AINeutralarXiv – CS AI · May 296/10
🧠Researchers introduce AgentSchool, an LLM-powered multi-agent simulator that models student learning through state transitions rather than simple role-play, featuring cognitively growable student agents with knowledge graphs and adaptive teachers operating within the Zone of Proximal Development. The system addresses the challenge of validating educational AI interventions in real classrooms by creating a configurable simulation environment that reproduces plausible learning outcomes and social dynamics without requiring institutional constraints or ethical compromises of live trials.
AIBullisharXiv – CS AI · May 296/10
🧠Researchers have released mcp-proto-okn, a Python-based server that enables AI assistants to query and integrate scientific knowledge graphs through natural language via the Model Context Protocol. The tool democratizes access to complex biomedical and scientific data by removing technical barriers to cross-domain knowledge graph analysis.
AINeutralarXiv – CS AI · May 286/10
🧠DiagramRAG is a new retrieval-augmented framework that converts rough sketches into publication-quality scientific diagrams by retrieving semantically and topologically compatible reference diagrams. The system achieves strong performance metrics (F1-scores of 0.848 and 0.802 on benchmark datasets) while maintaining efficient inference at 35.48 seconds per sample.
🏢 Hugging Face
AINeutralarXiv – CS AI · May 286/10
🧠Researchers developed an LLM-based pipeline that automatically tags learning resources with competencies from structured frameworks, combining language models with graph constraints and evidence extraction. The system achieved strong performance metrics (0.57 micro-F1, 0.82 MRR) while providing transparent, auditable evidence spans—outperforming traditional baselines and addressing the labor-intensive challenge of manual resource tagging in educational systems.
AINeutralarXiv – CS AI · May 286/10
🧠Researchers propose a novel multimodal multi-agent framework that uses graph-based knowledge construction and adaptive retrieval-augmented generation to enable autonomous agents to execute complex workflows more effectively. The system combines offline discovery of workflow topology from execution logs with real-time collaborative verification, demonstrating improved performance in novel scenarios with limited training data.
AINeutralarXiv – CS AI · May 286/10
🧠Researchers propose a snippet-driven method using large language models to construct supply chain knowledge graphs for Chinese firms, achieving 7.2× greater coverage than traditional disclosure databases while reducing computational costs by 251× compared to full-text processing.
AINeutralarXiv – CS AI · May 286/10
🧠Researchers introduce VibeSearchBench, a new benchmark that exposes significant gaps between LLM agent performance on existing search tasks and real-world user satisfaction. The benchmark uses multi-turn dialogue and schema-free evaluation across 200 bilingual tasks, revealing that even frontier models achieve only 30.30% F1 scores, indicating fundamental deficiencies in long-context reasoning and intent elicitation.
AINeutralarXiv – CS AI · May 286/10
🧠Researchers introduce LegalGraphRAG, a framework that combines hierarchical graph structures with multi-agent verification to improve legal reasoning in AI systems. The approach addresses critical limitations in applying retrieval-augmented generation to legal domains by organizing heterogeneous legal knowledge at multiple abstraction levels and implementing transparent, audited reasoning processes.
AINeutralarXiv – CS AI · May 286/10
🧠Researchers introduce BIRDNet, a neurosymbolic deep learning architecture that mines Boolean implication relationships from tabular data and encodes them as sparse, interpretable neural networks. The model achieves near-baseline performance on biomedical datasets while using 96× fewer active parameters and maintaining human-readable symbolic rules without external rule bases.
AINeutralarXiv – CS AI · May 286/10
🧠MetaboT is an open-source LLM-based framework that translates natural-language questions into SPARQL queries for metabolomics knowledge graphs, significantly lowering technical barriers for researchers without programming expertise. The multi-agent architecture addresses hallucination and schema-compliance issues through specialized agents for validation, entity resolution, and query refinement, validated on the Experimental Natural Products Knowledge Graph.
AIBullisharXiv – CS AI · May 286/10
🧠Researchers introduce HGMem, a hypergraph-based working memory system that enhances multi-step retrieval-augmented generation (RAG) for large language models by modeling complex relational dependencies among facts. Unlike traditional RAG systems that treat memory as passive storage, HGMem dynamically structures information as interconnected high-order relationships, demonstrating improved performance on global sense-making benchmarks requiring complex reasoning across extended contexts.
AIBullisharXiv – CS AI · May 286/10
🧠Researchers propose a case-aware medical image classification framework that leverages multimodal knowledge graphs to retrieve similar historical cases and integrate external clinical knowledge, improving diagnostic accuracy through interpretable evidence-based reasoning rather than relying solely on isolated visual analysis.
AIBullisharXiv – CS AI · May 276/10
🧠Researchers introduce POLAR, a memory-augmented framework that enables multimodal AI agents to personalize their behavior based on accumulated long-term user interactions. The system organizes past interactions into semantic and episodic memory, allowing embodied agents to interpret implicit user requests and improve task execution performance across multiple interaction cycles.
AIBullisharXiv – CS AI · May 276/10
🧠Researchers present two autonomous AI agent frameworks—DeepTS/DeepCollector for time-series dataset curation and DeepScribe for converting physics lectures into structured reports—demonstrating how agentic AI can overcome current LLM limitations in scientific workflows through hybrid local-remote architectures and advanced systems engineering techniques.
AINeutralarXiv – CS AI · May 276/10
🧠Researchers introduce Helicase, an autonomous multi-agent LLM system designed to construct supply chain knowledge graphs by synthesizing fragmented web data through multi-hop reasoning. The system incorporates uncertainty quantification across three layers to enable calibrated confidence assessment, addressing a critical gap in complex supply chain intelligence tasks that cannot be solved by single-document queries.
AINeutralarXiv – CS AI · May 276/10
🧠Researchers have extended LELA, an LLM-based entity linking framework, into a practical Python library that combines zero-shot Named Entity Recognition with entity disambiguation. The end-to-end pipeline addresses limitations in existing approaches by offering domain-agnostic capabilities and demonstrating robust performance across diverse entity linking tasks, making it more applicable to real-world usage scenarios.
AINeutralarXiv – CS AI · May 276/10
🧠Researchers propose KMAS, an adaptive negative sampling method that enhances knowledge graph foundation models by constructing higher-quality hard negative triples and dynamically adjusting their ratio throughout training. The approach improves multiple state-of-the-art KGFMs across 44 datasets without significant computational overhead, advancing zero-shot knowledge graph completion for unseen relational vocabularies.
AIBullisharXiv – CS AI · May 276/10
🧠Researchers developed Chat-ISV, an LLM-enhanced knowledge graph system that organizes fragmented steel industry VOCs literature into a queryable database with 27,180 nodes and 81,779 semantic edges. The system achieved 96.93% precision in answering specialized industrial questions, demonstrating a scalable approach to deploying reliable LLMs in domain-specific applications where hallucination risks are high.
AINeutralarXiv – CS AI · May 276/10
🧠Researchers introduce DualGraph, a retrieval-augmented generation framework that combines semantic and symbolic approaches to improve question answering on semi-structured data. The system uses dual knowledge graph representations alongside a new benchmark dataset (SpecsQA) from e-commerce, demonstrating superior performance over existing dense-retrieval and graph-based methods.
AINeutralarXiv – CS AI · May 276/10
🧠Researchers evaluated how knowledge graphs (KGs) influence hypothesis generation in large language models across multiple models, finding that compact subgraphs often perform comparably to full graphs. The study reveals that KG utility is selective and model-dependent, with useful signal often recoverable from structured, compressed subsets rather than complete local graphs.
🧠 Gemini🧠 Llama
AINeutralarXiv – CS AI · May 275/10
🧠RAGEAR is a neurosymbolic recommender system that combines dense retrieval of lecture transcripts with knowledge graphs to improve academic course recommendations. The system demonstrates that fine-grained instructional content outperforms metadata-only approaches, with a novel graph-aware aggregation function that effectively propagates evidence from transcript chunks to course-level rankings.
AIBullisharXiv – CS AI · May 276/10
🧠Researchers demonstrate that knowledge graphs significantly outperform traditional document stores for LLM-based industrial asset operations, achieving 100% accuracy on 467 maintenance scenarios compared to 65% with flat data structures. The study reveals that data architecture, not LLM orchestration design, is the primary performance bottleneck in structured operational domains.
🏢 Hugging Face🧠 GPT-4
AINeutralarXiv – CS AI · May 276/10
🧠Researchers propose a formal framework for describing knowledge graph affordances to agents, extending decades-old semantic web service standards to address modern KG discovery and composition challenges. The framework introduces the Agentic Affordance Profile (AAP), a metadata layer that enables principled selection and failure diagnosis by specifying what agents can prove from a knowledge graph and under what epistemic conditions.
AINeutralarXiv – CS AI · May 276/10
🧠SEAL introduces a two-stage semantic parsing framework that combines large language models with agentic learning to improve conversational question answering over knowledge graphs. The system self-evolves through dialog history and execution feedback without retraining, achieving state-of-the-art results on complex multi-hop reasoning and aggregation tasks while reducing computational costs.
AINeutralarXiv – CS AI · May 126/10
🧠Open Ontologies is an open-source Rust-based system that combines LLM-driven ontology engineering with formal OWL reasoning and stable matching alignment. The research demonstrates that stable 1-to-1 matching is the critical factor for ontology alignment quality, achieving F1 scores competitive with state-of-the-art systems, while structured tool access via Model Context Protocol significantly outperforms raw file reading for LLM interaction.
