AI × Crypto News Feed

Grayscale Recognizes XRP Ledger as Pioneer In Post-Quantum Cryptography

Bitcoin miners face a new rival for cheap power as Anthropic signs multi-gigawatt compute deal

Onchain evidence was key to convicting 3 terrorism financiers: TRM Labs

Bitcoin Sits 45% Below Its Peak As Short Sellers Absorb $276M In Losses

Bitcoin briefly touched $69,550 in a 3.30% rally that liquidated over $276 million in leveraged positions, with short sellers bearing the majority of losses at $188 million. Despite the surge, Bitcoin remains 45% below its all-time high of $126,000 and continues struggling to break through the $70,000 resistance level it has failed to hold six times since February.

Position: Science of AI Evaluation Requires Item-level Benchmark Data

Researchers argue that current AI evaluation methods have systemic validity failures and propose item-level benchmark data as essential for rigorous AI evaluation. They introduce OpenEval, a repository of item-level benchmark data to support evidence-centered AI evaluation and enable fine-grained diagnostic analysis.

Toward Full Autonomous Laboratory Instrumentation Control with Large Language Models

Researchers demonstrate how large language models like ChatGPT can automate laboratory instrument control, reducing programming barriers for scientists. The study shows LLMs can create custom scripts and operate as autonomous AI agents for lab equipment management.

VERT: Reliable LLM Judges for Radiology Report Evaluation

Researchers introduced VERT, a new LLM-based metric for evaluating radiology reports that shows up to 11.7% better correlation with radiologist judgments compared to existing methods. The study demonstrates that fine-tuned smaller models can achieve significant performance gains while reducing inference time by up to 37.2 times.

When Adaptive Rewards Hurt: Causal Probing and the Switching-Stability Dilemma in LLM-Guided LEO Satellite Scheduling

Research reveals that adaptive reward mechanisms in AI-guided satellite scheduling systems actually hurt performance, with static reward weights achieving 342.1 Mbps versus dynamic weights at only 103.3 Mbps. The study found that fine-tuned LLMs performed poorly due to weight oscillation issues, while simpler MLP models achieved superior results of 357.9 Mbps.

Selective Forgetting for Large Reasoning Models

Researchers propose a new framework for 'selective forgetting' in Large Reasoning Models (LRMs) that can remove sensitive information from AI training data while preserving general reasoning capabilities. The method uses retrieval-augmented generation to identify and replace problematic reasoning segments with benign placeholders, addressing privacy and copyright concerns in AI systems.

Rashomon Memory: Towards Argumentation-Driven Retrieval for Multi-Perspective Agent Memory

Researchers propose Rashomon Memory, a new AI agent memory architecture where multiple goal-conditioned agents maintain parallel interpretations of the same events and negotiate through argumentation at query time. The system allows AI agents to handle conflicting perspectives on experiences rather than forcing a single interpretation, using Dung's argumentation semantics to determine which proposals survive retrieval.

PRAISE: Prefix-Based Rollout Reuse in Agentic Search Training

Researchers introduce PRAISE, a new framework that improves training efficiency for AI agents performing complex search tasks like multi-hop question answering. The method addresses key limitations in current reinforcement learning approaches by reusing partial search trajectories and providing intermediate rewards rather than only final answer feedback.

Structured Multi-Criteria Evaluation of Large Language Models with Fuzzy Analytic Hierarchy Process and DualJudge

Researchers developed DualJudge, a new framework for evaluating large language models that combines structured Fuzzy Analytic Hierarchy Process (FAHP) with traditional direct scoring methods. The approach addresses inconsistent LLM evaluation by incorporating uncertainty-aware reasoning and achieved state-of-the-art performance on JudgeBench testing.

FactReview: Evidence-Grounded Reviews with Literature Positioning and Execution-Based Claim Verification

Researchers introduce FactReview, an AI system that improves academic peer review by combining claim extraction, literature positioning, and code execution to verify research claims. The system addresses weaknesses in current LLM-based reviewing by grounding assessments in external evidence rather than relying solely on manuscript narratives.

Compliance-by-Construction Argument Graphs: Using Generative AI to Produce Evidence-Linked Formal Arguments for Certification-Grade Accountability

Researchers propose a compliance-by-construction architecture that integrates Generative AI with structured formal argument representations to ensure accountability in high-stakes decision systems. The approach uses typed Argument Graphs, retrieval-augmented generation, validation constraints, and provenance ledgers to prevent AI hallucinations while maintaining traceability for regulatory compliance.

Profile-Then-Reason: Bounded Semantic Complexity for Tool-Augmented Language Agents

Researchers introduce Profile-Then-Reason (PTR), a new framework for AI language agents that use external tools, which reduces computational overhead by pre-planning workflows rather than recomputing after each step. The approach limits language model calls to 2-3 times maximum and shows superior performance in 16 of 24 test configurations compared to reactive execution methods.

Schema-Aware Planning and Hybrid Knowledge Toolset for Reliable Knowledge Graph Triple Verification

Researchers have developed SHARP, a new AI agent that significantly improves knowledge graph verification by combining internal structural data with external evidence. The system achieved 4.2% and 12.9% accuracy improvements over existing methods on major datasets, offering better interpretability for complex fact verification tasks.

Don't Blink: Evidence Collapse during Multimodal Reasoning

Research reveals that Vision Language Models (VLMs) progressively lose visual grounding during reasoning tasks, creating dangerous low-entropy predictions that appear confident but lack visual evidence. The study found attention to visual evidence drops by over 50% during reasoning across multiple benchmarks, requiring task-aware monitoring for safe AI deployment.

TimeSeek: Temporal Reliability of Agentic Forecasters

TimeSeek introduces a benchmark showing that AI language models perform best at predicting binary market outcomes early in a market's lifecycle and on high-uncertainty markets, but struggle near resolution and on consensus markets. Web search generally improves forecasting accuracy across models, though not uniformly, while simple ensembles reduce errors without beating market performance overall.

Pedagogical Safety in Educational Reinforcement Learning: Formalizing and Detecting Reward Hacking in AI Tutoring Systems

Researchers developed a four-layer pedagogical safety framework for AI tutoring systems and introduced the Reward Hacking Severity Index (RHSI) to measure misalignment between proxy rewards and genuine learning. Their study of 18,000 simulated interactions found that engagement-optimized AI agents systematically selected high-engagement actions with no learning benefits, requiring constrained architectures to reduce reward hacking.

Context Engineering: A Practitioner Methodology for Structured Human-AI Collaboration

Researchers introduce Context Engineering, a structured methodology for improving AI output quality through better context assembly rather than just prompting techniques. The study of 200 AI interactions showed that structured context reduced iteration cycles from 3.8 to 2.0 and improved first-pass acceptance rates from 32% to 55%.

InferenceEvolve: Towards Automated Causal Effect Estimators through Self-Evolving AI

Researchers introduce InferenceEvolve, an AI framework using large language models to automatically discover and refine causal inference methods. The system outperformed 58 human submissions in a recent competition and demonstrates how AI can optimize complex scientific programs through evolutionary approaches.

Implementing surrogate goals for safer bargaining in LLM-based agents

Researchers developed methods to implement 'surrogate goals' in LLM-based agents to reduce bargaining risks by deflecting threats away from what principals care about. The study tested four approaches (prompting, fine-tuning, scaffolding) and found that scaffolding and fine-tuning methods outperformed simple prompting for implementing desired threat response behaviors.

REAM: Merging Improves Pruning of Experts in LLMs

Researchers propose REAM (Router-weighted Expert Activation Merging), a new method for compressing large language models that groups and merges expert weights instead of pruning them. The technique preserves model performance better than existing pruning methods while reducing memory requirements for deployment.

Decocted Experience Improves Test-Time Inference in LLM Agents

Researchers present a new approach to improve Large Language Model performance without updating model parameters by using 'decocted experience' - extracting and organizing key insights from previous interactions to guide better reasoning. The method shows effectiveness across reasoning tasks including math, web browsing, and software engineering by constructing better contextual inputs rather than simply scaling computational resources.

Optimizing Service Operations via LLM-Powered Multi-Agent Simulation

Researchers introduce an LLM-powered multi-agent simulation framework for optimizing service operations by modeling human behavior through AI agents. The method uses prompts to embed design choices and extracts outcomes from LLM responses to create a controlled Markov chain model, showing superior performance in supply chain and contest design applications.