OpenAI's CEO explains that its large language model has been misunderstood--and that he's changed his attitude to AGI. OpenAI's August launch of its GPT-5 large language model was somewhat of a disaster. There were glitches during the livestream, with the model generating charts with obviously inaccurate numbers. In a Reddit AMA with OpenAI employees, users complained that the new model wasn't friendly, and called for the company to restore the previous version. Most of all, critics griped that GPT-5 fell short of the stratospheric expectations that OpenAI has been juicing for years.

Why Are Car Software Updates Still So Bad? Over-the-air upgrades can not only transform your ride, they can help carmakers slash costs. Despite years of effort and the outlay of billions of dollars, none of the world's automakers have yet to match Tesla's prowess in delivering over-the-air (OTA) software updates. Just like with your phone and laptop, these operating system refreshes allow owners to upgrade their cars remotely. Tesla introduced OTAs in 2012, but now Elon Musk's company pumps out these updates like no other automaker. "Tesla once issued 42 updates within six months," Jean-Marie Lapeyre, Capgemini's CTO for automotive, tells WIRED. But for many other automakers, says Lapeyre, OTAs ship "maybe once a year."

Neural Information Processing Systems http://nips.cc/

"NIPS Neural Information Processing Systems 8-11th December 2014, Montreal, Canada",,, "Paper ID:","1807" "Title:","Zero-shot recognition with unreliable attributes" Current Reviews First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper strives to bridge the gap between the theory and practice of attribute-based zero-shot learning. The theory is that novel classes can be recognized automatically using pre-trained attribute predictors; in practice, however, learning these attribute classifiers can be as difficult or even more so than learning the object classes themselves. Random forests are trained to predict unseen classes from attribute vectors, and the training procedure takes into account the reliability of the attribute detectors by propagating a validation set through each decision tree at training time. The authors show how the method can be extended to handle training with a few training examples of test categories.

Neural Information Processing Systems http://nips.cc/

For Open-domain QA we report test numbers using 15 retrieved documents for RAG-Token models. Thorough Decoding approach since answers are generally short. Decoding approach for RAG-Sequence models, as Thorough Decoding did not improve performance. Figure 4 shows the user interface for human evaluation. Annotators were encouraged to research the topic using the internet, and were given detailed instructions and worked examples in a full instructions tab.

NeurIPS Rebuttal for "Retrieval-Augmented Generation for Knowledge-Intensive NLP T asks" We thank reviewers for their thoughtful, detailed reviews. "information retrieval strategy to improve the the generation Pre-trained seq2seq models have only become available in the last year (T5, BART) or two (GPT2). We study two RAG models. RAG-Sequence's formulation is similar to REALM, but RAG-Token is novel and Further, we explore novel decoding strategies for these models. "contribution [...] is not very specific, since R1 suggested that "A figure or example about P AG-Sequence Model and P AG-Token Model is needed", and R3 mentions "description of the model is quite concise (due to space restrictions)".

Counterfactual reasoning typically involves considering alternatives to actual events. While often applied to understand past events, a distinct form-forward counterfactual reasoning-focuses on anticipating plausible future developments. This type of reasoning is invaluable in dynamic financial markets, where anticipating market developments can powerfully unveil potential risks and opportunities for stakeholders, guiding their decision-making. However, performing this at scale is challenging due to the cognitive demands involved, underscoring the need for automated solutions. LLMs offer promise, but remain unexplored for this application. To address this gap, we introduce a novel benchmark, FIN-FORCE-FINancial FORward Counterfactual Evaluation. By curating financial news headlines and providing structured evaluation, FIN-FORCE supports LLM based forward counterfactual generation. This paves the way for scalable and automated solutions for exploring and anticipating future market developments, thereby providing structured insights for decision-making. Through experiments on FIN-FORCE, we evaluate state-of-the-art LLMs and counterfactual generation methods, analyzing their limitations and proposing insights for future research. We release the benchmark, supplementary data and all experimental codes at the following link: https://github.com/keanepotato/fin_force

We study the problem of posterior sampling using pretrained discrete diffusion foundation models, aiming to recover images from noisy measurements without retraining task-specific models. While diffusion models have achieved remarkable success in generative modeling, most advances rely on continuous Gaussian diffusion. In contrast, discrete diffusion offers a unified framework for jointly modeling categorical data such as text and images. Beyond unification, discrete diffusion provides faster inference, finer control, and principled training-free Bayesian inference, making it particularly well-suited for posterior sampling. However, existing approaches to discrete diffusion posterior sampling face severe challenges: derivative-free guidance yields sparse signals, continuous relaxations limit applicability, and split Gibbs samplers suffer from the curse of dimensionality. To overcome these limitations, we introduce Anchored Posterior Sampling (APS) for masked diffusion foundation models, built on two key innovations -- quantized expectation for gradient-like guidance in discrete embedding space, and anchored remasking for adaptive decoding. Our approach achieves state-of-the-art performance among discrete diffusion samplers across linear and nonlinear inverse problems on the standard benchmarks. We further demonstrate the benefits of our approach in training-free stylization and text-guided editing.

Image denoising is a fundamental problem in computer vision and medical imaging. However, real-world images are often degraded by structured noise with strong anisotropic correlations that existing methods struggle to remove. Most data-driven approaches rely on large datasets with high-quality labels and still suffer from limited generalizability, whereas existing zero-shot methods avoid this limitation but remain effective only for independent and identically distributed (i.i.d.) noise. To address this gap, we propose Median2Median (M2M), a zero-shot denoising framework designed for structured noise. M2M introduces a novel sampling strategy that generates pseudo-independent sub-image pairs from a single noisy input. This strategy leverages directional interpolation and generalized median filtering to adaptively exclude values distorted by structured artifacts. To further enlarge the effective sampling space and eliminate systematic bias, a randomized assignment strategy is employed, ensuring that the sampled sub-image pairs are suitable for Noise2Noise training. In our realistic simulation studies, M2M performs on par with state-of-the-art zero-shot methods under i.i.d. noise, while consistently outperforming them under correlated noise. These findings establish M2M as an efficient, data-free solution for structured noise suppression and mark the first step toward effective zero-shot denoising beyond the strict i.i.d. assumption.