Are All Marine Species Created Equal? Performance Disparities in Underwater Object Detection

Underwater object detection is critical for monitoring marine ecosystems but poses unique challenges, including degraded image quality, imbalanced class distribution, and distinct visual characteristics. Not every species is detected equally well, yet underlying causes remain unclear. We address two key research questions: 1) What factors beyond data quantity drive class-specific performance disparities? 2) How can we systematically improve detection of under-performing marine species? We manipulate the DUO dataset to separate the object detection task into localization and classification and investigate the under-performance of the scallop class. Localization analysis using YOLO11 and TIDE finds that foreground-background discrimination is the most problematic stage regardless of data quantity. Classification experiments reveal persistent precision gaps even with balanced data, indicating intrinsic feature-based challenges beyond data scarcity and inter-class dependencies. We recommend imbalanced distributions when prioritizing precision, and balanced distributions when prioritizing recall. Improving under-performing classes should focus on algorithmic advances, especially within localization modules. We publicly release our code and datasets.

Recycling History: Efficient Recommendations from Contextual Dueling Bandits

The contextual duelling bandit problem models adaptive recommender systems, where the algorithm presents a set of items to the user, and the user's choice reveals their preference. This setup is well suited for implicit choices users make when navigating a content platform, but does not capture other possible comparison queries. Motivated by the fact that users provide more reliable feedback after consuming items, we propose a new bandit model that can be described as follows. The algorithm recommends one item per time step; after consuming that item, the user is asked to compare it with another item chosen from the user's consumption history. Importantly, in our model, this comparison item can be chosen without incurring any additional regret, potentially leading to better performance. However, the regret analysis is challenging because of the temporal dependency in the user's history. To overcome this challenge, we first show that the algorithm can construct informative queries provided the history is rich, i.e., satisfies a certain diversity condition. We then show that a short initial random exploration phase is sufficient for the algorithm to accumulate a rich history with high probability. This result, proven via matrix concentration bounds, yields $O(\sqrt{T})$ regret guarantees. Additionally, our simulations show that reusing past items for comparisons can lead to significantly lower regret than only comparing between simultaneously recommended items.

Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training

ID-based embeddings are widely used in web-scale online recommendation systems. However, their susceptibility to overfitting, particularly due to the long-tail nature of data distributions, often limits training to a single epoch, a phenomenon known as the "one-epoch problem." This challenge has driven research efforts to optimize performance within the first epoch by enhancing convergence speed or feature sparsity. In this study, we introduce a novel two-stage training strategy that incorporates a pre-training phase using a minimal model with contrastive loss, enabling broader data coverage for the embedding system. Our offline experiments demonstrate that multi-epoch training during the pre-training phase does not lead to overfitting, and the resulting embeddings improve online generalization when fine-tuned for more complex downstream recommendation tasks. We deployed the proposed system in live traffic at Pinterest, achieving significant site-wide engagement gains.

Membership Inference Attacks on LLM-based Recommender Systems

Large language models (LLMs) based Recommender Systems (RecSys) can flexibly adapt recommendation systems to different domains. It utilizes in-context learning (ICL), i.e., the prompts, to customize the recommendation functions, which include sensitive historical user-specific item interactions, e.g., implicit feedback like clicked items or explicit product reviews. Such private information may be exposed to novel privacy attack. However, no study has been done on this important issue. We design four membership inference attacks (MIAs), aiming to reveal whether victims' historical interactions have been used by system prompts. They are \emph{direct inquiry, hallucination, similarity, and poisoning attacks}, each of which utilizes the unique features of LLMs or RecSys. We have carefully evaluated them on three LLMs that have been used to develop ICL-LLM RecSys and two well-known RecSys benchmark datasets. The results confirm that the MIA threat on LLM RecSys is realistic: direct inquiry and poisoning attacks showing significantly high attack advantages. We have also analyzed the factors affecting these attacks, such as the number of shots in system prompts and the position of the victim in the shots.

Evaluating the Evaluators: Are readability metrics good measures of readability?

Plain Language Summarization (PLS) aims to distill complex documents into accessible summaries for non-expert audiences. In this paper, we conduct a thorough survey of PLS literature, and identify that the current standard practice for readability evaluation is to use traditional readability metrics, such as Flesch-Kincaid Grade Level (FKGL). However, despite proven utility in other fields, these metrics have not been compared to human readability judgments in PLS. We evaluate 8 readability metrics and show that most correlate poorly with human judgments, including the most popular metric, FKGL. We then show that Language Models (LMs) are better judges of readability, with the best-performing model achieving a Pearson correlation of 0.56 with human judgments. Extending our analysis to PLS datasets, which contain summaries aimed at non-expert audiences, we find that LMs better capture deeper measures of readability, such as required background knowledge, and lead to different conclusions than the traditional metrics. Based on these findings, we offer recommendations for best practices in the evaluation of plain language summaries. We release our analysis code and survey data.

LaQual: A Novel Framework for Automated Evaluation of LLM App Quality

LLM app stores are quickly emerging as platforms that gather a wide range of intelligent applications based on LLMs, giving users many choices for content creation, coding support, education, and more. However, the current methods for ranking and recommending apps in these stores mostly rely on static metrics like user activity and favorites, which makes it hard for users to efficiently find high-quality apps. To address these challenges, we propose LaQual, an automated framework for evaluating the quality of LLM apps. LaQual consists of three main stages: first, it labels and classifies LLM apps in a hierarchical way to accurately match them to different scenarios; second, it uses static indicators, such as time-weighted user engagement and functional capability metrics, to filter out low-quality apps; and third, it conducts a dynamic, scenario-adaptive evaluation, where the LLM itself generates scenario-specific evaluation metrics, scoring rules, and tasks for a thorough quality assessment. Experiments on a popular LLM app store show that LaQual is effective. Its automated scores are highly consistent with human judgments (with Spearman's rho of 0.62 and p=0.006 in legal consulting, and rho of 0.60 and p=0.009 in travel planning). By effectively screening, LaQual can reduce the pool of candidate LLM apps by 66.7% to 81.3%. User studies further confirm that LaQual significantly outperforms baseline systems in decision confidence, comparison efficiency (with average scores of 5.45 compared to 3.30), and the perceived value of its evaluation reports (4.75 versus 2.25). Overall, these results demonstrate that LaQual offers a scalable, objective, and user-centered solution for finding and recommending high-quality LLM apps in real-world use cases.

STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning

While modern recommender systems are instrumental in navigating information abundance, they remain fundamentally limited by static user modeling and reactive decision-making paradigms. Current large language model (LLM)-based agents inherit these shortcomings through their overreliance on heuristic pattern matching, yielding recommendations prone to shallow correlation bias, limited causal inference, and brittleness in sparse-data scenarios. We introduce STARec, a slow-thinking augmented agent framework that endows recommender systems with autonomous deliberative reasoning capabilities. Each user is modeled as an agent with parallel cognitions: fast response for immediate interactions and slow reasoning that performs chain-of-thought rationales. To cultivate intrinsic slow thinking, we develop anchored reinforcement training - a two-stage paradigm combining structured knowledge distillation from advanced reasoning models with preference-aligned reward shaping. This hybrid approach scaffolds agents in acquiring foundational capabilities (preference summarization, rationale generation) while enabling dynamic policy adaptation through simulated feedback loops. Experiments on MovieLens 1M and Amazon CDs benchmarks demonstrate that STARec achieves substantial performance gains compared with state-of-the-art baselines, despite using only 0.4% of the full training data.

Preference Trajectory Modeling via Flow Matching for Sequential Recommendation

Sequential recommendation predicts each user's next item based on their historical interaction sequence. Recently, diffusion models have attracted significant attention in this area due to their strong ability to model user interest distributions. They typically generate target items by denoising Gaussian noise conditioned on historical interactions. However, these models face two critical limitations. First, they exhibit high sensitivity to the condition, making it difficult to recover target items from pure Gaussian noise. Second, the inference process is computationally expensive, limiting practical deployment. To address these issues, we propose FlowRec, a simple yet effective sequential recommendation framework which leverages flow matching to explicitly model user preference trajectories from current states to future interests. Flow matching is an emerging generative paradigm, which offers greater flexibility in initial distributions and enables more efficient sampling. Based on this, we construct a personalized behavior-based prior distribution to replace Gaussian noise and learn a vector field to model user preference trajectories. To better align flow matching with the recommendation objective, we further design a single-step alignment loss incorporating both positive and negative samples, improving sampling efficiency and generation quality. Extensive experiments on four benchmark datasets verify the superiority of FlowRec over the state-of-the-art baselines.

A Universal Framework for Offline Serendipity Evaluation in Recommender Systems via Large Language Models

Serendipity in recommender systems (RSs) has attracted increasing attention as a concept that enhances user satisfaction by presenting unexpected and useful items. However, evaluating serendipitous performance remains challenging because its ground truth is generally unobservable. The existing offline metrics often depend on ambiguous definitions or are tailored to specific datasets and RSs, thereby limiting their generalizability. To address this issue, we propose a universally applicable evaluation framework that leverages large language models (LLMs) known for their extensive knowledge and reasoning capabilities, as evaluators. First, to improve the evaluation performance of the proposed framework, we assessed the serendipity prediction accuracy of LLMs using four different prompt strategies on a dataset containing user-annotated serendipitous ground truth and found that the chain-of-thought prompt achieved the highest accuracy. Next, we re-evaluated the serendipitous performance of both serendipity-oriented and general RSs using the proposed framework on three commonly used real-world datasets, without the ground truth. The results indicated that there was no serendipity-oriented RS that consistently outperformed across all datasets, and even a general RS sometimes achieved higher performance than the serendipity-oriented RS.

Previously on... Automating Code Review

Modern Code Review (MCR) is a standard practice in software engineering, yet it demands substantial time and resource investments. Recent research has increasingly explored automating core review tasks using machine learning (ML) and deep learning (DL). As a result, there is substantial variability in task definitions, datasets, and evaluation procedures. This study provides the first comprehensive analysis of MCR automation research, aiming to characterize the field's evolution, formalize learning tasks, highlight methodological challenges, and offer actionable recommendations to guide future research. Focusing on the primary code review tasks, we systematically surveyed 691 publications and identified 24 relevant studies published between May 2015 and April 2024. Each study was analyzed in terms of tasks, models, metrics, baselines, results, validity concerns, and artifact availability. In particular, our analysis reveals significant potential for standardization, including 48 task metric combinations, 22 of which were unique to their original paper, and limited dataset reuse. We highlight challenges and derive concrete recommendations for examples such as the temporal bias threat, which are rarely addressed so far. Our work contributes to a clearer overview of the field, supports the framing of new research, helps to avoid pitfalls, and promotes greater standardization in evaluation practices.