AI Insights

• Adaptive heuristics: Heuristics that adapt to changing conditions or environments. (ML: 0.97)👍👎
• The paper assumes that the true dominant reward and cost functions are known, which may not be realistic in practice. (ML: 0.95)👍👎
• The authors propose a neural optimization approach using adaptive heuristics for intelligent marketing systems. (ML: 0.93)👍👎
• The proposed approach can effectively handle complex marketing systems with multiple stakeholders and constraints. (ML: 0.92)👍👎
• The offline experiment setup involves generating synthetic data with 500 users and 100 items, each assigned to one of five disjoint sets of items. (ML: 0.91)👍👎
• Neural optimization approach: An approach that uses neural networks to optimize a function subject to constraints. (ML: 0.90)👍👎
• Multi-stakeholder contextual bandit problem: A problem where the goal is to maximize the cumulative reward while satisfying multiple constraints. (ML: 0.89)👍👎
• The paper presents a multi-stakeholder contextual bandit problem, where the goal is to maximize the cumulative reward while satisfying multiple constraints. (ML: 0.88)👍👎
• The offline experiment setup is limited to 500 users and 100 items, which may not generalize well to larger-scale problems. (ML: 0.85)👍👎
• The offline experiment results demonstrate the effectiveness of the approach in maximizing cumulative reward while satisfying constraints. (ML: 0.81)👍👎

Abstract
We present BanditLP, a scalable multi-stakeholder contextual bandit framework that unifies neural Thompson Sampling for learning objective-specific outcomes with a large-scale linear program for constrained action selection at serving time. The methodology is application-agnostic, compatible with arbitrary neural architectures, and deployable at web scale, with an LP solver capable of handling billions of variables. Experiments on public benchmarks and synthetic data show consistent gains over strong baselines. We apply this approach in LinkedIn's email marketing system and demonstrate business win, illustrating the value of integrated exploration and constrained optimization in production.

Why we are recommending this paper?
Due to your Interest in Personalization

This paper directly addresses personalization through a scalable contextual bandit framework, aligning with your interest in data-driven CRM and personalization platforms. The use of linear programming for action selection is particularly relevant to optimizing recommendations.

AI Insights

• The OP-Bench benchmark may not capture all possible scenarios of over-personalization. (ML: 0.97)👍👎
• The benchmark construction process involves human annotation and evaluation to ensure that the generated synthetic queries are relevant and effective in triggering over-personalization behaviors. (ML: 0.96)👍👎
• The use of synthetic queries may not accurately reflect real-world user interactions. (ML: 0.95)👍👎
• Over-personalization: The phenomenon where a memory-augmented agent inappropriately retrieves and applies user information. (ML: 0.94)👍👎
• The OP-Bench benchmark is designed to isolate over-personalization behaviors triggered by carefully designed user queries, allowing for the evaluation of memory-augmented agents in a controlled environment. (ML: 0.91)👍👎
• OP-Bench: A benchmark construction that aims to evaluate over-personalization behaviors in memory-augmented agents. (ML: 0.90)👍👎
• OP-Bench is a benchmark construction that aims to evaluate over-personalization behaviors in memory-augmented agents. (ML: 0.88)👍👎
• A collection of task-specific prompt templates are used to generate synthetic queries for irrelevance, sycophancy, and repetition, as well as auxiliary prompts for persona extraction, topic control, and automated filtering. (ML: 0.86)👍👎
• LoCoMo: A dataset that provides high-quality long-horizon dialogue histories and user memories. (ML: 0.80)👍👎
• The benchmark is built on top of LoCoMo, which provides high-quality long-horizon dialogue histories and user memories. (ML: 0.78)👍👎

Abstract
Memory-augmented conversational agents enable personalized interactions using long-term user memory and have gained substantial traction. However, existing benchmarks primarily focus on whether agents can recall and apply user information, while overlooking whether such personalization is used appropriately. In fact, agents may overuse personal information, producing responses that feel forced, intrusive, or socially inappropriate to users. We refer to this issue as \emph{over-personalization}. In this work, we formalize over-personalization into three types: Irrelevance, Repetition, and Sycophancy, and introduce \textbf{OP-Bench} a benchmark of 1,700 verified instances constructed from long-horizon dialogue histories. Using \textbf{OP-Bench}, we evaluate multiple large language models and memory-augmentation methods, and find that over-personalization is widespread when memory is introduced. Further analysis reveals that agents tend to retrieve and over-attend to user memories even when unnecessary. To address this issue, we propose \textbf{Self-ReCheck}, a lightweight, model-agnostic memory filtering mechanism that mitigates over-personalization while preserving personalization performance. Our work takes an initial step toward more controllable and appropriate personalization in memory-augmented dialogue systems.

Why we are recommending this paper?
Due to your Interest in Personalization Platform

Given your interest in CRM optimization, this paper’s focus on over-personalization within conversational agents is highly relevant. It investigates a critical area for improving user engagement and tailoring interactions.

AI Insights

• Machine psychology: A research trend advocating for new data collection methodologies, sophisticated simulation tasks, and human-centered training regimes to align LLMs with human cognition and behavior. (ML: 0.98)👍👎
• Role-playing and theory-of-mind tasks are commonly used to assess whether models can understand and replicate human behavior. (ML: 0.98)👍👎
• Further research is needed to improve the performance of human-centric LLMs and address the limitations of current models. (ML: 0.98)👍👎
• The development of HumanLLM demonstrates a significant step towards bridging the gap between academic and social capabilities in LLMs. (ML: 0.97)👍👎
• Human-centric LLMs are needed to capture the intricacies of real human behaviors. (ML: 0.96)👍👎
• The need for systematic evaluation of LLMs' human-like abilities has given rise to specialized benchmarks and datasets. (ML: 0.96)👍👎
• Current LLMs have limited ability to simulate individual personalities, motivations, and dynamic social contexts. (ML: 0.95)👍👎
• Current LLMs are limited in their ability to simulate individual personalities, motivations, and dynamic social contexts. (ML: 0.94)👍👎
• Human-centric LLMs are necessary for authentic social intelligence in applications that interact directly with humans. (ML: 0.92)👍👎
• Human-centric LLMs: LLMs that prioritize capturing the intricacies of real human behaviors and structured persona-scenario-behavior data for advanced social simulation. (ML: 0.89)👍👎

Abstract
Motivated by the remarkable progress of large language models (LLMs) in objective tasks like mathematics and coding, there is growing interest in their potential to simulate human behavior--a capability with profound implications for transforming social science research and customer-centric business insights. However, LLMs often lack a nuanced understanding of human cognition and behavior, limiting their effectiveness in social simulation and personalized applications. We posit that this limitation stems from a fundamental misalignment: standard LLM pretraining on vast, uncontextualized web data does not capture the continuous, situated context of an individual's decisions, thoughts, and behaviors over time. To bridge this gap, we introduce HumanLLM, a foundation model designed for personalized understanding and simulation of individuals. We first construct the Cognitive Genome Dataset, a large-scale corpus curated from real-world user data on platforms like Reddit, Twitter, Blogger, and Amazon. Through a rigorous, multi-stage pipeline involving data filtering, synthesis, and quality control, we automatically extract over 5.5 million user logs to distill rich profiles, behaviors, and thinking patterns. We then formulate diverse learning tasks and perform supervised fine-tuning to empower the model to predict a wide range of individualized human behaviors, thoughts, and experiences. Comprehensive evaluations demonstrate that HumanLLM achieves superior performance in predicting user actions and inner thoughts, more accurately mimics user writing styles and preferences, and generates more authentic user profiles compared to base models. Furthermore, HumanLLM shows significant gains on out-of-domain social intelligence benchmarks, indicating enhanced generalization.

Why we are recommending this paper?
Due to your Interest in Personalization

This research from the University of Science and Technology of China explores simulating human behavior using LLMs, which could be valuable for understanding customer needs and preferences. The focus on transforming social science research aligns with your interest in personalization.

AI Insights

• They also note that the results may not generalize to other domains or tasks beyond text-to-SQL. (ML: 0.99)👍👎
• The authors acknowledge the limitations of their study, including the small size of the dataset and the potential bias in the persona modeling prompt. (ML: 0.98)👍👎
• The authors propose a framework for generating high-quality datasets that align with business intelligence (BI) settings and evaluate the performance of various LLMs using this framework. (ML: 0.97)👍👎
• Previous studies have shown that LLMs can be effective on certain NLP tasks, but struggle with others, highlighting the need for more research in this area. (ML: 0.96)👍👎
• The proposed framework generates high-quality datasets that align with BI settings and evaluates the performance of various LLMs using this framework. (ML: 0.96)👍👎
• The text is a research paper on developing a benchmark for evaluating large language models (LLMs) on real-world text-to-SQL tasks. (ML: 0.96)👍👎
• The results show that some LLMs perform well on certain aspects of text-to-SQL, but struggle with others, highlighting the need for more research in this area. (ML: 0.95)👍👎
• Text-to-SQL: A task where a natural language question is converted into an SQL query to retrieve relevant data from a database. (ML: 0.94)👍👎
• LLMs: Large Language Models, which are artificial intelligence models that can process and generate human-like text. (ML: 0.94)👍👎
• ReAct paradigm: A prompt-based agentic framework for evaluating LLMs on complex tasks such as text-to-SQL. (ML: 0.93)👍👎

Abstract
Evaluating Text-to-SQL agents in private business intelligence (BI) settings is challenging due to the scarcity of realistic, domain-specific data. While synthetic evaluation data offers a scalable solution, existing generation methods fail to capture business realism--whether questions reflect realistic business logic and workflows. We propose a Business Logic-Driven Data Synthesis framework that generates data grounded in business personas, work scenarios, and workflows. In addition, we improve the data quality by imposing a business reasoning complexity control strategy that diversifies the analytical reasoning steps required to answer the questions. Experiments on a production-scale Salesforce database show that our synthesized data achieves high business realism (98.44%), substantially outperforming OmniSQL (+19.5%) and SQL-Factory (+54.7%), while maintaining strong question-SQL alignment (98.59%). Our synthetic data also reveals that state-of-the-art Text-to-SQL models still have significant performance gaps, achieving only 42.86% execution accuracy on the most complex business queries.

Why we are recommending this paper?
Due to your Interest in Data Driven CRM

This paper tackles the challenge of data synthesis for BI, a key component of data-driven CRM strategies. The focus on realism in synthetic data generation is directly applicable to your interests in personalized data solutions.

AI Insights

• Imagine you have multiple goals that you want to achieve, like getting the best grades, being healthy, and having fun. (ML: 0.97)👍👎
• In multi-objective optimization problems, we try to find a solution that balances all these goals at the same time. (ML: 0.87)👍👎
• A set P ⊆ X is an A-approximation for Πp if every feasible solution x′ ∈ X is α-approximated by a feasible solution x ∈ P for some α ∈ A. (ML: 0.85)👍👎
• The paper assumes that the outcome space is closed, which may not be the case in general. (ML: 0.85)👍👎
• The paper discusses the concept of approximation in multi-objective optimization problems. (ML: 0.82)👍👎
• A set P ⊆ X is an α-approximation of Πp if every feasible solution x′ ∈ X is α-approximated by a solution x ∈ P. (ML: 0.81)👍👎
• It introduces a new definition of approximation that allows for a set of approximation factors, and provides an algorithm to find all supported efficient solutions for multi-objective integer network flow problems. (ML: 0.80)👍👎
• The set XSE is an A-approximation of all efficient solutions, where A = {(α1,...,αp): αi ≥ 1 for all i, αj = 1 for some j, and ∑i:αi>1αi=p}. (ML: 0.74)👍👎
• The paper discusses how to approximate these solutions, which means finding a set of good enough solutions that are close to the optimal ones. (ML: 0.73)👍👎
• The paper builds upon previous work by Benson (1998), Chlumsky-Harttmann (2025), DS92, Ehrgott (2005), HR94, KS25, Mie98, Nemhauser and Wolsey (1988), PGE10, and Roc70. (ML: 0.60)👍👎

Abstract
In this paper, we present and prove some results in multi-objective optimisation that are considered folklore. For the most part, proofs for these results exist in special cases, but they are used in more general settings since their proofs can be (largely) transferred. We do this transfer explicitly and try to state the results as generally as possible. In particular, we also aim at providing clean and complete proofs for results where the original papers are not rigorous.

Why we are recommending this paper?
Due to your Interest in CRM Optimization

This paper’s exploration of multi-objective optimization techniques could provide valuable insights for optimizing your CRM strategies. The focus on transferable proofs is relevant to developing efficient and scalable solutions.