The detection of manipulated content, a prevalent form of fake news, has been widely studied in recent years. While existing solutions have been proven effective in fact-checking and analyzing fake news based on historical events, the reliance on either intrinsic knowledge obtained during training or manually curated context hinders them from tackling zero-day manipulated content, which can only be recognized with real-time contextual information. In this work, we propose Manicod, a tool designed for detecting zero-day manipulated content. Manicod first sources contextual information about the input claim from mainstream search engines, and subsequently vectorizes the context for the large language model (LLM) through retrieval-augmented generation (RAG). The LLM-based inference can produce a "truthful" or "manipulated" decision and offer a textual explanation for the decision. To validate the effectiveness of Manicod, we also propose a dataset comprising 4270 pieces of manipulated fake news derived from 2500 recent real-world news headlines. Manicod achieves an overall F1 score of 0.856 on this dataset and outperforms existing methods by up to 1.9x in F1 score on their benchmarks on fact-checking and claim verification.

Efficient mission planning for cooperative systems involving Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) requires addressing energy constraints, scalability, and coordination challenges between agents. UAVs excel in rapidly covering large areas but are constrained by limited battery life, while UGVs, with their extended operational range and capability to serve as mobile recharging stations, are hindered by slower speeds. This heterogeneity makes coordination between UAVs and UGVs critical for achieving optimal mission outcomes. In this work, we propose a scalable deep reinforcement learning (DRL) framework to address the energy-constrained cooperative routing problem for multi-agent UAV-UGV teams, aiming to visit a set of task points in minimal time with UAVs relying on UGVs for recharging during the mission. The framework incorporates sortie-wise agent switching to efficiently manage multiple agents, by allocating task points and coordinating actions. Using an encoder-decoder transformer architecture, it optimizes routes and recharging rendezvous for the UAV-UGV team in the task scenario. Extensive computational experiments demonstrate the framework's superior performance over heuristic methods and a DRL baseline, delivering significant improvements in solution quality and runtime efficiency across diverse scenarios. Generalization studies validate its robustness, while dynamic scenario highlights its adaptability to real-time changes with a case study. This work advances UAV-UGV cooperative routing by providing a scalable, efficient, and robust solution for multi-agent mission planning.

Machine learning detects patterns, block chain guarantees trust and immutability, and modern causal inference identifies directional linkages, yet none alone exposes the full energetic anatomy of complex systems; the Hamiltonian Higher Order Elasticity Dynamics(2HOED) framework bridges these gaps. Grounded in classical mechanics but extended to Economics order elasticity terms, 2HOED represents economic, social, and physical systems as energy-based Hamiltonians whose position, velocity, acceleration, and jerk of elasticity jointly determine systemic power, Inertia, policy sensitivity, and marginal responses. Because the formalism is scaling free and coordinate agnostic, it transfers seamlessly from financial markets to climate science, from supply chain logistics to epidemiology, thus any discipline in which adaptation and shocks coexist. By embedding standard econometric variables inside a Hamiltonian, 2HOED enriches conventional economic analysis with rigorous diagnostics of resilience, tipping points, and feedback loops, revealing failure modes invisible to linear models. Wavelet spectra, phase space attractors, and topological persistence diagrams derived from 2HOED expose multistage policy leverage that machine learning detects only empirically and block chain secures only after the fact. For economists, physicians and other scientists, the method opens a new causal energetic channel linking biological or mechanical elasticity to macro level outcomes. Portable, interpretable, and computationally light, 2HOED turns data streams into dynamical energy maps, empowering decision makers to anticipate crises, design adaptive policies, and engineer robust systems delivering the predictive punch of AI with the explanatory clarity of physics.

As transformer-based large language models (LLMs) increasingly permeate society, they have revolutionized domains such as software engineering, creative writing, and digital arts. However, their adoption in cybersecurity remains limited due to challenges like scarcity of specialized training data and complexity of representing cybersecurity-specific knowledge. To address these gaps, we present Foundation-Sec-8B, a cybersecurity-focused LLM built on the Llama 3.1 architecture and enhanced through continued pretraining on a carefully curated cyber-security corpus. We evaluate Foundation-Sec-8B across both established and new cybersecurity benchmarks, showing that it matches Llama 3.1-70B and GPT -4o-mini in certain cybersecurity-specific tasks. By releasing our model to the public, we aim to accelerate progress and adoption of AI-driven tools in both public and private cybersecurity contexts.

The perceived quality of the explanations accompanying e-government services is key to gaining trust in these institutions, consequently amplifying further usage of these services. Recent advances in generative AI, and concretely in Large Language Models (LLMs) allow the automation of such content articulations, eliciting explanations' interpretability and fidelity, and more generally, adapting content to various audiences. However, selecting the right LLM type for this has become a non-trivial task for e-government service providers. In this work, we adapted a previously developed scale to assist with this selection, providing a systematic approach for the comparative analysis of the perceived quality of explanations generated by various LLMs. We further demonstrated its applicability through the tax-return process, using it as an exemplar use case that could benefit from employing an LLM to generate explanations about tax refund decisions. This was attained through a user study with 128 survey respondents who were asked to rate different versions of LLM-generated explanations about tax refund decisions, providing a methodological basis for selecting the most appropriate LLM. Recognizing the practical challenges of conducting such a survey, we also began exploring the automation of this process by attempting to replicate human feedback using a selection of cutting-edge predictive techniques.

Prompt injection attacks have emerged as a serious threat in curr ent large language models (LLMs), where adversaries may alter model behav ior by injecting malicious instructions into the prompt [2]. Existing approach es - such as input sanitization, fixed prompt templates, and heuristic-based filtering - often mix trusted system instructions with untrusted us er inputs, leading to brittle defenses that are easily circumvented. For examp le, an adversary could include a cleverly worded request that causes the model to "forget its internal guidelines," thereby triggering unintended beh avior. Our PICO (Prompt Isolation and Cybersecurity Oversight) propos al circumvents these limitations, first of all, by architecturally segregat ing the system prompt and user input into distinct channels. In doing so, we ensure that the trusted instructions remain intact while only the untruste d user input is subject to adaptation. Furthermore, we augment the mode l with a dedicated Security Expert Agent and a Cybersecurity Knowledge G raph [4] to provide supplemental, domain-specific signals that reinforce the invariant. In what follows, we first present a mathematical formalization of th e PICO security strategy, and then we describe its concrete realiza tion, both via PICO-based retraining of transformer models from the bottom up, and via a more efficient if less ideal fine-tuning strategy. We flesh out theapproach by considering how it would be expected to handle two specific example situations, including a basic prompt injection and then a subtler Policy Puppetry attack.

Graph neural networks have been widely utilized to solve graph-related tasks because of their strong learning power in utilizing the local information of neighbors. However, recent studies on graph adversarial attacks have proven that current graph neural networks are not robust against malicious attacks. Yet much of the existing work has focused on the optimization objective based on attack performance to obtain (near) optimal perturbations, but paid less attention to the strength quantification of each perturbation such as the injection of a particular node/link, which makes the choice of perturbations a black-box model that lacks interpretability. In this work, we propose the concept of noise to quantify the attack strength of each adversarial link. Furthermore, we propose three attack strategies based on the defined noise and classification margins in terms of single and multiple steps optimization. Extensive experiments conducted on benchmark datasets against three representative graph neural networks demonstrate the effectiveness of the proposed attack strategies. Particularly, we also investigate the preferred patterns of effective adversarial perturbations by analyzing the corresponding properties of the selected perturbation nodes.

Large Language Models (LLMs) are powerful but often require extensive fine-tuning and large datasets for specialized domains like law. General-purpose pre-training may not capture legal nuances, and acquiring sufficient legal data is challenging. We introduce SynLexLM, a novel approach to efficiently pre-train a legal LLM. Our method employs curriculum learning, progressing from simple to complex legal texts and queries, combined with synthetic data augmentation using models like Gemini Pro to address data scarcity. We aim to achieve improved performance on legal benchmarks (BigLaw-Bench, EUR-Lex-Sum) compared to traditional models and fine-tuned versions. Preliminary work involves generating synthetic QA pairs reflecting legal reasoning. This work aims to enhance legal document analysis and research tools, potentially democratizing access to advanced legal AI.

University of California Santa Barbara, Santa Barbara, USA Abstract This paper demonstrates a probabilistic bit physics - inspired solver with 440 spins configured in a Chimera graph and occupying an area of 0. 44 mm . Area efficiency was maximized through a current - mode implementation of neuron update circuit, standard cell design for analog blocks pitch - matched to digital block, and a shared power supply for digital and analog components. Process variation related m ismatches introduced by this approach were effectively mitigated using a hardware - aware contrastive divergence algorithm during training. We validate the chip's ability to perform probabilistic computing tasks, such as modeling logic gates and full adde rs and optimization tasks, such as Max - Cut. demonstrate its potential for AI and machine learning. Keywords (optional): Ising, p - bit, hardware - aware learning Introduction Probabilistic bits (p - bits) have emerged as a hardware friendly approach for solving optimization problems, machine learning, quantum inspired computing and AI [1].

On 28 March, Briony Swire-Thompson began seeing reports online that the National Institutes of Health (NIH) might cancel grants for research on misinformation. At first, she didn't think she would be affected. Swire-Thompson, a psychologist at Northeastern University, studies misinformation--but not the political lies that get most of the attention. She's interested in false information about cancer, and why people fall for it. "There's a lot of people online trying to sell their snake oil," she says.