Are your current image searches missing the full story behind what you're looking for? Researchers from Renmin University of China and OPPO Research Institute are pioneering a new approach to visual search. They introduce DeepImageSearch, a novel AI agent that treats image retrieval as an autonomous exploration task. Instead of just matching isolated images, this agent learns to plan and perform multi-step reasoning across entire visual histories, using a sophisticated dual-memory system to uncover implicit contextual cues. They also present DISBench, a challenging new benchmark built on interconnected visual data. This groundbreaking agentic paradigm reveals significant limitations in current state-of-the-art models, demonstrating the critical need for advanced contextual reasoning in next-generation image retrieval systems for truly intelligent search. DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Histories Project: github.com/RUC-NLPIR/DeepIma… Paper: arxiv.org/abs/2602.10809 Huggingface: huggingface.co/datasets/RUC-… Leaderboard: huggingface.co/spaces/RUC-NL… Our report: mp.weixin.qq.com/s/dIV0uYTCc… 📬 #PapersAccepted by Jiqizhixin

Top AI Papers of The Week (Feb 16-22) - Less is Enough: Synthesizing Diverse Data in Feature Space of LLMs - SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval under Acoustic Noise - GLM-5: from Vibe Coding to Agentic Engineering by @zhipuAI - Experiential Reinforcement Learning - MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs - Zooming without Zooming: Region-to-Image Distillation by @InclusionAI - Sanity Checks for Sparse Autoencoders: Do SAEs Beat Random Baselines? - DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval - SLA2: Sparse-Linear Attention with Learnable Routing and QAT - SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks Find them below:

DeepImageSearch A new agentic paradigm for image retrieval over visual histories. Instead of matching single images, models must explore photo collections and perform multi-step reasoning to find targets based on contextual cues across time.

DeepImageSearch Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Histories huggingface.co/papers/2602.1…

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