Aug 2026· Symmetry· Vol 18, pp. 1340· 0 citations· 24 references
TL;DR
This work introduces GRACE (Graph Reasoning with Adaptive Controller and Evidence), which feeds rejection back into retrieval and restores accept–reject symmetry in the search, and improves KG-grounded reasoning on WebQSP and ComplexWebQuestions.
Abstract
A standard verification-based knowledge graph question answering (KGQA) pipeline retains a reasoning path only when retrieved evidence supports it. Requiring evidence makes large language model (LLM) output easier to audit and reduces unsupported answers. The verifier’s two outcomes are not equally informative: acceptance provides strong evidence, whereas rejection on an incomplete graph may indicate either an incorrect path or a missing proof. A one-pass pipeline discards both cases and loses the information contained in the failure. We introduce GRACE (Graph Reasoning with Adaptive Controller and Evidence), which feeds rejection back into retrieval and restores accept–reject symmetry in the search. GRACE assigns a failure type to each rejected path. Its reflective feedback loop (RFL) revises subsequent retrieval, whereas hypothetical bridging (HB) admits budgeted and penalized virtual edges for plausible missing links. A virtual edge remains marked as a hypothesis rather than a verified triple; the bridge budget bounds answer-level hallucination exposure, and the marking makes that exposure observable. With a fixed open-source LLM backbone, GRACE improves KG-grounded reasoning on WebQSP and ComplexWebQuestions. Diagnostic results indicate that RFL and HB recover different types of failure.
This work presents an evidence-carrying validation interface: every selected node-shape check returns either a satisfaction trace or failure witness, and shows how programs combine passing and failing evidence to diagnose missing information and guide repair.
Experiments show that precision improves by filtering invalid candidates, while recall is preserved due to retaining candidates whose constraints are not explicitly violated, and a three-valued constraint semantics that avoids incorrect rejections under open-world assumptions.
E. Kitzelmann· Deutsche Jahrestagung für Kü...· 0 citations
RA-QGQA is presented, which recasts triple verification as a question-driven, corpus-grounded task, and demonstrates RA-QGQA as an interactive web system in which users import a KG and its source corpus, verify all triples in a single pass, and inspect the passages that justify its verdict.
Si-Yang Liu, Hong Duc Nguyen, Yun-Miao Li et al.· Proceedings of the 2026 ACM...· 0 citations
Piping and Instrumentation Diagrams (P&IDs) are the authoritative maps of process plants: isolation, maintenance, and HAZOP decisions depend on what connects to what. Vision-language models describe these sheets fluently, yet they often invent or miss process connections---and an invented or missed link can reverse an isolation or reachability call, so a plant decision cannot trust a fluent answer that was never checked against the linework. We instead recover an explicit graph of the drawing---its symbols, the process connections between them, and the tags that name them---and then require the model to answer only by querying that graph through seven read-only operators, so a topology claim is returned only when it cites the query results that support it. On TopoPID-VQA, a new suite of 3000 topology questions over these sheets, Graph-Grounded Harness (Ours) raises exact match accuracy from 36.7--41.3% under image-only prompting to 74.3--76.0% for Qwen3-VL-4B, Qwen3-VL-8B, and Gemma-4-E4B. It does so on an imperfect substrate: on Digitize-PID dataset the recovered graph scores F1 0.742 on exact process connections, and 0.801 once symbols and tags are pooled in. The residual errors track that gap---grounding pays off where the recovered graph is right, and perception error still breaks topology questions where it is not.
P. Gadekar, Sagar Srinivas Sakhinana, Venkataramana Runkana· 0 citations
Knowledge graph question answering (KGQA) and knowledge-graph-based retrieval-augmented generation (KG-RAG) aim to ground answers in explicit graph evidence, but real-world knowledge graphs are often sparse, outdated, and incomplete. Existing robustness evaluations usually report aggregate changes in answer quality after evidence is removed or perturbed, which measures sensitivity to incomplete support but leaves the source of degradation under-specified: the same score change can conflate the type of missing evidence, the response of the evaluated system, and the sensitivity of the answer-matching protocol. To address this gap, we propose \textbf{MissDiag}, a diagnostic evaluation framework for incomplete-knowledge robustness in KGQA and KG-RAG. MissDiag keeps the question and gold answer fixed while applying structurally typed missingness interventions to benchmark-provided support graphs, enabling paired comparisons that decompose robustness changes by evidence type, system response, and evaluation protocol rather than reducing them to a single aggregate score drop. Experiments across multiple system families show that incomplete-knowledge robustness is better understood as a typed degradation phenomenon than as a uniform property: answer-adjacent evidence loss produces the largest observed degradation, source-context removal is often neutral and can be beneficial, and semantic answer matching changes absolute scores while preserving the main typed degradation patterns. By transforming aggregate robustness measurement into typed diagnostic attribution, MissDiag provides a more interpretable basis for comparing, diagnosing, and stress-testing KGQA and KG-RAG systems under incomplete knowledge.
The proposed TRACE-QA, a training-free multi-agent protocol that routes each instance to a sparse set of reasoning operators, constructs option-blind necessity constraints, audits every candidate in a structured elimination ledger, revisits risky eliminations through global risk-aware rescue, and aggregates role-specialized judge votes, supports constraint-guided elimination as an auditable alternative to answer-first debate.
Jia-Xin Lu, Hao Chen, Yan-Cheng Zhu et al.· Journal of King Saud Univers...· 0 citations
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