Complex Retrieval Tasks in Modern IR

• Complex retrieval tasks are scenarios where queries satisfy multiple interdependent criteria by integrating structured and unstructured data with advanced reasoning.
• Benchmarks like CRUMB, BIRCO, and TREC CAR are designed to evaluate multi-faceted queries and guide the development of specialized retrieval architectures.
• Hybrid methodologies combining query expansion, multi-task models, and dense-sparse fusion improve performance yet continue to face challenges in scalability and precision.

Complex retrieval tasks denote scenarios where queries require finding information that satisfies multiple interdependent criteria, integrates structured and unstructured data, or involves sophisticated reasoning beyond simple keyword matching. In information retrieval (IR) research, such tasks present distinct challenges—including vocabulary mismatch, ambiguous intent, multi-hop reasoning, and the necessity to synthesize heterogeneous modalities or knowledge sources. The evolution from simple, atomic queries to complex user objectives has directly motivated the construction of specialized benchmarks, new retrieval architectures, and composite evaluation protocols to accurately assess system capability in these realistic, demanding settings.

1. Defining Complex Retrieval Tasks

Complex retrieval tasks are characterized by queries containing multiple facets, constraints, or requirements expressed in natural language. Unlike standard retrieval settings—where inputs may be narrowly focused or single-aspect (e.g., a keyword or entity name)—complex queries may require:

• Satisfying multi-faceted objectives (e.g., “Find research papers about neural retrievers co-authored by both A and B, then summarize unique claims not found in prior work”).
• Integrating or reasoning over structured (e.g., relational graphs, knowledge bases) and unstructured (e.g., passages, images) content.
• Handling stepwise or multi-hop constraints, as in multi-step question answering and cross-modal search.
• Producing results that align with explicit user intent or task instructions rather than implicit contextual similarity.

Such complex queries more accurately reflect contemporary user needs and system applications, including fact verification, product search with attribute filtering, regulatory document retrieval, and evidence aggregation for scientific and medical domains (Nanni et al., 2017, Killingback et al., 8 Sep 2025, Wang et al., 21 Feb 2024, Wu et al., 19 Apr 2024).

2. Benchmarks and Evaluation Protocols

Addressing these challenges has led to the development of several dedicated benchmarks:

Benchmark / Paper	Domain Scope	Complexity Criteria
CRUMB (Killingback et al., 8 Sep 2025)	Multi-domain: movies, science, law, code, QA	Faceted, compositional, logical queries
BIRCO (Wang et al., 21 Feb 2024)	Science, debate, literature, biomedicine	Multi-faceted (up to 10+ facets per query)
STaRK (Wu et al., 19 Apr 2024)	Retail, academic search, precision medicine	Joint textual and relational constraints
SORCE (Liu et al., 30 May 2025)	Text-to-image retrieval (small object focus)	Fine-grained, region-level signal association
TREC CAR (Nanni et al., 2017)	Wikipedia article population	Section-level, hierarchical, context-driven

These resources standardize both queries and documents (often in markdown or semi-structured format), allow evaluation of passage/document-level criteria, and support multi-granularity (chunked) retrieval. Performance metrics go beyond accuracy, often prioritizing normalized Discounted Cumulative Gain (nDCG@k), Recall@k, or Sufficiency@k—for evaluating both the quality and breadth of retrieved results in multi-relevant settings.

A contextualized chunking strategy (Killingback et al., 8 Sep 2025) enhances retrieval on long-form documents by splitting them based on markdown hierarchy, ensuring local context is preserved while respecting tokenization constraints.

3. Methodologies: Architectures, Models, and Strategies

A broad spectrum of methodologies has been proposed to advance performance on complex retrieval tasks:

• Query Expansion and Enrichment: Pseudo relevance feedback (RM1), entity-based expansion (ent-RM1), and section-structure inspired Rocchio expansion mitigate vocabulary mismatch and broaden the recall space, as demonstrated in the TREC CAR benchmark (Nanni et al., 2017).
• Multi-task and Instruction-Tuned Models: Multi-task dense retrievers (e.g., based on DPR, BERT) share representations across diverse tasks and domains, using contrastive losses and adversarial confounder selection for robust out-of-domain generalization (Maillard et al., 2021). Instruction-tuning (as with TART (Asai et al., 2022)), where models are conditioned on explicit user instructions, enables dynamic adaptation to changing or previously unseen objectives.
• Hybrid and Modular Approaches: Modern frameworks integrate both dense retrieval (using semantic embeddings) and sparse lexical scoring (e.g., BM25). Methods such as LeSeR (Lexical Reranking of Semantic Retrieval) decouple dense retrieval from BM25 reranking to combine semantic recall with domain-aligned lexical precision (Purbey et al., 8 Dec 2024).
• Multimodal and Multi-granularity Retrieval: For tasks involving visually rich documents or cross-modal search, architectures combine hierarchical encoding (full-page vs. region-level embeddings), vision-LLM (VLM) candidate filtering, and late-stage fusion or reranking (e.g., Reciprocal Rank Fusion) to leverage complementary signals from text, images, and layout (Xu et al., 1 May 2025).
• Decomposition and Planning Paradigms: Decomposed Prompting (Khot et al., 2022) modularizes complex queries into sub-tasks handled by separate prompts or sub-models, facilitating multi-hop reasoning and integration with external retrieval APIs. Critic-guided planning frameworks (Li et al., 2 Oct 2024) employ critic models paired with planners (often leveraging MCTS) to iteratively select, execute, and validate retrieval actions, thus optimizing both reasoning and document selection for hard tasks including competitive programming and formal math QA.

4. Performance Insights and Limitations

Comprehensive benchmarking studies reveal that contemporary retrieval models—including instruction-aware LLM-based rerankers and dense retrievers—show persistent difficulty with complex queries:

• The highest average nDCG@10 across tasks in CRUMB is 0.346 and R@100 is 0.587, even for the best models (Killingback et al., 8 Sep 2025).
• Similarly, on BIRCO, no single approach achieves satisfactory performance across all tasks, and “even state-of-the-art models (e.g., GPT‑4 variants) struggle to handle all facets consistently” (Wang et al., 21 Feb 2024).
• LLM-based query rewriting/expansion, while potentially beneficial for weaker models, often leads to degraded performance for the strongest models (e.g., GTE Qwen 7B), plausibly due to a mismatch between rewritten queries and the models’ learned representations (Killingback et al., 8 Sep 2025). Different expansion strategies differentially affect recall and precision.
• In specialized domains (e.g., regulatory retrieval, biomedical QA), hybrid methods outperform monolithic dense or sparse models. The LeSeR pipeline, for instance, achieves Recall@10 of 0.8201 and mAP@10 of 0.6655—significantly ahead of baseline BM25 (0.7611/0.6237) (Purbey et al., 8 Dec 2024).
• In cross-modal and fine-grained retrieval (e.g., SORCE small object retrieval), standard single-embedding methods underperform. Multi-embedding strategies directed by regional prompts in MLLMs yield substantial improvements and are necessary to address fine-grained or regionally-scoped queries (Liu et al., 30 May 2025).

5. Planning, Scalability, and Reasoning

Advanced systems for retrieval planning and reasoning—such as REAPER (Joshi et al., 26 Jul 2024) and CR-Planner (Li et al., 2 Oct 2024)—shift from interleaved, iterative retrieval to plan-based execution:

• REAPER generates a retrieval plan in a single model call, dramatically reducing latency (e.g., ~207ms vs. seconds for multi-step LLM chains). It demonstrates high adaptability to new tools or sources with few training samples and supports scalable integration into real-time applications (e.g., retail conversational assistants).
• Critic-guided planners leverage action selection policies of the form $a_t = \arg\max_{a \in A_{s_t}} R(s_t, a)$, selecting and evaluating reasoning vs. retrieval actions via critic models trained using data collected from MCTS simulations. This leads to higher pass rates in competitive benchmarks, especially on reasoning- and retrieval-intensive tasks.
• Multi-tool and tool-augmented retrieval (e.g., QTA framework in (Zhang et al., 4 Oct 2024)) handles system- or API-rich environments by rewriting queries and aligning them to the style and capabilities of candidate tools. Direct Preference Optimization (DPO) is used to optimize query-tool matching efficiently, even with minimal supervision.

6. Practical Implications and Research Directions

The cumulative findings across benchmarks and methods suggest the following:

• There is no monolithic retrieval model yet matching general-purpose LLMs in flexibility or robustness on complex, multi-dimensional retrieval tasks (Killingback et al., 8 Sep 2025).
• Hybrid architectures—incorporating structured reasoning, instruction following, and signal fusion from dense and sparse representations—are more competitive, especially for domain-sensitive or multi-modal scenarios.
• Evaluation protocols must reflect real-world complexity by using realistic candidate pools, multi-faceted user objectives, passage-level granularity, and metrics such as nDCG@k, Recall@k, Sufficiency@k, and task-specific measures.
• Improving performance further will require new retrieval paradigms that inherently model compositional user intent, multi-hop constraints, and reasoning (e.g., via multi-agent planning, modular LLM architectures, or dynamic instruction routing).
• Efficiency and scalability remain open challenges, particularly when deploying LLM rerankers (due to latency) or integrating rapidly expanding tool ecosystems.

A plausible implication is that future progress in complex retrieval will hinge on synergizing efficient neural retrieval architectures with explicit planning, context-rich instruction following, and enhanced multi-modal and multi-granular representation learning—all evaluated under diverse, realistic, and challenging benchmarks that reflect actual user needs and open research problems in real-world retrieval settings.