RAG

ADAM combines Anchor extraction, Distribution estimation, Anchor selection, and Query generation to adaptively probe agent memory via an auxiliary generator and entropy based selection. On the EHRAgent benchmark with Llama2-7b-chat, ADAM reaches EQ=77 and ASR=1.00, compared to MEXTRA’s EQ=44 and ASR=0.89.

Adaptive RAG Memory (ARM) augments a standard retriever–generator stack with a Dynamic Embedding Layer and Remembrance Engine that track usage statistics and apply selective remembrance and decay to embeddings. On a lightweight retrieval benchmark, ARM achieves NDCG@5 ≈ 0.9401 and Recall@5 = 1.000 with 22M parameters, matching larger baselines like gte-small while providing the best efficiency among ultra-efficient models.

HingeMem combines Boundary Guided Long-Term Memory, Dialogue Boundary Extraction, Memory Construction, Query Adaptive Retrieval, Hyperedge Rerank, and Adaptive Stop to segment dialogues into element-indexed hyperedges and plan query-specific retrieval. On LOCOMO, HingeMem achieves 63.9 overall F1 and 75.1 LLM-as-a-Judge score, surpassing the best baseline Zep (56.9 F1) by 7.0 F1 without using category-specific QA formats.

Knowledge Packs pre-compute KV Cache Injection, KV–Prefix Equivalence, Banked Routing, and KV Composition to deliver retrieved knowledge and steering via KV states instead of prompt tokens. On HotpotQA, Knowledge Packs’ KV-chat matches RAG at 65.2% EM on Qwen3-8B with 0/500 divergences while eliminating 284 tokens of retrieval text per query.

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.

Memory as Metabolism defines companion knowledge systems with five retention operations (TRIAGE, DECAY, CONTEXTUALIZE, CONSOLIDATE, AUDIT) plus memory gravity and minority-hypothesis retention over a raw buffer, active wiki, and cold memory. Instead of benchmark gains, Memory as Metabolism’s main result is a governance specification that separates descriptive, taxonomic, and normative claims and predicts improved coherence stability, fragility resistance, monoculture resistance, and effective minority-hypothesis influence for companion wikis.

Memory for Autonomous LLM Agents decomposes agent memory into a POMDP-grounded write–manage–read loop, a three-dimensional taxonomy, and five mechanism families spanning context compression, retrieval stores, reflection, hierarchical virtual context, and policy-learned management. Memory for Autonomous LLM Agents synthesizes results like Voyager’s 15.3× tech-tree speedup and MemoryArena’s 80%→45% drop to show that memory architecture often matters more than backbone choice.

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.

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.

HGMEM represents working memory as a hypergraph with Hypergraph-based Memory Storage, Adaptive Memory-based Evidence Retrieval, and Dynamic Memory Evolving to build high-order correlations across entities and facts. On Prelude long narrative understanding, HGMEM with GPT-4o achieves 73.81% accuracy compared to 72.22% for HippoRAG v2, while also reaching 69.74 comprehensiveness on Longbench generative sense-making QA.

Learning from Supervision with Semantic and Episodic Memory combines a performance agent, critic agent, semantic memory, episodic memory, and memory retriever to turn label-grounded critiques into reusable supervision without parameter updates. On the Multi-Condition Ranking dataset with Mixtral 8x22B and o4-mini as critic, Learning from Supervision with Semantic and Episodic Memory reaches 85.6% accuracy, a 24.8% gain over the EP_LABEL baseline at 60.8%.

LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household Robotics coordinates a routing agent, task planning agent, and knowledge base agent over RAG and ChromaDB to translate household commands into grounded robot actions. In three tabletop scenarios, Qwen2.5-32B in LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household Robotics achieves 84.3% total lenient task planning accuracy versus 68.7% for Gemma2-27B and 61.1% for LLaMa3.1-8B.

MemInsight augments agent memory using Attribute Mining, Annotation and Attribute Prioritization, and Memory Retrieval modules that generate and exploit structured attributes over past interactions. On the LoCoMo question answering benchmark, MemInsight with Claude-3-Sonnet priority augmentation achieves 60.5% Recall@5 versus 26.5% for DPR, a 34.0-point improvement.

DM-RAG augments Phi-4-mini with a Short-Term Memory (STM) buffer, Long-Term Memory (LTM) FAISS store, Bayesian fusion, and a logistic regression confidence model for structured log analysis. On UNSW-NB15, DM-RAG reaches 98.70% recall and 69.59% F1, beating the Phi-4 + RAG (MITRE) baseline in F1 by 17.89 points.

Memory Decoder combines a Pre-training stage that aligns with kNN-LM distributions and an Inference interpolation mechanism that mixes Memory Decoder and base LLM outputs without changing base parameters. On Wikitext-103, Memory Decoder with 124M parameters reaches 13.36 perplexity on GPT2-small versus 14.76 for DAPT, and on specialized domains a single 0.5B Memory Decoder reduces average perplexity from 14.88 to 4.05 on Qwen2-0.5B.

MemVid combines a memory model, memorizer, retriever, and generator so a reasoning-oriented KV-cache memory can produce task-specific clues that drive video moment retrieval. On VideoMME (with subtitles, Avg column), MemVid scores 65.7% compared to 61.0% for Video-XL-7B, a +4.7 point gain under the same 7B scale.

Memory in the Age of AI Agents formalizes agent memory with Memory Formation, Memory Evolution, and Memory Retrieval operators, and classifies memories into token-level, parametric, and latent forms plus factual, experiential, and working functions. Memory in the Age of AI Agents’ main result is a unified Forms–Functions–Dynamics framework that consolidates fragmented LLM agent memory work, benchmarks, and open-source frameworks into a coherent taxonomy.

MEPIC extends vLLM with a Chunk Cache Coordinator, Chunk Matcher, Hybrid KV Manager, Chunk LRU Manager, and Chunk Processor to manage canonical, page-aligned, position-independent KV chunks in HBM. On long-context workloads, MEPIC reduces HBM usage by up to 5.21× and lowers latency by up to 11.48% compared to CacheBlend on Mistral-7B-Instruct-v0.3.

MeVe decomposes retrieval into Initial Retrieval, Relevance Verification, Fallback Retrieval, Context Prioritization, and Token Budgeting to tightly control what enters the LLM context. On a Wikipedia subset and HotpotQA, MeVe reduces average context from 188.8 to 79.8 tokens and from 308.6 to 78.5 tokens respectively compared to Standard RAG while keeping retrieval time comparable.

Mnemosyne combines a Commitment pipeline with substance and redundancy filters, a probabilistic Recall traversal over a graph-structured store, asynchronous Core Summary updates, and a Pruning module to manage long-term memory on edge devices. On the LoCoMo benchmark, Mnemosyne reaches 60.42% temporal reasoning J-score and a 54.55% overall J-score, compared to 51.55% temporal reasoning and 62.74% overall for Memory-R1, and achieves a 65.8% win rate over a 31.07% naive RAG baseline in human evaluations.

MobileRAG builds a fully on-device RAG stack by integrating EcoVector, Selective Content Reduction, DB Construction, and Chat Application over a local SQLite store. On SQuAD with Qwen2.5 1.5B, MobileRAG reaches 65.1% accuracy while reducing TTFT from 11.78s for Advanced RAG to 7.41s and cutting power from 89.09J to 56.28J.

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.

Semantic Anchoring enriches conversational memory by combining a hybrid memory store with dense and symbolic indexes, structured memory representation tuples, hybrid storage and indexing, and a retrieval scoring method. On MultiWOZ-Long, Semantic Anchoring reaches 83.5% Factual Recall and 80.8% Discourse Coherence, beating Entity-RAG by 7.6 and 8.6 points respectively.

TeleMem converts interactions into unified semantic nodes via the representation layer, organizes them in a memory graph with Insert and ReInsert, and reads them using closure-based retrieval and a ReAct-style multimodal agent. On ZH-4O, TeleMem reaches 86.33% QA Accuracy, beating the Mem0 baseline at 70.20% and the RAG baseline at 62.45%.

Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory analyzes users' mental models, privacy calculus, and expectations around RAG-based memory across generation, management, usage, and updating. Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory finds users demand explicit consent, fine-grained editing and deletion, and visibility into inferred information to trust RAG-based memory systems.

EMG-RAG combines Editable Memory Graphs, a two-stage MDP for Selecting Memories on EMGs, and Data Collection from real smartphone assistants to support editable, graph-structured personal memories. On a 2,500-user business dataset, EMG-RAG with GPT-4 reaches 75.99 BLEU on question answering, a +11.83 gain over M-RAG.

Deciphering the Interplay of Parametric and Non-parametric Memory instruments causal mediation analysis, Experiment 1, Experiment 2, and Path Specific Effects (PSE) inside ATLAS to trace how parametric and non-parametric memories compete token-by-token. Deciphering the Interplay of Parametric and Non-parametric Memory reports a strong shift toward counterfactual answers in altered contexts, with a t-test p-value of 1.60e-4 and Cohen’s d of -0.9851 for non-parametric versus parametric behavior.

Embodied-RAG builds a multimodal Topological Map and a hierarchical Semantic Forest and then runs Top-down Retrieval with LLM-based selection and hybrid re-ranking to drive Generation of waypoints and explanations. On the E-multimodal Embodied-Experiences dataset, Embodied-RAG reaches P(Q|A)=0.67 for implicit queries (Q only), compared to 0.13 for LightRAG, while building graph memory 9.76× faster than LightRAG.

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.

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.

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.