All AI memory papers

Wontopos Tablet 2 measures how a retrieval engine interacts with components like LongMemEval-S, BEAM-1M, Crossmodal-3600, and the re-ask mechanism without relying on lexical matching. It reports controlled multilingual and multimodal results, including a negative finding that adding captions can reduce cross-lingual photograph retrieval quality compared to pure visual matching.

WorldMemArena evaluates multimodal agent memory via an Action World Interaction Loop and a four-stage lifecycle of Observe to Write, Update and Consolidate, Retrieve for Decision, and Use and Act across 461 multi-session tasks. On WorldMemArena, systems like M2A reach 86.83% memory Recall but only 50.14% QA-C, revealing that storing more correct memories does not guarantee better performance compared to baselines like UniversalRAG at 39.62% QA-C.

Zombie Agents formalizes a two-phase Infection and Trigger pipeline over Sliding Window, Retrieval-Augmented Memory (RAG), and Memory Evolution Mechanisms to implant and persist malicious payloads in long-term memory. Zombie Agents achieves cumulative Attack Success Rates near 100% in sliding-window settings and ~77% under Raw History evolution while standard indirect prompt injection baselines decay to near 0 after context eviction.

ACON combines History Compression, Observation Compression, Compression Guideline Optimization, and Compressor Distillation to rewrite agent histories and observations into concise, task-aware summaries. On AppWorld, ACON UTCO with gpt-4.1 achieves 56.5% accuracy with 7.33k peak tokens, versus 56.0% accuracy with 9.93k peak tokens for No compression.

AgentSafe secures LLM-based multi-agent systems by combining ThreatSieve for permissioned communication and identity checks with HierarCache for hierarchical memory and junk isolation. On RIOH and WCEI, AgentSafe achieves up to 96.88% defense rate in memory-based attacks and maintains CSR above 0.65, compared to baselines dropping below 0.4 without AgentSafe.

A-MEM organizes agent memory via Note Construction, Link Generation, Memory Evolution, and Retrieve Relative Memory to build an evolving, interconnected note graph. On the LoCoMo dataset, A-MEM with GPT-4o-mini reaches 27.02 F1 on Multi Hop questions, +17.87 over ReadAgent, while cutting average token length from 16,910 to 2,520.

A-MemGuard combines consensus-based validation, dual-memory structure, lesson memory, and path divergence scoring to sanitize retrieved memories and revise actions using past failures. On EHRAgent under AgentPoison, A-MemGuard reduces ASR-r from 100.0% to 2.13% and ASR-t from 100.0% to 6.38%, far below LLM Auditor and Distil Classifier.

LIGHT augments LLMs with Retrieval from the Conversation, Scratchpad Formation and Utilization, and a Working Memory buffer plus noise filtering to answer BEAM’s long-context probing questions. On the BEAM benchmark, LIGHT raises GPT-4.1-nano’s average score at 10M-token conversations from 0.109 to 0.226, a +107.3% gain over the vanilla long-context baseline.

DAM decomposes agent memory into a Read Policy, Write Policy with Sub-policy, Value Function, Uncertainty Estimator, and an Aggregate Policy that arbitrates storage operations. DAM’s main result is a formal decision-theoretic framework that clarifies the limits of heuristic memory and unifies existing memory-augmented agents under a single uncertainty-aware control perspective.

CAM builds hierarchical schemata using an incremental overlapping clustering algorithm, ego centric disentanglement, and a Prune and Grow associative strategy for retrieval. On NovelQA, CAM achieves 52.3 ACC-L versus RAPTOR’s 47.8, a +4.5 point gain while also improving efficiency in long-text reading comprehension.

CarMem manages in-car user preferences via Preference Extraction, Preference Maintenance, and Preference Retrieval over a predefined category hierarchy. On the CARMEM dataset, CarMem reaches detail-level F1 of 0.78 and optimal retrieval accuracy of 0.87 using enriched embeddings.

CodeMem reframes agents as workflow architects that combine search_functions, write_todos, execute_code, and register_skill to build deterministic, reusable Python skills. On a 25-task benchmark, CodeMem with Gemini 3 Full achieves 96% minimum correctness with 7.00 calls and 2.02M tokens, compared to 68% correctness and 2.80 calls for GPT-5 Chat.

CAT organizes context into a structured workspace with Fixed Segment, Long-Term Memory Segment, and High-Fidelity Working Memory Segment, and trains SWE-Compressor via the CAT-GENERATOR trajectory pipeline to call a context tool as a first-class action. On SWE-Bench-Verified, SWE-Compressor achieves 57.6% Pass@1 using CAT, beating the Threshold-Compression Agent at 53.8% and ReAct Agent at 49.8% under the same 32B backbone and OpenHands scaffold.

Echo combines the Multi-Agent Data Generation Framework (MADGF), EM-Train, EM-Test, and a modified user–time–assistant training paradigm to give ChatGLM3-6B temporal episodic memory. Echo scores 6.7 on easy EM-Test questions, beating GPT-4’s 5.8, and reaches 84.0 similarity vs GPT-4’s 72.3 on the same benchmark.

Evaluating Long-Term Memory for Long-Context Question Answering compares Full Context, RAG, A-Mem, RAG+PromptOpt, and RAG+EpMem memory components across semantic, episodic, and procedural memory for long conversational QA. On LoCoMo, RAG+EpMem reaches an average F1 ranking of 1.83 for Llama 3.2-3B Instruct and 1.80 for GPT-4o mini while using around 1,000 tokens per query versus over 23,000 for Full Context.

MemoryAgentBench standardizes multi-turn datasets into chunked conversations with memorization prompts, then evaluates long-context agents, RAG agents, and agentic memory agents across Accurate Retrieval, Test-Time Learning, Long-Range Understanding, and Selective Forgetting. On the overall score in Table 3, the GPT-4.1-mini long-context agent reaches 71.8 on Accurate Retrieval tasks compared to 49.2 for the GPT-4o-mini long-context baseline.

Evo-Memory restructures datasets into streaming task trajectories and plugs in components like ExpRAG, ReMem, Search, Synthesis, and Evolve to benchmark self-evolving memory in LLM agents. On multi-turn embodied reasoning benchmarks such as BabyAI and PDDL with Claude 3.7 Sonnet, Evo-Memory with ReMem reaches 0.92/0.96 and 0.83/0.95 success/progress, improving over History and ReAct baselines.

MaRS organizes agent memory into episodic, semantic, social, and task nodes with provenance, scored by a privacy-aware retention controller and governed by FIFO, LRU, Priority Decay, Reflection-Summary, Random-Drop, and Hybrid policies. On the FiFA benchmark, the Hybrid policy in MaRS achieves a composite score of ≈0.911 across 300 runs and five memory budgets, outperforming simpler policies while preserving privacy and cost efficiency.

From Human Memory to AI Memory maps human memory categories onto AI memory using the 3D-8Q taxonomy with Personal Memory, System Memory, and the Three-Dimensional Eight-Quadrant Memory Taxonomy. The main result is that From Human Memory to AI Memory systematically organizes memory in LLM-driven AI systems across eight quadrants defined by object, form, and time, connecting them to human memory types.

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.