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• The paper discusses the problem of finding a strict improvement via the framework of Theorem 3 for non-defective cases. [3]
• The authors conduct an exhaustive search and find no counterexamples in every non-defective case with r ≤ 10, suggesting a positive answer to the question. [3]
• However, verifying this requires improvements in numerical techniques since the degree alone does not directly determine q in higher codimensions. [3]
• Non-defective case: A case where the secant variety σr(V) is non-defective, meaning it has no singularities and its dimension equals the expected value. [3]
• Theorem 3: A framework for finding a strict improvement via the degree of the secant variety σr(V). [3]
• The authors' exhaustive search suggests that every non-defective case admits a strict improvement via the framework of Theorem 3. [3]
• However, verifying this requires improvements in numerical techniques since the degree alone does not directly determine q in higher codimensions. [3]
• Improved bound: A bound that is stricter than the geometric bounds provided by Theorem 3. [2]
• The authors rely on numerical techniques, which may not be accurate or reliable in higher codimensions. [0]

Abstract
We systematically compute improved asymptotic rank bounds for tensors. Using numerical implicitization, we implement the geometric framework of Kaski and Michałek across all computationally feasible cases. By detecting the absence of low-degree vanishing polynomials on secant varieties, we obtain new asymptotic rank bounds that improve upon the generic border rank bounds. The results provide numerical data supporting Strassen's asymptotic rank conjecture and clarify the computational barriers posed by current numerical methods.

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

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• The authors propose a novel approach to modeling user behavior using a graph neural network (GNN), which allows for more accurate predictions and better personalization. [3]
• Conversational AI: A type of artificial intelligence that enables computers to understand and respond to human language in a conversational manner. [3]
• Graph Neural Network (GNN): A type of neural network designed to handle graph-structured data, which is particularly useful for modeling complex relationships between entities. [3]
• The development of web-based conversational AI systems has the potential to revolutionize the way we interact with technology and access information. [3]
• The paper cites several studies on conversational AI and user behavior modeling, including the use of GNNs for predicting user interactions. [3]
• The system uses a combination of natural language processing (NLP) and machine learning (ML) techniques to understand user queries and provide relevant responses. [2]
• The paper discusses the development of a web-based conversational AI system that can simulate user interactions and search behaviors. [1]

Abstract
A fundamental tension exists between the demand for sophisticated AI assistance in web search and the need for user data privacy. Current centralized models require users to transmit sensitive browsing data to external services, which limits user control. In this paper, we present a browser extension that provides a viable in-browser alternative. We introduce a hybrid architecture that functions entirely on the client side, combining two components: (1) an adaptive probabilistic model that learns a user's behavioral policy from direct feedback, and (2) a Small Language Model (SLM), running in the browser, which is grounded by the probabilistic model to generate context-aware suggestions. To evaluate this approach, we conducted a three-week longitudinal user study with 18 participants. Our results show that this privacy-preserving approach is highly effective at adapting to individual user behavior, leading to measurably improved search efficiency. This work demonstrates that sophisticated AI assistance is achievable without compromising user privacy or data control.

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

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• The double descent phenomenon in machine learning refers to the observation that as the model size and training data increase, the generalization error of a model can first decrease and then increase again. [3]
• This phenomenon has been observed in various contexts, including linear regression, neural networks, and graph convolutional networks. [3]
• The double descent curve is characterized by three phases: underfitting, overfitting, and double descent. [3]
• In the underfitting phase, the model is too simple to capture the underlying patterns in the data, leading to poor generalization performance. [3]
• In the overfitting phase, the model is too complex and captures noise in the data, also leading to poor generalization performance. [3]
• The double descent phase occurs when the model size increases beyond a certain point, causing the model to start capturing the underlying patterns in the data again, but with increased capacity for overfitting. [3]
• Some of these explanations include the bias-variance trade-off, the effect of noise on fitting linear regression models, and the role of regularization in mitigating double descent. [3]
• Researchers have also proposed various methods to mitigate or understand the double descent phenomenon, including optimal regularization, early stopping, and multi-scale feature learning dynamics. [3]
• These methods aim to balance the capacity of the model with its ability to generalize well to new data. [3]
• The study of double descent has significant implications for machine learning research and practice. [3]
• It highlights the importance of understanding the trade-offs between model complexity and generalization performance, and provides insights into how to design models that can generalize well to new data. [3]
• However, the existing literature provides valuable insights into this phenomenon and highlights the importance of continued investigation in this area. [3]
• Double Descent: A phenomenon where as the model size and training data increase, the generalization error of a model can first decrease and then increase again. [3]
• Underfitting: When a model is too simple to capture the underlying patterns in the data, leading to poor generalization performance. [3]
• Overfitting: When a model is too complex and captures noise in the data, also leading to poor generalization performance. [3]
• Double Descent Curve: A curve that characterizes the three phases of the double descent phenomenon: underfitting, overfitting, and double descent. [3]
• The double descent phenomenon has been studied extensively in recent years, and various explanations have been proposed. [1]

Abstract
Deep double descent is one of the key phenomena underlying the generalization capability of deep learning models. In this study, epoch-wise double descent, which is delayed generalization following overfitting, was empirically investigated by focusing on the evolution of internal structures. Fully connected neural networks of three different sizes were trained on the CIFAR-10 dataset with 30% label noise. By decomposing the loss curves into signal contributions from clean and noisy training data, the epoch-wise evolutions of internal signals were analyzed separately. Three main findings were obtained from this analysis. First, the model achieved strong re-generalization on test data even after perfectly fitting noisy training data during the double descent phase, corresponding to a "benign overfitting" state. Second, noisy data were learned after clean data, and as learning progressed, their corresponding internal activations became increasingly separated in outer layers; this enabled the model to overfit only noisy data. Third, a single, very large activation emerged in the shallow layer across all models; this phenomenon is referred as "outliers," "massive activa-tions," and "super activations" in recent large language models and evolves with re-generalization. The magnitude of large activation correlated with input patterns but not with output patterns. These empirical findings directly link the recent key phenomena of "deep double descent," "benign overfitting," and "large activation", and support the proposal of a novel scenario for understanding deep double descent.

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

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• The paper discusses a new benchmark called BEIR (Benchmarking Evaluation in Retrieval) for evaluating the performance of information retrieval models. [3]
• The benchmark includes 15 datasets and covers various languages. [3]
• BEIR: Benchmarking Evaluation in Retrieval LLMs: Large Language Models Position Bias: A phenomenon where the ranking of documents is influenced by their position in the result list, rather than their relevance to the query. [3]
• The paper highlights the importance of evaluating information retrieval models using diverse and representative datasets. [3]
• The paper does not provide any empirical evaluation or results from experiments conducted using the proposed methods. [3]
• The authors also discuss the use of large language models (LLMs) to generate synthetic test collections, which can be used to evaluate the performance of retrieval systems without requiring human annotators. [2]

Abstract
While dense retrieval models have achieved remarkable success, rigorous evaluation of their sensitivity to the position of relevant information (i.e., position bias) remains largely unexplored. Existing benchmarks typically employ position-agnostic relevance labels, conflating the challenge of processing long contexts with the bias against specific evidence locations. To address this challenge, we introduce PosIR (Position-Aware Information Retrieval), a comprehensive benchmark designed to diagnose position bias in diverse retrieval scenarios. PosIR comprises 310 datasets spanning 10 languages and 31 domains, constructed through a rigorous pipeline that ties relevance to precise reference spans, enabling the strict disentanglement of document length from information position. Extensive experiments with 10 state-of-the-art embedding models reveal that: (1) Performance on PosIR in long-context settings correlates poorly with the MMTEB benchmark, exposing limitations in current short-text benchmarks; (2) Position bias is pervasive and intensifies with document length, with most models exhibiting primacy bias while certain models show unexpected recency bias; (3) Gradient-based saliency analysis further uncovers the distinct internal attention mechanisms driving these positional preferences. In summary, PosIR serves as a valuable diagnostic framework to foster the development of position-robust retrieval systems.

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

AI Insights

• Agricultural large language models (LLMs) have been increasingly used in various applications such as knowledge transfer and practical application. [2]

Abstract
As the volume of unstructured text continues to grow across domains, there is an urgent need for scalable methods that enable interpretable organization, summarization, and retrieval of information. This work presents a unified framework for interpretable topic modeling, zero-shot topic labeling, and topic-guided semantic retrieval over large agricultural text corpora. Leveraging BERTopic, we extract semantically coherent topics. Each topic is converted into a structured prompt, enabling a language model to generate meaningful topic labels and summaries in a zero-shot manner. Querying and document exploration are supported via dense embeddings and vector search, while a dedicated evaluation module assesses topical coherence and bias. This framework supports scalable and interpretable information access in specialized domains where labeled data is limited.

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