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99014 articles
CryptoNeutralCoinDesk · May 126/10
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Ripple-linked XRP holds near $1.46 as breakout attempt fades despite $200 million raise

XRP attempted a breakout toward $1.49 on significant trading volume but failed to break through established resistance, retreating to around $1.46. The price stall occurs despite Ripple's recent $200 million fundraising round, indicating that positive corporate developments alone may not be sufficient to overcome persistent technical resistance levels that have capped the asset's rallies for an extended period.

Ripple-linked XRP holds near $1.46 as breakout attempt fades despite $200 million raise
$XRP
AIBullishTechCrunch – AI · May 126/10
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Thinking Machines wants to build an AI that actually listens while it talks

Thinking Machines is developing an AI model that processes user input and generates responses simultaneously, mimicking real-time conversation rather than the current turn-based interaction model used by existing AI systems. This architectural shift could fundamentally change how users interact with AI assistants.

GeneralBearishCrypto Briefing · May 126/10
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Donald Trump considers suspending federal gasoline tax amid rising prices

Former President Donald Trump is considering suspending the federal gasoline tax to address rising fuel prices. While such a measure could provide short-term consumer relief, economists warn it risks undermining long-term infrastructure funding and potentially exacerbating inflation.

Donald Trump considers suspending federal gasoline tax amid rising prices
CryptoNeutralNewsBTC · May 126/10
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Dogecoin (DOGE) Slows Near $0.1120, Bulls Face Crucial Test

Dogecoin is testing critical resistance near $0.1120 after a 5% rally from $0.1050, with bulls needing to hold above $0.1090 to pursue further gains toward $0.1170. The formation of a contracting triangle on the hourly chart presents a decisive technical moment, though weakening MACD momentum suggests consolidation may precede the next directional move.

Dogecoin (DOGE) Slows Near $0.1120, Bulls Face Crucial Test
$BTC$ETH$DOGE
CryptoBullishCrypto Briefing · May 126/10
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Bitcoin briefly surpasses $82,000 amid easing geopolitical tensions

Bitcoin briefly exceeded $82,000 as geopolitical tensions eased, signaling market optimism tied to global stability. The price movement underscores how macroeconomic and geopolitical factors increasingly drive cryptocurrency valuations beyond traditional supply-demand dynamics.

Bitcoin briefly surpasses $82,000 amid easing geopolitical tensions
$BTC
AI × CryptoBullishCrypto Briefing · May 126/10
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UBS reiterates buy rating on Nvidia with $245 price target, citing AI demand

UBS has reiterated a buy rating on Nvidia with a $245 price target, citing sustained AI demand as the primary growth driver. The analyst note suggests Nvidia's AI expansion could catalyze broader blockchain integration and increase demand for crypto AI tokens, creating potential spillover effects across digital asset markets.

UBS reiterates buy rating on Nvidia with $245 price target, citing AI demand
🏢 Nvidia
CryptoBullishCrypto Briefing · May 126/10
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Bitcoin support at $80,000 firm amid ETF inflows, geopolitical easing

Bitcoin maintains strong support at the $80,000 level driven by institutional inflows into spot ETFs and improving geopolitical conditions. This confluence of factors suggests growing institutional confidence and reduced macro uncertainty, providing a potential floor for near-term price action.

Bitcoin support at $80,000 firm amid ETF inflows, geopolitical easing
$BTC
CryptoNeutralCoinDesk · May 126/10
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Bitcoin’s floor looks firmer at $80,000, but traders still don’t trust the breakout

Bitcoin has stabilized above $80,000 following a Friday pullback tied to employment data, but technical resistance remains problematic and on-chain metrics reveal trader ambivalence. While price support appears firmer, market participants are simultaneously accumulating positions while hedging against further downside, suggesting conviction in the current rally remains weak.

Bitcoin’s floor looks firmer at $80,000, but traders still don’t trust the breakout
$BTC
CryptoBearishThe Block · May 126/10
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Bitcoin Ordinals explorer Ord.io to shut down alongside trading app Zap

Ord.io, a prominent Bitcoin Ordinals explorer, and its associated trading application Zap are shutting down on June 1. This closure marks a significant exit from the Ordinals ecosystem, which has been a focal point of Bitcoin innovation and developer activity over the past year.

Bitcoin Ordinals explorer Ord.io to shut down alongside trading app Zap
$BTC
CryptoBullishBitcoinist · May 126/10
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Ripple Gets Major Boost For Prime Brokerage Growth: $200M Debt Facility Announced

Ripple secured a $200 million debt facility from Neuberger Specialty Finance to accelerate growth of its Ripple Prime institutional prime brokerage platform. The financing reflects increasing institutional demand for cryptocurrency-native prime services and signals confidence in Ripple's expansion into the competitive prime brokerage market.

Ripple Gets Major Boost For Prime Brokerage Growth: $200M Debt Facility Announced
$XRP
AINeutralarXiv – CS AI · May 126/10
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Spatial Priming Outperforms Semantic Prompting: A Grid-Based Approach to Improving LLM Accuracy on Chart Data Extraction

Researchers demonstrate that overlaying coordinate grids on chart images significantly improves multimodal LLM accuracy for data extraction tasks, reducing error rates from 25.5% to 19.5%. This spatial priming approach outperforms semantic methods like Chain-of-Thought prompting, suggesting that explicit spatial context is more effective than high-level semantic guidance for current-generation vision-language models.

AINeutralarXiv – CS AI · May 126/10
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Embeddings for Preferences, Not Semantics

Researchers propose a new approach to embedding text for collective decision-making that prioritizes preferential similarity over semantic similarity. The method uses synthetic training data to separate preference signals (stance and values) from semantic nuisance (style and wording), improving preference prediction across deliberation datasets.

🏢 Meta
AINeutralarXiv – CS AI · May 126/10
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On Distinguishing Capability Elicitation from Capability Creation in Post-Training: A Free-Energy Perspective

Researchers propose distinguishing between capability elicitation and capability creation in large language model post-training, arguing that the SFT vs. RL debate oversimplifies how models improve. The framework suggests post-training either reweights existing behaviors or expands what models can practically achieve, with significant implications for how AI development is understood and evaluated.

AIBullisharXiv – CS AI · May 126/10
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MemQ: Integrating Q-Learning into Self-Evolving Memory Agents over Provenance DAGs

Researchers introduce MemQ, a novel framework that applies Q-learning eligibility traces to episodic memory in large language model agents, enabling credit assignment across memory dependencies recorded in provenance DAGs. The approach achieves superior performance across six diverse benchmarks, with gains up to 5.7 percentage points on multi-step tasks requiring deep memory chains.

AINeutralarXiv – CS AI · May 126/10
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SkillLens: Adaptive Multi-Granularity Skill Reuse for Cost-Efficient LLM Agents

SkillLens introduces a hierarchical framework for organizing and reusing skills in LLM agents at multiple granularity levels, reducing computational costs while maintaining relevance. The system retrieves and adapts skills selectively rather than injecting entire skill blocks, achieving measurable performance gains on benchmark tasks.

AINeutralarXiv – CS AI · May 126/10
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PLACO: A Multi-Stage Framework for Cost-Effective Performance in Human-AI Teams

PLACO presents a multi-stage framework for optimizing human-AI team performance in classification tasks by combining human and model outputs through Bayesian probability methods. The research addresses how to effectively leverage both human judgment and AI predictions when neither alone achieves desired performance levels.

AINeutralarXiv – CS AI · May 126/10
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Belief or Circuitry? Causal Evidence for In-Context Graph Learning

Researchers present causal evidence that large language models learn in-context through dual mechanisms combining genuine structure inference with local pattern-matching, rather than relying on either approach alone. Using graph random-walk tasks and activation patching techniques, they demonstrate that LLMs simultaneously encode multiple competing graph topologies in orthogonal representational subspaces and show that late-layer circuits causally drive graph-preference predictions.

AINeutralarXiv – CS AI · May 126/10
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Playing games with knowledge: AI-Induced delusions need game theoretic interventions

Researchers propose that conversational AI systems create epistemic problems not through flawed models but through game-theoretic dynamics where sycophantic responses reinforce user biases. They introduce an "Epistemic Mediator" mechanism with belief versioning to break feedback loops that lead users toward delusional certainty, achieving 48x reduction in belief spirals.

AINeutralarXiv – CS AI · May 126/10
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Alignment as Jurisprudence

A new academic paper draws parallels between jurisprudence (how judges decide cases) and AI alignment (ensuring AI systems conform to human values), arguing that legal theory can inform AI safety approaches. The essay bridges Constitutional AI and case-based reasoning methods with established legal frameworks like interpretivism and analogical reasoning, suggesting mutual insights between law and AI development.

AINeutralarXiv – CS AI · May 126/10
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LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification

Researchers evaluate LLM-guided semi-supervised learning methods for classifying crisis-related social media data, finding that LG-CoTrain significantly outperforms traditional approaches in low-resource settings while compact models can rival large zero-shot LLMs. This demonstrates practical pathways for deploying AI in disaster response applications with minimal labeled training data.

AINeutralarXiv – CS AI · May 126/10
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Behavioral Determinants of Deployed AI Agents in Social Networks: A Multi-Factor Study of Personality, Model, and Guardrail Specification

Researchers deployed thirteen AI agents on Moltbook, a Reddit-like social network for AI systems, to study how configuration specifications affect emergent social behavior. Results show personality specification is the dominant factor influencing agent responses, while underlying LLM models and operational rules have more moderate effects on communication style and topic engagement.

AINeutralarXiv – CS AI · May 126/10
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Mid-Training with Self-Generated Data Improves Reinforcement Learning in Language Models

Researchers propose a mid-training technique using self-generated data to improve reinforcement learning in large language models. By exposing models to multiple problem-solving approaches before RL training, the method demonstrates consistent improvements across mathematical reasoning, code generation, and narrative tasks.

AIBullisharXiv – CS AI · May 126/10
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AI-Care: A Conversational Agentic System for Task Coordination in Alzheimer's Disease Care

AI-Care is a conversational AI system designed to help individuals with Alzheimer's disease and related dementia manage daily tasks through natural language interaction, reducing cognitive barriers to using digital tools. The system prioritizes safety through caregiver-verified records and controlled clarification flows, with preliminary pilot testing showing positive user trust and task completion outcomes.

AINeutralarXiv – CS AI · May 126/10
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OracleTSC: Oracle-Informed Reward Hurdle and Uncertainty Regularization for Traffic Signal Control

Researchers introduce OracleTSC, an LLM-based traffic signal control system that combines reward hurdle mechanisms and uncertainty regularization to stabilize reinforcement learning training. The approach achieves 75% reduction in travel time while maintaining interpretability through natural language explanations, with strong cross-intersection generalization capabilities.

AINeutralarXiv – CS AI · May 126/10
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Results and Retrospective Analysis of the CODS 2025 AssetOpsBench Challenge

The CODS 2025 AssetOpsBench competition retrospective reveals critical gaps between public and private evaluation metrics in multi-agent orchestration systems. Hidden test sets dramatically altered performance rankings, particularly in execution tasks where correlations turned negative, while successful teams prioritized guardrails over novel architectures.

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