All AI memory papers

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.

CareCall combines a memory management layer, LLM summarizer, and memory-augmented input over HyperCLOVA to store and reuse summaries of users’ Health, Meals, Sleep, Visited Places, and Pets across weekly calls. In deployment to 147 socially isolated adults, CareCall with long-term memory yielded higher Health-detail and Clinical-detail disclosure counts per call than CareCall without memory, and longer average call durations (87.89s vs 75.48s).

WISE introduces a dual parametric memory with Side Memory, Memory Routing, and Knowledge Sharding and Merging to separate pretrained and edited knowledge inside LLaMA, Mistral, and GPT-J. On ZsRE with LLaMA-2-7B at T = 100 edits, WISE reaches an average score of 0.90 versus 0.76 for MEMIT-MASS, while maintaining 1.00 locality.

LONGMEM augments a frozen GPT-2*-style backbone with a Residual SideNet, Cached Memory Bank, Memory Retrieval and Fusion, and Cross-Network Residual Connections to read and use long-term key–value memories. On ChapterBreak AO3, LONGMEM reaches 40.5% suffix identification accuracy with infinite in-memory context, compared to 28.3% for Memorizing Transformer under the same 1k in-context window.

Empowering Working Memory for Large Language Model Agents introduces a Working Memory Hub, Episodic Buffer, Interaction History Window, Central Processor, and External Environment Interface to give LLM agents persistent, structured working and episodic memory. Empowering Working Memory for Large Language Model Agents is a conceptual blueprint rather than a benchmarked system, so no quantitative MAIN_RESULT against specific baselines is reported.

HimNet combines a GNN Encoder, Node Memory Module, Graph Memory Module, and Graph Decoder to reconstruct graphs via stored normal patterns and score anomalies by reconstruction and approximation errors. On the DD biochemical dataset, HimNet achieves 80.6% AUC compared to 70.6% for PK-iF, a +10.0 point gain over this two-step baseline.