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PhotoCraft: Agentic Reasoning with Hierarchical Self-Evolving Memory for Deep Image Search
arXiv:2606.03099v1 Announce Type: new Abstract: Deep Image Search requires multi-step reasoning over rich contextual cues, such as time, location, and event relations. However, most existing LLM-based agents are stateless and reactive, lacking persistent memory to maintain long-horizon context or transfer experience across tasks, which often leads to execution drift and experience isolation. To address these limitations, we propose PhotoCraft, a training-free, hierarchical memory system for...
Show HN: Uruky (EU-based Kagi alternative) now has Image Search and URL Rewrites
You can get a 2h free trial by solving a proof-of-work captcha when topping up your account for the first time. If you'd like to learn more, an independent interview was posted a couple of weeks ago [1], and the FAQ [2] has a lot of information as well.
Exposing Hidden Biases in Text-to-Image Models via Automated Prompt Search
arXiv:2512.08724v3 Announce Type: replace Abstract: Text-to-image (TTI) diffusion models have achieved remarkable visual quality, yet they have been repeatedly shown to exhibit social biases across sensitive attributes such as gender, race and age. To mitigate these biases, existing approaches frequently depend on curated prompt datasets - either manually constructed or generated with large language models (LLMs) - as part of their training and/or evaluation procedures. Beside the curation...
Resolving Ambiguity in Composed Image Retrieval via Calibrated Interaction
arXiv:2605.24634v3 Announce Type: replace Abstract: Composed image retrieval (CIR) searches a corpus with a reference image and a text describing how to modify it. Despite rapid progress from triplet-trained compositors to zero-shot and generative methods, essentially all systems share one assumption: that a query maps to a single target, scored by Recall@K against one annotation. We argue this is fundamentally at odds with the task.
DuckDuckGo makes its 'no-AI' search engine easier to access as its traffic booms
As its traffic continues to climb, alternative search engine DuckDuckGo is leaning into anti-AI sentiment with the launch of new browser extensions that allow users to set its no-AI search experience, noai.duckduckgo.com, as their default search engine. Once enabled, users will be directed to DuckDuckGo’s AI-free search page, where there are no AI-assisted answers, no chat prompts, and fewer AI images in the search results, the company claims. The extensions are currently available for...
PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning
Announce Type: new Abstract: Multimodal federated graph learning (MM-FGL) aims to collaboratively learn from decentralized graphs with text and images. However, real-world clients may not share a common modality basis: a visual-search client may contain image--interaction graphs but no seller descriptions, while a catalog client may provide text but no product images. We refer to this practical setting as client-level modality deficiency.
Towards On-Policy Data Evolution for Visual-Native Multimodal Deep Search Agents
Announce Type: replace Abstract: Multimodal deep search requires an agent to solve open-world problems by chaining search, tool use, and visual reasoning over evolving textual and visual context. Two bottlenecks limit current systems. First, existing tool-use harnesses treat images returned by search, browsing, or transformation as transient outputs, so intermediate visual evidence cannot be re-consumed by later tools.
FOCUS: Forcing In-Context Object Localization through Visual Support Constraints and Policy Optimization
arXiv:2605.31145v1 Announce Type: new Abstract: In-context localization (ICL) seeks to localize a target object specified by a small set of support examples in a query image, operating on the fly without training or parameter updates. Despite rapid advances in vision-language models (VLMs), achieving category-agnostic and visually grounded ICL remains an open problem, even though it is essential for applications such as image editing, personalized visual search, and retrieval. Existing...
LLM-Guided Evolution for Medical Decision Pipelines
Announce Type: new Abstract: Adapting large language models (LLMs) to clinical workflows often requires costly fine-tuning or manual prompt and pipeline engineering. We study LLM-guided MAP-Elites evolution as an inference-time alternative for discovering medical decision strategies and provide an implementation repository at https://github.com/univanxx/llm_guided_evo_medical. We formulate urgency triage, interactive consultation, and medical image classification as evolutionary searches...
ROGLE: Robust Global-Local Alignment with Automated Region Supervision for Text-Based Person Search
Announce Type: new Abstract: Text-Based Person Search (TBPS) aims to retrieve pedestrian images using natural language queries. However, existing TBPS models, especially those based on CLIP, struggle with fine-grained understanding due to global representational bias and semantic sparsity inherited from training on short captions. This results in weak fine-grained alignment, exacerbated by the scarcity of region-level annotations.