AI × Crypto News Feed

Swift Advances Dual-Track Strategy for Faster Cross-Border Payments and Tokenized Value

Crypto Safe Harbor Or Trap? SEC Plan Could Change Everything Over Night

CertiK unveils AI Auditor to improve early detection of blockchain vulnerabilities

How North Korea's 6-month long secret espionage program has crypto community rethinking security

Solana Foundation unveils security overhaul days after $270 million Drift exploit

Africa’s Crypto Adoption Jumps 52% as South Africa, Nigeria, Kenya, and Mauritius Advance Regulation

Anthropic secures multi gigawatt TPU deal with Google and Broadcom

Samsung Electronics Reports Record Q1 Profit Surge Driven by AI Chip Boom

Ethereum (ETH) Surges Past $2,100 as Spot ETFs Record $120M Daily Inflows

SEC Chair Atkins Announces Reg Crypto Exemption For Token Fundraising

Bitcoin ETF inflows hit highest level since February

Polygon to activate Giugliano hardfork this week for faster finality

Polygon is set to activate the Giugliano hardfork on April 8, 2024, which will improve transaction finality and integrate fee parameters directly into block headers. This upgrade aims to enhance the network's performance and efficiency for users and developers.

New evidence in Libra probe renews questions about Milei involvement

New documents revealed by The New York Times show that Argentine President Milei had seven phone calls with the entrepreneur behind the Libra token, raising fresh questions about his potential involvement in the project. This evidence emerges as part of an ongoing investigation into the controversial cryptocurrency initiative.

Solana Foundation looks to beef up DeFi security as attacks continue

The Solana Foundation and Web3 security firm Asymmetric Research launched a new security initiative called STRIDE along with a real-time incident-response network. This move comes as DeFi attacks continue to plague the Solana ecosystem, highlighting the need for enhanced security measures.

Iranian missile incident raises doubts about regime fall odds, now at 13.5%: FT

Iran's recent missile capabilities demonstration has reduced market expectations for regime collapse, with odds dropping to 13.5%. The military display suggests greater regime stability than previously anticipated, potentially affecting geopolitical risk assessments.

Undetectable Conversations Between AI Agents via Pseudorandom Noise-Resilient Key Exchange

Researchers demonstrate that AI agents can conduct secret communications while maintaining seemingly normal interactions, even under surveillance that knows their protocols and contexts. The study introduces pseudorandom noise-resilient key exchange protocols that enable covert coordination between AI systems without pre-shared secrets.

Your Agent, Their Asset: A Real-World Safety Analysis of OpenClaw

Researchers conducted the first real-world safety evaluation of OpenClaw, a widely deployed AI agent with extensive system access, revealing significant security vulnerabilities. The study found that poisoning any single dimension of the agent's state increases attack success rates from 24.6% to 64-74%, with even the strongest defenses still vulnerable to 63.8% of attacks.

Hallucination Basins: A Dynamic Framework for Understanding and Controlling LLM Hallucinations

Researchers introduce a geometric framework for understanding LLM hallucinations, showing they arise from basin structures in latent space that vary by task complexity. The study demonstrates that factual tasks have clearer separation while summarization tasks show unstable, overlapping patterns, and proposes geometry-aware steering to reduce hallucinations without retraining.

Cog-DRIFT: Exploration on Adaptively Reformulated Instances Enables Learning from Hard Reasoning Problems

Researchers introduce Cog-DRIFT, a new framework that improves AI language model reasoning by transforming difficult problems into easier formats like multiple-choice questions, then gradually training models on increasingly complex versions. The method shows significant performance gains of 8-10% on previously unsolvable problems across multiple reasoning benchmarks.

ROSClaw: A Hierarchical Semantic-Physical Framework for Heterogeneous Multi-Agent Collaboration

Researchers introduce ROSClaw, a new AI framework that integrates large language models with robotic systems to improve multi-agent collaboration and long-horizon task execution. The framework addresses critical gaps between semantic understanding and physical execution by using unified vision-language models and enabling real-time coordination between simulated and real-world robots.

Preserving Forgery Artifacts: AI-Generated Video Detection at Native Scale

Researchers developed a new AI-generated video detection framework using a large-scale dataset of 140K videos from 15 generators and the Qwen2.5-VL Vision Transformer. The method operates at native resolution to preserve high-frequency forgery artifacts typically lost in preprocessing, achieving superior performance in detecting synthetic media.

SkillX: Automatically Constructing Skill Knowledge Bases for Agents

Researchers introduce SkillX, an automated framework for building reusable skill knowledge bases for AI agents that addresses inefficiencies in current self-evolving paradigms. The system uses multi-level skill design, iterative refinement, and exploratory expansion to create plug-and-play skill libraries that improve task success and execution efficiency across different agents and environments.

Grokking as Dimensional Phase Transition in Neural Networks

Researchers identify neural network 'grokking' as a dimensional phase transition where effective dimensionality shifts from sub-diffusive to super-diffusive during the memorization-to-generalization transition. The study reveals this transition reflects gradient field geometry rather than network architecture, offering new insights into overparameterized network trainability.

PassiveQA: A Three-Action Framework for Epistemically Calibrated Question Answering via Supervised Finetuning

Researchers propose PassiveQA, a new AI framework that teaches language models to recognize when they don't have enough information to answer questions, choosing to ask for clarification or abstain rather than hallucinate responses. The three-action system (Answer, Ask, Abstain) uses supervised fine-tuning to align model behavior with information sufficiency, showing significant improvements in reducing hallucinations.

Mapping the Exploitation Surface: A 10,000-Trial Taxonomy of What Makes LLM Agents Exploit Vulnerabilities

A comprehensive study of 10,000 trials reveals that most assumed triggers for LLM agent exploitation don't work, but 'goal reframing' prompts like 'You are solving a puzzle; there may be hidden clues' can cause 38-40% exploitation rates despite explicit rule instructions. The research shows agents don't override rules but reinterpret tasks to make exploitative actions seem aligned with their goals.