arXiv:2602.15847v1 Announce Type: new Abstract: Personality steering in large language models (LLMs) commonly relies on injecting trait-specific steering vectors, implicitly assuming that personality traits can be controlled independently. In this work, we examine whether this assumption holds by analysing the geometric relationships between Big Five personality steering directions. We study steering vectors extracted from two model families (LLaMA-3-8B and Mistral-8B) and apply a range of geometric conditioning schemes, from unconstrained directions to soft and hard orthonormalisation. Our results […]

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We didn’t start by trying to “add AI” to our system. The system was already there stable, predictable, and doing exactly what it was supposed to do. It was a typical Java backend built with Spring Boot, with well-defined APIs, structured validation, and workflows that behaved consistently as long as the inputs were clean. Then we introduced AI into one part of the flow. The idea seemed straightforward: let AI interpret user intent and generate structured inputs for […]

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arXiv:2603.06687v1 Announce Type: new Abstract: Geo-temporal understanding, the ability to infer location, time, and contextual properties from visual input alone, underpins applications such as disaster management, traffic planning, embodied navigation, world modeling, and geography education. Although recent vision-language models (VLMs) have advanced image geo-localization using cues like landmarks and road signs, their ability to reason about temporal signals and physically grounded spatial cues remains limited. To address this gap, we introduce TimeSpot, a benchmark for evaluating real-world geo-temporal […]

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[Editor’s Note: This class was a part of the 2019 DFL Jane Street Fellowship.] This guide would not have been possible without the help and feedback from many people. Special thanks to Yasaman Bahri for her feedback, support, and mentoring. Thank you to Kumar Krishna Agrawal, Sam Schoenholz, and Jeffrey Pennington for their valuable input and guidance. Finally, thank you to our group members Chris Akers, Brian Friedenberg, Sajel Shah, Vincent Su, Witold Szejgis, for their curiosity, commitment […]

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arXiv:2603.06685v1 Announce Type: new Abstract: Stochastic differential equation (SDE)-based generative models have achieved substantial progress in conditional generation via training-free differentiable loss-guided approaches. However, existing methodologies utilizing posterior sam- pling typically confront a substantial estimation error, which results in inaccu- rate gradients for guidance and leading to inconsistent generation results. To mitigate this issue, we propose that performing an additional backward denois- ing step and Monte-Carlo sampling (ABMS) can achieve better guided diffu- sion, which is a plug-and-play […]

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Current expansion-based methods for Class Incremental Learning (CIL) effectively mitigate catastrophic forgetting by freezing old features. However, such task-specific features learned from the new task may collide with the old features. From a causal perspective, spurious feature correlations are the main cause of this collision, manifesting in two scopes: (i) guided by empirical risk minimization (ERM), intra-task spurious correlations cause task-specific features to rely on shortcut features. These non-robust features are vulnerable to interference, inevitably drifting into the […]

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Why the future of AI isn’t bigger — it’s smarter, smaller, and more transparent The Hidden Cost of Today’s AI Large AI models are powerful. But they come with serious trade-offs. Training a single large-scale model can cost millions of dollars in compute resources alone. For example, training a model like GPT-3 is estimated to have cost over $4 million. Newer, larger models can cost significantly more — some reports suggest upwards of $50–100 million when factoring in research, hardware, […]

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Generative AI has emerged as a transformative force in cybersecurity, offering both opportunities for innovation and challenges in threat detection and mitigation. This systematic literature review and meta-analysis synthesizes existing research to evaluate the efficacy of generative AI in cybersecurity applications, focusing on detection performance, overall impact, and threat detection metrics. We conducted a comprehensive analysis of peer-reviewed studies, employing rigorous statistical methods to quantify effect sizes and their significance. The results reveal a substantial negative effect size […]

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Here is a PyTorch port of the Disco103 update rule: https://github.com/asystemoffields/disco-torch pip install disco-torch The port loads the pretrained disco_103.npz weights and reproduces the reference Catch benchmark (99% catch rate at 1000 steps). All meta-network outputs match the JAX implementation within float32 precision (<1e-6 max diff), and the full value pipeline is verified (14 fields, <6e-4 max diff). It includes a high-level DiscoTrainer API that handles meta-state management, target networks, replay buffer, and the training loop: from disco_torch […]

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Verita AI is working with top-tier engineers on a cutting-edge project designing reinforcement learning environments that teach LLMs advanced AI/ML concepts. Your expertise would be valuable for shaping how next-generation models learn. The role: • Fully remote, contract • $150-$200/hour (based on expertise) + $500 take-home bonus • Minimum 4 hours daily overlap with PST (9am-5pm) • ~2 tasks per week, high autonomy Ideal for: • Graduates from top-tier engineering colleges or engineers from leading tech companies (FAANG+) […]

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