Multi-Step Information-Seeking Tasks

• Multi-step information-seeking tasks are defined by sequential, multi-hop reasoning and structured data collection, integrating depth (e.g., 4+ retrieval steps) and width (e.g., 200+ data points).
• They utilize unified benchmarks and task synthesis methodologies like DeepWideSearch to enforce simultaneous reasoning and combinatorial data assembly via agentic loops and set-theoretic formulations.
• Modern system architectures, such as ReAct and HG-MCTS, leverage reward modeling and layered planning to address challenges like context overflow and overreliance on internal knowledge.

Multi-step information-seeking tasks are defined by the requirement that an agent, system, or user systematically gathers, synthesizes, and verifies information through a sequence of interdependent operations. These tasks span complex search, open-domain question answering, structured data collection, decision-making under partial observability, and adaptive conversational exploration. Recent research has focused on unified evaluation frameworks, architectures, and cognitive behavioral analyses for agents operating in regimes that demand both depth (multi-hop reasoning and retrieval) and width (large-scale evidence organization)—dimensions that interact to produce formidable planning and memory challenges (Lan et al., 23 Oct 2025).

1. Formalization of Depth and Width in Multi-Step Information Seeking

The DeepWideSearch benchmark provides a canonical formalism to operationalize "depth" and "width":

• Depth ($\mathrm{Depth}(Q)$): The expected minimum number of retrieval or reasoning steps to verify each core entity for query $Q$. Formally, for a $k$-hop path, an agent generates a chain $D_1,\ldots,D_k$ of tool calls or document accesses, with $k$ quantifying the retrieval path length.
• Width ($\mathrm{Vol}(Q)$): The cardinality of structured data required, measured as $|R| \times |C|$ for an output table with entities ($R$) and attributes ($C$). Tasks with $\mathrm{Vol}(Q) \gg 1$ are termed wide.

Benchmarks such as DeepWideSearch set thresholds of $\mathrm{Depth}(Q) \gtrsim 4$ and $\mathrm{Vol}(Q) \gtrsim 200$ to enforce simultaneous reasoning and collection pressures—regimes in which prior benchmarks (multi-hop QA, broad data extraction) only explored one variable at a time (Lan et al., 23 Oct 2025).

2. Benchmark Construction and Data Generation Methodologies

Task construction strategies must ensure both multi-step reasoning and combinatorial data assembly:

• Pipeline conversion: Deep2Wide transforms multi-hop benchmarks by extracting core entities, designing rich, attribute-indexed table schemas, and exhaustively populating ground truths via manual multi-source web research. Wide2Deep extends wide benchmarks by adding subquestions that necessitate additional retrieval hops per entity, maintaining the original table structure but enforcing deeper evidence chains (Lan et al., 23 Oct 2025, Wu et al., 28 May 2025, Tao et al., 20 Jul 2025).
• Formal task synthesis with knowledge projections: WebShaper formalizes IS tasks using set-theoretic compositions. Knowledge Projections (KP) iteratively define answer sets via operations such as $r(V)$ for relation $r$ and seed $V$, supporting both union and intersection to enable explicit reasoning graph control at each expansion step. Layer-wise expansion prohibits shortcutting and redundant connections, yielding deep, answer-consistent multi-step trajectories (Tao et al., 20 Jul 2025).

Synthetic agents and annotators ensure that each instance demands multiple distinct URLs, hundreds of entities, and checks for time stability, consistency, and redundancy, supporting RL- or SFT-based policy learning (Wu et al., 28 May 2025, Tao et al., 20 Jul 2025).

3. System Architectures and Planning Paradigms for Long-Horizon Tasks

Modern multi-step information-seeking agents instantiate:

• ReAct-style agentic loops: Each episode alternates between a generative reasoning module ("Thought") and a structured tool-calling policy ("Action"), cycling through search, visit, answer calls, and accumulating a structured history $\mathcal{H}_t$ (Wu et al., 28 May 2025, Tao et al., 20 Jul 2025).
• Tree search with adaptive checklists: HG-MCTS applies Monte Carlo Tree Search over state representations that include checklist sub-goals and a "knowledge memory" of accumulated evidence. Multi-perspective reward modeling (exploration/retrieval/progress feedback) guides expansion, selection, and backpropagation, ensuring global coverage and minimizing redundancy (Ren et al., 7 Feb 2025).
• Process reward modeling and context compression: PRInTS unites a dense information-gain scoring head and a summarizer that maintains a fixed-size, recursively updated summary $h_t$ rather than unbounded raw trajectory history. At inference, best-of-$n$ sampling selects high-value next actions, while full-context compression enables scaling to long-horizon regimes (Lee et al., 24 Nov 2025).
• Modular multi-modal architectures: M$^3$Searcher decouples evidence acquisition (retrieval-oriented planner) from answer synthesis (generator), operating multimodal tool calls and optimizing a multi-objective RL reward combining factuality, reasoning soundness, and retrieval fidelity. Each policy is tuned via explicit stepwise rewards. Dataset support for multi-hop, multi-modal reasoning enables transferability and robust adaptation (Yu et al., 14 Jan 2026).

4. Empirical Evaluation Regimes and Core Findings

Evaluation protocols span multiple axes:

Metric	Definition	Notable Findings
Success Rate	Fraction of exact matches to ground truth tables (binary)	Peak: 2.39% (WebSailor, DeepWideSearch)
Column-Level F1	Entity-discovery accuracy across unique columns	Peaks at 45.3% (Gemini 2.5 Pro)
Core Entity Acc.	Correct naming of core entities per question	Peaks at 74.3% (WebSailor + GPT-5)
Row-/Item-Level	Row completeness and per-cell accuracy	Strict and partial credit, width-focused
Efficiency	Token consumption, API cost	Tracked to monitor resource use

Additional structures (best-of-$n$ sampling, pass@k, trajectory summarization) measure agent reliability under nondeterministic or stochastic policy rollout (Lee et al., 24 Nov 2025, Lan et al., 23 Oct 2025).

Key findings:

• No agent demonstrates robust integration of depth and width; entity identification can be boosted by tool orchestration, but column/width metrics degrade under deep frameworks.
• Wide-only LLMs sometimes outperform tool-augmented agents on breadth, but rely on static, parametric knowledge instead of live retrieval.
• Effect sizes of agentic methods (e.g., WebDancer, WebShaper, PRInTS) yield absolute accuracy gains of 3–15 percentage points over vanilla retrieval/QA agents, but still fall below shallow/atomic benchmarks (Lee et al., 24 Nov 2025, Wu et al., 28 May 2025, Tao et al., 20 Jul 2025).

5. Behavioral, Cognitive, and User-Centric Analyses

User and agent behavior across multi-step information-seeking follows structured, psychologically measurable phases:

• Canonical stages: Information Need (IN), Query Formulation (QF), Query Submission (QS), and Relevance Judgment (RJ) are distinguishable via EEG, EDA, PPG, and pupillometry markers, enabling fine-grained mapping of cognitive load, arousal, and engagement across the search cycle (Ji et al., 2024).
• Evidence context tracking: Large-scale empirical analyses show that more than half of new query terms at each step originate in previously retrieved evidence, with specialized trajectories reaching ~78% reuse (CTAR). Declarative sessions display high repetition and shallow retrieval, while reasoning tasks sustain broader exploration and semantic drift (Ning et al., 24 Jan 2026).
• Conversational agents: Multi-step conversational search agents (Macaw, ChatShop) and intent prediction systems use dialog structural features (turn index, role, utterance length) as primary predictors in multi-label intent detection, providing actionable clues for strategic intervention, clarification policy, and mixed-initiative balancing (Zamani et al., 2019, Chen et al., 2024, Qu et al., 2019).
• Subtopic-aware learning: SACSM models participant needs as an ordered sequence of subtopics; greedy or greedy-skip selection strategies improve both coverage and pedagogically sound learning gain compared to random or reverse ordering (Câmara et al., 2022).

Implications for system design include explicit context memory, turn-index tracking, targeted feedback mechanisms, and adaptive retrieval budgeting based on inferred session intent (Ji et al., 2024, Ning et al., 24 Jan 2026).

6. Failure Modes and Open Challenges

Systematic error analyses reveal distinctive breakdowns unique to deep+wide regimes:

• Lack of reflection: Agents abort early or with vacuous outputs when search trajectories go astray, lacking self-repair or alternative planning (Lan et al., 23 Oct 2025).
• Overreliance on internal knowledge: Agents sometimes eschew retrieval, producing outdated or fabricated content from static LLM knowledge.
• Insufficient retrieval: Agents invoke tools but fail to extract or summarize all required facts, leading to incomplete rows/columns.
• Context overflow: Long trajectories saturate context windows, leading to truncated histories or partial tables.
• Over-exploration: Reward models may encourage excessive information gathering, missing early correct answers if not properly balanced (Lee et al., 24 Nov 2025).
• Repetition loops: Empirical studies highlight declarative sessions stalling in high-overlap retry cycles, emphasizing the need for early stopping and forced exploration (Ning et al., 24 Jan 2026).

These failures indicate limitations in planning, memory management, reward shaping, and trajectory repair.

7. Prospects for Future Research and Practical Recommendations

Current frontiers center on:

• Adaptive planning: Dynamic interleaving of deep and wide operations based on intermediate task progress signals, context summaries, and self-reflective modules (Lan et al., 23 Oct 2025).
• Memory augmentation: Incremental summarization of evidence with question-aware pruning and cross-step term prioritization (Lee et al., 24 Nov 2025, Ning et al., 24 Jan 2026).
• Meta-reasoning: Integrated modules capable of self-reflection and automatic repair on contradictory or vacuous retrieval trajectories (Lan et al., 23 Oct 2025).
• Automated data generation and evaluation metrics: Formalization-driven task synthesis (WebShaper) and reference-free metrics expand benchmarks to new domains and languages at scale (Tao et al., 20 Jul 2025).
• Multimodal agency: Decoupled acquisition/synthesis architectures (M$^3$Searcher) exhibit greater transfer robustness and stepwise fidelity in multi-hop multimodal benchmarks (Yu et al., 14 Jan 2026).
• Bandit-based constraint completion: Contextual multi-armed bandit policies efficiently surface and refine implicit constraints for complex requests, reducing hallucination and improving retrieval relevance (Ahmadvand et al., 2022).

By integrating these advances with rigorous evaluation (success rate, multi-granular F1, efficiency, behavioral/cognitive traces), the field aims toward robust, generalizable, and human-aligned multi-step information-seeking agents.