Lightricks/LTX-2.5
New image-to-video model. Tags: diffusion-single-file, image-to-video, text-to-video, video-to-video, image-text-to-video, audio-to-video, text-to-audio, video-to-audio
it♥ 1303
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2026 BAIR Graduate Showcase
Congratulations to the Berkeley Artificial Intelligence Research (BAIR) Lab class of 2026! This year, BAIR celebrates another remarkable group of Ph.D. graduates whose curiosity, creativity, and perseverance have pushed the frontiers of artificial intelligence and machine learning. Their work spans the breadth of modern AI — robotics and embodied intelligence, large language models and reasoning, computer vision, generative modeling, AI…
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Google just redesigned the search box for the first time in 25 years — here’s why it matters more than you think.
For a quarter century, the Google search box has been one of the most recognizable interfaces in computing: a thin white rectangle, a blinking cursor…
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perplexity-ai/pplx-decider-v1.1-27b
New text-classification model. Tags: pytorch, qwen3_5, classification, multimodal, custom-code, decider, decision-model, text-classification
it♥ 88
LiquidAI/d1-omni-600M
New image-text-to-text model. Tags: d1_omni, feature-extraction, liquid, lfm2.5, edge, decision, classification, calibration
en♥ 90
nerkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2
New image-text-to-text model. Tags: gguf, qwen3.8, efficient-thinking, reasoning, coding, uncensored, sft, simpo
en♥ 98
Qwen/Qwen-Image-2.1-Turbo
New text-to-image model. Tags: diffusers, qwen, image-generation, image-editing, text-to-image, base_model:Qwen/Qwen-Image-2.1, base_model:finetune:Qwen/Qwen-Image-2.1, license:other
en♥ 122
Instinct was the buzziest AI agent around — can it survive Muse?
Before there were cute little guys, there was Instinct. In August, the startup got its AI agent to market with an unusual playbook: invite-only, no…
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Phocinae/Phocinae-Largha-150M-v1
New text-classification model. Tags: modernbert, fill-mask, decision-making, typed-decisions, text-classification, small-model, bilingual, calibration
en♥ 108
ConwayResearch/Underdog-Saluki-27B-1.0
New text-generation model. Tags: gguf, llama.cpp, 2-bit, tool-calling, function-calling, agents, qwen3.8, text-generation
en♥ 99
HANS: A Handwritten Answer Sheet Dataset for Noisy Hybrid Document Parsing
Intelligent grading and automated scoring technologies constitute critical infrastructure for smart education. However, existing document parsing and handwriting recognition benchmarks are predominantly designed for well-structured printed documents or isolated mathematical expressions, lacking datasets that capture the complex characteristics inherent to student answer sheets, including multi-line derivation processes, heterogeneous mixtures of text and mathematical formulae, and noise artifacts such as strikethroughs. To address this gap, we introduce HANS, the first dataset explicitly constructed for real-world educational scenarios, encompassing mathematical expressions, natural language text, hand-drawn tables, and diverse noise patterns including corrections and deletions, accompanied by fine-grained annotations that establish a reliable foundation for robust recognition research. Building upon HANS, we propose NA-GOT, an end-to-end framework that achieves two-stage noise suppression through a lightweight noise suppression module operating at the feature level, complemented by a noiseaware attention mechanism incorporated into the decoding stage. Experimental results demonstrate that HANS poses substantial challenges to existing methods, while NA-GOT achieves significant improvements in both accuracy and stability for answer process recognition. The dataset will be made publicly available upon publication.
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Adapting English Quality Classifiers for Multilingual LLM Pretraining Data Selection
Recent advances in large language model (LLM) pretraining highlight the role of high-quality training data in improving performance. While model-based filtering has proven effective in selecting high-quality subsets from web-scale corpora, especially for high-resource languages, low-resource languages face challenges due to limited availability of annotated data. This work explores extending quality filtering to over 100 languages by proposing a multilingual adaptation approach that converts an existing English quality classifier into a multilingual variant. Our approach proposes training a small multi-layer perceptron on top of Transformer encoder-only model embeddings, using multilingual text as input and scores obtained from English classifiers applied to machine-translated text as labels. Our 1B, 3B and 8B scale experiments show that our approach maintains the downstream LLM benchmark performance of existing multilingual model-based filtering baselines, without harming regional and cultural knowledge benchmarks. To further evaluate cross-lingual generalization, we compare classifier scores of high-quality synthetic data and web samples, and the correlation of classifier scores with LLM-based ones, revealing that the classifier can learn the scoring criteria of its original English variant, even for languages not included in its training data.
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autotrust/GLM5.3-Flash-E224-DGX-Spark
New image-text-to-text model. Tags: vllm, glm5_next, autotrust, moe, nvfp4, modelopt, dgx-spark, gb10
en♥ 108
ChatGPT’s ‘Intelligent UI’ update fills its responses with pictures, charts, and buttons
OpenAI is launching a new Intelligent UI feature in ChatGPT that allows the chatbot to answer your questions with interactive visuals. The update, which is…
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Document-Level Text Simplification in Estonian Using Large Language Models
Document-level text simplification involves transformations that go beyond sentence-internal edits, addressing discourse coherence, anaphora resolution, and cross-paragraph consistency. Despite advances in sentence-level simplification for high-resource languages, document-level simplification in morphologically rich, low-resource languages such as Estonian remains largely unexplored. This study presents a comprehensive evaluation of five state-of-the-art multilingual large language models (LLMs) for document-level simplification in Estonian. Three prompting strategies are examined: single-pass generation, pipeline-based modular agents, and guideline-augmented pipelines. The evaluation framework integrates automatic metrics assessing readability, semantic preservation, and discourse coherence, alongside a structured manual annotation protocol. The findings indicate that Gemini-2.0 and LLaMA-3.3 produce outputs with near-native fluency and strong meaning preservation, whereas other models display notable grammatical and semantic limitations. This work contributes novel document-level coherence metrics, evidence-based prompting strategies, and publicly available resources for reproducibility.
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Automate remediation post AWS DevOps Agent investigation
AWS DevOps Agent can diagnose production incidents but is kept in observe-and-report mode so it does not change resources directly. This post shows how to use AWS Lambda Durable Functions, Amazon EventBridge, and Amazon Bedrock to turn its investigation summaries into pre-validated fixes an on-call engineer can approve with a single action.
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How Cornerstone OnDemand cut database diagnosis by 78% with Amazon Bedrock
Cornerstone OnDemand built Orion AI, a multi-agent system on Amazon Bedrock and Strands Agents, to turn database operations from reactive firefighting into proactive automation. A three-person team cut database diagnosis from 45 minutes to 10, a 78% reduction, in six months. See the design decisions other teams can reuse.
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