knowledge-representation

An essay exploring knowledge representation in artificial intelligence, differentiating between model and state knowledge, and discussing the challenges of enabling computers to understand nuanced contexts like those in address books.

Introduces BoardroomAI, a framework for human-steerable multi-agent deliberation using dependency-aware evolving decision graphs, enabling selective repair of affected artifacts after human interventions. Evaluated on synthetic interventions, showing efficiency while preserving unaffected nodes.

This paper investigates how different formal knowledge representation notations affect language models' syllogistic reasoning, extending FOLIO and P-FOLIO datasets and introducing the CLGC library for generating syllogisms.

This paper proposes a task-based evaluation methodology for measuring semantic preservation in ontology learning, comparing LLM performance on source documents versus transformed representations in the legal domain.

This paper introduces NeurOWL, a neuro-symbolic framework using LLMs and ontology embeddings to perform subsumption verification and abduction on incomplete OWL ontologies, enabling reasoning when axioms are missing.

This paper proposes a theoretical framework for intelligence as atomic compression and compositional reuse, introducing the Compression Calculus and the Compounding Cascade thesis.

This comprehensive survey systematically reviews graph neural network-based methods across the entire knowledge graph pipeline, proposing a novel two-level taxonomy and discussing challenges and future research directions.

Kevin Kelly argues that the latent space within AI models represents a compressed form of all human knowledge, and that this latent space will become a new medium for creativity, enabling novel artistic and scientific exploration.

This proposal introduces the Dynamic Concept Graph (DCG), a hybrid cognitive architecture that combines neural representation learning, symbolic knowledge structures, multimodal perception, and analogical reasoning to provide persistent, evolving world models for AI, addressing limitations such as inconsistent reasoning and lack of causal understanding in large language models.

This paper presents a novel fuzzy-logic-based extension of Answer Set Programming (ASP) that integrates numerical information with qualitative reasoning using membership functions, demonstrated through a travel recommendation case study.

Google Cloud introduces the Open Knowledge Format (OKF), an open specification for representing metadata and curated knowledge in markdown files to improve data sharing and context for AI agents. The format aims to make knowledge from fragmented internal systems portable and interoperable.

Google Cloud introduces the Open Knowledge Format (OKF), an open specification that standardizes the LLM-wiki pattern for representing structured knowledge in markdown with YAML frontmatter, aiming to improve data sharing and interoperability for AI agents.

The Agent Instruction Protocol (AIP) proposes modeling AI agent skills as directed execution graphs with schema-validated YAML specifications, replacing free-form prose instructions. Experiments show AIP compilation raised Claude Sonnet's task reward from 0.60 to 0.71 and pass rate from 53% to 67% across 27 real agent tasks.

This paper proposes MONIR, a Modalized-Output Normative Intermediate Representation that bridges LLM-assisted norm extraction and ASP-based compliance reasoning for technical standards. The framework is instantiated on Chinese ADAS regulations, combining symbolic reasoning with LLM pipelines for explainable compliance checking.

This paper argues that consensus-seeking in multi-agent LLM systems is insufficient for value-laden tasks, proposing a knowledge-representation layer that classifies agent reasoning-trace disagreements into four symbolic states to enable strategic routing in systems like content moderation.

This paper presents a prototype framework for managing uncertainty in LLM-generated procedural knowledge for virtual laboratory planning, using structured domain representations to repair uncertain procedural steps.

This paper investigates the phenomenon where large language models hallucinate despite having the correct answer available in their generation-time distribution. By introducing a semantic notion of answer availability, the authors show that 16-47% of instruction-tuned model hallucinations occur when the correct concept is already represented, and that this rate increases with scale. They identify that instruction tuning sharpens answer commitment, making helpfulness and confident hallucination two sides of the same coin.

This paper introduces a quantitative notion of diversity of extensions in abstract argumentation based on symmetric difference, and provides a systematic complexity classification for related reasoning tasks.

DALM proposes a domain-algebraic language model that generates text under exact structural constraints derived from a domain lattice, addressing hallucination by organizing knowledge into separate domain fibers with algebraic guarantees. The model uses three-phase structured denoising (domain → relation → concept) with domain-annotated training data to prevent cross-domain contamination.