Video Corpus Moment Retrieval (VCMR) requires models to efficiently retrieve and precisely locate specific moments relevant to natural language queries within a massive, untrimmed video corpus. However, existing discriminative approaches typically rely on shallow visual-textual feature matching mechanisms, which often struggle to capture fine-grained semantic differences. To address this limitation, we propose Video-GAR, a novel framework that reframes the conventional retrieval task from superficial matching to generative understanding, positing that the capability for query reconstruction evidences deep semantic comprehension. Specifically, Video-GAR orchestrates three synergistic components: To overcome the computational efficiency bottleneck, we construct a Bi-Mamba backbone that leverages the linear complexity of state-space models for efficient global context modeling. Building on these representations, we introduce a generation-augmented fusion module, in which a training-only decoder acts as a semantic regularizer to implicitly calibrate cross-modal attention without increasing inference overhead. Finally, to ensure fine-grained precision, we propose a boundary-aware localization strategy that integrates boundary modeling with categorical supervision. Experiments on two benchmark datasets demonstrate that Video-GAR significantly improves retrieval and localization accuracy while maintaining outstanding inference speed.

The advent of generative artificial intelligence (AI), driven by advancements in large language models (LLMs), has unlocked transformative possibilities for information retrieval (IR), giving rise to a new wave of interactive and conversational paradigms. This workshop, titled New Interaction Paradigms for Information Retrieval in the Era of Generative AI, aims to serve as a collaborative platform for researchers and practitioners to explore the challenges and opportunities of integrating generative AI into IR systems. By focusing on tasks such as multi-turn conversational search, adaptive retrieval interfaces, and context-aware response generation, this workshop will address key areas including system design, user engagement, and evaluation methodologies. The workshop will also delve into broader concerns such as trust, transparency, and fairness, emphasizing the ethical implications of deploying generative AI in IR systems. Through panel discussions, poster sessions, and interactive roundtables, this workshop will foster critical dialogue and innovation, paving the way for a new era of user-centric, generative AI-powered IR systems.

Recent studies indicate that dense retrieval models struggle to perform well on a wide variety of retrieval tasks that lack dedicated training data, as different retrieval tasks often entail distinct search intents. To address this challenge, in this work we leverage instructions to flexibly describe retrieval intents and introduce I3, a unified retrieval system that performs Intent-Introspective retrieval across various tasks, conditioned on Instructions without any task-specific training. I3 innovatively incorporates a pluggable introspector in a parameter-isolated manner to comprehend specific retrieval intents by jointly reasoning over the input query and instruction, and seamlessly integrates the introspected intent into the original retrieval model for intent-aware retrieval. Furthermore, we propose progressively-pruned intent learning. It utilizes extensive LLM-generated data to train I3 phase-by-phase, embodying two key designs: progressive structure pruning and drawback extrapolation-based data refinement. Extensive experiments show that in the BEIR benchmark, I3 significantly outperforms baseline methods designed with task-specific retrievers, achieving state-of-the-art zero-shot performance without any task-specific tuning.

The booming development and huge market of micro-videos bring new e-commerce channels for merchants. Currently, more micro-video publishers prefer to embed relevant ads into their micro-videos, which not only provides them with business income but helps the audiences to discover their interesting products. However, due to the micro-video recording by unprofessional equipment, involving various topics and including multiple modalities, it is challenging to locate the products related to micro-videos efficiently, appropriately, and accurately. We formulate the microvideo-product retrieval task, which is the first attempt to explore the retrieval between the multi-modal and multi-modal instances.

Generating fluent and informative natural responses while main- taining representative internal states for search optimization is critical for conversational search systems. Existing approaches ei- ther 1) predict structured dialog acts first and then generate natural response; or 2) map conversation context to natural responses di- rectly in an end-to-end manner. Both kinds of approaches have shortcomings. The former suffers from error accumulation while the semantic associations between structured acts and natural re- sponses are confined in single direction. The latter emphasizes generating natural responses but fails to predict structured acts. Therefore, we propose a neural co-generation model that gener- ates the two concurrently. The key lies in a shared latent space shaped by two informed priors. Specifically, we design structured dialog acts and natural response auto-encoding as two auxiliary tasks in an interconnected network architecture. It allows for the concurrent generation and bidirectional semantic associations. The shared latent space also enables asynchronous reinforcement learn- ing for further joint optimization. Experiments show that our model achieves significant performance improvements.

We tackle the task of video moment retrieval (VMR), which aims to localize a specific moment in a video according to a textual query. Existing methods primarily model the matching relationship between query and moment by complex cross-modal interactions. Despite their effectiveness, current models mostly exploit dataset biases while ignoring the video content, thus leading to poor generalizability. We argue that the issue is caused by the hidden confounder in VMR, i.e., temporal location of moments, that spuriously correlates the model input and prediction. How to design robust matching models against the temporal location biases is crucial but, as far as we know, has not been studied yet for VMR.