Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· 1 citation· 17 references
TL;DR
This work delineates LLM reasoning boundaries and presents a new paradigm for fine-grained capability assessment, which suggests that genuine reasoning is demonstrated only when a model follows logical rules despite conflicting prior knowledge.
Abstract
Despite the success of Large Language Models (LLMs) on reasoning benchmarks, it remains unclear whether their performance stems from genuine logical deduction or the memorization of training patterns. Existing benchmarks often fail to disentangle reasoning from prior knowledge, as tasks grounded in real-world facts allow models to take ''knowledge shortcuts''. In this paper, we propose a novel diagnostic benchmark to decouple knowledge memorization from logical reasoning. Built on the DBpedia KG, our framework constructs multi-hop reasoning chains (from Q1 to Q5) across three task dimensions: Factural Questions (FQ), Counterfactual Questions (CQ) with logically consistent but counterfactual conclusions, and Questions with Similar-Entity Options (SO) to evaluate the dependence on prior knowledge. Questions without Context serve only as an intermediate form: they contain solely queries with no triples or options, so LLMs cannot answer them directly. Our core hypothesis is that genuine reasoning is demonstrated only when a model follows logical rules despite conflicting prior knowledge. Evaluating seven state-of-the-art LLMs (8B to ultra-large) reveals strong prior knowledge dependence, with performance degrading sharply on counterfactual tasks as reasoning depth grows. This work delineates LLM reasoning boundaries and presents a new paradigm for fine-grained capability assessment.
TKFQA, a factuality consistency benchmark comprising 10,130 question-answering (QA) pairs grounded in tables, texts, and knowledge graphs, is introduced and ORLF, an LLM-agnostic training framework that models cross-context topological relations through knowledge-specific latent vectors is proposed.
Shibo Chu, Yuze Liu, Tiehua Zhang et al.· 0 citations
Evaluating the multi-hop reasoning capabilities of large language models remains a significant challenge. Although current models achieve strong results on existing multi-hop question answering datasets, such performance often masks two critical vulnerabilities: (1) reliance on internal parametric knowledge rather than adherence to the provided context, and (2) exploitation of dataset shortcuts, such as single-document cues or type-matching, that diminish the need for genuine evidence aggregation across multiple documents. We introduce CRiT-QA (Counterfactual Reasoning with Traps), a dataset explicitly designed to address both limitations. To neutralize reliance on memorized knowledge and enforce strict context dependency, CRiT-QA transforms factual reasoning chains with counterfactual entities. Furthermore, it injects multi-anchor distractor chains, plausible but incorrect reasoning paths that diverge at different hops. These traps require models to follow the entire reasoning process rather than exploiting shallow heuristics. Our experiments show that LLMs exhibit substantial performance degradation on CRiT-QA compared to standard datasets, exposing their vulnerability to counterfactual conditions and distractor traps. CRiT-QA thus serves as a rigorous diagnostic tool for evaluating genuine multi-hop reasoning and provides a foundation for developing more reliable, evidence-grounded LLMs.
Large language models (LLMs) often produce reasoning steps that are superficially coherent yet internally inconsistent, leading to unreliable outputs. Since such failures typically arise from implicit or poorly-grounded knowledge, we introduce Grounded Reasoning in Dependency (GRiD) , a novel dependency-aware reasoning framework that explicitly grounds reasoning steps in structured knowledge. GRiD represents reasoning as a graph consisting of interconnected knowledge extraction nodes and reasoning nodes, enforcing logical consistency through explicit dependencies. Each reasoning step is validated via a lightweight, step-wise verifier that ensures logical correctness relative to its premises. Extensive experiments across diverse reasoning benchmarks—including StrategyQA, CommonsenseQA, GPQA, and TruthfulQA—demonstrate that GRiD substantially improves reasoning accuracy, consistency, and faithfulness compared to recent state-of-the-art structured reasoning methods. Notably, GRiD enhances performance even when applied purely as a lightweight verification module at inference time, underscoring its generalizability and practical utility † .
Xiangyu Wen, Min Li, Junhua Huang et al.· Neural Information Processin...· 2 citations
Large Language Models (LLMs) explore problems through chain-of-thought, but this exploration is buried in unstructured prose. On high-stakes tasks, users cannot tell which steps are well-supported, which alternatives were seriously considered, or how the final conclusion compares to those the model discarded. We propose a framework that ensembles the reasoning structure, not just the answers, of multiple LLMs by weighted merging of Directed Acyclic Graphs (DAGs) extracted from reasoning chains. We weight each step by how many traces independently attest to it, to return"Consensus Reasoning". Across six benchmarks spanning statutory interpretation, graduate-level science, narrative multi-hop reasoning, and first-order logic, our ensemble outperforms a matched-budget majority-vote baseline, with a maximum accuracy gain of 3.1% on MuSR-MM (narrative multi-hop reasoning). On a single model, the framework matches or exceeds self-consistency at the same trace budget while additionally exposing an inspectable consensus reasoning graph. Ensemble weights correlate with LLM-judge rankings of reasoning quality at Spearman $\rho = 0.30$-$0.51$, and consensus subgraphs are preferred over alternatives leading to the majority-vote answer in 54.4-65.4% of head-to-head comparisons across five of six datasets. We observe that our framework can also be used to analyze diverse reasoning perspectives for a problem.
Amruta Parulekar, Jinu Lee, Dilek Z. Hakkani-Tür et al.· 0 citations
Retrieval-augmented generation (RAG) improves question answering by grounding large language models (LLMs) in external knowledge such as text corpora. However, its reasoning process remains largely opaque: intermediate reasoning steps are difficult to verify and cannot be reliably attributed to specific evidence. Moreover, missing user-specific context is rarely detected systematically, often leading to incomplete or incorrect output. We propose NeSy-RAG, a modular neuro-symbolic RAG framework that synthesizes attributable Prolog modules from retrieved text chunks. For each chunk, the system generates semantically meaningful predicates that encode Boolean claims, which may depend on user facts. Using joint natural language-code embeddings, predicates are retrieved and composed into Prolog queries. To address incomplete user context, we introduce a symbolic knowledge-gap detection mechanism that identifies missing user facts whose truth values affect the query outcome and automatically triggers follow-up interactions. Executing the resulting Prolog queries yields deterministic answers together with transparent execution traces that link each reasoning step to its originating source. On the ShARC benchmark, without domain-specific training, NeSy-RAG achieves 61.1% accuracy, outperforming a same-model RAG baseline that achieves 42.8% accuracy.
MulTR, a comprehensive benchmark designed for multi-answer temporal reasoning from long unstructured contexts, is introduced, designed to systematically decouple temporal reasoning capabilities from the uncertainty of the number of answers.
Shaojuan Wu· Annual International ACM SIG...· 0 citations