Personalized large language models (LLMs) adapt model behavior to individual users to enhance user satisfaction, yet personalization can inadvertently distort factual reasoning. We show that when personalized LLMs face factual queries, there exists a phenomenon where the model generates answers aligned with a user’s prior history rather than the objective truth, resulting in **personalization-induced hallucinations** that degrade factual reliability and may propagate incorrect beliefs, due to representational entanglement between personalization and factual representations. To address this issue, we propose **Factuality-Preserving Personalized Steering (FPPS)**, a lightweight inference-time approach that mitigates personalization-induced factual distortions while preserving personalized behavior. We further introduce **PFQABench**, the first benchmark designed to jointly evaluate factual and personalized question answering under personalization. Experiments across multiple LLM backbones and personalization methods show that FPPS substantially improves factual accuracy while maintaining personalized performance.

Retrieval-Augmented Generation (RAG) systems are widely used to mitigate the stateless nature of Large Language Models (LLMs) in long-term and personalized interactions by incorporating external memory. However, existing approaches often prioritize memory organization, such as knowledge graphs, while overlooking a critical semantic gap between implicit, intent-driven queries and explicit, narrative-based memories. To bridge this gap, we propose QueryLink, a novel framework that leverages Query-Memory Alignment to project both queries and memories into a shared semantic space. It significantly boosts recall by facilitating multi-grained retrieval of semantically relevant information. To further enhance memory retrieval, we leverage Coherent Memory Chunking, a mechanism that processes memories in multi-turn dialogue units, preserving semantic integrity, rather than relying on fixed-size segments. Extensive experiments on the LoCoMo and LongMemEval benchmark demonstrate that QueryLink significantly outperforms SOTA methods, achieving at least a 7% improvement in reasoning accuracy (measured by LLM). Additionally, QueryLink can be integrated as a plug-and-play component to boost existing vector-based systems like A-MEM, leading to improvements of over 6% in both F1 and B1 scores.The code is available at https://github.com/Dontplay0112/querylink.

Self-reflection for Large LanguageModels (LLMs) has gained significant attention. Existing approaches involve models iterating and improving their previous responses based on LLMs’ internal reflection ability or external feedback. However, recent research has raised doubts about whether intrinsic self-correction without external feedback may even degrade performance. Based on our empirical evidence, we find that current static reflection methods may lead to redundant, drift, and stubborn issues. To mitigate this, we introduce **I**nstruct-**o**f-**R**eflec**t**ion (**IoRT**), a novel and general reflection framework that leverages dynamic-meta instruction to enhance the iterative reflection capability of LLMs. Specifically, we propose the instructor driven by the meta-thoughts and self-consistency classifier, generates various instructions, including refresh, stop, and select, to guide the next reflection iteration. Our experiments demonstrate that IoRT achieves an average improvement of 10.1% over established baselines in mathematical and commonsense reasoning tasks, highlighting its efficacy and applicability. Our code is available at https://github.com/llp635/IoRT.

Personalized product search aims to retrieve and rank items that match users’ preferences and search intent. Despite their effectiveness, existing approaches typically assume that users’ query fully captures their real motivation. However, our analysis of a real-world e-commerce platform reveals that users often engage in relevant consultations before searching, indicating they refine intents through consultations based on motivation and need. The implied motivation in consultations is a key enhancing factor for personalized search. This unexplored area comes with new challenges including aligning contextual motivations with concise queries, bridging the category-text gap, and filtering noise within sequence history. To address these, we propose a Motivation-Aware Personalized Search (MAPS) method. It embeds queries and consultations into a unified semantic space via LLMs, utilizes a Mixture of Attention Experts (MoAE) to prioritize critical semantics, and introduces dual alignment: (1) contrastive learning aligns consultations, reviews, and product features; (2) bidirectional attention integrates motivation-aware embeddings with user preferences. Extensive experiments on real and synthetic data show MAPS outperforms existing methods in both retrieval and ranking tasks. Code and supplementary materials are available at: https://github.com/E-qin/MAPS.

Personalized search systems in e-commerce platforms increasingly involve user interactions with AI assistants, where users consult about products, usage scenarios, and more. Leveraging consultation to personalize search services is trending. Existing methods typically rely on semantic similarity to align historical consultations with current queries due to the absence of ‘value’ labels, but we observe that semantic similarity alone often fails to capture the true value of consultation for personalization. To address this, we propose a consultation value assessment framework that evaluates historical consultations from three novel perspectives: (1) Scenario Scope Value, (2) Posterior Action Value, and (3) Time Decay Value. Based on this, we introduce VAPS, a value-aware personalized search model that selectively incorporates high-value consultations through a consultation–user action interaction module and an explicit objective that aligns consultations with user actions. Experiments on both public and commercial datasets show that VAPS consistently outperforms baselines in both retrieval and ranking tasks. Codes are available at https://github.com/E-qin/VAPS.

Operations research (OR) is widely deployed to solve critical decision-making problems with complex objectives and constraints, impacting manufacturing, logistics, finance, and healthcare outcomes. While Large Language Models (LLMs) have shown promising results in various domains, their practical application in industry-relevant operations research (OR) problems presents significant challenges and opportunities. Preliminary industrial applications of LLMs for operations research face two critical deployment challenges: 1) Self-correction focuses on code syntax rather than mathematical accuracy, causing costly errors; 2) Complex expert selection creates unpredictable workflows that reduce transparency and increase maintenance costs, making them impractical for time-sensitive business applications. To address these business limitations, we introduce ORMind, a cognitive-inspired framework that enhances optimization through counterfactual reasoning. Our approach emulates human cognition—implementing an end-to-end workflow that systematically transforms requirements into mathematical models and executable solver code. It is currently being tested internally in Lenovo’s AI Assistant, with plans to enhance optimization capabilities for both business and consumer customers. Experiments demonstrate that ORMind outperforms existing methods, achieving a 9.5% improvement on the NL4Opt dataset and a 14.6% improvement on the ComplexOR dataset.