AI News Archive: July 13, 2026 — Part 18
Sourced from 500+ daily AI sources, scored by relevance.
- Orbis AI
Local-first AI that turns prompts into real designs
- PromptVault Pro
The offline AI Prompt Operating System that runs anywhere.
- CraftVis AI Image Editor & Generator
Edit images by just describing the change.
- AiVideoFree
AiVideoFree
- MusicAura AI
MusicAura AI
- Sidekick Pro
Sidekick Pro
- Timi AI
Timi AI
- Can LLMs Perform Deep Technical Comprehension of Computer Architecture Papers?
Can large language models perform deep technical comprehension of computer architecture papers -- not summarization, but structured critique that names the core mechanism, surfaces buried assumptions, and connects a contribution beyond its own scope? We study Gauntlet, an open-source pipeline that a...
- Multi-Agent Reinforcement Learning for C-V2X RAT Selection
Vehicles are increasingly equipped with advanced V2X communication capabilities. While early V2X apps utilized services such as Cooperative Awareness Messages, recent developments have allowed more advanced applications including cooperative driving, shared perception, and sensor-sharing services. T...
- Multi-Agent LLMs Fail to Explore Each Other
Exploration is essential for reliable autonomy in multi-agent systems, yet it remains unclear whether large language model (LLM) agents can explore effectively when interacting with one another. We show that modern LLM agents fail to do so, often exhibiting myopic and polarized interaction patterns ...
- Mako: A Self-Evolving Agentic Operating System (SE-AOS) for Autonomous Web Exploitation
We introduce the Self-Evolving Agentic Operating System (SE-AOS): a new class of AI agent that treats exploit capability as a mutable, versioned kernel it extends at runtime, observing its own failures, synthesising new capabilities, proving them against a live target, and hot-loading them back into...
- Supporting Reflection in LLM-based Exploratory Search
Large Language Models (LLMs) can make exploratory search more efficient but may undermine the reflection and iterative sensemaking needed in unfamiliar domains. Existing LLM tools often prioritize rapid answers over supporting users in tracking how their understanding evolves and how well their stra...
- ManiScope: LLM-Assisted Visual Analytics of Cryptocurrency Manipulation Risk
Cryptocurrency markets are vulnerable to trade-based manipulation, such as wash trading, which can distort price signals and mislead investors. Prior research has mainly focused on detecting manipulation using fixed rules or labeled examples, offering limited flexibility and interpretability for ass...
- Same Stories, Different Journeys: From Social Comparison to Sensemaking in AI-Mediated Peer Career Exploration
Young job seekers frequently turn to social media to compare themselves with peers and make sense of career possibilities. However, passive feed browsing creates a paradox: the authentic peer content that provides emotional grounding also triggers potentially detrimental upward social comparison and...
- EquiFusion: Kinematics-Agnostic Human Motion Prediction via Equivariant Latent Diffusion
Existing Stochastic 3D Human Motion Prediction models are fundamentally constrained by hard-coding the skeleton kinematics, severely limiting generalization, preventing cross-dataset training, and requiring complex data retargeting. We introduce EquiFusion, the first kinematics-agnostic model to sol...
- Graph-Based Structural Evaluation of LLM-Translated Adversary Emulation Procedures
Adversary emulation plans describe multi-step attacker procedures using MITRE ATT&CK techniques, privilege requirements, and observable telemetry. Translating them across operating systems supports cross-platform defender evaluation, and large language models (LLMs) can automate this task. However, ...
- LLM-Guided Program Evolution for Targeted Black-Box Attacks on Perceptual Hash Algorithms
Perceptual hash algorithms (PHAs) are widely deployed to detect image forgery under benign transformations, yet their robustness against adversarially chosen perturbations remains poorly understood and rarely comes with provable guarantees. We propose a novel evolutionary framework based on GigaEvo ...
- AMT-X: Phase-Structured Multi-Turn Red-Teaming with Checklist-Gated Evaluation
Safety evaluation of large language models (LLMs) relies largely on single-turn attack datasets and single-judge scoring, underestimating risk from adaptive multi-turn adversaries and reporting a single success rate that does not separate partially actionable outputs from those carrying complete ope...
- When cheap gradients fail: the measurement cost of attacking quantum classifiers
Adversarial perturbations threaten machine learning classifiers, including variational quantum classifiers. We show that finite quantum measurement statistics (shot noise) act as a built-in defense against gradient-based test-time attacks whose cost scales unfavorably for the attacker. Because every...
- Rethinking MCP Security: A Large-Scale Study of Runtime MCP Servers and Security Scanner Reliability
The Model Context Protocol (MCP) has rapidly established itself as a standard interface for enabling LLM-based agents to interact with external tools and services. As MCP servers are increasingly entrusted with security-sensitive operations, understanding their real-world risks has become critical. ...