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AI agents gain advanced reasoning and personalization with new frameworks

By PulseAugur Editorial · [9 sources] ·

Researchers have developed two novel frameworks for enhancing AI agent capabilities in information retrieval and reasoning. The first, SPARK, utilizes coordinated persona-based LLM agents to deliver task-specific retrieval and emergent personalization by modeling user needs through a defined persona space. The second, LLM-Wiki, operationalizes a 'retrieval-as-reasoning' paradigm by structuring external knowledge into a self-evolving Wiki format, enabling agents to search, read, and traverse information more effectively than traditional RAG systems. AI

IMPACT These frameworks advance AI agent reasoning and personalization, potentially improving search systems and complex task execution.

RANK_REASON Two research papers introduce novel frameworks for AI agent retrieval and reasoning.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 9 sources. How we write summaries →

COVERAGE [9]

  1. arXiv cs.AI TIER_1 English(EN) · Melissa Z. Pan, Negar Arabzadeh, Mathew Jacob, Fiodar Kazhamiaka, Esha Choukse, Matei Zaharia ·

    Natural Language Query to Configuration for Retrieval Agents

    arXiv:2605.27361v1 Announce Type: new Abstract: Modern retrieval agents expose many configuration choices -- LLM, retriever, number of documents, number of hops, and synthesis strategy -- each shaping both answer quality and serving cost. Today, these pipelines are typically hand…

  2. arXiv cs.LG TIER_1 English(EN) · Nilesh Gupta, Wei-Cheng Chang, Ngot Bui, Cho-Jui Hsieh, Inderjit S. Dhillon ·

    LLM-guided Hierarchical Search for End-to-end Reasoning Intensive Retrieval

    arXiv:2510.13217v2 Announce Type: replace-cross Abstract: Search systems are increasingly used for reasoning-intensive queries, where what makes a document relevant requires understanding or reasoning over the query-document relation rather than relying on surface vocabulary or t…

  3. arXiv cs.AI TIER_1 English(EN) · Hao Wang, Jialun Zhong, Changcheng Wang, Zhujun Nie, Zheng Li, Shunyu Yao, Yanzeng Li, Xinchi Li ·

    SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs

    arXiv:2512.04868v2 Announce Type: replace-cross Abstract: Knowledge-based conversational question answering (KBCQA) confronts persistent challenges in resolving coreference, modeling contextual dependencies, and executing complex logical reasoning. Existing approaches often suffe…

  4. arXiv cs.AI TIER_1 English(EN) · Matei Zaharia ·

    Natural Language Query to Configuration for Retrieval Agents

    Modern retrieval agents expose many configuration choices -- LLM, retriever, number of documents, number of hops, and synthesis strategy -- each shaping both answer quality and serving cost. Today, these pipelines are typically hand-tuned once per workload, leaving substantial pe…

  5. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Xuanjing Huang ·

    Rethinking Agentic RAG: Toward LLM-Driven Logical Retrieval Beyond Embeddings

    Recent advances in RAG have shifted toward an agentic paradigm, where LLMs interact with retrieval systems over multiple turns and iteratively refine queries based on intermediate results. At the same time, LLMs have demonstrated a strong ability to construct structured queries t…

  6. arXiv cs.AI TIER_1 English(EN) · Gaurab Chhetri, Subasish Das, Tausif Islam Chowdhury ·

    SPARK: Search Personalization via Agent-Driven Retrieval and Knowledge-sharing

    arXiv:2512.24008v3 Announce Type: replace Abstract: Personalized search demands the ability to model users' evolving, multi-dimensional information needs; a challenge for systems constrained by static profiles or monolithic retrieval pipelines. We present SPARK (Search Personaliz…

  7. arXiv cs.CL TIER_1 English(EN) · Haoliang Ming, Feifei Li, Xiaoqing Wu, Wenhui Que ·

    Retrieval as Reasoning: Self-Evolving Agent-Native Retrieval via LLM-Wiki

    arXiv:2605.25480v1 Announce Type: new Abstract: LLM agents require retrieval to behave less like one-shot context fetching and more like reasoning: searching, reading, traversing, and deciding when evidence is sufficient. However, Retrieval-Augmented Generation (RAG) typically or…

  8. arXiv cs.CL TIER_1 English(EN) · Wenhui Que ·

    Retrieval as Reasoning: Self-Evolving Agent-Native Retrieval via LLM-Wiki

    LLM agents require retrieval to behave less like one-shot context fetching and more like reasoning: searching, reading, traversing, and deciding when evidence is sufficient. However, Retrieval-Augmented Generation (RAG) typically organizes external knowledge as flat chunks retrie…

  9. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Lingtao Mao ·

    Search-E1: Self-Distillation Drives Self-Evolution in Search-Augmented Reasoning

    Post-training has become the dominant recipe for turning a language model into a competent search-augmented reasoning agent. A line of recent work pushes its performance further by adding elaborate machinery on top of this standard pipeline. These augmentations import external su…

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