AI News Archive: August 13, 2026 — Part 14
Sourced from 500+ daily AI sources, scored by relevance.
- Lumina by ByteDance
Create images, videos, and apps with AI.
- Vosko AI
Your voice, in any language.
- Prime Intellect
Train, deploy, and improve your own AI models.
- OlmoEarth by Ai2
Earth data turned into decision-ready insightsno AI expertise needed.
- Fly.io
Real computers for AI agents.
- Visual Studio Code Agents
From prompt to finished task, autonomously.
- Polar: AI Browser
The AI browser that actually does work for you.
- isgen.ai
Instantly detect AI-written text with pinpoint accuracy.
- Connector | Hostinger
Deploy and manage websites directly from your IDE.
- AI Agents | Hostinger
Your AI business team, one subscription.
- Ecommerce | Hostinger
Sell everywhere from one platform.
- AI Builder | Hostinger Horizons
Build web apps and websites without writing any code.
- Grok 4.6
Grok 4.6
- LTX 2.5
LTX 2.5
- GrokBot
GrokBot
- Wan-Animate-2
Wan-Animate-2
- MAI-Image-2.6
MAI-Image-2.6
- TennisPredictions.ai
TennisPredictions.ai
- Heterogeneity-Aware Belief Synchronization for Semantic Communication in AI-Native 6G Networks
6G networks will not be serving as communication infrastructures only; rather, they are expected to evolve into intelligent systems, where thousands of autonomous artificial intelligence (AI) agents are interconnected. The agents are deployed across a wide range of platforms including low Earth orbi...
- InterSAGE: The Secure and Verifiable Interoperability Protocol for An Internet of Agents
The emerging Internet of Agents enables LLM-powered agents to discover peers, invoke tools, and delegate tasks across organizational boundaries. Existing protocols increasingly define how agents exchange messages, but not how an agent proves its identity, authorization, advertised capabilities, or a...
- OGR-MARL: Option-Guided Residual Multi-Agent Reinforcement Learning for Heterogeneous USV Cooperative Pursuit in Constrained Port Waterways
Heterogeneous USV cooperative pursuit in constrained port waterways requires evader interception under navigation, traffic, and role constraints. This paper proposes OGR-MARL, an option-guided residual multi-agent reinforcement learning framework that is decoupled from a specific MARL algorithm. OGR...
- Discovering Efficient and Explainable Communication Topologies for LLM-based Multi-Agent Systems via Causal Inference
The performance of large language model (LLM)-based multi-agent systems (MAS) largely depends on effective communication topologies. Existing topology generation methods, however, typically learn communication topologies through black-box optimization driven solely by task-level rewards. While effec...
- Before You Say It: Anticipating Verbal Behavior from Longitudinal Everyday Conversations with LLMs
Knowing someone deeply means not just understanding what they say or do but also how they will likely think, react, and engage across situations. Such predictions could eventually inform systems to anticipate when the individual is about to deviate from their goal, catch regrettable behaviors before...
- CogChat: Knowledge Graph-Augmented Conversational AI with Heterogeneous Graph Transformer for Cognitive Grounding in Design Generation
LLM-based chat systems have become valuable tools for design practice, enabling rapid ideation and flexible task support. Yet these systems process designer utterances as generic sequences, maintaining context through recency rather than through any model of how the speaker organizes knowledge. In d...
- How LLMs Respond to Escalating Delusions: Four Longitudinal Trajectories of Model Behavior
The widespread use of LLMs among psychiatric populations has raised concerns regarding their safety and potential iatrogenic impact in the context of AI psychosis. While growing literature conceptualizes AI psychosis and documents case studies, empirical evidence tracing AI-exacerbated psychotic pro...
- PolyPresentation: A Multimodal AI Platform for Slide-Aware Iterative Presentation Practice
Presentations are essential for students, researchers, and professionals to communicate ideas persuasively, yet delivering them effectively requires repeated practice that coordinates content, delivery, visual materials, and audience interaction. Existing AI-assisted rehearsal tools provide scalable...
- PatientAct: Theory-Grounded Mental Health Client Simulation
LLM-based simulated clients are increasingly used to train novice counselors, evaluate LLM therapists, and generate synthetic data. However, current simulators produce overly cooperative clients that disclose too readily, accept therapeutic reframes without resistance, and resolve core issues within...
- Does Fixing Break Security? An Empirical Study of Security Degradation in Iterative LLM-Driven Infrastructure-as-Code Repair
Background: Iterative feedback loops are the dominant paradigm for improving LLM-generated Infrastructure-as-Code (IaC): validators such as Checkov and terraform validate feed error signals back for successive repair attempts. Prior work reports cumulative-best metrics, which are non-decreasing by c...
- ATOBench: Tracing How Autonomous Penetration-Testing Agents Verify Vulnerabilities When Target Evidence Lies
Autonomous penetration-testing agents rely on target responses. These responses guide both subsequent actions and the final report. A deceptive response can therefore redirect both the attack trajectory and the agent's verification process. However, final reports reveal little about how an agent int...
- Adversarial Robustness in Smishing Detection: A Comparative Analysis of Adversarial Fragility in Classical vs. Transformer-Based Detection Systems
Smishing detection systems are commonly trained and evaluated on clean, monolingual text. In low-resource settings, however, attackers frequently circumvent these systems through character obfuscation, cross-lingual code-switching, and structural perturbation. This study evaluates adversarial robust...
- Labels Are Not Endpoints: Treatment Leakage and Construct Validity in MCP Agent Security Evaluation
Security evaluations of tool-using agents often equate stored labels with behavioral facts. We audit a preserved campaign by tracing 10,200 execution rows to 180 model-bound requests, 45 semantic requests, and 15 observable stimuli. Two schema treatments were delivered, but the planned external payl...
- PIPES: Securing Agent Perception with Provenance and Priors
Tool-using agents consume external data from sources with different levels of trust, yet tool responses rarely identify who produced each component or what it should convey. We show that this gap enables state-corruption attacks, in which attacker-controlled content makes environmental claims beyond...
- Correct Is Not Governed: Provenance Integrity in Agentic Workflows
Agentic workflows are commonly evaluated by whether they reach the correct outcome. That is insufficient in institutional settings, where a correct action may rely on the wrong authority, an unsupported completion claim, or work made stale by a later change. We define governed execution as work whos...
- QuoteBench: How Matched Scores Can Hide Command-Path Failures
LLM coding agents issue Bash commands through interfaces that may serialize, wrap, and reparse model output. Matched execution scores alone cannot distinguish command-generation errors from failures introduced after generation. QuoteBench measures this boundary with exact final-state validation on 5...
- CAPRI: Contract-Aware Proof Repair for Isabelle
We address the use of large language models (LLMs) to help discover Isabelle proofs. An Isabelle build establishes that the submitted theory is accepted, but not that an LLM changed only what the developer authorised. We present CAPRI, a contract-aware repair workflow in which Isabelle checks the pr...
- Refine After Generation: Toward Correct and Concise Patches in LLM-based Program Repair
Large language models (LLMs) have advanced automatic program repair (APR) to the point where agentic systems routinely resolve real-world, repository-level issues. Yet the generated patch has received little scrutiny beyond whether it passes tests. In this paper, we identify patch verbosity as a m...
- Can Formal Specifications Be Synthesized from Tests Alone?
Formal specifications offer strong guarantees, but remain costly to write manually. Recent LLM-based approaches automate this by inferring specifications from source code, yet their reliance on white-box access poses barriers to industrial adoption due to intellectual property risks and deployment c...
- How Powerful are LLMs in Generating Formal Program Specifications?
Formal verification provides strong guarantees of software correctness, but its adoption is limited by the high cost of writing precise formal specifications. While recent large language models (LLMs) have shown strong capabilities in theorem proving and verified code generation, their true ability ...
- Requirements-Augmented Generation for Trustworthy Acceptance Testing of LLM-Based Software
LLM-based software (LBS) integrates large language models as core components to deliver flexible, personalised responses. Unlike traditional software with deterministic outputs, LBSs exhibit context-dependent, stochastic behaviour that renders classical acceptance testing and test oracles insufficie...
- Memorization Diagnostics for Code LLMs Should be Scale-Aware
The extent to which large language models for code rely on memorization over genuine understanding remains highly debated. While current literature frequently reports widespread memorization, evaluating the underlying probing techniques across dense architectures reveals a severe breakdown in their ...
- Static analysis-guided agentic AI translation enables Rust as a full stack bioinformatics language
The field of bioinformatics struggles with legacy code - old code that is commonly used but may no longer have a maintainer, or may be written in an now-unfamiliar language (e.g. Perl, Fortran). This incurs maintenance cost (technical debt), but dynamically typed languages also negatively impacts th...
- Structure then Query: Enabling Precise Analytical Queries over Unstructured Documents
Unstructured documents constitute the majority of enterprise and web data. With the rapid development of large language models(LLMs), researchers have started to build data systems that analyze unstructured textual documents like operating on databases. However, because mainstream retrieval methods ...
- Generative Universal Multimodal Retrieval with Dual-role Identifiers
Generative information retrieval (GIR) has emerged as a compelling alternative to the conventional index-retrieve-then-rank retrieval pipeline by training a generator to produce the identifiers of relevant items directly. Despite its promise, a number of open challenges still remain. First, constrai...
- A Comprehensive Empirical Evaluation of Vector Database Systems for Approximate Nearest Neighbor Search: Performance, Quality, and Resource Trade-offs
Vector databases have emerged as critical infrastructure for modern artificial intelligence applications, particularly retrieval-augmented generation (RAG), semantic search, and recommendation systems. Despite their growing importance, there remains a significant gap in comprehensive, reproducible b...
- CRAFT: LLM-Based Iterative Refinement for Temporal Reasoning over Clinical Narratives
Understanding the temporal progression of symptoms in clinical narratives is critical for disease monitoring, safety surveillance, and causality assessment. Clinical narratives, however, rarely provide explicit temporal anchors. Current approaches to temporal information reasoning focus predominantl...
- Knowledge Synthesis Review Framework: Task-Level Benchmarking of LLM-Based Systems for Multi-Source Evidence Synthesis
Evidence in rapidly evolving fields is fragmented across academic studies, industry reports, policy documents, and media sources that differ in quality, structure, and purpose, making timely synthesis difficult. Large language models (LLMs) may accelerate this work, but their reliability across the ...
- STAR: Structured Tokenization and Target-Aware Interest Representation for PCVR Prediction
Post-click conversion rate (PCVR) prediction is a core ranking task in industrial recommender systems. Modern ranking models must jointly capture heterogeneous non-sequential features, multi-behavior user sequences, and target-item-aware user interests, while remaining robust to high-cardinality spa...
- DTAMLP: Denoise Time-aware MLP for Session-based Recommendation
This paper reports two empirical findings on session-based recommendation (SBR), unified in a single model, DTAMLP. First, existing time-aware and GNN-based models (e.g., TiSASRec, SR-GNN) treat every click-time interval as equally informative, even though very short dwell times often reflect accide...
- FSGR: Mitigating Token Frequency Bias for Fair SID-Based Generative Recommendation
Semantic ID (SID)-based generative recommendation has recently achieved remarkable success. However, existing methods suffer from a previously overlooked fairness issue, which we term \textbf{Token Frequency Bias}, where high-frequency SID tokens are systematically over-predicted while low-frequency...
- Query Translation vs. Cross-Lingual Embeddings for Sinhala-Tamil E-Government Information Retrieval
This paper presents a comparative evaluation of cross-lingual information retrieval (CLIR) methods for retrieving English government information using Sinhala and Tamil queries. Two CLIR paradigms are investigated: Query Translation (QT), employing Google Translate, NLLB, and mBART50, and Cross-Ling...