arXiv:2606. 15246v1 Announce Type: cross Abstract: Provenance-enhanced statements of the form "according to $X$, $\varphi$" are pervasive in contemporary knowledge graphs, especially in domains where graph content primarily represents claims, interpretations, and hypotheses (\emph{capta}) rather than observer-independent facts (\emph{data}).
arXiv:2608.29753v1 Announce Type: new Abstract: Multi-hop question answering in retrieval-augmented gener?ation (RAG) often benefits from retrieving beyond the few candidates that will finally be rea...
arXiv:2611.07432v1 Announce Type: cross Abstract: Type theories designed for mathematics or program verification need only that a proposition holds; proof irrelevance in conversion erases, at the ker...
LatWeave is a deterministic multi‑hop question‑answering framework that structures knowledge into a multidimensional lattice and reduces QA to three operators—meet, compare, and abstain—while limiting LLM use to extraction and planning. It achieves near‑lossless performance on complete knowledge benchmarks (e.g., MetaQA) and strong results on templated multi‑hop datasets (e.g., 2WikiMultihopQA), while transparently handling incomplete knowledge through abstention. The approach offers reproducible, auditable answer paths with no performance penalty within its operating envelope.
ArcRAG is a retrieval-augmented generation system that operates over knowledge graphs without relying on construction‑specific schemas or relation names. It dynamically induces a query‑local graph interface at inference time, plans multi‑hop traversals, and executes them as Cypher queries. Experiments on MuSiQue, HotpotQA, and TriviaQA show that ArcRAG remains effective across both schema‑guided and open‑information‑extraction graph constructions, demonstrating that retrieval can be construction‑agnostic.
arXiv:2607. 03447v1 Announce Type: cross Abstract: Knowledge graphs (KGs) that underpin Graph-based Retrieval-Augmented Generation (Graph-RAG) are increasingly built automatically by LLM-driven extraction rather than curated by experts.
arXiv:2608. 04457v1 Announce Type: cross Abstract: As "AI Scientists" emerge to drive research via the Model Context Protocol (MCP), systems relying on ephemeral scripts will fail.
arXiv:2608. 08118v1 Announce Type: new Abstract: There are several methods for searching for graphs with prescribed properties, such as SAT solvers and specialized generators.
As "AI Scientists" emerge to drive research via the Model Context Protocol (MCP), systems relying on ephemeral scripts will fail. The sheer scale of stateful, interconnected evidence requires a machine-walkable warranty grounded in a purpose-built database architecture.
arXiv:2606. 28841v1 Announce Type: cross Abstract: Large language models are increasingly capable of mathematical reasoning, but the proofs they generate are often unreliable and hard to verify.
The paper introduces a hierarchy of four increasingly strict faithfulness criteria—discrimination, logical admissibility, monotonic logical faithfulness, and probabilistic logical faithfulness—for evaluating knowledge base completion models, particularly when the target is a description logic knowledge base. It demonstrates that ranking accuracy alone does not guarantee logical faithfulness and shows that current embedding models fail to satisfy any of the criteria across the hierarchy. The authors provide a formal grounding for the strongest criterion using relative model counts and evaluate several models on εL ontologies, revealing gaps between performance metrics and logical correctness.
arXiv:2605. 26874v2 Announce Type: replace-cross Abstract: LLM-based agents for industrial asset operations show limited accuracy when reasoning over flat document stores.