Personalization

ATM-Bench structurally evaluates long-term multimodal personal memory using Memory Ingestion, Retrieval, and Answer Generation with Schema-Guided Memory and Descriptive Memory variants. On ATM-Bench-Hard, Oracle with SGM reaches 47.3% QS while the best full system stays under 20% accuracy, revealing a large gap.

AdaMem organizes dialogue history into Working Memory, Episodic Memory, Persona Memory, and Graph Memory coordinated by a Memory Agent, Research Agent, and Working Agent. On LoCoMo with GPT-4.1-mini, AdaMem achieves 44.65 F1 overall, beating the best baseline LangMem at 41.76 F1 by +2.89.

AIM orchestrates an Operation Detection Agent, Privacy-Aware Classification Engine, Structured Memory Extraction, In-Context Semantic Deduplication, and Cascaded Memory Retrieval over a shared vector-indexed store. On MUMBench, AIM with GPT-5.4-mini achieves 58.8% strict and 70.5% state-aware operation accuracy while reaching 96.0% visibility classification accuracy with GPT-5.4.

A-MBER builds multi-session conversational scenarios via a staged pipeline of persona specification, long-horizon planning, conversation generation, annotation, question construction, and benchmark-unit packaging. On A-MBER, a structured memory system reaches 0.69 judgment accuracy, 0.66 retrieval, and 0.65 explanation versus 0.34, 0.29, and 0.31 for a no-memory baseline.

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.

CoreMem combines Riemannian retrieval, Fisher-guided discrete token distillation, Residual Metric Fusion, and an Edge-Cloud Hybrid Architecture to retrieve and compress long-term dialogue memories under strict VRAM budgets. On LOCOMO with MiniLM-L6, CoreMem-Fusion reaches Judge accuracy 0.540 versus 0.531 for NaiveRAG, with +4.51 pp Open-domain and +4.17 pp Temporal gains.

ES-MemEval evaluates conversational agents’ long-term memory via Question Answering, Summarization, and Dialogue Generation on the EvoEmo dataset, which combines User Profile Construction, Event Timeline Expansion, and Chat Data Generation. On ES-MemEval QA, Mistral-24B + RAG reaches 18.8% overall F1 and 1.27 LLM-as-Judge score, improving over base Mistral-24B and revealing how explicit memory and retrieval reshape performance.

FinPerMA combines Persona Synthesis, Event Timeline Construction, the three-layer Impact Model, Dialogue Synthesis, and Evaluation Task Design to generate frozen, event-grounded investor trajectories for memory benchmarking. On the FinPerMA v8gold corpus, full-context Qwen-3.8 reaches 46.9% overall accuracy and 38.7% MCQ, while retrieval-based memory on Qwen3.7-Max recovers ≈88% of the no-memory–full-context gap with only 1.40k tokens of context.

Graph-Based Personalized Memory for LLM Agents organizes memory representation, memory evolution, memory retrieval, and memory evaluation into a single lifecycle for LLM personalization. This unified taxonomy clarifies how graph-based memory supports long-horizon personalized assistants across benchmarks like LoCoMo, PersonaMem-v2, and EngramaBench.

GroupMemBench combines Graph-grounded Message Synthesis, Path-Based Context Sampling, Message Generation, Adversarial Question Synthesis, and a Solve–Judge–Refine Loop to stress-test group memory in LLM agents. On GroupMemBench, Hindsight reaches 46.01% average accuracy while a simple BM25 baseline attains 43.22%, revealing that current memory ingestion pipelines fail to preserve crucial multi-user structure.

MeClear evaluates retrieved memories via Local Counterfactual Screening, Counterfactual Cooperative Attribution, and Verified Query-Scoped Clearance to decide which records to suppress per query. On ten LoCoMo-based long dialogue memory pools, MeClear attains 85.9% target recall and 82.3% binary task recovery, a +25.5 percentage point gain over Leave-One-Out.

MemMachine combines Short-term memory, Long-term memory, Profile memory, and the Retrieval Agent to store raw conversational episodes and retrieve clustered context around nucleus matches. On LoCoMo, MemMachine scores 0.9169 with gpt-4.1-mini while using about 80% fewer input tokens than Mem0, and reaches 93.0% on LongMemEvalS with GPT-5-mini.

MEMORYCD builds a user memory pool Mu from lifelong Amazon Review histories and evaluates long-context prompting, Mem0, LoCoMo, ReadAgent, MemoryBank, and A-Mem across rating, ranking, and personalized text tasks. On Books and Home & Kitchen, MEMORYCD shows GPT-5 reaches RMSE 0.551–0.624 and NDCG@3 up to 0.610, while Gemini-2.5 Pro peaks at ROUGE-L 0.222 for generation, revealing substantial remaining gaps to real user behavior.

MemPrivacy combines a Local MemPrivacy Model, Four-Level Privacy Taxonomy, Local Secure Mapping DB, and Uplink Desensitization / Downlink Restoration to sanitize edge inputs and restore them after cloud processing. On MemPrivacy-Bench, MemPrivacy-4B-RL achieves 85.97 F1 vs 68.99 for GPT-5.2, with ~2.05s latency instead of 4.62s.

PairPref constructs controlled pairs using Stored preferences, Companion memory pool, Multi-stage validation, and a dual Selection and Free generation protocol to test when preferences should guide answers. On the 1,227-pair benchmark, PairPref reports selection selectivity up to 64.9 points for Claude Opus 5 but free-generation Pair success of only 3.6–18.3% across eight models.

Personalize-then-Store combines PerMem-Bench, PerMem-Benchs, PerMem-Benchd, session-level storage gating, and structural note modeling to study personalized memory for long-horizon agents. On PerMem-Bench, Oracle gating substantially boosts Memory Retention Rate over Universal policies, especially at 100–200 entry budgets, while existing gating baselines like Greedy and Structure-aware recover only incremental gains.

PACMEM in PERSONATRAIL builds Factual Memory, Preference Memory, Trajectory Segmentation, and Memory Retrieval to structure browser-level histories for personalized web navigation. On PERSONATRAIL, PACMEM achieves 53.61% Task Success Rate on single-hop preference inference with Qwen3.6-27B, compared to 46.45% for ReasoningBank, and 39.03% vs 17.84% on multi-hop episodic grounding.

PRAGMA constructs synthetic User Schema, Event Sessions, Trajectory Sessions, and annotated Queries to test how systems retrieve and use long-term conversational memories. On Traj-Align, Oracle-Summary in PRAGMA reaches 99.00 alignment and 86.00 grounding, far above Oracle-Session, revealing a large utilization gap.

QUMem combines Dynamic Episode Construction, Typed Memory Decomposition, and Query-Conditioned User-State Inference to segment histories into semantically coherent episodes and split them into factual, preference, and transferable insight memories. On PersonaMem, QUMem reaches 70.58% overall accuracy with Gemini-3.5-flash, improving over Mem0’s 63.29% and Zep’s 54.46%.

STALE evaluates latent user-state tracking by probing State Resolution, Premise Resistance, and Implicit Policy Adaptation across 400 conflict scenarios packaged into long user-assistant histories. CUPMEM applies structured state consolidation and propagation-aware search on STALE, reaching 68.0% overall accuracy versus 55.2% for Gemini-3.1-pro.

SP-Mem uses Privacy-Aware Memory Writing, Partitioned Storage with Privacy Mapping, and Privacy-Aware Query-Time Reasoning and Authorized Retrieval over a hybrid vector plus graph memory layer to separate sanitized facts from exact private values. On the privacy-aware benchmark, SP-Mem reaches 0.996 privacy-entity identification accuracy and reduces unnecessary privacy usage on preference-only tasks from 16.00% for Full-context to 0.33%.

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.

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.

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.

MemOS standardizes memory as MemCube units orchestrated by MemReader, MemScheduler, MemLifecycle, MemOperator, MemVault, and MemGovernance to manage parametric, activation, and plaintext memory as one system. MemOS does not report benchmark numbers but instead contributes a unified architecture and Memory Interchange Protocol for cross-LLM memory sharing and lifecycle governance.

Mem-PAL uses Log Graph, Background, Topic Outline, and Principle memories to encode long-term user logs and dialogues into structured, retrievable personas. On PAL-Bench’s Requirement Restatement task, H2Memory in Mem-PAL achieves a GPT-4 G-Score of 32.54, +3.21 over Session-level RAG, and boosts BLEU-1 to 26.67 vs 23.81.

MIRIX organizes Core Memory, Episodic Memory, Semantic Memory, Procedural Memory, Resource Memory, and Knowledge Vault under multi-agent control with Active Retrieval for topic-driven access. On ScreenshotVQA, MIRIX reaches 0.5950 accuracy vs 0.4410 for SigLIP@50 while shrinking storage from 15.07GB to 15.89MB, and on LOCOMO MIRIX scores 85.38% vs 79.09% for Zep.

MR.Rec unifies User-specific Local Memory, Cross-user Global Memory, Reasoning-enhanced Memory Retrieval, and Reinforcement Learning for Memory-synergized Reasoning into a single recommendation assistant pipeline. On the Amazon-C4–based benchmark, MR.Rec achieves NDCG@100 = 0.113 and Recall@100 = 0.270, improving over the best baseline Rec-R1 (NDCG@100 = 0.104, Recall@100 = 0.260).

PersonaMem-v2 combines PERSONAMEM-V2: IMPLICIT PERSONAS, RL with Long-Context Reasoning, RL with Agentic Memory, and a User Privacy-Aware Design to train Qwen3-4B with GRPO on implicit user preferences from long, noisy histories. PersonaMem-v2 achieves 55.2% MCQ and 60.7% open-ended accuracy on PERSONAMEM-V2, surpassing GPT-5-Chat’s 45.6% and 46.2% while using a 2k-token agentic memory instead of full 32k–128k contexts.

PREMem builds long term dialogue memory by combining Episodic Memory Extraction, Pre Storage Memory Reasoning, semantic clustering, a persistent memory pool, and an inference phase over enriched memory fragments. PREMem reaches 71.4 LLM as a judge on LongMemEval with gpt 4.1 base, a +15.5 gain over HippoRAG 2 and +9.6 over A Mem.

RGMem builds a multi-scale memory state using Microscopic Evidence Space DL0, Structured Knowledge Space G, and renormalization operators RK1, RK2, RK3 to evolve user profiles. On PersonaMem with GPT-4.1, RGMem reaches 74.01% Avg., beating Memory OS by 8.98 points.

MemoryBank combines Memory Storage, Memory Retrieval, and a Memory Updating Mechanism to maintain daily conversations, event summaries, and user portraits for long-term personalization. On a 10-day, 15-user simulated dialog benchmark with 194 probing questions, MemoryBank-powered SiliconFriend ChatGPT achieves 0.716 correctness and 0.912 contextual coherence, surpassing SiliconFriend ChatGLM and SiliconFriend BELLE.