June 2026

Find the best open-source OCR models in one place at Papers with Code [P]

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Hi, I’ve created an overview of the most important OCR benchmarks, along with the top open models, and links to their paper and code: https://paperswithcode.co/tasks/ocr. This week, new OCR models were released by Baidu and Mistral. Baidu released Unlimited OCR, a 3B-parameter model that introduces a key innovation called Reference Sliding Window Attention (R-SWA) and builds on top of DeepSeek OCR. Mistral released OCR 4, which is available via an API. OCR, or Optical-Character Recognition, is the task […]

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Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization

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We present HiReLC, a hierarchical ensemble-reinforcement learning framework for automated joint quantization and structured pruning of deep neural networks. The framework decomposes the compression search across two levels of abstraction: low-level agents (LLAs) operate independently per block, selecting per-kernel configurations over a multi-discrete action space spanning bitwidth, pruning keep-ratio, quantization type, and granularity, while high-level agents (HLAs) coordinate global budget allocation via ensemble voting guided by Fisher Information-based sensitivity estimates. To mitigate the computational cost of policy evaluation, […]

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Taxonomy-aware deep learning for hierarchical marine species classification in underwater imagery

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Automated classification of marine species from underwater imagery is essential for scalable ocean biodiversity monitoring and conservation policy. Existing approaches struggle with severe domain shift across collection platforms, fine-grained visual similarity between closely related species, and uneven annotation granularity, where many specimens can only be identified to genus or a coarser taxonomic rank. We present a taxonomy-aware deep learning framework that aligns both the training loss and the inference rule with the hierarchical structure of biological classification, combining […]

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Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound

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Multi-agent goal recognition asks an observer to jointly infer which agents act together and what each team is trying to achieve, so the hypothesis space grows combinatorially with the number of team partitions and goals per team. Real applications such as drone surveillance and collaborative robotics expose only the agents’ trajectory, which forces the observer to rank team-goal hypotheses from behavior alone. Multi-Agent Goal Recognition with Branch-and-Bound (MAGR-BB) addresses this setting with a shared team- and goal-conditioned policy […]

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Bayesian Optimisation

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Is there another disadvantage with Bayesian Optimisation for Hyperparameter of Actor-Critic-RL Controller, than being computationally expensive? I have remote access to a PC at my university Would it make sense, to run Optimisation permanently on the remote PC and just stop when I am working on other things there? submitted by /u/Keran137 [link] [comments]

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Context Rot: Why Longer Windows Are Making Your AI Dumber, Not Smarter

Context Rot: Why Longer Windows Are Making Your AI Dumber, Not Smarter

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Your million-token context window isn’t a superpower — it’s a liability you haven’t measured yet. The promise that didn’t quite deliver Two years ago, a 200,000-token context window felt like magic. Today it’s table stakes — some models now advertise windows in the millions of tokens. The pitch is seductive: stuff in your entire codebase, your whole knowledge base, every past conversation, and the model will just know everything. But engineers running these systems in production are noticing something uncomfortable. Bigger context isn’t making […]

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