AI News Archive: July 20, 2026 — Part 15
Sourced from 500+ daily AI sources, scored by relevance.
- Wholesale Management System
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- DevNotes
Local-first dev productivity app with full source code
- Pure Path
Best app to fight porn addiction with weekly doctors
- ReBot
An AI companion with voice, games, and shared ReBot Links
- CCPayment
Seamless crypto payment gateway for global business
- Zenless Zone Zero Global Top-Up
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- Practice With Robin
Coaches, find your next practice partner.
- SignalEye™: Software-Defined RF Sensing
Software-Defined RF Sensing for Modern Defense
- Busuiness Automator
AI automations to help businesses automate repetitive work.
- FaceUnlock
Hands-free Mac unlock using your face - private, local, free
- Managed Hermes Agent | Hostinger
Hermes Agent with zero setup, maintenance, or infrastructure management.
- AI Agents | Hostinger
Your AI business team, one subscription.
- Kimi K3
Kimi K3
- Referent
Referent
- Borade AI
Borade AI
- Autonomous Discovery of Wireless Communications Algorithms
Large language model (LLM)-driven evolutionary search is an emerging algorithm-discovery paradigm that has already produced novel results in several scientific fields. Yet its application to wireless communications remains largely unexplored. To bridge this gap, we introduce The AI Telco Engineer (A...
- ETAS: An Effect-Typed Language for Agent Systems
ETAS is a programming language for agent systems that treats model-backed agents, tool calls, prompts, typed memory, human approvals, policies, and execution traces as semantic program elements rather than library conventions. It separates deterministic computation from agentic nondeterminism and ex...
- Lifelong Multi-Subsystem Pickup and Delivery with Buffer-Limited Handover Stations
Coordinating payload transfers between subsystems is a critical challenge in lifelong Multi-Agent Pickup and Delivery (MAPD). We study systems where agents are confined to separate regions and must exchange payloads through shared handover stations. These stations, equipped with single docks and fin...
- SR-Agent: An Experience-Driven Agentic Framework for Post-Ranking Strategies Refinement in E-Commerce Recommendation
User experience is a first-class objective in industrial e-commerce recommender systems (RS). Post-ranking strategies, which govern diversity, similarity, and exposure over a ranked list, are widely deployed in industrial RS for their simplicity and low serving cost. However, as the online recommend...
- I wanted it to feel more personal: Customization of social AI as AI individualism in practice
Despite the growing availability of customizable social artificial intelligence (AI), such as ChatGPT, Grok, and Character.ai, we know little about how users actively shape social AI to reflect their personal preferences. This study examines why and how users (N = 169) customize social AI through th...
- Persona-as-Configuration: Generative Stakeholder Reporting for Agricultural Floods
Cyber-physical systems built on deterministic edge inference, such as on-vehicle flood detection for agricultural fields, produce structured decision logs that must be interpreted differently by heterogeneous stakeholders. Pairing such systems with large language models (LLMs) to generate stakeholde...
- Informal Learning Emerges in Everyday Human-LLM Interaction
As LLMs become increasingly capable of completing tasks for users, a central concern is that everyday AI use may become primarily cognitive offloading, eroding the opportunities through which people develop their own capabilities. We analyse large-scale human--LLM conversations to ask whether inform...
- Human-in-the-Loop User Feedback Affects Perceived Accuracy and Trust, but Task Subjectivity Matters
While ML can produce complex models beyond those that a human could produce manually, incorporating human input can often improve performance beyond purely data-driven models. While this feedback could come from system designers or domain experts, in many cases, the end users who regularly use the s...
- Sidekick: Designing Communication for Effective Multitasking with Computer Use Agents
Computer Use Agents (CUAs) can autonomously execute complex, multi-step tasks within GUIs, enhancing efficiency through parallel multitasking. However, our formative studies with CUA experts and GenAI users indicated that current feedback is primarily text-based, requiring sustained attention to mon...
- HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation
The Matthew effect is a notorious issue in Recommender Systems (RSs), \emph{i.e.}, the rich get richer and the poor get poorer, wherein popular items are overexposed while less popular ones are regularly ignored. Most methods examine Matthew effect in static or nearly-static recommendation scenarios...
- RRAM-DP: Device-Calibrated Differential Privacy for In-Memory Edge Learning
Edge Artificial Intelligence of Things (AIoT) systems often collect sensitive data in situ, raising serious privacy concerns. Resistive-switching random-access memory (RRAM) is an attractive substrate for efficient AIoT thanks to its multi-bit storage and compute-in-memory (CiM) capabilities, while ...
- CutBackdoor: A Circuit Cut Triggered Backdoor Attack on Variational Quantum Algorithms
Variational Quantum Algorithms (VQAs) are a leading paradigm for near-term quantum computing, combining parameterized quantum circuits with classical optimization across quantum chemistry, combinatorial optimization, and quantum machine learning. Since real-world VQA deployments routinely require ci...
- GARAGE: Characterizing the Automation Boundary in LLM-based Attack Graph Generation
While modern vehicle security depends on effective Cyber Threat Intelligence (CTI) synthesis, current automated tools struggle with unstructured data and automotive-specific architectural nuances. To bridge this gap, we introduce GARAGE, a RAG-powered framework that converts fragmented CTI into an a...
- Residual Observability and Attack Detectability in Encrypted OPC UA Traffic
OPC Unified Architecture (OPC UA) encryption conceals application-layer semantics and restricts intrusion detection to residual communication structure. Although machine learning-based intrusion detection systems (IDSs) can detect attacks in encrypted OPC UA traffic, the relationship between residua...
- Insecure Coding Preferences in Long-Term Memory: Security Risks for LLM-based Code Generation
LLM-based systems increasingly incorporate long-term memory to improve cross-session continuity. However, once insecure coding preferences are stored, they may silently influence security-critical decisions in subsequent generations. In this study, we conduct the first systematic empirical study on ...
- Protecting Floating-Point Computation for DNN Binaries with MBA Obfuscation
Deep neural networks (DNNs) have become a foundational component of modern computing systems with a wide range of applications, such as computer vision, edge intelligence, etc. For the sake of low latency and data privacy, DNN models are increasingly compiled into executables and deployed on local d...
- (A)iSpy: Parasitic Trojans for Machine Learning Infrastructure
Modern machine learning (ML) pipelines depend heavily on third party libraries for graph compilation and hardware acceleration. While current practices audit data and model artifacts or rely on file integrity checks, the execution environment remains implicitly trusted. This blind spot enables activ...
- ShadowPickle: Evading Machine Learning Model Scanners via Stealthy Pickle Deserialization Attacks
Model hosting hubs (e.g., Hugging Face) are vulnerable to supply chain attacks that enable remote code execution on trusted user environments. Attackers often distribute malicious Pre-trained ML models (PTMs) via model hubs. In this paper, we present novel attacks against PTMs and model hubs called ...
- MeshScope-Region: Distribution, Road-Network Accessibility, and Nine-Year Evolution of ICU and HCU Capacity Across Japan's 330 Secondary Medical Areas
Background: In Japan, health planning is organized around secondary medical areas (SMAs; niji-iryo-ken; 330 areas in the 2025 classification), yet nationwide analyses of intensive care unit (ICU) capacity have been conducted mainly at the prefecture level, and a recent SMA-level study addressed only the presence or absence of ICUs. The full supply structure of intensive and intermediate critical care - ICU and high care unit (HCU) beds - has not been characterized at the SMA level with respect to its composition, road-network accessibility, and evolution over time. Methods: We developed MeshScope-Region, an analytical platform built on the Hospital Bed Function Reports (byosho-kino-hokoku) for fiscal years 2016-2024, in which ICU and HCU beds were identified from notified reimbursement categories and aggregated to SMAs. Three analytical layers were integrated: (1) cross-sectional distribution of ICU/HCU beds; (2) nationwide road-network accessibility computed with the Open Source Routing Machine (OSRM) from 176,962 populated 1-km census grid cells to all facilities reporting ICU or HCU beds; and (3) a nine-year longitudinal analysis of supply-structure types, classified by k-means (k = 6) in an 8-dimensional PCA space anchored to fiscal year 2024, with earlier years projected into the same space. Results: In fiscal year 2024, 20,631 ICU/HCU beds were reported nationally (7,114 ICU-type; 13,517 HCU-type) at 1,044 facilities. Zone-level totals among SMAs with any beds ranged 229-fold (3-688 beds); the 90th/10th percentile ratio of per-capita density was 3.6. In total, 90.1% of the population resided within 30 minutes' drive of a facility with ICU beds and 97.8% within 60 minutes; only 0.8% resided beyond 90 minutes. Although 140 of the 330 SMAs had no ICU facility within their own boundaries, 84.7% of their residents could reach an ICU facility in an adjacent area within 60 minutes' drive. Longitudinally, supply structures were highly persistent: 63.0% of SMAs (208/330) retained the same structural type across all nine years, adjacent-year rank correlations of a supply-vulnerability index were 0.887-0.924 (2016 vs. 2024: rho = 0.711), and the number of SMAs with zero ICU beds remained frozen at 133-141. The Gini coefficient of bed distribution declined from 0.384 to 0.262 - although computed on ICU-type beds alone it remained 0.365 in fiscal year 2024 - and capacity growth (total +27.9%) was driven predominantly by HCU beds (+41.6%) while ICU beds grew only +8.0%. Conclusions: Japan's critical care supply structure is regionally rigid, with a stable set of approximately 140 SMAs lacking ICU beds for nearly a decade, yet road-network accessibility substantially mitigates the consequences of zone-level absence. Recent capacity growth - and much of the apparent equalization - has occurred predominantly in intermediate care. MeshScope-Region provides a standing, reproducible evidence base at the geographic unit of Japan's medical planning cycles.
- Detection, Attribution, Narration: An End-to-End Pipeline for Explainable Money Mule Identification
Money mule accounts are critical facilitators of financial fraud, yet detecting them at scale remains challenging due to the heterogeneous nature of transactional and behavioural data. We present an end-to-end pipeline for customer-level mule detection comprising three stages: (1) a LightGBM classif...
- Testing Retrieval-Augmented Generation Systems with Chunk Coverage
Retrieval-Augmented Generation (RAG)-based systems\footnote{For brevity, RAG-based systems are referred to as RAG systems throughout this paper.} are increasingly deployed in high-stakes settings where correct behaviour depends not only on the language model but also on the retrieval component that ...
- DepRepair: LLM-Based Source-Code Repair for Dependency Breaking Changes
Modern software projects depend on numerous third-party libraries, whose updates often introduce breaking changes. Adapting consumer code to such changes remains labor-intensive and error-prone. Existing work either characterizes dependency breaking changes without producing a verified consumer-side...
- KernelDiag: Agent-Based Root Cause Diagnosis for Kernel Crashes
The Linux kernel is one of the most complex software systems, where automated fuzzing continuously exposes thousands of crashes, yet root-cause diagnosis remains a manual and time-consuming bottleneck. Existing LLM-based root cause analysis (RCA) techniques, effective for distributed systems, do not...
- Verify, Repair, Repeat, or Stop? Robust Stopping for Noisy Verify-Repair Loops in LLM Agents
Verify-repair loops are a standard means for large language model (LLM) agents to correct faulty plans in code generation, mathematical reasoning, and tool use. When both the verifier and the repairer are noisy, repair can damage already-correct plans, and reported acceptance keeps rising while true...
- FailureAtlas: A Taxonomy of Failure Modes in Multi-Provider LLM Serving Infrastructure
Multi-provider LLM gateways reverse proxies that route, load-balance, and rate-limit requests across foundation-model APIs have become critical production infrastructure. Yet the failure modes specific to this architectural layer remain undocumented, scattered across issue trackers and post-mortems ...
- Test Coverage Analysis of Agentic Pull Requests
AI coding agents increasingly submit complete pull requests (PRs) with minimal human intervention, shifting software development from AI-assisted to autonomous workflows. As these agents become more prevalent, ensuring the code they generate is adequately tested, by existing tests or by tests the ag...
- How Agent Skills Fail under Long Contexts: A White-Box Study in Code Auditing
Agent Skills package procedural instructions and checks for use by general-purpose agents, but loading a skill does not guarantee that every requirement remains active throughout a long tool-using trajectory. We study this problem in a production-derived, white-box code-audit workflow. Holding the t...
- (Over)Reliance on Test Agents in AI-Assisted Software Testing
AI-based test agents promise to accelerate software testing by shortening feedback loops in continuous development and improving scalability and maintainability. To realize these benefits, engineers must still be able to assess if agent outputs are useful, valid, and reliable, rather than treating t...
- CommitLLM: A Fine-Tuned Pipeline for Git Commit Message Generation
Developers frequently write uninformative git commit messages such as "fix" or "update stuff", degrading the value of version-control history for code review, debugging, and onboarding. We present CommitLLM, a three-stage pipeline that generates concise, Conventional Commits-compliant messages from ...
- The tttAI System for the TSA-ASR Task of the SmartGlasses Challenge 2026
This paper presents the tttAI system submitted to the TSA-ASR task of the SmartGlasses Challenge 2026, evaluated on both two-person dialogues (Track 1) and multi-party meetings (Track 2). The task requires time-stamped speaker-attributed speech recognition from smart-glasses recordings. This is part...
- X-Translator: A Real-Time Multilingual Speaker-Aware Speech-to-Speech Translation System
Real-time speech-to-speech translation (S2ST) systems must balance translation quality, latency, speech naturalness, and speaker consistency. Publicly documented S2ST systems have advanced direct, multilingual, streaming, and expressive modeling, while proprietary products and APIs increasingly expo...
- Evidence-in-the-Loop: Trace-Driven Optimization for Customer-Service LLM Agents
Production customer-service bots must improve answer quality across iterative releases, yet large language models must not bypass evidence boundaries, policy rules, or human-handoff safeguards. We present an \textbf{Evidence-Grounded Customer-Service Agent Workflow} deployed in a real-world customer...
- ANNLib: A Development Framework for Efficient Approximate Nearest Neighbor Search
Approximate Nearest Neighbor Search (ANNS) plays a pivotal role in modern deep learning pipelines. Recently, many ANNS systems have been proposed to either provide broad functionality or reach high performance. However, it is yet difficult to achieve both with minimal programming efforts. We propose...
- The Matryoshka Hypencoder
The Hypencoder is a recently-proposed retrieval approach that encodes queries as shallow neural networks ("Q-Nets") that estimate relevance over pre-computed document embeddings. Inspired by Matryoshka Representation Learning, we show that the Hypencoder can be extended to support multiple sizes of ...
- jina-reranker-v3.5: An Efficient Listwise Reranker with Hybrid Attention and Self-Distillation
Listwise rerankers are the discriminative core of agentic retrieval pipelines, yet production deployment demands efficiency, domain robustness, and fluency on semi-structured data at the same time. We present jina-reranker-v3.5, a 0.6B-parameter listwise reranker that meets these demands together wi...