Deep Image Search
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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...
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.
ErA: Error-Aware Deep Unrolling Network for Single Image Defocus Deblurring
Electrical Engineering and Systems Science > Image and Video Processing [Submitted on 4 Jun 2026] Title:ErA: Error-Aware Deep Unrolling Network for Single Image Defocus Deblurring View PDF HTML (experimental)Abstract:We introduce ErA (Error-Aware Deep Unrolling Network), an end-to-end frame work for single-image defocus deblurring.
Towards Verifiable Multimodal Deep Research: A Multi-Agent Harness for Interleaved Report Generation
arXiv:2605.29861v2 Announce Type: replace Abstract: Large Language Models (LLMs) have advanced autonomous agents from deep search, which retrieves concise factual answers, to deep research, which synthesizes scattered evidence into long-form reports. However, verifiable multimodal deep research remains challenging due to open-ended synthesis without deterministic ground truth and the need to interleave textual arguments with visual evidence. We propose Ptah, a multi-agent harness for...
I resisted smart telescopes for years — then one changed my life
I resisted smart telescopes for years — then one changed my life The last dark skies of spring are the perfect time to get to know a new smart telescope and go deep-sky stargazing. When I go in search of dark skies, I like to travel light. With a full-frame camera, a tripod and some binoculars on my back, I can get everything I want from the night sky, save for the close-up views that only a telescope could bring.
Human-Like Neural Nets by Catapulting
Human-like Neural Nets by Catapulting Speculative proposal to create artificial neural nets with human-like performance by high-learning-rate/regularization training of overparameterized NNs to trigger catapulting/grokking. Over-parameterization as a route to true generalization would resolve many outstanding mysteries of artificial versus natural intelligence. There are many mysteries about deep learning and human intelligence, but we could describe the biggest anomaly this way: why are...
Unsupervised Learning Based Focal Stack Camera Depth Estimation
Electrical Engineering and Systems Science > Image and Video Processing [Submitted on 14 Mar 2022 (v1), last revised 3 Jun 2026 (this version, v3)] Title:Unsupervised Learning Based Focal Stack Camera Depth Estimation View PDFAbstract:We propose an unsupervised deep learning based method to estimate depth from focal stack camera images. On the NYU-v2 dataset, our method achieves much better depth estimation accuracy compared to single-image based methods.
Odysseus – self-hosted AI workspace
─────────────────────────────────────────────── ⊹ ࣪ ˖ ૮( ˶ᵔ ᵕ ᵔ˶ )っ Odysseus vers. 1.0 ─────────────────────────────────────────────── A self-hosted AI workspace -- meant to be the self-hosted version of the UI experience you get from ChatGPT and Claude. But with more jank and fun.
Beyond Humans: Multispecies Animal Face Recognition Using Transfer Learning
arXiv:2606.09353v1 Announce Type: new Abstract: Individual animal recognition can be useful in the search for lost or stolen pets, the tracking of individuals of endangered species, and the recognition of animals in crowded farms. Present recognition techniques mostly use physical devices, e.g., microchips, often impractical and difficult to apply. These could be replaced by remote recognition via the animal's face; if accurate enough, it provides several advantages: it is non-invasive, can...
Learning What's Real: Disentangling Signal and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics
arXiv:2604.09787v2 Announce Type: replace-cross Abstract: Data collected from the physical world is always a combination of multiple sources: an underlying signal from the physical process of interest and a signal from measurement-dependent artifacts from the sensor or instrument. This secondary signal acts as a confounding factor, limiting our ability to extract information about the physics underlying the phenomena we observe.