The paper proposes a four‑layer architecture—Signals, Domains, Claims, and Dimensions—to preserve and revise organizational interpretations of evidence. It introduces a truth‑maintenance network that updates the standing of interpretations as new evidence arrives and uses validated, versioned Dimensions to position these interpretations along named axes. Experiments show that incorporating Dimensions improves retrieval performance, with a 0.028 nDCG@10 gain for the Dimension term and overall nDCG@10 increases of 0.081 and 0.053 over baseline configurations.
arXiv:2604. 03588v3 Announce Type: replace Abstract: AI agents operating over extended time horizons accumulate experiences that serve multiple concurrent goals, and must often maintain conflicting interpretations of the same events.
arXiv:2607. 02609v1 Announce Type: cross Abstract: For decades, data engineering has developed mature architectural principles for integrating, governing, validating, cataloging, and serving organizational data.
The paper investigates whether a knowledge graph can enhance retrieval for enterprise retrieval‑augmented generation (RAG) systems compared to traditional similarity search. Using a snapshot of SEC EDGAR filings, the authors built a graph with 430,257 nodes and 561,144 edges, then evaluated three architectures—text‑to‑SQL, dense‑vector RAG, and a two‑stage GraphRAG. GraphRAG outperformed the other methods, correctly answering 16 of 20 questions across all attempts, while the other approaches lagged behind, especially in structured aggregation and single‑document queries.
Agent Zero Memory is a provenance‑aware long‑term memory system for large language model agents that distills user interactions into three parallel memory structures: an episodic timeline, an associative entity‑event knowledge graph, and a semantic, citation‑locked hierarchical documentary memory. Retrieval is performed via an intent gate, source router, and concurrent searches across the three systems, producing integrated, cited answers that exclude fabrication and require evidence the reader has opened. The system achieves state‑of‑the‑art performance on LongMemEval (95.60%) and LoCoMo (93.60%) while offering a favorable accuracy‑cost‑latency trade‑off across multiple backbone LLMs.
arXiv:2608. 15193v1 Announce Type: cross Abstract: As large language model (LLM) agents are increasingly adopted in scientific research, external knowledge bases, knowledge graphs, and long-term memory have improved information retrieval and task continuity.
arXiv:2608.21418v1 Announce Type: new Abstract: Manufacturing knowledge graphs that integrate data from heterogeneous industrial systems face a trust deficit: consumers cannot determine whether queri...
arXiv:2608.28642v1 Announce Type: new Abstract: Knowledge graphs used by agentic systems are often treated as flat stores of extracted triples, with little record of who owns a fact, why it was admit...
The paper introduces CogMem, a cognitive memory architecture for long‑term dialogue agents that uses a PEC$^2$F graph schema to separate claims, facts, and events. Dialogue turns are incrementally turned into provenance‑aware graph records, consolidated into higher‑level facts, and reconciled into temporally scoped claim views when conflicting updates arise. Retrieval is performed by a rule‑based controller that applies deterministic graph operators—anchoring, traversal, intersection, and evidence grounding—guided by LLM intent parsing, achieving strong performance on multi‑hop, temporal, and knowledge‑update tasks in LoCoMo and LongMemEval benchmarks.
The paper introduces Knowledge Cards, a new structured artefact designed to capture validated knowledge about specific concepts that AI systems use to make decisions. Unlike existing model, data, and system cards, Knowledge Cards focus on the layer between inputs and outputs, documenting entities, relationships, reasoning patterns, conditions for validity, and provenance, all grounded in a formal domain ontology and signed off by a domain expert. Prototype cards have been created in the energy and pharmaceutical domains, and the schema is released as a public draft for community engagement.
EGCForest is a multi‑view hierarchical memory framework designed for enterprise group chats. It organizes the immutable message log into three complementary forests—Channel, Actor, and Artifact—each capturing temporal workflows, speaker‑conditioned context, and deliverable lifecycles, respectively. By using a Placement Map and sparse structural bridges, EGCForest jointly leverages message‑level relevance, forest summaries, and cross‑view links to retrieve and assemble distributed evidence, outperforming RAG and memory‑system baselines by up to 20.85 points on three benchmarks.
arXiv:2609. 04377v1 Announce Type: new Abstract: Enterprise AI deployments fail not from model inadequacy, but because organizations lack a structured substrate encoding how they decide, negotiate, and execute.
arXiv:2608.21755v2 Announce Type: replace Abstract: Long-horizon agents require more than retrieval over stored conversations: they must preserve source evidence, revise outdated knowledge, and disti...