5arXiv cs.AI (Artificial Intelligence)·
SearchOS: Multi-agent framework for robust open-domain information-seeking with explicit shared state
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.
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WebSwarm: Recursive multi-agent framework for deep-and-wide web search
WebSwarm is a new multi-agent orchestration framework for LLM-based web search that uses progressive recursive delegation to handle both depth and breadth simultaneously. Each agent node couples a local objective with a search mode and can either solve its task or delegate to child nodes, passing evidence upward for parent-level expansion and aggregation. The system outperforms single-agent and multi-agent baselines on BrowseComp-Plus, WideSearch, DeepWideSearch, and GISA benchmarks. The work addresses known limitations of ReAct-style single-agent search and flat parallel multi-agent approaches.
Open-source DeepResearch – Freeing our search agents
Hugging Face published a blog post introducing Open Deep Research, an open-source replication of agentic deep research capabilities (similar to OpenAI's Deep Research). The project aims to build open-weight search agents capable of multi-step web research and synthesis. The post details the architecture, tooling, and early benchmark results of the system.
OptiAgent: Multi-agent framework for end-to-end operations research optimization modeling
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.
FinSAgent: Multi-agent RAG framework for evidence-grounded SEC filing question answering
Researchers introduce FinSAgent, a multi-agent retrieval-augmented generation framework designed to address 'prior-corpus misalignment' in financial QA over SEC filings. The system combines role-specialized agents anchored to 10-K item structure, database-aware query decomposition, and a learned feature-gated reranker that separates evidential validity from semantic similarity. Evaluated across five offline financial QA benchmarks and a 1,000-user online experiment, FinSAgent outperforms single-agent and multi-agent baselines on retrieval coverage and answer correctness. The work targets a narrow but practically important domain where standard RAG pipelines fail due to structural and terminological mismatch.
SearchGEO framework measures LLM search agent vulnerability to web content manipulation
Researchers introduce SearchGEO, a controlled evaluation framework for measuring endorsement corruption in LLM-based web-search agents, combining a manipulation pipeline, five-mode attack taxonomy, and multiple output metrics. Evaluating 13 LLM backends on 308 cases each, they find attack success rates ranging from 0.0% on Claude-Sonnet-4.6 to 31.4% on Gemini-3-Flash, with model-family-specific vulnerability patterns. An auxiliary probe escalating endorsement to install commands reveals a behavioral split: Claude over-rejects while GPT over-trusts. The findings argue for treating adversarial search content robustness as a first-class safety evaluation dimension for deployed agents.
Comparative Study: Semantic Metadata vs. Unstructured Web Retrieval for Agentic Data Discovery
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.
MemOS: Self-Evolving Memory OS for LLM Agents with Hybrid Retrieval and Token Savings
MemOS is an open-source TypeScript project providing a memory operating system layer for LLM and AI agents, featuring ultra-persistent memory, hybrid retrieval, and cross-task skill reuse. The project claims 35.24% token savings through its memory management approach. It has accumulated 9,329 GitHub stars with moderate daily momentum (+67). The system targets agent memory persistence and efficiency as a foundational infrastructure component.
AgentScope: Open-Source Multi-Agent Framework for Transparent, Trustworthy Agents
AgentScope is an open-source Python framework for building and running AI agents with an emphasis on observability and trustworthiness. The repository has accumulated 25,755 total GitHub stars with 95 new stars today, indicating sustained community interest. It targets developers building multi-agent systems and positions itself around interpretability and reliability of agent behavior.