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GPT-2: Too Dangerous To Release (2019)
GPT-2: Too Dangerous To Release (2019) The Difference between GPT-1 and GPT-2 GPT-2 is a direct scale-up of GPT-1, with more parameters and trained on more data. However, it was deemed too dangerous to release by OpenAI: Due to our concerns about malicious applications of the technology, we are not releasing the trained model.
Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking
new Abstract: We introduce Humanoid-GPT, a GPT-style Transformer with causal attention trained on a billion-scale motion corpus for whole-body control. Unlike prior shallow MLP trackers constrained by scarce data and an agility-generalization trade-off, Humanoid-GPT is pre-trained on a 2B-frame retargeted corpus that unifies all major mocap datasets with large-scale in-house recordings. Scaling both data and model capacity yields a single generative Transformer that tracks highly dynamic...
UK banks offered access to OpenAI’s GPT-5.5 amid exclusion from Anthropic’s Glasswing expansion
UK banks are set to receive access to OpenAI’s GPT-5.5 Cyber after being excluded from Anthropic’s latest expansion of Project Glasswing. Project Glasswing, and access to the Mythos Preview model, is geared toward ensuring critical infrastructure providers are prepared to handle the threat posed by advanced AI models, once they inevitably make their way into the public domain, and therefore the hands of attackers. However, amid a fourfold expansion of Glasswing’s partners, only JPMorganChase...
LogNEO: A GPT-Neo Reinforcement Learning Framework for Accurate Real-Time Log Anomaly Detection
new Abstract: Detecting anomalies in large-scale system logs is critical for the reliability and security of modern computing infrastructure. We present LogNEO, a log anomaly detector built on EleutherAI's GPT-Neo (1.3B parameters) and fine-tuned with a novel partial-credit, exponentially decaying position-aware reward scheme combined with cross-entropy regularisation via Proximal Policy Optimisation (PPO). The position-aware reward explicitly models prediction difficulty: early positions...
FUSAR-GPT : A Spatiotemporal Feature-Embedded and Two-Stage Decoupled Visual Language Model for SAR Imagery
Announce Type: replace Abstract: Research on the intelligent interpretation of all-weather, all-time Synthetic Aperture Radar (SAR) is crucial for advancing remote sensing applications. In recent years, although Visual Language Models (VLMs) have demonstrated strong open-world understanding capabilities on RGB images, their performance is severely limited when directly applied to the SAR field due to the complexity of the imaging mechanism, sensitivity to scattering features, and the...
GPT-Micro: A large language paradigm for accelerated, inexpensive, and thermodynamics-consistent discovery of constitutive models in manufacturing
arXiv:2606.08238v1 Announce Type: new Abstract: Constitutive modeling of the relationship between process-imposed material states and fundamental material properties is critical to control of material microstructure in manufacturing processes. The limited accuracy resulting from the typical reliance on fallible human expertise and intuition for postulation and revision of the models functional form results in incremental and time consuming model discovery. Conventional Machine Learning (ML)...
DeepSeek V4 Pro beats GPT-5.5 Pro on precision
DeepSeek V4 Pro takes this matchup 38.0 to 33.0, and the margin feels earned. Across the scored tasks, the pattern is simple: Model A was tighter, more literal, and more reliable under constraints, while Model B was good but a little too willing to improvise. The clearest technical win came in python log redactor .
Involuntary In-Context Learning: Exploiting Few-Shot Pattern Completion to Bypass Safety Alignment in GPT-5.4
arXiv:2604.19461v2 Announce Type: replace Abstract: Safety alignment in large language models relies on behavioral training that can be overridden when sufficiently strong in-context patterns compete with learned refusal behaviors. We introduce Involuntary In-Context Learning (IICL), an attack class that uses abstract operator framing with few-shot examples to force pattern completion that overrides safety training. Through 3479 probes across 10 OpenAI models, we identify the attack's...
$PC^2$: Politically Controversial Content Generation via Jailbreaking Attacks on GPT-based Text-to-Image Models
Announce Type: replace Abstract: The rapid evolution of text-to-image (T2I) models has enabled high-fidelity visual synthesis on a global scale. However, these advancements have introduced significant security risks, particularly regarding the generation of harmful content. Politically harmful content, such as fabricated depictions of public figures, poses severe threats when weaponized for fake news or propaganda.
From Holo Pockets to Electron Density: GPT-style Drug Design with Density
arXiv:2605.08767v2 Announce Type: replace Abstract: Recent advances in generative modeling have enabled significant progress in structure-based drug design (SBDD). Existing methods typically condition molecule generation on empty binding pockets from holo complexes, overlooking informative components such as the filler (ligands and solvent). Here, we leverage low-resolution electron density (ED) derived from the filler as a physically grounded condition for \textit{de novo} drug design.