Information-Seeking Agents

Updated 24 October 2025

Information-seeking agents are autonomous systems designed to actively gather, process, and integrate data from complex, dynamic environments.
They combine traditional crawling techniques with advanced LLM capabilities and reinforcement learning to optimize information gain and uncertainty reduction.
Their modular design, incorporating navigators, extractors, and controllers, supports proactive decision-making, error recovery, and adaptive resource management.

Information-seeking agents are autonomous or semi-autonomous systems engineered to actively acquire, process, and integrate information from complex, often partially observable or dynamic environments. These agents encompass a wide spectrum of designs, from traditional crawlers and distributed multi-agent networks to sophisticated LLM-powered agents employing tool use, deep reasoning, and interactive dialogue. Core to their operation are mechanisms for discovering relevant data, reducing uncertainty, optimizing information acquisition strategies, and supporting high-value downstream tasks such as decision making, search, summarization, or question answering.

1. Foundational Architectures and Taxonomy

Early information-seeking agents were primarily realized as autonomous programs (crawlers, spiders, robots) designed for exhaustively exploring, downloading, and indexing web content to support search engines (Bhute et al., 2013). These agents typically operate via a recursive process: starting from a set of seed URLs, they download and parse page contents, extract hyperlinks to grow a queue ("crawl frontier"), and follow systematic policies (e.g., prioritizing high PageRank) to balance coverage, freshness, and system politeness.

Modern architectures hierarchically and functionally expand on this paradigm, exemplifying multiple core modules:

Component	Example Implementations	Typical Role
Navigator	Autonomous crawler, LLM with web API/browser access	Exploration, page/endpoint selection
Extractor	HTML parser, text/image extractor, question genusis	Information extraction from sources
Aggregator/Integrator	Deduplication engine, LLM-based synthesis/validation	Merge, deduplicate, validate facts
Planner/Controller	RL agent, heuristic rule engine, LLM-based decision core	Sequential decision making
Memory/State Tracker	Explicit memory buffer, vector DB, retrieval module	Track queries, states, observations

LLM-based frameworks such as KwaiAgents (Pan et al., 2023), InfoAgent (Zhang et al., 29 Sep 2025), WebDancer (Wu et al., 28 May 2025), and AppAgent-Pro (Zhao et al., 26 Aug 2025) augment these with deeply integrated planning, tool-use, reflection, and multi-domain compositional reasoning.

2. Control Principles and Information-Seeking Mechanisms

A distinguishing principle of information-seeking agents is the explicit modeling and optimization of information gain, uncertainty reduction, or epistemic competence.

Crawlers and Search Agents: Web crawlers employ selection policies to target high-value pages (by PageRank, backlinks, freshness), revisit policies (e.g., uniform vs. proportional for content update rates), politeness policies for server load moderation, and parallelization/distribution strategies (Bhute et al., 2013).

Distributed Agent Networks: In decentralized agent settings, information-seeking is formalized by maximizing expected information gain as measured by the (negative) posterior joint entropy over hidden states, using sample-based distributed gradient ascent. Given differential entropy $h(\cdot)$ , the control objective is $D_h(u^+) = -h(\mathbf{x}^+ \mid \mathbf{y}^+)$ and each agent's control is updated by computing gradients with respect to future actions, factoring in mutual information and transition Jacobians (Meyer et al., 2014).

Probabilistic Objectives:

Agents may optimize evidence (reward-maximizing) objectives or divergence objectives. The latter, such as $\arg\min_{\mathbf{a}_{t:T}} \mathrm{KL}[p(o_{t:T}|\mathbf{a}_{t:T}) || \tilde{p}(o_{t:T})],$ imbue agents with an intrinsic exploratory drive by ensuring broad, information-rich future predictions, as opposed to reward-seeking, mode-focused exploitation (Millidge et al., 2021).

RL and Intrinsic Motivation: In deep information-seeking agents, exploration is incentivized by combining extrinsic task rewards with uncertainty reduction bonuses (intrinsic rewards). The total reward at time $t$ is $R_t = r_t^E + r_t^I,$ with $r_t^I$ based on the agent's modeled reduction in entropy or cross-entropy of its belief state (Bachman et al., 2016).

3. Interactive and Proactive Information Gathering

Agents increasingly operate in interactive or partially observable domains. Here, information seeking is cast as sequential decision making (POMDPs), with agents issuing actions to selectively reveal, search for, or clarify environment state:

Interactive MRC agents act in environments where most of the information is "occluded," and must iteratively issue commands (e.g., navigation, search, or query reformulation) to reveal evidence (Yuan et al., 2019).
Proactive dialogue agents and GUI assistants shift from passive query response to anticipating latent user needs, dynamically decomposing, and executing sub-queries across multiple domains (Lee et al., 20 Oct 2024, Zhao et al., 26 Aug 2025). This involves methods for need anticipation, deep task decomposition, and recursive execution with information integration.

Proactivity in dialogue and decision-making is further refined by decomposing each response into an explicit answer plus a proactive element (e.g., follow-up question or additional information). Chain-of-Thought prompting is employed to ensure the generation pipeline surfaces new, relevant information to sustain the interaction (Lee et al., 20 Oct 2024).

4. Data Synthesis, Benchmarks, and Evaluation

The evaluation and advancement of information-seeking agents demand tailored datasets and benchmarks for both depth (multi-hop reasoning) and width (large-scale aggregation):

WebDancer (Wu et al., 28 May 2025) formalizes agent training as a multi-stage process: (1) web data construction, (2) high-quality trajectory sampling (with both short/long chain-of-thought), (3) supervised fine-tuning on agentic episodes, and (4) reinforcement learning with dynamic policy optimization (DAPO). Loss functions are masked to only optimize agent-decision tokens, and SFT+RL yields significant gains in both correctness and consistency.
WebShaper (Tao et al., 20 Jul 2025) introduces a formalization-driven data synthesis paradigm, using set theory and Knowledge Projections (KP) to design tasks whose structure and required reasoning are tightly controlled. The data synthesis pipeline involves iterative, agentic expansion and validation, reducing redundancy and enforcing precise compositionality.
WideSearch (Wong et al., 11 Aug 2025) and DeepWideSearch (Lan et al., 23 Oct 2025) expose a major capability gap in current systems: when tasked with filling large, multi-attribute tables via both broad retrieval and deep evidence chains, even state-of-the-art agents achieve <5% success rates. Error analysis reveals barriers such as failure to decompose queries, inadequate reflection, hallucination, and context overflow.
SeekBench (Shao et al., 26 Sep 2025) goes beyond accuracy to define epistemic competence, coding agent traces for evidence-grounded reasoning (Reasoning Quality Index), adaptive recovery via search reformulation (Evidence Recovery Function), and proper calibration regarding answer sufficiency (Calibration Error).

Benchmark	Principal Focus	Key Metric(s)	Highest Agent Pass Rate
WideSearch	Wide-scale info collection	Success Rate, F1 Scores	≈5%
DeepWideSearch	Depth+Width reasoning	Success Rate, Col-F1	2.39%
SeekBench	Epistemic competence	RQI, ERF, CE	N/A (process-level)
WebDancer/WebShaper	Data-centric pipeline/SFT+RL	Pass@k, Consistency	Outperforms open-source

5. Applications, Limitations, and Implications

Information-seeking agents are deployed in web-scale search (crawlers, aggregation, document retrieval), robotics (distributed self-localization, target tracking, embodied control), multi-domain assistants, healthcare triage, and automated research. Cutting-edge systems integrate precise planning, hybrid search–browse tools, memory management, and deep reflection (e.g., KwaiAgents (Pan et al., 2023), Infogent (Reddy et al., 24 Oct 2024), InfoSeeker (Fang et al., 2 Oct 2025)).

Nevertheless, persistent limitations include:

Failure at Scale: Agents exhibit low pass rates on broad/deep info-seeking tasks due to partial retrieval, context window overflows, and lack of error recovery (Wong et al., 11 Aug 2025, Lan et al., 23 Oct 2025).
Reflection/Recovery Gaps: Few architectures systematically revisit and revise failed search strategies in complex domains.
Epistemic Calibration: Agents may answer prematurely or without sufficient evidence, highlighting the need for better assessment of information completeness (Shao et al., 26 Sep 2025).
Reliance on Internal Knowledge: Overuse of parametric memory leads to non-updated, possibly obsolete responses.

The modularization of navigator, extractor, and aggregator roles, together with feedback mechanisms and explicit uncertainty estimation, are observed to improve information diversity and accuracy in aggregation tasks (Reddy et al., 24 Oct 2024, Dass et al., 24 Oct 2024).

6. Mathematical Models and Formalizations

Information-seeking behaviors are mathematically formalized across several lines:

Entropy-based Control: Differential entropy $h(x)$ , posterior joint entropy, and gradient ascent optimization for information gain (Meyer et al., 2014).
Divergence Objectives: $\arg\min \mathrm{KL}[p(o_{t:T}|a_{t:T}) \| \tilde{p}(o_{t:T})],$ decomposed into reward maximization and entropy augmentation (Millidge et al., 2021).
Set-theoretic Formalization: Information-seeking queries as compositional Knowledge Projections, e.g. $T = \bigcap_{i=1}^p \left( R_i(S_{i1}) \cup R_i(S_{i2}) \cup \dots \cup R_i(S_{it_i}) \right)$ (Tao et al., 20 Jul 2025).
Tabular Benchmarks: Precision–Recall–F1 on structured outputs, with rigorous constraints for completeness and correctness (Wong et al., 11 Aug 2025, Lan et al., 23 Oct 2025).
Cmdp Factorization: $A = A_{IS} \cup A_{IR}$ ; with policy factorization, intrinsic reward, and uncertainty-based policy switching for balancing exploration/exploitation (Dass et al., 24 Oct 2024).

7. Future Directions

Opportunities for progress, as indicated in the literature, include:

Improved error correction and reflective agents, possibly by integrating multi-agent cross-validation or agent-synthesis (combining strengths of complementary agents) (Wong et al., 11 Aug 2025, Shao et al., 26 Sep 2025).
Enhanced context management, memory architectures, and reasoning over long trajectories to address overflow and information forgetting (Lan et al., 23 Oct 2025, Pan et al., 2023).
Unified frameworks combining proactive planning, information-seeking, and robust execution in partially observable worlds (see InfoSeeker (Fang et al., 2 Oct 2025)).
Scalable, formalization-driven data synthesis to support transferability and generalization (Tao et al., 20 Jul 2025).
Persistent benchmarking on both processual (step-level) and outcome (answer-level) epistemic competence to drive agent design toward true, transparent information-seeking (Shao et al., 26 Sep 2025).

Information-seeking agents thus stand at the confluence of formal models of exploration, reinforcement learning, distributed estimation, proactive dialogue, and scalable, data-centric evaluation. Continued advancement hinges on resolving the integration of depth and width in information gathering, consistent grounding in high-quality evidence, and adaptive, robust decision making in dynamic environments.