All AI memory papers

APEX-MEM constructs a semi-structured temporal property graph using Ontology, Entity and Property Resolution, Fact Extraction, and Graph Agents to store and query conversational memory. On LOCOMO, APEX-MEM with GPT5 achieves 88.88% overall accuracy, beating MIRIX at 85.38% by 3.50 percentage points.

ARKV dynamically combines Per-layer OQ ratio estimation, Token importance scoring, and Tri-state cache assignment to manage KV cache precision under a global memory budget. On LongBench, ARKV reaches 0.972 relative performance versus 0.979 for Origin while achieving 4× KV memory reduction and maintaining ~86% Tokens Per Second.

PROACTAGENT combines Experience-Enhanced Online Evolution (EXPONEVO), a structured EXPERIENCE BASE, and Proactive Reinforcement Learning-based Retrieval (PROACTRL) to jointly evolve memory and policy with retrieval as an explicit action. On SciWorld, PROACTAGENT reaches 73.50% SR versus 55.50% for GRPO+Reflexion, while cutting interaction rounds from 27.52 to 18.38.

Mnemonic Sovereignty analyzes long term Write, Store, Retrieve, Execute, Share, and Forget Rollback phases against integrity, confidentiality, availability, and governance objectives for agent memory. Mnemonic Sovereignty’s lifecycle matrix shows most of the ~70 works cluster on write and retrieve integrity, leaving store, availability, and governance primitives like write gate validation and post deletion verification almost entirely unexplored.

ATANT v1.1 structurally analyzes seven benchmarks using the 7 v1.0 continuity properties, the 10 checkpoints, a property-coverage matrix, and the Kenotic v1.0 reference implementation. ATANT v1.1 reports 96% ATANT cumulative-scale versus 8.8% LOCOMO substring accuracy, showing that LOCOMO, LongMemEval, BEAM, MemoryBench, Zep eval, MemGPT/Letta, and RULER measure different properties from continuity.

AtomMem reframes agent memory as a POMDP and composes atomic CRUD operations over a hybrid scratchpad plus vector memory storage using a GRPO-based RL policy. On HotpotQA, 2WikiMultiHopQA, Musique, GAIA, and WebWalkerQA, AtomMem reaches an average 58.8 score, beating MemAgent’s 56.7 with the same Qwen3-8B backbone.

AutoMem composes long-term memory from modular Encode, Store, Retrieve, and Manage components, optimized via Experience-Guided Architecture Search and Failure-Guided Module Diagnosis. On GAIA with Qwen3.5-122B-A10B, AutoMem achieves 71.5% accuracy vs 67.8% for MemoryBank, and reduces token cost by 14.3%.

AutoSkill composes prompt-driven modules Query Rewriting, Hybrid Skill Retrieval, Skill Extraction, Skill Management Decision, and Versioned Skill Merging to externalize user preferences and workflows into SKILL.md artifacts stored in a SkillBank. AutoSkill builds a multilingual SkillBank with 1,858 skills across four WildChat-1M subsets, showing that explicit skill artifacts can be continuously refined (e.g., professional_text_rewrite at version 0.1.34) without any parameter updates.

Auxiliary-predicted Compress Memory Model (ApCM Model) combines an Invertible Dimensionality Reduction and Predictor (IDRP) module with a Memory Read-Write Controller, including a global Memory Bank, cosine-similarity read, and access-frequency write policy. ApCM Model achieves lower MSE (0.987171 vs 1.001440) than a Key-Value Memory Network while compressing memory from 1024 to 128 dimensions on random data.

BeliefMem maintains an external belief-based memory bank with Add, Merge, and Belief-aware Retrieval over attribute-level hypotheses. On LoCoMo, BeliefMem reaches 42.38 F1 with GPT-4o-mini, beating Mem0’s 40.99 F1, and on ALFWorld it attains 59.88% success rate vs ReadAgent’s 54.03% (+5.85).

MEMORYARENA orchestrates Memory-Agent-Environment Loops, Multi-Session Working Flow, Bundled Web Shopping, Group Travel Planning, and Progressive Web Search to stress-test how agents store and reuse information across sessions. MEMORYARENA’s main result is that agents with near-saturated scores on long-context benchmarks like LoCoMo still obtain Task Success Rates as low as 0.00–0.12 across its four environments.

BenchPreS combines Contexts, User Profiles, Preference Attributes, Gold Labeling, and an LLM-as-Judge framework to test context-aware preference selectivity in persistent-memory LLMs. BenchPreS shows GPT-5.2 reaches 87.33% Appropriate Application Rate on BenchPreS while still having a 40.95% Misapplication Rate compared to Gemini 3 Pro’s 86.48% Misapplication Rate.

CAMA combines Neuro-Symbolic Evidence Assignment, Effective Independent Evidence Estimation, Factor-Level Conflict Arbitration, and Active Independent-Evidence Recovery to decouple correlated memories into latent evidence slots and recover missing sources. On LongMemEval with DeepSeek V4 Flash, CAMA reaches 87.9 EM compared to MADAM RAG’s 86.8 EM while also cutting Replication Sensitivity from 15.3 to 7.8.

xMemory organises agent memory via segments, memory components, groups, and dynamic maintenance with a sparsity plus semantic faithfulness objective. On LoCoMo with GPT-5 nano, xMemory reaches 38.71 BLEU and 50.00 F1 versus Nemori’s 36.65 BLEU and 48.17 F1, while cutting tokens per query from 9154.76 to 6581.20.

Beyond the Context Window compares Conversation Segmentation, Fact Extraction, Embedding and Storage, and Retrieval Mechanism in a Mem0-based memory system against long-context GPT-5-mini. On LongMemEval, Beyond the Context Window finds LC GPT-5-mini reaches 82.40% accuracy, 33.4 percentage points above the memory system baseline.

Fan Duality Model (FDM) uses the Fan Operator, Local-Global Cache, Freeze-Scan Training, and Holographic Reference Beam Decoding to separate wave-like compression from particle-like associative recall. On WikiText-103, Fan Duality Model (FDM) reaches 64.9 perplexity with Freeze-Scan and 62.79 with holographic decoding, while achieving 0.966 MQAR accuracy compared to Transformer at 0.606.

ByteRover organizes memory through an LLM-driven Agent Layer, a sequential Execution Layer, and a file-based Context Tree with an Adaptive Knowledge Lifecycle. On LoCoMo, ByteRover reaches 96.1% overall accuracy versus 89.9% for HonCho, and on LongMemEval-S ByteRover scores 92.8% overall.

StateMem represents conversations as structured StateStore entries built by a TurnEncoder, updated via deterministic Rechecker passes, and queried through a guided test-time recomputation prompt. On StateMemBench, StateMem reaches 0.363 gold rate on DeepSeek-V4-Flash, a +0.158 gain over the best retrieval baseline (Dense at 0.205) and +0.164 over long-context (0.149).

CAST builds views, scenes, character profiles, a semantic index, and an episodic index to organize dialogue into person-conditioned event structures. CAST reaches 62.02 F1 and 81.21 J on LOCOMO open questions, beating Zep by +13.24 F1 and +8.32 J and vanilla RAG by +25.15 F1 and +24.61 J.

ChronoMem wraps ADK’s LocalMemoryService, SQLite memory store, Version Index, and Reranker module into a semantic version-control layer that snapshots agent memory on every write and rolls back via natural-language queries. ChronoMem reaches 55.1% rollback-consistent QA accuracy on MemoryAgentBench with Qwen2.5-7B, compared to 35.5% for the RAG-only baseline.

Chronos decomposes dialogue into structured events via the Event Extraction pipeline, stores them in dual event calendar and turn calendar indexes, and uses Dynamic Prompting, Initial Retrieval, and the Chronos Agent for temporal-aware tool-calling. On LongMemEvalS, Chronos Low reaches 92.60% overall accuracy and Chronos High 95.60%, beating EmergenceMem Internal by 7.67 percentage points and Mastra’s OM by 3.02 points.

ClawVM manages agent state as typed pages via the SessionPageTable, RepresentationSelector, FaultObserver, WritebackJournal, and ClawVMEngine inside the agent harness. Across four OpenClaw-derived workloads and six token budgets, ClawVM cuts explicit faults from 67.8 (retrieval baseline) and 1.5 (Compaction-Hybrid) to 0.0 while adding median <50 μs policy-engine overhead per turn.

Codebase-Memory parses repositories with a multi-pass pipeline using the Parse stage, Build stage, Serve stage, FunctionRegistry, Louvain communities, and MCP tool interface to build a persistent SQLite knowledge graph. On a 31-language benchmark, Codebase-Memory reaches 0.83 quality versus 0.92 for an Explorer Agent while using ten times fewer tokens and 2.1 times fewer tool calls.

D-Mem combines Mem0∗, Quality Gating, and Full Deliberation into a dual-process memory system that incrementally stores vector memories and selectively scans raw history. On LoCoMo with GPT-4o-mini, D-Mem’s Quality Gating reaches 53.5 F1 versus the Mem0∗ baseline’s 51.2 F1, recovering 96.7% of the 55.3 F1 Full Deliberation performance with far fewer tokens.