arXiv:2608.30437v1 Announce Type: new Abstract: Graph-augmented large language models often assume that graph evidence produced by external computation and placed in the input can be used by the nati...
The paper introduces a two-stage diagnostic evaluation to assess the structural reliability of reasoning graphs used in large language models for tasks like multi-hop question answering. Stage 1 examines how graph reliability depends on topology, prompt design, and model family, while Stage 2 evaluates the impact of graph errors on uncertainty estimation. Findings show that graph-based computation is vulnerable to upstream errors, offers limited benefit as prompt context alone, but improves confidence-based answer discrimination and enables node-level diagnostics.
arXiv:2602. 11745v2 Announce Type: replace Abstract: Graph models are fundamental to data analysis in domains rich with complex relationships.
arXiv:2606. 11562v1 Announce Type: new Abstract: Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repurposing, user preference, and scientific theme are all inferred from a node together with its neighbourhood.
GraphDecide is a model‑independent benchmark designed to evaluate System One models—such as Jev—that make decisions directly from supplied options on graph‑related tasks. The benchmark combines structural task profiles, matched graph‑text input contrasts, and heuristic‑proposal controls to diagnose graph decision performance. In testing fourteen model‑interface configurations, GraphDecide shows that accurate adjacency recognition does not guarantee broader structural correctness, joint graph‑text input does not consistently improve prediction, and feasible construction does not ensure high solution quality.
GraphDecisionBench is a benchmark designed to evaluate System One models—such as Jev—that make decisions directly from supplied options on graph-related tasks. The benchmark is model‑independent and incorporates structural task profiles, matched graph‑text input contrasts, and heuristic‑proposal controls to diagnose graph decision performance. Evaluations across fourteen model‑interface configurations reveal that accurate adjacency recognition does not guarantee broader structural correctness, joint graph‑text input does not consistently improve prediction, and feasible construction does not ensure high solution quality.
ThreadRAG is a retrieval‑augmented generation framework that preserves contextual consistency by structuring retrieved documents into Semantic Nodes and building a Semantic Topology Graph (STG). It uses Energy‑Driven Pathfinding (EDP) to identify query‑relevant subgraphs and linearize them into coherent textual evidence paths. Experiments on HotpotQA, 2WikiMultihop, and another benchmark show ThreadRAG outperforms strong baselines, achieving a 14.4% EM gain on HotpotQA and a 5.8% EM lead on 2WikiMultihop while cutting token cost to 23.3% of the strongest baseline.
The paper investigates whether natural language inference (NLI) can be performed using only interpretable, graph-based representations of evidence. It introduces a pipeline that decomposes sentences into atomic propositions, maps them to ConceptNet triples, and feeds three graphs—premise, hypothesis, and a retrieved ConceptNet subgraph—into a fine‑tuned 0.8‑billion‑parameter language model. On the SNLI dataset the graph‑only model reaches 89.7% accuracy, close to a text‑based baseline, while on ANLI it matches RoBERTa‑large on rounds R2 and R3 but lags on R1, illustrating a trade‑off between interpretability and performance.
arXiv:2606. 16603v1 Announce Type: cross Abstract: LLM-based agents have demonstrated strong capabilities in data-intensive analytical tasks, yet their outputs are rarely verifiable: a reliance on linear text trajectories makes their reasoning difficult to audit.
arXiv:2608. 12391v1 Announce Type: cross Abstract: Graph reasoning provides a promising testbed for evaluating the reasoning ability of large language models (LLMs), as graph instances can be programmatically generated, structurally controlled, and naturally scaled to long-input settings.
The paper proposes a layered verification approach for argument graphs generated by large language models (LLMs). It suggests starting with deterministic checks, then using solver-based methods, followed by LLM reviews, and resorting to debate only when uncertainty remains, reserving human judgment for cases no other instrument can resolve. A study on human-annotated graphs with injected defects shows that while LLM reviewers can detect structural issues with high recall, they also produce false positives, exhibit inconsistent verdicts, and are slower than exact checks, underscoring the need for deterministic verification gates.
The paper introduces QRAKEN, a training‑free, ontology‑agnostic neurosymbolic pipeline that grounds natural‑language queries to RDF knowledge graphs using empirical graph evidence instead of schema expectations. QRAKEN distills a compact TTQL representation offline, then uses it online to guide large language models, providing deterministic checks and an iterative refinement loop. On the CK25 benchmark, QRAKEN achieves a strict F1 of 0.643–0.652 with GPT‑4.1 mini and GPT‑5.4, outperforming state‑of‑the‑art systems and demonstrating the effectiveness of empirical pattern distillation over schema‑only approaches.