I Wasn't Sure I Wanted Anthropic to Pay Me for My Books--I Do Now Anthropic agreed to a $1.5 billion settlement for authors whose books were used to train its AI model. As an author who fits that description, I've come around to the idea. A billion dollars isn't what it used to be--but it still focuses the mind. At least it did for me when I heard that the AI company Anthropic agreed to an at least $1.5 billion settlement This came after a judge issued a summary judgement that it had pirated the books it used. The proposed agreement--which is still under scrutiny by the wary judge--would reportedly grant authors a minimum $3,000 per book.

Powerful moment Charlie Kirk's widow Erika holds hands with Usha Vance on his final journey on Air Force Two REVEALED: The truth about the'vanishing plane' five miles from Charlie Kirk's assassination... as private jet owner is unmasked Charlie Kirk's incredible welcome to young gay man who wants to join his conservative movement And the armed militia mystery. FBI terror hunter blows the lid on search for Charlie Kirk's assassin... and the vital clue cops are desperate for Kristin Chenoweth fans surprised over her grieving comment on Charlie Kirk's final video about abortion Charlotte Tilbury reveals the secrets behind the Dallas Cowboys cheerleaders' flawless look Go inside the killing that has rocked America - on Daily Mail's podcast The Assassination of Charlie Kirk Charlie Kirk's gesture to my son tells you everything about the man: JILLIAN MICHAELS on her unlikely camaraderie with the conservative giant Joe Rogan is speechless as he learns of Charlie Kirk's assassination on his podcast McDonald's fans disgusted by what customer thinks is'parasite' found in Filet-O-Fish READ MORE: Scientists finally discover what is inside mysterious'halo barrels' The failure of a vital ocean upwelling has sparked concerns of catastrophic effects for life, according to scientists. Every year, between December and April, northerly winds create a rising current in the deep waters of the Gulf of Panama. This upwelling brings cold, nutrient-rich waters to the surface, protecting vulnerable coral reefs and triggering an explosion of ocean life. However, researchers now say the Panama Pacific upwelling has failed for the first time in over 40 years of records - and it could be a permanent change.

Powerful moment Charlie Kirk's widow Erika holds hands with Usha Vance on his final journey on Air Force Two REVEALED: The truth about the'vanishing plane' five miles from Charlie Kirk's assassination... as private jet owner is unmasked Charlie Kirk's incredible welcome to young gay man who wants to join his conservative movement And the armed militia mystery. FBI terror hunter blows the lid on search for Charlie Kirk's assassin... and the vital clue cops are desperate for Kristin Chenoweth fans surprised over her grieving comment on Charlie Kirk's final video about abortion Charlotte Tilbury reveals the secrets behind the Dallas Cowboys cheerleaders' flawless look Go inside the killing that has rocked America - on Daily Mail's podcast The Assassination of Charlie Kirk Charlie Kirk's gesture to my son tells you everything about the man: JILLIAN MICHAELS on her unlikely camaraderie with the conservative giant Joe Rogan is speechless as he learns of Charlie Kirk's assassination on his podcast McDonald's fans disgusted by what customer thinks is'parasite' found in Filet-O-Fish Walkers and hikers have an exciting opportunity to find meteorite fragments that scattered over Scotland this summer, scientists say. The bright meteor was witnessed by some Scots as it streaked across the sky in the early hours of Thursday July 3. It is believed to have exploded over northern Scotland, with the'fall zone' straddling Loch Treig in Lochaber, Highland. The aerial event was captured on some cameras and shared on social media, showing a big yellow spark soaring through the dark sky.

Retrieval-Augmented Generation (RAG) systems in the legal domain face a critical challenge: standard, flat-text retrieval is blind to the hierarchical, diachronic, and causal structure of law, leading to anachronistic and unreliable answers. This paper introduces the Structure-Aware Temporal Graph RAG (SAT-Graph RAG), an ontology-driven framework designed to overcome these limitations by explicitly modeling the formal structure and diachronic nature of legal norms. We ground our knowledge graph in a formal, LRMoo-inspired model that distinguishes abstract legal Works from their versioned Expressions. We model temporal states as efficient aggregations that reuse the versioned expressions (CTVs) of unchanged components, and we reify legislative events as first-class Action nodes to make causality explicit and queryable. This structured backbone enables a unified, planner-guided query strategy that applies explicit policies to deterministically resolve complex requests for (i) point-in-time retrieval, (ii) hierarchical impact analysis, and (iii) auditable provenance reconstruction. Through a case study on the Brazilian Constitution, we demonstrate how this approach provides a verifiable, temporally-correct substrate for LLMs, enabling higher-order analytical capabilities while drastically reducing the risk of factual errors. The result is a practical framework for building more trustworthy and explainable legal AI systems.

Large language models (LLMs)-empowered autonomous agents are transforming both digital and physical environments by enabling adaptive, multi-agent collaboration. While these agents offer significant opportunities across domains such as finance, healthcare, and smart manufacturing, their unpredictable behaviors and heterogeneous capabilities pose substantial governance and accountability challenges. In this paper, we propose a blockchain-enabled layered architecture for regulatory agent collaboration, comprising an agent layer, a blockchain data layer, and a regulatory application layer. Within this framework, we design three key modules: (i) an agent behavior tracing and arbitration module for automated accountability, (ii) a dynamic reputation evaluation module for trust assessment in collaborative scenarios, and (iii) a malicious behavior forecasting module for early detection of adversarial activities. Our approach establishes a systematic foundation for trustworthy, resilient, and scalable regulatory mechanisms in large-scale agent ecosystems. Finally, we discuss the future research directions for blockchain-enabled regulatory frameworks in multi-agent systems.

The proliferation of Large Language Models (LLMs) in real-world applications poses unprecedented risks of generating harmful, biased, or misleading information to vulnerable populations including LGBTQ+ individuals, single parents, and marginalized communities. While existing safety approaches rely on post-hoc filtering or generic alignment techniques, they fail to proactively prevent harmful outputs at the generation source. This paper introduces PromptGuard, a novel modular prompting framework with our breakthrough contribution: VulnGuard Prompt, a hybrid technique that prevents harmful information generation using real-world data-driven contrastive learning. VulnGuard integrates few-shot examples from curated GitHub repositories, ethical chain-of-thought reasoning, and adaptive role-prompting to create population-specific protective barriers. Our framework employs theoretical multi-objective optimization with formal proofs demonstrating 25-30% analytical harm reduction through entropy bounds and Pareto optimality. PromptGuard orchestrates six core modules: Input Classification, VulnGuard Prompting, Ethical Principles Integration, External Tool Interaction, Output Validation, and User-System Interaction, creating an intelligent expert system for real-time harm prevention. We provide comprehensive mathematical formalization including convergence proofs, vulnerability analysis using information theory, and theoretical validation framework using GitHub-sourced datasets, establishing mathematical foundations for systematic empirical research.

Although popularized AI fairness metrics, e.g., demographic parity, have uncovered bias in AI-assisted decision-making outcomes, they do not consider how much effort one has spent to get to where one is today in the input feature space. However, the notion of effort is important in how Philosophy and humans understand fairness. We propose a philosophy-informed approach to conceptualize and evaluate Effort-aware Fairness (EaF), grounded in the concept of Force, which represents the temporal trajectory of predictive features coupled with inertia. Besides theoretical formulation, our empirical contributions include: (1) a pre-registered human subjects experiment, which shows that for both stages of the (individual) fairness evaluation process, people consider the temporal trajectory of a predictive feature more than its aggregate value; (2) pipelines to compute Effort-aware Individual/Group Fairness in the criminal justice and personal finance contexts. Our work may enable AI model auditors to uncover and potentially correct unfair decisions against individuals who have spent significant efforts to improve but are still stuck with systemic disadvantages outside their control.

LLM-integrated app systems extend the utility of Large Language Models (LLMs) with third-party apps that are invoked by a system LLM using interleaved planning and execution phases to answer user queries. These systems introduce new attack vectors where malicious apps can cause integrity violation of planning or execution, availability breakdown, or privacy compromise during execution. In this work, we identify new attacks impacting the integrity of planning, as well as the integrity and availability of execution in LLM-integrated apps, and demonstrate them against IsolateGPT, a recent solution designed to mitigate attacks from malicious apps. We propose Abstract-Concrete-Execute (ACE), a new secure architecture for LLM-integrated app systems that provides security guarantees for system planning and execution. Specifically, ACE decouples planning into two phases by first creating an abstract execution plan using only trusted information, and then mapping the abstract plan to a concrete plan using installed system apps. We verify that the plans generated by our system satisfy user-specified secure information flow constraints via static analysis on the structured plan output. During execution, ACE enforces data and capability barriers between apps, and ensures that the execution is conducted according to the trusted abstract plan. We show experimentally that ACE is secure against attacks from the InjecAgent and Agent Security Bench benchmarks for indirect prompt injection, and our newly introduced attacks. We also evaluate the utility of ACE in realistic environments, using the Tool Usage suite from the LangChain benchmark. Our architecture represents a significant advancement towards hardening LLM-based systems using system security principles.

Mixture-of-Experts (MoE) in Large Language Models (LLMs) routes each token through a subset of specialized Feed-Forward Networks (FFN), known as experts. We present SteerMoE, a framework for steering MoE models by detecting and controlling behavior-linked experts. Our detection method identifies experts with distinct activation patterns across paired inputs exhibiting contrasting behaviors. By selectively (de)activating such experts during inference, we control behaviors like faithfulness and safety without retraining or modifying weights. Across 11 benchmarks and 6 LLMs, our steering raises safety by up to +20% and faithfulness by +27%. In adversarial attack mode, it drops safety by -41% alone, and -100% when combined with existing jailbreak methods, bypassing all safety guardrails and exposing a new dimension of alignment faking hidden within experts.

Semantic Textual Relatedness (STR) captures nuanced relationships between texts that extend beyond superficial lexical similarity. In this study, we investigate STR in the context of job title matching - a key challenge in resume recommendation systems, where overlapping terms are often limited or misleading. We introduce a self-supervised hybrid architecture that combines dense sentence embeddings with domain-specific Knowledge Graphs (KGs) to improve both semantic alignment and explainability. Unlike previous work that evaluated models on aggregate performance, our approach emphasizes data stratification by partitioning the STR score continuum into distinct regions: low, medium, and high semantic relatedness. This stratified evaluation enables a fine-grained analysis of model performance across semantically meaningful subspaces. We evaluate several embedding models, both with and without KG integration via graph neural networks. The results show that fine-tuned SBERT models augmented with KGs produce consistent improvements in the high-STR region, where the RMSE is reduced by 25% over strong baselines. Our findings highlight not only the benefits of combining KGs with text embeddings, but also the importance of regional performance analysis in understanding model behavior. This granular approach reveals strengths and weaknesses hidden by global metrics, and supports more targeted model selection for use in Human Resources (HR) systems and applications where fairness, explainability, and contextual matching are essential.