OpenAI plans to allow a wider range of content, including erotica, on its popular chatbot ChatGPT as part of its push to treat adult users like adults, says its boss Sam Altman. In a post on X on Tuesday, Mr Altman said upcoming versions of the popular chatbot would enable it to behave in a more human-like way - but only if you want it, not because we are usage maxxing. The move, reminiscent of Elon Musk's xAI recent introduction of two sexually explicit chatbots to Grok, could help OpenAI attract more paying subscribers. It is also likely to intensify pressure on lawmakers to introduce tighter restrictions on chatbot companions. OpenAI did not respond to the BBC's requests for comment following Mr Altman's post.

The former Australian Paralympic swimmer wanted to vamp up her headshot and uploaded a full-length photo of her and prompted it really specifically that she was missing her left arm from below the elbow. But ChatGPT couldn't create the image she was asking for and despite various prompts, the results were largely the same - a woman with two arms or one with a metal device to represent a prosthetic. She asked the AI why it was so hard to create the image and it said it was because it didn't have enough data to work with. That was an important realisation for me that of course AI is a reflection of the world we live in today and the level of inequality and discrimination that exists, she says. Smith recently tried to generate the image again on ChatGPT and was amazed to find it could now produce an accurate picture of a woman with one arm, just like her.

The company launched a dedicated ChatGPT experience for under-18 users in September. The company launched a dedicated ChatGPT experience for under-18 users in September. New version will allow users to customize AI assistant's personality in what firm calls'treat adults users like adults' policy OpenAI announced plans on Tuesday to relax restrictions on its ChatGPT chatbot, including allowing erotic content for verified adult users as part of what the company calls a "treat adult users like adults" principle. OpenAI's plan includes the release of an updated version of ChatGPT that will allow users to customize their AI assistant's personality, including options for more human-like responses, heavy emoji use, or friend-like behavior. The most significant change will come in December, when OpenAI plans to roll out more comprehensive age-gating that would permit erotic content for adults who have verified their ages.

'Sovereign AI' Has Become a New Front in the US-China Tech War OpenAI has announced "AI sovereignty partnerships with governments around the world, but can proprietary models compete with Beijing's open source offerings? OpenAI has announced a number of projects this year with foreign governments to help build out what it has called their "sovereign AI" systems. The company says the deals, some of which are being coordinated with the US government, are part of a broader push to give national leaders more control over a technology that could reshape their economies. Over the past few months, sovereign AI has become something of a buzzword in both Washington and Silicon Valley. Proponents of the concept argue it's crucial that AI systems developed in democratic nations are able to proliferate globally, particularly as China races to deploy its own AI technology abroad.

Lorenzo Servadei and his team at Sony AI are focused on researching and developing machine learning models to aid chip design and manufacturing. In this interview, Lorenzo tells us more about Electronic Design Automation, and how machine learning has been added into the mix to further advance the field of semiconductor chip design. What was your inspiration for pursuing a career in AI and semiconductors? When I was pursuing my Master's degree, I studied subjects related to traditional computer science and algorithmics - before AI was seen as a specific area of study - which led me into the field of software development. While working in software development, I had the opportunity to join a semiconductor company that was seeking AI experts, which allowed me to explore the algorithmic aspects of AI.

We introduce AbBiBench (Antibody Binding Benchmarking), a benchmarking framework for antibody binding affinity maturation and design. Unlike previous strategies that evaluate antibodies in isolation, typically by comparing them to natural sequences with metrics such as amino acid recovery rate or structural RMSD, AbBiBench instead treats the antibody-antigen (Ab-Ag) complex as the fundamental unit. It evaluates an antibody design's binding potential by measuring how well a protein model scores the full Ab-Ag complex. We first curate, standardize, and share more than 184,500 experimental measurements of antibody mutants across 14 antibodies and 9 antigens-including influenza, lysozyme, HER2, VEGF, integrin, Ang2, and SARS-CoV-2-covering both heavy-chain and light-chain mutations. Using these datasets, we systematically compare 15 protein models including masked language models, autoregressive language models, inverse folding models, diffusion-based generative models, and geometric graph models by comparing the correlation between model likelihood and experimental affinity values. Additionally, to demonstrate AbBiBench's generative utility, we apply it to antibody F045-092 in order to introduce binding to influenza H1N1. We sample new antibody variants with the top-performing models, rank them by the structural integrity and biophysical properties of the Ab-Ag complex, and assess them with in vitro ELISA binding assays. Our findings show that structure-conditioned inverse folding models outperform others in both affinity correlation and generation tasks. Overall, AbBiBench provides a unified, biologically grounded evaluation framework to facilitate the development of more effective, function-aware antibody design models.

Recent progress in reasoning with large language models (LLMs), such as DeepSeek-R1, demonstrates impressive capabilities in domains like mathematics and coding, by exhibiting complex cognitive behaviors such as verification, goal decomposition, and self-reflection. However, it is unclear what behavior is effective and what behavior is missing for long-horizon AI agents tasks. In this work, we propose Dyna-Think, a thinking framework that integrates planning with an internal world model with reasoning and acting to enhance AI agent performance. To enable Dyna-Think, we propose Dyna-Think Imitation Learning (DIT) and Dyna-Think Dyna Training (DDT). To initialize a policy with Dyna-Think, DIT reconstructs the thinking process of R1 to focus on performing world model simulation relevant to the proposed (and planned) action, and trains the policy using this reconstructed data. To enhance Dyna-Think, DDT uses a two-stage training process to first improve the agent's world modeling ability via objectives such as state prediction or critique generation, and then improve the agent's action via policy training. We evaluate our methods on OSWorld and WindowsAgentArena, and demonstrate that Dyna-Think improves the agent's in-domain and out-of-domain performance, achieving similar best-of-n performance compared to R1 while generating 2x less tokens on average. Our extensive empirical studies reveal that 1) using critique generation for world model training is effective to improve policy performance; and 2) AI agents with better performance correlate with better world modeling abilities. We believe our results suggest a promising research direction to integrate world model simulation into AI agents to enhance their reasoning, planning, and acting capabilities.

Despite their strong performance in multimodal emotion reasoning, existing Multimodal Large Language Models (MLLMs) often overlook the scenarios involving emotion conflicts, where emotional cues from different modalities are inconsistent. To fill this gap, we first introduce CA-MER, a new benchmark designed to examine MLLMs under realistic emotion conflicts. It consists of three subsets: video-aligned, audio-aligned, and consistent, where only one or all modalities reflect the true emotion. However, evaluations on our CA-MER reveal that current state-of-the-art emotion MLLMs systematically over-rely on audio signal during emotion conflicts, neglecting critical cues from visual modality. To mitigate this bias, we propose MoSEAR, a parameter-efficient framework that promotes balanced modality integration. MoSEAR consists of two modules: (1)MoSE, modality-specific experts with a regularized gating mechanism that reduces modality bias in the fine-tuning heads; and (2)AR, an attention reallocation mechanism that rebalances modality contributions in frozen backbones during inference. Our framework offers two key advantages: it mitigates emotion conflicts and improves performance on consistent samples-without incurring a trade-off between audio and visual modalities. Experiments on multiple benchmarks-including MER2023, EMER, DFEW, and our CA-MER-demonstrate that MoSEAR achieves state-of-the-art performance, particularly under modality conflict conditions.

In-car multi-zone speech separation, which captures voices from different speech zones, plays a crucial role in human-vehicle interaction. Although previous SpatialNet has achieved notable results, its high computational cost still hinders real-time applications in vehicles. To this end, this paper proposes LSZone, a lightweight spatial information modeling architecture for real-time in-car multi-zone speech separation. We design a spatial information extraction-compression (SpaIEC) module that combines Mel spectrogram and Interaural Phase Difference (IPD) to reduce computational burden while maintaining performance. Additionally, to efficiently model spatial information, we introduce an extremely lightweight Conv-GRU crossband-narrowband processing (CNP) module. Experimental results demonstrate that LSZone, with a complexity of 0.56G MACs and a real-time factor (RTF) of 0.37, delivers impressive performance in complex noise and multi-speaker scenarios.

Large language model (LLM) query routers are critical to modern AI platforms as they seek to improve efficiency by assigning inference queries to accurate, yet low-cost models. Parametric routers typically use trained neural networks for LLM selection but suffer from retraining and maintenance overheads. Nonparametric routers are training-free, instead estimating LLM accuracy and cost via similarity between encodings of the input query and training set queries. However, like their parametric counterparts, nonparametric routers struggle to generalize to outlier queries, an issue exacerbated by limited diversity in training sets which are costly to expand and difficult to keep current with ever-evolving use cases. We propose ProxRouter, which applies an exponentially tilted aggregation mechanism to balance bias and variance in nonparametric routers, improving their robustness to outliers. Experiments show ProxRouter enhances outlier routing while preserving inlier performance with minimal overhead.