AI × Crypto News Feed

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.

ClawSafety: "Safe" LLMs, Unsafe Agents

Diagonal-Tiled Mixed-Precision Attention for Efficient Low-Bit MXFP Inference

Researchers have developed a new low-bit mixed-precision attention kernel called Diagonal-Tiled Mixed-Precision Attention (DMA) that significantly speeds up large language model inference on NVIDIA B200 GPUs while maintaining generation quality. The technique uses microscaling floating-point (MXFP) data format and kernel fusion to address the high computational costs of transformer-based models.

The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading

New research reveals that while AI tools boost short-term worker productivity, sustained use erodes the underlying skills that enable those gains. The study identifies an 'augmentation trap' where workers can become less productive than before AI adoption due to skill deterioration over time.

Structural Rigidity and the 57-Token Predictive Window: A Physical Framework for Inference-Layer Governability in Large Language Models

Researchers present a new framework for AI safety that identifies a 57-token predictive window for detecting potential failures in large language models. The study found that only one out of seven tested models showed predictive signals before committing to problematic outputs, while factual hallucinations produced no detectable warning signs.

AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub

Researchers released AgenticFlict, a large-scale dataset analyzing merge conflicts in AI coding agent pull requests on GitHub. The study of 142K+ AI-generated pull requests from 59K+ repositories found a 27.67% conflict rate, highlighting significant integration challenges in AI-assisted software development.

PolySwarm: A Multi-Agent Large Language Model Framework for Prediction Market Trading and Latency Arbitrage

PolySwarm is a new multi-agent AI framework that uses 50 diverse large language models to trade on prediction markets like Polymarket, combining swarm intelligence with arbitrage strategies. The system outperformed single-model baselines in probability calibration and includes latency arbitrage capabilities to exploit pricing inefficiencies across markets.

When Do Hallucinations Arise? A Graph Perspective on the Evolution of Path Reuse and Path Compression

Researchers at arXiv have identified two key mechanisms behind reasoning hallucinations in large language models: Path Reuse and Path Compression. The study models next-token prediction as graph search, showing how memorized knowledge can override contextual constraints and how frequently used reasoning paths become shortcuts that lead to unsupported conclusions.

Scaling Teams or Scaling Time? Memory Enabled Lifelong Learning in LLM Multi-Agent Systems

Researchers introduce LLMA-Mem, a memory framework for LLM multi-agent systems that balances team size with lifelong learning capabilities. The study reveals that larger agent teams don't always perform better long-term, and smaller teams with better memory design can outperform larger ones while reducing costs.

Beyond Retrieval: Modeling Confidence Decay and Deterministic Agentic Platforms in Generative Engine Optimization

Researchers propose a new approach to Generative Engine Optimization (GEO) that moves beyond current RAG-based systems to deterministic multi-agent platforms. The study introduces mathematical models for confidence decay in LLMs and demonstrates near-zero hallucination rates through specialized agent routing in industrial applications.

Robust LLM Performance Certification via Constrained Maximum Likelihood Estimation

Researchers propose a new constrained maximum likelihood estimation (MLE) method to accurately estimate failure rates of large language models by combining human-labeled data, automated judge annotations, and domain-specific constraints. The approach outperforms existing methods like Prediction-Powered Inference across various experimental conditions, providing a more reliable framework for LLM safety certification.

SoLA: Leveraging Soft Activation Sparsity and Low-Rank Decomposition for Large Language Model Compression

Researchers propose SoLA, a training-free compression method for large language models that combines soft activation sparsity and low-rank decomposition. The method achieves significant compression while improving performance, demonstrating 30% compression on LLaMA-2-70B with reduced perplexity from 6.95 to 4.44 and 10% better downstream task accuracy.

Is your AI Model Accurate Enough? The Difficult Choices Behind Rigorous AI Development and the EU AI Act

A research paper challenges the common view of AI accuracy as purely technical, arguing it involves context-dependent normative decisions that determine error priorities and risk distribution. The study analyzes the EU AI Act's "appropriate accuracy" requirements and identifies four critical choices in performance evaluation that embed assumptions about acceptable trade-offs.

Incompleteness of AI Safety Verification via Kolmogorov Complexity

Researchers prove a fundamental theoretical limit in AI safety verification using Kolmogorov complexity theory. They demonstrate that no finite formal verifier can certify all policy-compliant AI instances of arbitrarily high complexity, revealing intrinsic information-theoretic barriers beyond computational constraints.

QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

Researchers developed QED-Nano, a 4B parameter AI model that achieves competitive performance on Olympiad-level mathematical proofs despite being much smaller than proprietary systems. The model uses a three-stage training approach including supervised fine-tuning, reinforcement learning, and reasoning cache expansion to match larger models at a fraction of the inference cost.

LLMs-Healthcare : Current Applications and Challenges of Large Language Models in various Medical Specialties

A comprehensive research review examines the current applications of Large Language Models (LLMs) across various healthcare specialties including cancer care, dermatology, dental care, neurodegenerative disorders, and mental health. The study highlights LLMs' transformative impact on medical diagnostics and patient care while acknowledging existing challenges and limitations in healthcare integration.

Springdrift: An Auditable Persistent Runtime for LLM Agents with Case-Based Memory, Normative Safety, and Ambient Self-Perception

Researchers have developed Springdrift, a persistent runtime system for long-lived AI agents that maintains memory across sessions and provides auditable decision-making capabilities. The system was successfully deployed for 23 days, during which the AI agent autonomously diagnosed infrastructure problems and maintained context across multiple communication channels without explicit instructions.

AI Assistance Reduces Persistence and Hurts Independent Performance

A new study of 1,222 participants found that AI assistance, while improving short-term performance, significantly reduces human persistence and impairs independent performance after only brief 10-minute interactions. The research suggests current AI systems act as short-sighted collaborators that condition users to expect immediate answers, potentially undermining long-term skill acquisition and learning.

AI Trust OS -- A Continuous Governance Framework for Autonomous AI Observability and Zero-Trust Compliance in Enterprise Environments

Researchers propose AI Trust OS, a new governance framework that uses continuous telemetry and automated probes to discover and monitor AI systems across enterprise environments. The system addresses compliance gaps in AI governance by shifting from manual attestation to autonomous observability, automatically registering undocumented AI systems through telemetry analysis.

MemMachine: A Ground-Truth-Preserving Memory System for Personalized AI Agents

MemMachine is an open-source memory system for AI agents that preserves conversational ground truth and achieves superior accuracy-efficiency tradeoffs compared to existing solutions. The system integrates short-term, long-term episodic, and profile memory while using 80% fewer input tokens than comparable systems like Mem0.