Researchers introduce Hybrid Ontology Matching (HOM), a new task formulation that unifies equivalence and subsumption discovery in ontology matching, previously treated as separate problems. They propose AgentMap, an LLM-based multi-agent framework that combines semantic retrieval, hierarchical search, and collaborative reasoning to identify concept correspondences across ontologies. The system is evaluated on four extended OM datasets and outperforms baselines on both equivalence-only and subsumption-only settings in addition to the new hybrid setting.
OptiAgent is a multi-agent LLM framework that converts natural language descriptions of Operations Research problems into solver-ready mathematical formulations and executable code. The architecture uses dedicated agents for extracting decision variables and constraints, with a multi-loop validation system featuring four specialized feedback mechanisms targeting distinct failure modes. The system claims state-of-the-art performance on 3 of 4 benchmarks spanning LP, MILP, and Nonlinear Programming tasks, while also improving transparency through auditable agent reasoning.
SearchOS is a new multi-agent framework that addresses a core failure mode in web-search-augmented LLM agents: repetitive search loops caused by implicit, untracked task progress. The system externalizes agent state into structured components (Frontier Task, Evidence Graph, Coverage Map, Failure Memory) via a Search-Oriented Context Management (SOCM) layer, and adds pipeline-parallel scheduling and a middleware harness to intercept tool interactions and recover from stalls. Evaluated on WideSearch and GISA benchmarks, SearchOS outperforms all single- and multi-agent baselines across all reported metrics.
Researchers introduce a scalable benchmark for evaluating LLM agents on cooperative joint decision-making tasks where agents must exchange information under partial and asymmetric observations to reach a shared decision. A systematic evaluation of representative LLMs finds that state-of-the-art models still struggle with complex deliberative collaboration, failing in either information alignment or downstream reasoning even with external mathematical tools. Diagnostic analysis also reveals that deliberation can enable reflection and error correction, sometimes outperforming centralized baselines, offering a nuanced picture of multi-agent LLM capabilities.
This paper evaluates whether LLM-based agents still need structured semantic metadata (e.g., schema.org) for data retrieval, comparing a Baseline Agent searching open-web documents against a Semantic Agent leveraging 90 million schema.org-annotated datasets. Using an LLM-as-a-judge pipeline aligned to FAIR principles, the Semantic Agent achieves 65.7% higher overall precision in retrieving FAIR-compliant datasets, while the Baseline Agent answers 40% more questions but frequently returns prose-heavy or portal landing pages instead of actionable data. The study concludes that structured semantic ecosystems remain essential for reliable, execution-oriented agentic workflows despite LLMs' broad unstructured retrieval capabilities.
Researchers introduce Agora, a framework that uses an incentive-compatible auction mechanism to dynamically route reasoning subtasks to the most capable expert models or tools, rather than relying on coarse-grained function matching. Agents bid based on 'rectified competence' to prevent overconfident solvers from capturing critical logic steps. Evaluations across five benchmarks show improvements over single-model, routing, and cascade baselines, with a controllable cost-quality trade-off via a single auction parameter.
AgentMob is a training-free LLM-driven agent framework that formulates next-location prediction as adaptive evidence-controlled decision making, using a fast path for routine cases and iterative tool use for ambiguous ones. Evaluated on three mobility datasets, it achieves the strongest overall performance among training-free LLM-based methods, with GPT-5.4 reaching 71.42% Acc@1 on the BW dataset. The framework demonstrates that LLM controllers add most value in resolving ambiguous predictions through adaptive evidence gathering rather than routine cases.
A new arXiv preprint proposes a training-free multi-agent framework for multi-document summarization (MDS) that decomposes the task into specialized agents for extractive selection, knowledge-aware abstraction, and iterative refinement, unified via a multi-perspective consistency mechanism. The system integrates LLMs with knowledge graphs without task-specific fine-tuning. Experiments across four datasets in English and Vietnamese show state-of-the-art or competitive performance, with the authors emphasizing cross-domain and cross-lingual generalization.
Researchers introduce MyAG, an open-source framework that structures LLM agent systems using three graph abstractions: a component graph, a workflow graph, and a search graph for runtime execution. The separation enables flexible component reuse across different execution strategies, with support for hierarchical composition via recursive system nodes and built-in monitoring and visualization tools. Experiments on representative agent applications demonstrate performance-efficiency tradeoff analysis capabilities.