Semantic Web and Ontologies

Últimos artículos

• Online Verification of Language Model Responses Under Cost Constraints

  Erfan Hajihashemi, Yanning Shen · 5 de octubre de 2026

  As large language models are increasingly deployed for multi-step reasoning, verifying the correctness of their outputs has become essential for maintaining reliability at scale. Verifying the correctness of large language model outputs is often done by querying a costly ground-truth oracle, which i…

• Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching

  Songze Li, Zhiqiang Liu, Zhengke Gui, Huajun Chen, Wen Zhang · 5 de octubre de 2026

  Large Language Models (LLMs) exhibit strong reasoning capabilities in complex tasks. However, they still struggle with hallucinations and factual errors in knowledge-intensive scenarios like knowledge graph question answering (KGQA). We attribute this to the semantic gap between structured knowledge…

• The Geometry of Knowledge Accessibility in Large Language Models

  Lihu Chen · 5 de octubre de 2026

  Large language models (LLMs) contain broad knowledge, but they cannot access all of it reliably. We study this problem through knowledge accessibility, which describes whether the knowledge needed for a query can be recalled from the model. We find that knowledge accessibility has a simple geometric…

• HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning

  Donggyun Kim, Jack Lu, Chanwoo Kim, Mengye Ren, Seunghoon Hong · 5 de octubre de 2026

  Long-form thinking traces can substantially improve the multi-step reasoning performance of large language models (LLMs), but they introduce high inference-time overhead, with latency dominated by sequential decoding. We propose HyperThink, a text-to-parameter approach that amortizes this reasoning …

• ULTRADISCOVERY: Abductive Exploration in an Interconnected, Epistemically Open Universe

  Weihan Li, Tianshi Zheng, Yangqiu Song, Ginny Y. Wong, Simon See · 5 de octubre de 2026

  Scientific discovery often begins when scattered clues call for a new way of describing the world. Such abductive exploration can require constructing the representation in which an explanation is stated, when the world is epistemically open, and composing evidence scattered across contexts, when it…

• JOVE: Joint Execution and Verification for Resource-Aware LLM Task Graphs

  Haoran Zhang, Dongjun Kim, Seohyeon Cha, Kevin S Chan, Ananthram Swami, Gustavo De Veciana, Haris Vikalo · 5 de octubre de 2026

  Complex reasoning queries can be decomposed into directed acyclic task graphs and distributed across heterogeneous LLMs, reducing latency through parallelism and enabling smaller models to solve complex tasks. In practice, however, the suitability of an LLM for a given subtask may be a priori unknow…

• Decoupling Memory from Context: Structured Memory for Token-Efficient Test-Time Continual Learning

  Yehya Farhat, Michael Desmond, Anastasios Kyrillidis · 5 de octubre de 2026

  Large language models (LLMs) are increasingly deployed in enterprise, scientific, and medical applications, where agents must incorporate domain-specific knowledge and adapt from experience. Context engineering offers a practical alternative to weight updates by improving model behavior through inst…

• Selection-Based Structured Reasoning: Toward Efficient Multimodal Search Agents

  Feiyu Gavin Zhu, Xiaoyu Zhu, Jiqi Yang, Rui Yang, Arnab Kumar Mondal, Yancheng Wang, Xinke Deng, Jean Oh, Reid Simmons, Joerg Liebelt, Xiang Kong, Zhongyu Jiang · 2 de octubre de 2026

  Multimodal agents commonly generate free-form reasoning before each action. For small models, limited model capacity can result in lengthy reasoning that provides little useful guidance for action generation while incurring substantial inference cost. To address this challenge, we introduce Selectio…

• Auto-Formalizing Neuro-Symbolic Predictors

  Samuele Bortolotti, Weixin Chen, Han Zhao, Andrea Passerini, Stefano Teso, Antonio Vergari · 2 de octubre de 2026

  Neuro-Symbolic (NeSy) predictors incorporate prior knowledge into the prediction process of neural networks, ensuring that outputs satisfy specified constraints, making them particularly suitable for high-stakes applications where compliance with domain knowledge is essential. A key bottleneck in th…

• ReHoPER: Receding-Horizon Planning for Enhanced Reasoning

  Saeed Ahmadnia, Cornelia Caragea · 2 de octubre de 2026

  We propose ReHoPER, an inference-only, zero-shot method that improves large language models' reasoning by generating and answering intermediate questions along multiple paths before the final answer. It iteratively plans a horizon of candidate intermediate questions, selects one to answer, and repla…

• When Reasoning Helps Action: Monitoring and Steering Chain-of-Thought in Vision-Language-Action Policies

  Sathwik Karnik, Joseph JR. Lee, Aryaman Gupta, Somil Bansal · 2 de octubre de 2026

  Reasoning-enabled VLA policies expose chain-of-thought (CoT) traces that appear to explain and guide their actions, creating a potential interface for runtime safety through reasoning monitoring and correction. In this work, we define and operationalize two evaluation axes for assessing when this in…

• Random Recursive Models

  Jama Hussein Mohamud, Mirco Ravanelli · 2 de octubre de 2026

  Recursive models create computational depth through parameter reuse, offering a parameter-efficient alternative to increasing model size. However, most recursive models repeatedly apply one learned transformation or a prescribed sequence of transformations, restricting computation to a fixed layer o…

• Learning to Ask: Information Acquisition for SLM-LLM Collaboration, under a budget

  Yongjun Kim, Xiaoxiao Li, Jaeho Lee · 2 de octubre de 2026

  Collaboration between a small language model (SLM) and a large language model (LLM) offers an opportunity to combine the efficiency of smaller models with the strong reasoning capabilities of larger ones. Existing approaches primarily frame such collaboration as a computation allocation problem, det…

• Improving Math Reasoning through Value-guided Informative Search

  Shaohuai Liu, Yuning Wu, Haoran Liu, Enzo Jia, Devin Chen, Kai Wei · 2 de octubre de 2026

  Reinforcement learning with verifiable rewards (RLVR) has substantially improved the mathematical reasoning capabilities of large language models. Recent work introduces search into RLVR rollouts to increase trajectory diversity, but diversity alone does not ensure that the search-induced rollout po…

• Ontology-Grounded, Reasoner-Verified Benchmarks for Evaluating LLM Reasoning in Scientific AI

  Nishtha N. Vaidya, Stephan Grimm, Thomas Hubauer, Thomas A. Runkler · 2 de octubre de 2026

  Large language models (LLMs) increasingly underpin scientific AI applications that reason over structured knowledge, from biomedical question answering to materials informatics. However, their logical reasoning often falls short, producing factual inaccuracies unacceptable in these settings. Reliabl…

• EviGraph: Proof-Carrying Selective Recommendation over Temporal Public-Service Knowledge Graphs

  Yixi Zhou, Sikun Wang, Lei Fan, Fan Zhang · 2 de octubre de 2026

  Public-service recommendations require evidence that matches the requested service, scope, and date. Yet treating every missing detail as decisive can withhold useful recommendations. We introduce EviGraph, which distinguishes critical decision requirements from information that can remain unresolve…

• Interpreting Reasoning of Large Language Models via Partial Information Decomposition

  Barproda Halder, Qiuyi Zhang, Sanghamitra Dutta · 2 de octubre de 2026

  Large reasoning models (LRMs) have achieved substantial improvements in solving complex mathematical problems, but often produce lengthy, repetitive, or erroneous reasoning trajectories. In this work, we introduce a new interpretability framework, SLIDER, to evaluate the quality of the reasoning pro…

• CypherTurn: A Multi-Turn Benchmark for Conversational Text-to-Cypher Evaluation and the Autonomy Divergence

  Yuzhe Zhang, Weijie Zhu, Haolin Yang, Ziyun Zhang, Xianwei Xue, Mengke Chen, Qiutong Pan, Huaqian Cai · 1 de octubre de 2026

  Graph databases are increasingly queried through natural language, yet every existing benchmark evaluates isolated single-turn queries rather than the multi-turn sessions through which analysts actually work. We introduce CypherTurn, the first benchmark for conversational Text-to-Cypher evaluation, …

• The Canonical Order Problem: When Large Language Models Are Unreliable Knowledge Bases for Multi-Valued Relations

  Timo Pierre Schrader, Annemarie Friedrich, Simon Razniewski, Lukas Lange · 1 de octubre de 2026

  Large language models (LLMs) are increasingly used as knowledge bases (KBs) due to the vast amount of knowledge they acquire during pre-training. While many works focus on extracting single relational triples, most real-world relations are multi-valued and require generating sets of entities. In t…

• On the (In)effectiveness of AMR Augmentation for Large Language Models

  Hoa Quynh Nhung Nguyen, Jacopo Staiano, Michael Sullivan · 1 de octubre de 2026

  While Abstract Meaning Representation (AMR) has historically improved performance on a range of NLP tasks, the benefit---or lack thereof---of AMR augmentation for modern LLMs is thus far unclear. In this paper, we attempt to reproduce recent work that reported substantial downstream gains from AMR a…

• What Limits Recursive Reasoning Models: Optimization, Architecture and Test-Time Scaling

  Yuliana Shakhvalieva, Dmitrii Kharchev, Viacheslav Bezrukov, Inessa Fedorova, Dmitry Bocharov, Ivan Oseledets, Valerii Ternovskii · 1 de octubre de 2026

  Recursive reasoning models apply a small shared Transformer block many times to refine a latent state. This gives them large effective depth with few parameters and makes them strong on algorithmic tasks. Such compact solvers are natural candidates for tools that an LLM can call on narrow algorithmi…

• Making LLMs Say What They Think: Measuring and Improving CoT-Interpretability Alignment

  Yihuai Hong, Shauli Ravfogel, Chen Zhao, Eunsol Choi · 1 de octubre de 2026

  Chain-of-thought (CoT) traces often serve as a proxy for how Large Language Models (LLMs) arrive at their answers. However, growing evidence shows that models' CoT often fails to reflect their internal computations and can be changed without affecting their final answers. In this work, we measure an…

• Coverage Before Control: Route-Instruction Grounding and Steering for Controllable Retrosynthesis

  Xuemin Chen, Xiaozhuang Song, Xinjian Zhao, Yaoyao Xu, Tianshu Yu · 1 de octubre de 2026

  Single-step retrosynthesis models are commonly evaluated by their ability to recover recorded reactions. In practice, chemists may need to choose among several precursor sets for the same product, for example to preserve a particular motif. Recovering a recorded answer alone does not establish this …

• Diversity Combining for Multi-Path LLM Reasoning

  Guangsheng Yu, Litianyi Zhang, Qin Wang, Xu Wang, Mingyuan Li, Shaoxiong Ji, Ren Ping Liu, Massimo Piccardi · 1 de octubre de 2026

  Multi-path reasoning methods such as self-consistency (SC) sample $K$ reasoning paths and choose the most frequent answer. However, their gains quickly plateau as $K$ increases, and existing methods do not predict when this saturation will occur. We formalize multi-path LLM reasoning as a diversity …

• Concept-Grounded Attention: A Controlled Evaluation of Graph-Injected Attention, Temporal Versioning, and Epistemic Status

  Sachin Dev Duggal, Pradyumna Swarnalatha Ramanna, Alexandros Vassiliades · 1 de octubre de 2026

  Knowledge-intensive language-model systems typically represent external knowledge as text chunks or static graphs, with limited support for concept evolution, point-in-time reasoning, and distinctions between validated and inferred knowledge. We introduce the Concept Lifecycle Model (CLM), which rep…