon tokens, using a tailored compression workflow, compression-based auto-regression, and chunk-wise random compression ratio training to store information in activation space. On LongBench, Activation Beacon reaches 34.9 single-doc QA vs 34.8 for Full-FT Llama-2-7B while delivering 2x acceleration and 8x KV cache reduction on 128K-context NeedleBench-style tasks.

LongMemEval evaluates long-term interactive memory by running chat assistants through indexing, retrieval, and reading over 50k sessions with fact-augmented keys and time-aware query expansion. On LONGMEMEVALS, long-context LLMs like GPT-4o, Llama 3.1, and Phi-3 suffer 30%–60% accuracy drops compared to oracle evidence-only reading, revealing severe limitations in current long-context designs.

Long Term Memory equips OMNE with a Data Framework for LTM, Development Framework for LTM, and multi-agent collaboration over personalized memories to support AI self-evolution. On the GAIA benchmark, OMNE reaches first place, showing that LTM-driven multi-agent personalization can solve complex real-world tasks better than prior agent systems.

MemLong augments OpenLLaMA-3B with a Ret-Mem module, Memory Bank, Retriever, Retrieval Causal Attention, and Dynamic Memory Update to store and retrieve chunk-level K-V caches via dense embeddings. On PG19 at 16k tokens, MemLong with 32K Memory reaches perplexity 9.73 vs 10.37 for MemLong-3B* without memory, and achieves up to +10.2 percentage points over OpenLLaMA on retrieval-augmented in-context learning.

MemoNav composes Short-term memory, Selective forgetting module, Long-term memory, Working memory generation, and Transformer decoders to focus navigation on goal-relevant topological map nodes. On Gibson 1-goal, MemoNav reaches 74.7% SR vs 70.0% for VGM, and on Gibson 4-goal multi-goal tasks MemoNav achieves 28.9% PR vs 21.5% for VGM.

PerLTQA builds a synthetic personal memory database and runs questions through Memory Classification, Memory Retrieval, and Memory Synthesis to test how LLMs use semantic and episodic memories. On the PerLTQA benchmark, BERT-base achieves 95.7 F1 for memory classification, while gpt-3.5-turbo reaches MAP 0.756 for memory synthesis with retrieval and classification.

MD-DETR augments Deformable-DETR with frozen encoder–decoder, task-partitioned memory modules, localized query retrieval, and background thresholding to mitigate background relegation. On MS-COCO continual detection, MD-DETR reaches 50.2 mAP@A on Task 4, +11.0 over CL-DETR and +14.8 over ERD.

Retrieval-Augmented Decision Transformer (RA-DT) combines a vector index, embedding model g(·), maximum inner product search, experience reweighting, and cross-attention layers to retrieve and fuse relevant sub-trajectories into a Decision Transformer policy. On Dark-Room 10×10, RA-DT reaches near-optimal average reward over 40 in-context trials while using a 50-step context window, whereas baselines like Algorithm Distillation require entire episodes of up to 100 steps.

SAGE coordinates Iterative Feedback, Reflection, Short-Term Memory, Long-Term Memory, and MemorySyntax so the assistant, checker, and user co-evolve policies and memories over time. On AgentBench and long-context QA like HotpotQA, SAGE lifts GPT-3.5’s Database score from 25.9 to 37.6 and HotpotQA answer accuracy from 48.5% to 68.3%.

WM-Neurosymbolic combines External Working Memory, Working Memory Initialization, Symbolic Rule Grounding, and LLM-based Rule Implementation to execute multi-step rule application with explicit fact and rule tracking. On CLUTRR, WM-Neurosymbolic reaches 92.34% accuracy with GPT-4, a +6.81 point gain over Self-Consistency CoT.

Toward Conversational Agents with Context and Time Sensitive Long-term Memory integrates a Tabular Chat Database, Classifying Query Type, Chain-of-Tables for Meta-Data Retrieval, and Combining Meta-Data and Semantic Retrieval to handle time-sensitive and ambiguous conversational queries. On the LoCoMo-derived temporal benchmark, Toward Conversational Agents with Context and Time Sensitive Long-term Memory achieves 90.32 average recall vs 31.93 for the best Semantic w MetaD baseline.

TransformerFAM augments Block Sliding Window Attention with Feedback Attention Memory, where Feedback Attention Memory (FAM) tokens attend to and compress block activations while queries jointly attend to BSWA memory segments and past FAM. On PassKey retrieval, TransformerFAM maintains 100% accuracy up to 260k filler tokens, while TransformerBSWA with 12 memory segments collapses after 20k tokens.

Understanding Factual Recall in Transformers via Associative Memories analyzes linear associative memories, MLP associative memories, and a one-layer transformer with multi-head self-attention plus an MLP on a synthetic factual recall task. Understanding Factual Recall in Transformers via Associative Memories proves that storing N random associations requires Θ(N log M) bits and that a single-layer transformer achieves 100% accuracy whenever either self-attention or MLP parameters scale linearly with the number of facts.