All AI memory papers

OP-Bench benchmarks over-personalization using Irrelevance, Repetition, and Sycophancy across 1,700 long-horizon queries built from LoCoMo, then analyzes memory systems like RAG, LDAgent, Mem0, MemU, and MEMOS. Self-ReCheck, a lightweight memory filter, improves average OP-Bench scores by 29% for Qwen3-8B while preserving personalization on LoCoMo compared to BASE.

OS-SYMPHONY coordinates an Orchestrator, Reflection-Memory Agent, and Versatile Tool Agents (Multimodal Searcher, Grounders, Coder) to stabilize long-horizon GUI workflows and fetch visual tutorials on demand. On OSWorld-Verified, OS-SYMPHONY with GPT-5 scores 65.84% at 100 steps, beating Agent S3 w/ GPT-5 (62.63%) by 3.21 percentage points.

Pask combines Demand Detection (IntentFlow), Pask-MM hierarchical memory, and the Pask-PAS proactive agent system to continuously infer latent needs and act through tools and frontier models. On LatentNeeds-Bench, Pask’s IntentFlow achieves 84.2% balanced accuracy versus 80.8% for Gemini-3-Flash, under ~1.3–1.5 s per-turn latency.

PERMA constructs temporally ordered Interaction Events, a dynamic Persona State, and a dual-process Memory System over Clean, In-session Noise, and Style-aligned Long-context datasets. PERMA shows that structured memory agents like MemOS can compress raw 34k-token histories down to ~700 tokens while preserving higher MCQ accuracy than vanilla RAG in realistic, noisy multi-session personalization tasks.

PersistBench combines Monte Carlo Tree Search, Seed Initialization and Candidate Generation, Search and Scoring, and Human Verification to build realistic long-term memory test cases across cross-domain leakage, sycophancy, and beneficial memory use. PersistBench then reports a median 53% cross-domain leakage failure rate and 97.8% sycophancy failure rate on 18 LLMs using its 500-sample benchmark.

PLUGMEM standardizes episodic traces with the Structuring Module, retrieves via an abstraction-aware Retrieval Module, and compresses outputs through a Reasoning Module into a unified memory graph. On HotpotQA, PLUGMEM reaches 61.4 EM and 74.1 F1 with only 81.6 memory tokens, compared to 51.7 EM and 62.7 F1 with 659.2 tokens for Vanilla Retrieval.

eTAMP stores attacker-crafted payloads inside Trajectory Memory, then reactivates them via semantic Cross-Site Task Pairing and Chaos Monkey induced frustration during later tasks. On (Visual)WebArena, eTAMP achieves up to 32.5% ASRB on GPT-5-mini and 23.4% on GPT-5.2 under Frustration Exploitation, showing persistent cross-session compromise without direct memory access.

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%.

Ingest-Time Semantic Compilation (ISC) builds a semantic substrate in PostgreSQL using a geometric layer, symbolic layer, validation gate, and index_outbox for incremental maintenance and migration. On 500 broadcast-interview transcripts, ISC’s compiled claims reach 85.2% accuracy from roughly 2.2k reader tokens, beating the best chunk configuration at 72.5% from roughly 16.3k tokens and matching a contextualized stack that spends ~47.7k tokens.

REAL represents conversational memory as a temporal directed property graph using Atomic Fact Extraction, Incremental Graph Update with Non-Destructive Temporal Evolution, Confidence Stratification and Upgrade, and Exploration Intent Enrichment. On LoCoMo, REAL with DeepSeek-V3 reaches 59.98% EM versus 55.12% EM for A-MEM, and averages a 22.72% improvement over existing memory management methods.

RealMem constructs realistic long-term project dialogues via Project Foundation Construction, Multi-Agent Dialogue Generation, and Memory and Schedule Management over eleven scenarios and 2,000+ cross-session dialogues. On RealMem, the Oracle QA Score reaches 0.804 while the strongest memory system, MemoryOS, achieves only 0.567, quantifying the difficulty of real-world project memory.

Memory–Clarification Boundary (MCB) uses persist, ephemeral, verify, and clarify actions plus the MCB-Act tool-call variant to probe memory commitment decisions in LLM agents. On the 70-item held-out MCB test, Qwen3.5-9B few-shot prompting raises accuracy from 0.557 to 0.771 while Qwen3.5-9B act-mode accuracy falls to 0.343, showing that stated decisions and tool-call choices diverge.

REMem converts interaction histories into a hybrid memory graph via Gist Extraction, Fact Extraction, and Graph Construction, then queries it with Agentic Inference tools like semantic retrieve and find entity contexts. On the Test of Time benchmark, REMem-I achieves 93.1% EM compared to 66.9% for HippoRAG 2, a +26.2 point gain in episodic reasoning.

TriMem combines dense storage with efficient retrieval, scattered fact with integrated profile, and lifelong evolution with optimized prompt to build a three-level agent memory. On LoCoMo with GPT-4.1-mini, TriMem achieves 54.26 F1 average versus 50.30 F1 for SimpleMem, and up to 57.04 F1 with GPT-5-nano.

Rethinking Memory Mechanisms of Foundation Agents in the Second Half organizes foundation agent memory using a three-dimensional taxonomy of Memory Substrates, Memory Cognitive Mechanisms, and Memory Subjects plus an operation and optimization view. Rethinking Memory Mechanisms of Foundation Agents in the Second Half synthesizes 218 memory-related agent papers from 2023 Q1–2025 Q4, highlighting the sharp acceleration of external memory and working or episodic mechanisms in 2025.

RippleMem converts dialogues into Cue-Rich Episodic Memory Construction, organizes them in an Event-Centric Memory Graph, and queries them via Adaptive Associative Recollection and Evidence Assembly. On LoCoMo, RippleMem attains 87.14% LLM-as-a-Judge accuracy, 52.49% F1, and 44.05 BLEU-1, beating RF-Mem’s 83.83% judge accuracy.

Self-Correcting RAG combines a MMKP-based Context Selector, NLI-Guided MCTS Generator, and Self-Correcting RAG Optimizer to jointly optimize retrieval and reasoning under token and redundancy constraints. On six QA datasets, Self-Correcting RAG attains average EM 37.1 and F1 45.8, beating the strongest baseline CRAG (avg EM 34.3, F1 43.3).

SE-Search combines Memory Purification, Atomic Query, and Dense Rewards inside a GRPO-trained search agent that follows a Think-Search-Memorize-Answer loop. On seven QA benchmarks, SE-Search-3B attains 0.420 average EM, a 10.8 point absolute and 33.8% relative gain over Search-R1-Base.

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.

StructMem organizes conversational history via Event-Level Binding, Cross-Event Consolidation, Dual-Perspective Extraction, and Temporal Anchoring to maintain temporally grounded relational events. On LoCoMo, StructMem achieves 76.82 overall accuracy while using just 1.937M construction tokens, improving over Mem0g’s 68.44 overall with 35.825M tokens.

SYNAPSE combines a Unified Episodic Semantic Graph, Cognitive Dynamics with Uncertainty Gating, and Triple Hybrid Retrieval to turn long-term agent memory into a dynamic associative graph. On the LoCoMo benchmark, SYNAPSE achieves 40.5 weighted F1 versus 33.3 for A-Mem, with 95% token reduction compared to full-context methods.

TAME stores experiences as (query, experience, usefulness, trust annotation) in a shared Strategy Memory Bank, orchestrated by an Executor–Evaluator loop with feedback-driven memory evolution. On the GPT-5.2 AIME benchmark, TAME reaches 0.733 accuracy versus 0.587 for ReasoningBank, a +0.146 improvement while preserving trustworthiness on Trust-Memevo.

TA-Mem processes long conversations via an Episodic Memory Constructor, Multi-Indexed Database with Tools, and Memory Retrieval Agent that cooperate to chunk, index, and query structured memory pages. On the LoCoMo dataset, TA-Mem achieves 55.95 F1 and 51.47 BLEU-1 on temporal questions, beating Mem0 and MemoryOS while using 3755 tokens on average.

MemStrata combines a deterministic assertion path, bi temporal ledger, production triple extractor, and strict_object_supersede gate to track current code facts across GitHub histories. On 130 SWE bench Lite plus Verified atomic transitions, MemStrata reaches 0.908–0.985 accuracy versus naive_rag’s 0.569–0.615 while reducing stale fact error from 0.361–0.377 to ≈0.