The Download: OpenAI's caste bias problem, and how AI videos are made Plus: Taiwan has pushed back against America's chip request OpenAI is huge in India. Its models are steeped in caste bias. Caste bias is rampant in OpenAI's products, including ChatGPT, according to an MIT Technology Review investigation. Though CEO Sam Altman boasted about India being its second-largest market during the launch of GPT-5 in August, we found that both this new model, which now powers ChatGPT, as well as Sora, OpenAI's text-to-video generator, exhibit caste bias. This risks entrenching discriminatory views in ways that are currently going unaddressed. Mitigating caste bias in AI models is more pressing than ever.

Trump dollar coin design released by Treasury... and it's inspired by an iconic political photo Top plastic surgeons reveal secrets behind Taylor Swift's'changing' face: 'It is looking very full' Fans erupt at Taylor Swift's'dig' at Travis Kelce's ex Kayla Nicole in wild The Life of a Showgirl track Shroud of Turin mystery deepens as surgeon spots hidden detail that points to Jesus' resurrection Hollywood A-listers pay me $50,000 to cure their drug addicted nepo-babies because they can't afford for these secrets to go public I'm no longer sleeping with my husband - and never will again, says MOLLY RYDDELL. I love him, but counted down the moments until he climaxed. Then I couldn't bear it any more and the truth spilled out... so many women feel the same Lori Loughlin's husband Mossimo Giannulli seen with mystery brunette in tiny skirt day after shock split I'm a woman with autism... here are the signs you might be masking, even from yourself The truth about Keith Urban's guitarist'other woman' Maggie Baugh revealed amid Nicole Kidman divorce Taylor, your album should be'Life of a Callgirl'. KENNEDY's appalled take on Swift's new record... and its ultra-vivid sex shout outs for Travis the Sasquatch I was so happy after trying a trendy new cosmetic procedure. But 10 years later I suffered a devastating side effect... the doctor had lied The'middle-class kinks' saving marriages: Wives reveal the eight buzzy sex trends that revived their lagging libidos - including the fantasy husbands are secretly obsessed with Cake-faced 90s sitcom star looks unrecognizable as she ditches the heavy eyeshadow for an LA errand run can you guess who?

Trump dollar coin design released by Treasury... and it's inspired by an iconic political photo Top plastic surgeons reveal secrets behind Taylor Swift's'changing' face: 'It is looking very full' Shroud of Turin mystery deepens as surgeon spots hidden detail that points to Jesus' resurrection Hollywood A-listers pay me $50,000 to cure their drug addicted nepo-babies because they can't afford for these secrets to go public I'm no longer sleeping with my husband - and never will again, says MOLLY RYDDELL. I love him, but counted down the moments until he climaxed. Then I couldn't bear it any more and the truth spilled out... so many women feel the same Fans erupt at Taylor Swift's'dig' at Travis Kelce's ex Kayla Nicole in wild The Life of a Showgirl track Lori Loughlin's husband Mossimo Giannulli seen with mystery brunette in tiny skirt day after shock split The truth about Keith Urban's guitarist'other woman' Maggie Baugh revealed amid Nicole Kidman divorce Taylor, your album should be'Life of a Callgirl'. KENNEDY's appalled take on Swift's new record... and its ultra-vivid sex shout outs for Travis the Sasquatch I was so happy after trying a trendy new cosmetic procedure. But 10 years later I suffered a devastating side effect... the doctor had lied The'middle-class kinks' saving marriages: Wives reveal the eight buzzy sex trends that revived their lagging libidos - including the fantasy husbands are secretly obsessed with I'm a woman with autism... here are the signs you might be masking, even from yourself Cake-faced 90s sitcom star looks unrecognizable as she ditches the heavy eyeshadow for an LA errand run can you guess who?

A still from an AI generated Tilly Norwood video. A still from an AI generated Tilly Norwood video. 'It's too late to be scared': readers on the controversial rise of AI'actors' The birth of AI'actor' Tilly Norwood has caused a backlash in Hollywood and has sparked conversation from Guardian readers Tilly Norwood: how scared should we be of the viral AI'actor'? T he unveiling of AI'actor' Tilly Norwood - touted as the next Scarlett Johansson - was met with swift backlash in Hollywood. Here's what Guardian readers have to say about the controversial rise of AI actors.

Emily Blunt among Hollywood stars outraged over'AI actor' Tilly Norwood An AI actor named Tilly Norwood has been causing a stir after its Dutch creators said the synthetic performer is in talks with talent agencies. Norwood could be mistaken for a young, aspiring actress when one glances at its social media. The brunette poses for photos and showcases a fully AI-generated comedy sketch, where it is described as having girl next door vibes. I may be AI, but I'm feeling very real emotions right now, Tilly's creators wrote on her page. I am so excited for what's coming next!

While automatic music transcription is well-established in music information retrieval, most models are limited to transcribing pitch and timing information from audio, and thus omit crucial expressive and instrument-specific nuances. One example is playing technique on the violin, which affords its distinct palette of timbres for maximal emotional impact. Here, we propose VioPTT (Violin Playing Technique-aware Transcription), a lightweight, end-to-end model that directly transcribes violin playing technique in addition to pitch onset and offset. Furthermore, we release MOSA-VPT, a novel, high-quality synthetic violin playing technique dataset to circumvent the need for manually labeled annotations. Leveraging this dataset, our model demonstrated strong generalization to real-world note-level violin technique recordings in addition to achieving state-of-the-art transcription performance. To our knowledge, VioPTT is the first to jointly combine violin transcription and playing technique prediction within a unified framework.

Interpretability is essential for deploying deep learning models in symbolic music analysis, yet most research emphasizes model performance over explanation. To address this, we introduce MUSE-Explainer, a new method that helps reveal how music Graph Neural Network models make decisions by providing clear, human-friendly explanations. Our approach generates counterfactual explanations by making small, meaningful changes to musical score graphs that alter a model's prediction while ensuring the results remain musically coherent. Unlike existing methods, MUSE-Explainer tailors its explanations to the structure of musical data and avoids unrealistic or confusing outputs. We evaluate our method on a music analysis task and show it offers intuitive insights that can be visualized with standard music tools such as Verovio.

Existing Reward Models (RMs), typically trained on general preference data, struggle in Retrieval Augmented Generation (RAG) settings, which require judging responses for faithfulness to retrieved context, relevance to the user query, appropriate refusals when context is insufficient, completeness and conciseness of information. To address the lack of publicly available RAG-centric preference datasets and specialised RMs, we introduce RAGferee, a methodology that repurposes question-answering (QA) datasets into preference pairs that prioritise groundedness over stylistic features, enabling the training of contextual RMs better suited to judging RAG responses. Using RAGferee, we curate a small preference dataset of 4K samples and fine-tune RMs ranging from 7B to 24B parameters. Our RAG-centric RMs achieve state-of-the-art performance on ContextualJudgeBench, surpassing existing 70B+ RMs trained on much larger (up to 2.4M samples) general corpora, with an absolute improvement of +15.5%.

The internet is saturated with low-density, high-redundancy information, such as social media comments, repetitive news, and lengthy discussions, making it difficult to extract valuable insights efficiently. Multi-layer nested JSON structures provide an effective solution by compressing such information into semantically rich, hierarchical representations, which organize data into key-value pairs, arrays, and nested objects, preserving contextual relationships and enabling efficient storage, retrieval, and semantic querying. For instance, in news aggregation, a JSON object can nest an article's metadata (title, author, date), content (text, multimedia), and multimedia information (multimedia type, caption) hierarchically. Large Language Models (LLMs) play a transformative role in web data mining by parsing unstructured text and outputting structured results directly into complex JSON schemas. However, current benchmarks for evaluating LLMs' JSON output capabilities overemphasize pure JSON generation rather than assessing data comprehension and extraction abilities, a limitation that lacks relevance to practical web data mining tasks. To address this, we introduce DeepJSONEval, a novel benchmark featuring 2100 multi-domain instances with deep nested structures, categorized by difficulty. Experiments show significant performance gaps among LLMs in handling such complexity. Our benchmark and datasets are open-sourced to advance research in structured JSON generation.(https://github.com/GTS-AI-Infra-Lab-SotaS/DeepJSONEval).

Recent advancements in diffusion and flow-matching models have demonstrated remarkable capabilities in high-fidelity image synthesis. A prominent line of research involves reward-guided guidance, which steers the generation process during inference to align with specific objectives. However, leveraging this reward-guided approach to the task of image editing, which requires preserving the semantic content of the source image while enhancing a target reward, is largely unexplored. In this work, we introduce a novel framework for training-free, reward-guided image editing. We formulate the editing process as a trajectory optimal control problem where the reverse process of a diffusion model is treated as a controllable trajectory originating from the source image, and the adjoint states are iteratively updated to steer the editing process. Through extensive experiments across distinct editing tasks, we demonstrate that our approach significantly outperforms existing inversion-based training-free guidance baselines, achieving a superior balance between reward maximization and fidelity to the source image without reward hacking.