LLM-infused Search Engine

• LLM-infused search engines are advanced information retrieval systems that integrate large language models to understand queries, generate answers, and synthesize evidence.
• They decompose search tasks using techniques like retrieval-augmented generation and agentic planning to handle complex, multi-intent queries with improved accuracy.
• These systems employ validators and transparent attribution mechanisms to bolster reliability, mitigate hallucinations, and support accountable search outputs.

LLM-infused search engines are information retrieval systems that integrate LLMs into their core architecture to enable advanced natural language understanding, flexible semantic synthesis, and direct answer generation. These systems move beyond traditional retrieval methods—based on keyword and link analysis—by leveraging the generative, reasoning, and knowledge representation capabilities of LLMs, often enhanced through retrieval-augmented generation (RAG) and reinforced by agentic planning or adaptive feedback. As a result, LLM-infused search engines support more complex user queries, facilitate richer task-oriented interactions, and present novel challenges in reliability, transparency, and optimization.

1. Architectural Principles and System Design

Contemporary LLM-infused search engines either augment or partially replace the traditional multi-stage search stack with LLM-based modules. In the “large search model” framework (Wang et al., 2023), nearly all downstream search pipeline stages—query understanding, multi-stage ranking, snippet generation, question answering—are reformulated as conditional autoregressive text generation tasks, where each is posed as predicting $Y$ from $X$. The only remaining non-LLM component is the initial retrieval of a manageable set of documents (via lexical or embedding-based methods). Beyond that, the LLM generates ranked result lists, answer snippets, and direct responses conditioned on user input, retrieval context, and designer-defined prompts.

Agent-based search architectures (“search agents” (Xi et al., 3 Aug 2025)) further extend this paradigm, casting the search process as iterative loops of planning, acting (retrieval/tool use), and reflecting—potentially decomposing queries, issuing dynamic sub-queries, updating world models, and synthesizing evidence across multiple contexts into a final output:

• Plan: $\pi_0 = Plan(q, C)$
• Reflect after observing $o_t$: $\pi_{t+1} = Reflect(o_t, h_t, \pi_t)$
• Act: Decides next retrieval/tool invocation or answer generation.

Recent systems incorporate additional modules for trustworthiness (validation, evidence attribution (Shi et al., 2023, Strauss et al., 27 Jun 2025)), query routing (for multimodal or type-specific searches (Nguyen et al., 13 Jan 2025)), and entity extraction (for structured search interfaces (Yao et al., 7 Nov 2024)).

A typical modular architecture includes:

• Retriever(s): lexical (BM25), semantic (vector), or hybrid (Nguyen et al., 13 Jan 2025)
• LLM-based generator(s): for response and evidence synthesis, query expansion, and facet generation (Lee et al., 25 Mar 2024)
• Validator(s): assessing freshness, accessibility, factuality, and extracting evidence from web content (Shi et al., 2023)
• Optimizer(s): refining source/resource lists via critical feedback, online checks, and history mining (Shi et al., 2023)

2. Methods for Query Understanding, Generation, and Augmentation

LLM-infused systems are capable of handling complex, composite, or ambiguous queries, supporting both faceted exploration (e.g., multi-intent (Shi et al., 2023)) and natural language interaction (Yao et al., 7 Nov 2024). Notable advances include:

• Query entity extraction via LLMs and advanced prompt engineering for mapping natural language to structured search fields, using methods such as system messages, few-shot demonstrations, and chain-of-thought (CoT) reasoning (Yao et al., 7 Nov 2024).
• Facet prediction using multi-task learning, where the small model generates base predictions (facets, snippets, related queries) from only the query. These are further refined with LLM editing for enhanced semantic diversity (Lee et al., 25 Mar 2024).
• Synthetic query generation to bridge verbose or complex specialized queries into concise, search-engine-friendly Boolean queries, with uncertainty-based filtering to extract high-value knowledge from search engine results (Shi et al., 18 Feb 2025).
• In agentic frameworks, query decomposition (plan–act–reflect) allows agents to adapt their search strategy based on feedback from previous retrievals and LLM-generated sub-goals (Xi et al., 3 Aug 2025, Yang et al., 13 Apr 2024).

3. Information Synthesis, Reasoning, and Direct Answer Generation

A defining characteristic of LLM-infused search engines is their ability to synthesize information across multiple documents and modalities, state explicit reasoning via CoT or knowledge graphs, and produce direct, decision-oriented answers:

• Retrieval-augmented generation (RAG) is foundational: LLMs are conditioned on evidence from both static knowledge bases and real-time search engine results (Vu et al., 2023, Shi et al., 18 Feb 2025, Nguyen et al., 13 Jan 2025). The FreshPrompt method, for example, demonstrates that integrating a large, well-ordered set of retrieved evidences yields substantial gains in both strict and relaxed QA accuracy (Vu et al., 2023).
• Agentic frameworks (CuriousLLM (Yang et al., 13 Apr 2024)) and knowledge graph prompting leverage follow-up question generation to traverse knowledge graphs, iteratively filling evidence gaps with targeted retrieval, improving accuracy in multi-document QA and reducing hallucination.
• Confidence-based highlighting—visual cues tied to token-level LLM generation probabilities—significantly mitigates user overreliance on erroneous responses (Spatharioti et al., 2023), doubling accuracy on challenging queries when compared to no-highlighting controls.
• Direct answer generation, listwise ranking, snippet synthesis, and other tasks are all cast as sequence generation, enabling rapid iteration via prompt tuning and in-context learning (Wang et al., 2023).

4. Reliability, Trustworthiness, and Attribution

Current research confronts the LLM’s tendency toward hallucination, overreliance, and lack of transparent attribution:

• Overreliance arises when users accept LLM outputs uncritically, especially in low-verifiability domains. Experimental evidence highlights that LLM-based interfaces (absent explicit confidence or source cues) lead to higher satisfaction but persistent vulnerability to model error (Spatharioti et al., 2023).
• Frameworks such as (Shi et al., 2023) introduce a modular validator for real-time web validation, evidence consistency checks, and source accessibility, improving reliability metrics over standalone LLMs.
• The “attribution crisis” (Strauss et al., 27 Jun 2025) documents that mainstream web-enabled LLMs often give incomplete, missing, or omitted citations, leaving a systematic “attribution gap” between information consumed and credited. Per-model citation efficiency (e.g., 0.19 to 0.45 additional citations per unique web page visited) varies widely and is shaped more by retrieval pipeline design than by technical LLM limits. Transparent LLM search architectures, detailed search traces, and standardized telemetry are proposed to address this gap.

System/Behavior	% No Search	% No Citation	Median URLs Visited	Median Citations	Citation Efficiency
Gemini	34%	92%	~11	low	~0.43
GPT-4o	24%	(low, via selective logs)	~4	low	(selective)
Perplexity Sonar	(Not stated)	high	~10	3–4	~0.19–0.45

5. Optimization, Evaluation, and Post-Ranking

Optimization strategies vary along the spectrum from prompt engineering to full-reinforcement learning:

• Prompt engineering remains essential for both guiding entity extraction (Yao et al., 7 Nov 2024) and directing reasoning steps or evidence selection (Spatharioti et al., 2023, Shi et al., 2023, Vu et al., 2023).
• Supervised fine-tuning is used for aligning LLMs with task-specific representations or schema (e.g., efficient natural-language DAG search plans (Li et al., 24 May 2025)) and adapting to proprietary domain requirements.
• Reinforcement learning (RL) is applied to train reasoning-search interleaved agents: reward functions encompassing outcome correctness, format compliance, and (less effectively) intermediate retrieval quality (Jin et al., 21 May 2025). PPO and GRPO are common optimization algorithms, while reward ablations have shown that outcome + format rewards are particularly effective.
• Hybrid optimization (e.g., SFTS + RLSF (Li et al., 24 May 2025)) is used to support both token-efficient planning and high-accuracy, reasoning-intensive multimedia retrieval.
• Post-ranking is increasingly LLM-mediated: the LLM4PR paradigm (Yan et al., 2 Nov 2024) introduces a Query-Instructed Adapter (QIA) to fuse heterogeneous user/item features, a feature adaptation step for aligning representations, and a learning-to-post-rank loss for refining candidate result lists.

6. Evaluation Methodologies and Benchmarks

Evaluation strategies accommodate both closed-form QA (accuracy, EM, NDCG@k) and open-ended information synthesis:

• Objective metrics (e.g., NDCG, MRR, Success@k) remain standard for retrieval/ranking tasks (Yan et al., 2 Nov 2024, Jain et al., 5 Aug 2024, Wang et al., 2023).
• Dynamic benchmarks (FreshQA (Vu et al., 2023), SearchExpertBench-25 (Li et al., 24 May 2025)) account for shifting ground truths and real-time content. Updated benchmarks are critical for capturing model robustness against knowledge drift.
• Multi-axis, LLM-augmented rubrics (G-Eval 2.0 (Chen et al., 15 Aug 2025)) allow for fine-grained, human-aligned subjective evaluation (e.g., relevance, fluency, uniqueness, positional prominence).
• Citational analysis, using negative binomial hurdle models, quantifies the gap between sites visited and sites cited (Strauss et al., 27 Jun 2025).

7. User Behavior, Overreliance, and Emerging Human-Computer Dynamics

Experimental and qualitative studies (higher education (Divekar et al., 19 Sep 2024, Divekar et al., 3 Apr 2025)) indicate:

• LLM-infused search tools are perceived as more satisfying and accessible for narrative, summarizing, or scaffolding learning tasks, but raise concerns in information transparency, source verification, and potential erosion of critical evaluation skills.
• Hybrid usage—LLM for synthesis, search engine for verification—is common among power users and suggests a path forward in interface and system design. The provision of confidence signals or underlying sources is highly sought after.

8. Practical Implications and Future Directions

LLM-infused search engines offer substantial improvements in efficiency, complex query handling, and user experience, but introduce pronounced risks in error propagation, attribution, and auditability. Current best practices include:

• Integrating confidence-based cues or evidence highlighting to guide user trust and encourage verification (Spatharioti et al., 2023).
• Maintaining modular validator and optimizer modules for dynamic quality control (Shi et al., 2023).
• Systematizing telemetry, traceability, and attribution into search architectures (Strauss et al., 27 Jun 2025).
• Modeling intent and information roles to adapt content optimization strategies beyond keyword tuning, crucial for GSE (Generative Search Engine) SEO in the black-box LLM era (Chen et al., 15 Aug 2025).
• Investing in benchmarks and evaluation frameworks that reflect real-world, evolving knowledge needs (Vu et al., 2023, Li et al., 24 May 2025).

Open challenges include the integration of multimodal data, adaptation to privacy-preserving or proprietary corpora, agent self-evolution, the reliability of real-time retrieval, and the development of user-facing mechanisms that balance efficiency with rigorous evaluation and transparency.