Working Memory

Focus Agent adds start_focus, complete_focus, a persistent Knowledge block, and an optimized Persistent Bash plus String-Replace Editor scaffold to actively compress context during long software-engineering tasks. On five hard SWE-bench Lite instances against a Baseline ReAct agent, Focus Agent achieves 22.7% token reduction (14.9M → 11.5M) while matching 3/5 = 60% task success.

AgenticAI-DialogGen chains ChatPreprocessor, KnowledgeExtractor, TopicAnalyzer, KnowledgeGraphBuilder, PersonaGenerator, DuelingChat Agent, ConversationValidator, ConversationRefiner, QAGeneration, and PostProcessing to turn raw multi-session chats into topic-guided, persona-grounded conversations with explicit short- and long-term memories. On the TGC / KG memory QA benchmark, Mistral-7B fine-tuned within AgenticAI-DialogGen achieves 87.36 F1, compared to GPT-4’s 83.77 F1 in a zero-shot setting on the same task.

Agentic Memory (AgeMem) exposes memory management tools, a three-stage progressive RL strategy, and step-wise GRPO directly inside the agent policy to jointly control long-term and short-term memory. On Qwen3-4B-Instruct, AgeMem attains 54.31% average performance across ALFWorld, SciWorld, PDDL, BabyAI, and HotpotQA, exceeding the best baseline A-Mem at 45.74%.

AGENTSYS organizes a Main Agent, Worker Agents, Intent Schemas, and an Alignment Validator into a hierarchical memory system that isolates raw tool outputs and only admits schema-validated JSON. On AgentDojo, AGENTSYS reaches 52.87% attacked utility and 0.78% ASR versus 48.27% and 30.66% for the No Defense baseline.

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.

LightMem orchestrates SLM-1 Controller, SLM-2 Selector, SLM-3 Writer, and STM MTM LTM stores to modularize retrieval, writing, and offline consolidation. On LoCoMo, LightMem reaches 34.50 F1 for GPT-4o multi hop questions, +1.64 over A-MEM, while keeping median retrieval latency at 83 ms.

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.

LightMem pipelines a Cognitive-Inspired Sensory Memory, Topic Segmentation Submodule, Topic-Aware Short-Term Memory, and Long-Term Memory with Sleep-Time Update to filter, group, summarize, and asynchronously consolidate dialogue history. On LongMemEval-S with Qwen3-30B-A3B-Instruct-2507, LightMem reaches 70.20% ACC vs 65.20% for A-MEM (+5.00 points) while reducing total token usage by up to 21.8× and API calls by up to 17.1×.

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 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.

MMAG organizes conversational memory, long-term user memory, episodic and event-linked memories, sensory and context-aware memory, and short-term working memory under a modular memory controller integrated with Heero’s encrypted Firestore and S3 stores. MMAG delivers a 20% increase in user retention and a 30% increase in average conversation duration on the Heero language learning platform compared to its pre-memory deployment.