Hugging Face has officially released TRL (Transformer Reinforcement Learning) v1.0, marking a pivotal transition for the library from a research-oriented repository to a stable, production-ready framework. For AI professionals and developers, this release codifies the Post-Training pipeline—the essential sequence of Supervised Fine-Tuning (SFT), Reward Modeling, and Alignment—into a unified, standardized API. In the early stages […] The post Hugging Face Releases TRL v1.0: A Unified Post-Trainin

“There’s a myth in mainstream media and social media that insider trading doesn’t apply in the prediction markets … That is wrong,” said CFTC enforcement director David Miller.

arXiv:2603.28990v1 Announce Type: new Abstract: How much autonomy can multi-agent LLM systems sustain -- and what enables it? We present a 25,000-task computational experiment spanning 8 models, 4--256 agents, and 8 coordination protocols ranging from externally imposed hierarchy to emergent self-organization. We observe that autonomous behavior already emerges in current LLM agents: given minimal structural scaffolding (fixed ordering), agents spontaneously invent specialized roles, voluntaril

arXiv:2603.28998v1 Announce Type: cross Abstract: As Large Language Models (LLMs) and multi-agent AI systems are demonstrating increasing potential in cybersecurity operations, organizations, policymakers, model providers, and researchers in the AI and cybersecurity communities are interested in quantifying the capabilities of such AI systems to achieve more autonomous SOCs (security operation centers) and reduce manual effort. In particular, the AI and cybersecurity communities have recently d

arXiv:2603.29062v1 Announce Type: cross Abstract: LLM-based chatbots in government services face critical security gaps. Multi-turn adversarial attacks achieve over 90% success against current defenses, and single-layer guardrails are bypassed with similar rates. We present CivicShield, a cross-domain defense-in-depth framework for government-facing AI chatbots. Drawing on network security, formal verification, biological immune systems, aviation safety, and zero-trust cryptography, CivicShield

arXiv:2603.29356v1 Announce Type: cross Abstract: The rapid progress of generative adversarial networks (GANs) and diffusion models has enabled the creation of synthetic faces that are increasingly difficult to distinguish from real images. This progress, however, has also amplified the risks of misinformation, fraud, and identity abuse, underscoring the urgent need for detectors that remain robust across diverse generative models. In this work, we introduce Counterfeit Image Pattern High-level

arXiv:2603.29403v1 Announce Type: cross Abstract: LLM-as-a-Judge (LaaJ) is a novel paradigm in which powerful language models are used to assess the quality, safety, or correctness of generated outputs. While this paradigm has significantly improved the scalability and efficiency of evaluation processes, it also introduces novel security risks and reliability concerns that remain largely unexplored. In particular, LLM-based judges can become both targets of adversarial manipulation and instrume

arXiv:2603.29418v1 Announce Type: cross Abstract: Although multimodal large language models (MLLMs) are increasingly deployed in real-world applications, their instruction-following behavior leaves them vulnerable to prompt injection attacks. Existing prompt injection methods predominantly rely on textual prompts or perceptible visual prompts that are observable by human users. In this work, we study imperceptible visual prompt injection against powerful closed-source MLLMs, where adversarial i

arXiv:2603.30016v1 Announce Type: cross Abstract: AI agents, predominantly powered by large language models (LLMs), are vulnerable to indirect prompt injection, in which malicious instructions embedded in untrusted data can trigger dangerous agent actions. This position paper discusses our vision for system-level defenses against indirect prompt injection attacks. We articulate three positions: (1) dynamic replanning and security policy updates are often necessary for dynamic tasks and realisti

arXiv:2601.13358v2 Announce Type: replace Abstract: Scale does not uniformly improve reasoning - it restructures it. Analyzing 25,000+ chain-of-thought trajectories across four domains (Law, Science, Code, Math) and two scales (8B, 70B parameters), we discover that neural scaling laws trigger domain-specific phase transitions rather than uniform capability gains. Legal reasoning undergoes Crystallization: 45% collapse in representational dimensionality (d95: 501 -> 274), 31% increase in traject

arXiv:2509.05841v2 Announce Type: replace-cross Abstract: We give an overview on the emerging applications of GenAI in the financial industry, especially within investment banks. Inherent to these exciting opportunities is a new realm of risks that must be managed properly. By heeding both the Yin and Yang sides of GenAI, we can accelerate its organic growth while safeguarding the entire financial industry during this nascent era of AI.

arXiv:2601.04448v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have greatly advanced Natural Language Processing (NLP), particularly through instruction tuning, which enables broad task generalization without additional fine-tuning. However, their reliance on large-scale datasets-often collected from human or web sources-makes them vulnerable to backdoor attacks, where adversaries poison a small subset of data to implant hidden behaviors. Despite this growing risk, defen

Bittensor (TAO) has emerged as one of the market’s strongest performers this month, rallying roughly 73% over the past 30 days even as larger cryptocurrencies staged a more modest recovery.  NVIDIA Nod Fuels TAO Rally Market analyst Alex Carchidi argues that a key catalyst was public recognition from a major tech figure. NVIDIA CEO Jensen Huang recently acknowledged decentralized AI training — the core use case Bittensor champions — as a practical approach after hearing about the project’s lates

The AI recruiting startup confirmed a security incident after an extortion hacking crew took credit for stealing data from the company's systems.

Bitfarms said it was impacted by a decline in Bitcoin prices last year. The company is now five months into its pivot from Bitcoin mining to HPC and AI.