Large vision-language models (LVLMs) have shown significant progress in video understanding, but they struggle to scale to long videos due to limited context windows. Existing methods reduce input dimensionality via frame sampling and feature compression, yet discard details and incur high computational cost for post-training. In contrast, retrieval-augmented generation (RAG) that indexes long videos for query retrieval and memory-based methods that maintain evolving long-term stores, offer a lighter and deployment-friendly solution. Nevertheless, they rely on shallow retrieval that selects only top-ranked segments and fails to integrate information across multiple relevant video episodes. Inspired by Multiple-Trace Theory in cognitive psychology, we revisit long video understanding from a probe-echo perspective, in which human episodic memories are activated and integrated in parallel. Building on this insight, we propose ProEchoMem, a cognitive-inspired framework that simulates the probe-echo mechanism: (1) Incremental Episodic Memory Construction builds structured knowledge graphs from video streams; (2) Probe-Driven Memory Activation generates probe signals from user queries to activate all stored traces simultaneously; (3) Memory Echo Synthesis integrates activated traces into a coherent and structured memory echo. Experiments on LongerVideos, LVBench, and cross-domain settings demonstrate the effectiveness of ProEchoMem, with multi-trace probing achieving up to 14.2% higher relevance and ablation studies validating the contribution of each module. The code is available at https://github.com/Applied-Machine-Learning-Lab/SIGIR26_ProEchoMem

The widespread proliferation of online content has intensified concerns about clickbait, deceptive or exaggerated headlines designed to attract attention. While Large Language Models (LLMs) offer a promising avenue for addressing this issue, their effectiveness is often hindered by Sycophancy, a tendency to produce reasoning that matches users' beliefs over truthful ones, which deviates from instruction-following principles. Rather than treating sycophancy as a flaw to be eliminated, this work proposes a novel approach that initially harnesses this behavior to generate contrastive reasoning from opposing perspectives. Specifically, we design a Self-renewal Opposing-stance Reasoning Generation (SORG) framework that prompts LLMs to produce high-quality ''agree'' and ''disagree'' reasoning pairs for a given news title without requiring ground-truth labels. To utilize the generated reasoning, we develop a local Opposing Reasoning-based Clickbait Detection (ORCD) model that integrates three BERT encoders to represent the title and its associated reasoning. The model leverages contrastive learning, guided by soft labels derived from LLM-generated credibility scores, to enhance detection robustness. Experimental evaluations on three benchmark datasets demonstrate that our method consistently outperforms LLM prompting, fine-tuned smaller language models, and state-of-the-art clickbait detection baselines. Our code is available in https://github.com/126541/ORCD.

Multiple kernel clustering (MKC) has received increasing attention in the community of machine learning, which takes advantage of multiple pre-specified kernels to perform clustering tasks. Traditional MKC algorithms cannot effectively deal with the incomplete views where some samples are missing. Thus, incomplete MKC (IMKC) has been developed to solve this problem and obtained promising results. Nevertheless, the samples may be noisy or limited in real-world applications, which will result in the performance deterioration of existing IMKC algorithms. To address this issue, in this paper we propose a simple yet effective clustering method for incomplete data, termed fractional-order embedding incomplete multi-kernel k-means clustering (FE-MKKM-IK). Specifically, FE-MKKM-IK introduces the idea of fractional-order embedding to reconstruct the kernel matrix computed by the samples. On this basis, a new incomplete multiple kernel k-means clustering is developed. Performance evaluation is conducted on four widely used datasets, which shows that FE-MKKM-IK is effective to cluster the incomplete data.

In the short text, the extremely short length, feature sparsity, and high ambiguity pose huge challenges to classification tasks. Recently, as an effective method for tuning Pre-trained Language Models for specific downstream tasks, prompt-learning has attracted a vast amount of attention and research. The main intuition behind the prompt-learning is to insert the template into the input and convert the tasks into equivalent cloze-style tasks. However, most prompt-learning methods only consider the class name and monotonous strategy for knowledge incorporating in cloze-style prediction, which will inevitably incur omissions and bias in short text classification tasks. In this paper, we propose a short text classification method with prompt-learning. Specifically, the top$M$concepts related to the entity in the short text are retrieved from the open Knowledge Graph like Probase, these concepts are first selected by the distance with class labels, which takes both the short text itself and the class name into consideration during expanding label word space. Then, we conducted four additional strategies for the integration of the expanded concepts, and the union of these concepts are adopted finally in the verbalizer of prompt-learning. Experimental results show that the obvious improvement is obtained compared with other state-of-the-art methods on five well-known datasets.

Unsupervised domain adaptation aims to facilitate learning tasks in unlabeled target domain with knowledge in the related source domain, which has achieved awesome performance with the pre-trained language models (PLMs). Recently, inspired by GPT, the prompt-tuning model has been widely explored in stimulating rich knowledge in PLMs for language understanding. However, existing prompt-tuning methods still directly applied the model that was learned in the source domain into the target domain to minimize the discrepancy between different domains, e.g., the prompts or the template are trained separately to learn embeddings for transferring to the target domain, which is actually the intuition of end-to-end deep-based approach. In this paper, we propose an Iterative Soft Prompt-Tuning method (ItSPT) for better unsupervised domain adaptation. On the one hand, the prompt-tuning model learned in the source domain is converted into an iterative model to find the true label information in the target domain, the domain adaptation method is then regarded as a few-shot learning task. On the other hand, instead of hand-crafted templates, ItSPT adopts soft prompts for both considering the automatic template generation and classification performance. Experiments on both English and Chinese datasets demonstrate that our method surpasses the performance of SOTA methods.

Analyzing short texts infers discriminative and coherent latent topics that is a critical and fundamental task since many real-world applications require semantic understanding of short texts. Traditional long text topic modeling algorithms (e.g., PLSA and LDA) based on word co-occurrences cannot solve this problem very well since only very limited word co-occurrence information is available in short texts. Therefore, short text topic modeling has already attracted much attention from the machine learning research community in recent years, which aims at overcoming the problem of sparseness in short texts. In this survey, we conduct a comprehensive review of various short text topic modeling techniques proposed in the literature. We present three categories of methods based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation, with example of representative approaches in each category and analysis of their performance on various tasks. We develop the first comprehensive open-source library, called STTM, for use in Java that integrates all surveyed algorithms within a unified interface, benchmark datasets, to facilitate the expansion of new methods in this research field. Finally, we evaluate these state-of-the-art methods on many real-world datasets and compare their performance against one another and versus long text topic modeling algorithm.

Most recent approaches for Text Simplification (TS) have drawn on insights from machine translation to learn simplification rewrites from the monolingual parallel corpus of complex and simple sentences, yet their effectiveness strongly relies on large amounts of parallel sentences. However, there has been a serious problem haunting TS for decades, that is, the availability of parallel TS corpora is scarce or not fit for the learning task. In this paper, we will focus on one especially useful and challenging problem of unsupervised TS without a single parallel sentence. To the best of our knowledge, we present the first unsupervised text simplification system based on phrase-based machine translation system, which leverages a careful initialization of phrase tables and language models. On the widely used WikiLarge and WikiSmall benchmarks, our system respectively obtains 39.08 and 25.12 SARI points, even outperforms some supervised baselines.

More and more user comments like Tweets are available, which often contain user concerns. In order to meet the demands of users, a good summary generating from multiple documents should consider reader interests as reflected in reader comments. In this article, we focus on how to generate a summary from multi-document documents by considering reader comments, named as reader-aware multi-document summarization (RA-MDS). We present an innovative topic-based method for RA-MDA, which exploits latent topics to obtain the most salient and lessen redundancy summary from multiple documents. Since finding latent topics for RA-MDS is a crucial step, we also present a Heterogeneous-length Text Topic Modeling (HTTM) to extract topics from the corpus that includes both news reports and user comments, denoted as heterogeneous-length texts. In this case, the latent topics extract by HTTM cover not only important aspects of the event, but also aspects that attract reader interests. Comparisons on summary benchmark datasets also confirm that the proposed RA-MDS method is effective in improving the quality of extracted summaries. In addition, experimental results demonstrate that the proposed topic modeling method outperforms existing topic modeling algorithms.