This work introduces SearchOS, a system-level multi-agent framework that turns fragile, implicit search progress into explicit, persistent, and shared state, and introduces a Search Tool Middleware Harness that intercepts model and tool interactions to record grounded evidence and react to stalls or budget exhaustion.
Abstract
Recent advances in Tool-Integrated Large Language Models have made web search a core capability of information-seeking agents. However, as interaction histories grow, agents increasingly struggle to track task progress. When search attempts fail to yield useful evidence, current single- and multi-agent systems can become trapped in repetitive loops, wasting search budgets and ultimately compromising the quality and completeness of the final output. We introduce SearchOS, a system-level multi-agent framework that turns fragile, implicit search progress into explicit, persistent, and shared state. First, we formulate open-domain information seeking as relational schema completion with grounded citations, where agents discover entities, populate attributes across linked tables, and anchor each value to source evidence. Then we design Search-Oriented Context Management (SOCM), which externalizes the evolving state into Frontier Task, an Evidence Graph, a Coverage Map, and Failure Memory. Built on SOCM, SearchOS applies a pipeline-parallel scheduling mechanism that overlaps the execution of sub-agents and continuously refills freed slots with tasks targeting unresolved coverage gaps to improve utilization and throughput. To schedule and control the execution of search agents, SearchOS introduces a Search Tool Middleware Harness that intercepts model and tool interactions to record grounded evidence and react to stalls or budget exhaustion, and provides a reusable hierarchical skill system comprising strategy and access skills to augment the agents'search process and avoid repeating failed search patterns across runs. On WideSearch and GISA, SearchOS leads all metrics among the evaluated single- and multi-agent baselines, paving the way toward robust information-seeking collaboration.
Large language model (LLM)-based web search agents have demonstrated remarkable capabilities in autonomous information retrieval and multi-step reasoning. However, their robustness under real-world distribution shifts, such as evolving webpage structures, dynamic content layouts, and unseen task environments, remains a critical open challenge. Existing approaches predominantly rely on static supervised fine-tuning with human-annotated trajectories, which not only incurs substantial labeling cost but also lacks the adaptive capacity to handle the inherent stochasticity of live web environments. In this paper, we propose TAPE (Trajectory Alignment and exPerience pool Evolution), a novel self-evolving fine-tuning framework designed to enhance the generalization and robustness of web search agents without requiring large-scale human annotation. TAPE introduces a dual-stream experience pool that continuously accumulates both successful and failed agent trajectories during self-play execution. A trajectory alignment module maps heterogeneous execution paths into a unified semantic space, enabling contrastive learning to identify failure-inducing operations and reward generalizable search strategies. The framework further incorporates an adaptive pool evolution mechanism that filters, reweights, and distills experiences to prevent knowledge stagnation and distributional collapse. Extensive experiments on the GAIA benchmark and BrowseComp dataset demonstrate that TAPE consistently outperforms strong baselines across all three difficulty levels, achieving up to 6.2% absolute improvement in task success rate on GAIA Level-2 tasks (a 134.8% relative gain over the SFT-Only baseline on the GAIA validation split) and 1.3% absolute (217% relative) improvement on BrowseComp hard-tier queries, while exhibiting significantly greater resilience to webpage structure perturbations. These improvements are consistent across three open base models spanning two model families and the 7B–14B scale range. Our analysis further suggests that the contrastive self-play signal can serve as a useful partial surrogate for human preference labeling in agent trajectory optimization, substantially reducing, though not eliminating, reliance on human-annotated trajectories.
Minjae Rhee, Jitong Zou, Tianjun Mo et al.· IEEE Access· 0 citations
Large language model (LLM)-based web search agents are transforming information seeking from simple factoid question answering into complex, deep-and-wide search and research-oriented tasks. A single ReAct-style agent is constrained by one long trajectory and limited context, making it difficult to handle depth and coverage simultaneously. Existing multi-agent systems improve search coverage through parallel execution and aggregation, but still exhibit clear limitations in recursive depth, collaboration adaptability, and evidence-grounded expansion. We propose WebSwarm, a progressive recursive delegation framework that jointly constructs task decomposition, recursive expansion, and agent collaboration during inference. WebSwarm dynamically instantiates agentic search nodes, each coupling a local objective with a search mode that specifies how the node should organize search and collaboration. Each node can either solve its objective itself or further delegate child nodes; after solving, it returns evidence and results upward, enabling parent nodes to further expand, revise, or aggregate the search process. To guide this process, WebSwarm first probes how task-relevant information is organized on the web to ground subsequent node expansion, and reuses process-level experience across homogeneous sibling nodes. Experiments on BrowseComp-Plus, WideSearch, DeepWideSearch, and GISA show that WebSwarm consistently outperforms single-agent and multi-agent baselines on deep, wide, and interleaved deep-and-wide tasks. Further analyses of ablation, task difficulty, web tool efficiency, and model generalization explain WebSwarm's effectiveness and provide insights for multi-agent search systems.
Xiaoshuai Song, Lian Zhang, Kangzhi Zhao et al.· 0 citations
EvoGraph-R1 is introduced, a self-evolving GraphRAG framework that reconceptualizes knowledge graphs as dynamic environments shaped through agent interactions, establishing self-evolving knowledge graphs as a fundamental paradigm across modalities.
Jiashi Lin, Changhong Jiang, Xiangru Lin et al.· 1 citation
Information access has evolved from search to ask to act. For more than two decades, information retrieval made networked information usable through crawling, indexing, ranking, user modeling, and recommendation. My early work on Web page understanding, vertical search, learning-to-rank, personalized search, and recommendation followed this user-centered view. This keynote briefly revisits that trajectory as the context for a large shift recently: from retrieving documents to constructing systems that reason over evidence, generate grounded answers, and execute information-seeking tasks. I will discuss WenLan, YuLan, and LLaDA as examples of how multimodal foundation models, large language models, and diffusion language models reshape the interface between retrieval and generation. More importantly, I will focus on retrieval-augmented generation and AI search agents. FlashRAG provides a modular open-source platform for RAG research; Search-o1 and WebThinker connect large reasoning models with active search and deep research; and DeepAgent studies scalable tool use for general reasoning. Together, these works expose new IR problems: when and what to retrieve, how to align model and retriever knowledge preferences, how to refine long-context evidence, how to maintain memory and source credibility, and how to evaluate multi-step search behavior. I will close by arguing that future IR systems should not stop at answers. Large-model-driven agents can plan, search, verify, call tools, and support decisions. RAG and AI search agents turn information access into an iterative process of evidence gathering, grounded reasoning, tool use, and responsible action, linking retrieval to decision support and human-centered applications.
Ji-rong Wen· Annual International ACM SIG...· 0 citations
LLM-based search agents are widely used for information-seeking tasks, but their reliance on external tool returns introduces a critical security risk: web content retrieved during execution is untrusted, exposing agents to prompt injection and goal hijacking. Prior work on search-agent safety primarily focuses on static web-content injection, but modern agents issue follow-up queries and cross-check competing sources, so a single injected page is often diluted or rejected. We show that the channel delivering search and page observations is a fragile security boundary: beyond exposing the agent to a single poisoned page, a mediated search interface can repeatedly steer how the agent gathers evidence and forms its final answer. Under a constrained tool-intermediary threat model, appending only one controlled result per query can substantially increase attack success when the evidence is coordinated across the agent's trajectory. We study this setting with a strategy-driven long-horizon attack system and introduce Authority-Chain Hijack (ACH), an expert-refined strategy that turns isolated search-result and page-content manipulations into a coherent evidence chain across seemingly corroborating sources. ACH achieves the highest Overall ASR among all baselines, reaching 55.9% / 83.3% ASR / MaxN ASR on the full SafeSearch test split. We further introduce Trace-Guided Strategy Evolution (TGSE), which automatically improves attacker strategies from execution traces, replacing manual redesign with trace-driven refinement; its strongest single setting reaches 71.4% / 95.0% in held-out evaluation.
Xuebin Li, Hanqing Zhao, Siyuan Liang et al.· 0 citations
Code agents spend much of their effort simply locating the right code inside a repository. Two approaches dominate current practice. In Semantic Search, the agent retrieves code blocks from a vector index built from the repository in advance. In Deep Agentic Search (also known as grep-search by subagent), a planning agent delegates the exploration to a separate subagent that works in an isolated context window and returns only a condensed result. The second design, which is considered good context engineering practice, exists to protect the main agent from context pollution (also known as context rot), the loss of accuracy that occurs as unrelated material accumulates in the context window. Recent code agents (such as Claude Code, Codex, Antigravity, etc) have adopted it quickly, but there is little evidence on whether it produces better answers. We compare the two approaches on SWE-QA, a benchmark for repository-level code question answering. Semantic search answered 65.2% of questions correctly against 46.2% for deep agentic search, and it produced each correct answer at less than half the cost. To explain the gap, we then coded every failed run into a taxonomy of failure modes. The taxonomy shows that deep agentic search did not remove failures but introduced a new class of them: the single largest share of its failures, 41.8%, occurred at the hand-off between the planner and its sub-agent, and these were usually silent, ending in a fluent and confident answer that was wrong. Deep agentic search addresses a real problem and is now the preferred design in many code agents. However, our results show that the protection it offers may not be free, and that for read-only questions over a repository that can be indexed, retrieval was the stronger and cheaper option.
Amirkia Rafiei Oskooei, Bora Ilci, Alperen Kayim et al.· 0 citations