Beyond the Chatbox: Generative UI, AG-UI, and the Stack Behind Agent-Driven InterfacesMarkTechPost Most AI applications still showcase the model as a chat box. That interface is simple, but it hides what agents are actually doing, such as planning steps, calling tools, and updating state. Generative UI is about letting the agent drive real interface elements, for example tables, charts, forms, and progress indicators, so the experience feels
The post Beyond the Chatbox: Generative UI, AG-UI, and the Stack Behind Agent-Driven Interfaces appeared first on MarkTechPost.
Most AI applications still showcase the model as a chat box. That interface is simple, but it hides what agents are actually doing, such as planning steps, calling tools, and updating state. Generative UI is about letting the agent drive real interface elements, for example tables, charts, forms, and progress indicators, so the experience feels
The post Beyond the Chatbox: Generative UI, AG-UI, and the Stack Behind Agent-Driven Interfaces appeared first on MarkTechPost. Read More
Microsoft has linked recent reports of Windows 11 boot failures after installing the January 2026 updates to previously failed attempts to install the December 2025 security update, which left systems in an “improper state.” […] Read More
If an attacker splits a malicious prompt into discrete chunks, some large language models (LLMs) will get lost in the details and miss the true intent. Read More
A new Android malware campaign is using the Hugging Face platform as a repository for thousands of variations of an APK payload that collects credentials for popular financial and payment services. […] Read More
Seemingly harmless game mods can hide infostealer malware that quietly steals identities. Flare shows how Roblox mods can turn a home PC infection into corporate compromise. […] Read More
Google has introduced stronger Android authentication safeguards and enhanced recovery tools to make smartphones more challenging targets for thieves. […] Read More
The Aisuru/Kimwolf botnet launched a new massive distributed denial of service (DDoS) attack in December 2025, peaking at 31.4 Tbps and 200 million requests per second. […] Read More
A study by OMICRON has revealed widespread cybersecurity gaps in the operational technology (OT) networks of substations, power plants, and control centers worldwide. Drawing on data from more than 100 installations, the analysis highlights recurring technical, organizational, and functional issues that leave critical energy infrastructure vulnerable to cyber threats. The findings are based on Read More
Microsoft plans to introduce a call reporting feature in Teams by mid-March, allowing users to flag suspicious or unwanted calls as potential scams or phishing attempts. […] Read More
Lost in Simulation: LLM-Simulated Users are Unreliable Proxies for Human Users in Agentic Evaluationscs.AI updates on arXiv.org arXiv:2601.17087v2 Announce Type: replace-cross
Abstract: Agentic benchmarks increasingly rely on LLM-simulated users to scalably evaluate agent performance, yet the robustness, validity, and fairness of this approach remain unexamined. Through a user study with participants across the United States, India, Kenya, and Nigeria, we investigate whether LLM-simulated users serve as reliable proxies for real human users in evaluating agents on {tau}-Bench retail tasks. We find that user simulation lacks robustness, with agent success rates varying up to 9 percentage points across different user LLMs. Furthermore, evaluations using simulated users exhibit systematic miscalibration, underestimating agent performance on challenging tasks and overestimating it on moderately difficult ones. African American Vernacular English (AAVE) speakers experience consistently worse success rates and calibration errors than Standard American English (SAE) speakers, with disparities compounding significantly with age. We also find simulated users to be a differentially effective proxy for different populations, performing worst for AAVE and Indian English speakers. Additionally, simulated users introduce conversational artifacts and surface different failure patterns than human users. These findings demonstrate that current evaluation practices risk misrepresenting agent capabilities across diverse user populations and may obscure real-world deployment challenges.
arXiv:2601.17087v2 Announce Type: replace-cross
Abstract: Agentic benchmarks increasingly rely on LLM-simulated users to scalably evaluate agent performance, yet the robustness, validity, and fairness of this approach remain unexamined. Through a user study with participants across the United States, India, Kenya, and Nigeria, we investigate whether LLM-simulated users serve as reliable proxies for real human users in evaluating agents on {tau}-Bench retail tasks. We find that user simulation lacks robustness, with agent success rates varying up to 9 percentage points across different user LLMs. Furthermore, evaluations using simulated users exhibit systematic miscalibration, underestimating agent performance on challenging tasks and overestimating it on moderately difficult ones. African American Vernacular English (AAVE) speakers experience consistently worse success rates and calibration errors than Standard American English (SAE) speakers, with disparities compounding significantly with age. We also find simulated users to be a differentially effective proxy for different populations, performing worst for AAVE and Indian English speakers. Additionally, simulated users introduce conversational artifacts and surface different failure patterns than human users. These findings demonstrate that current evaluation practices risk misrepresenting agent capabilities across diverse user populations and may obscure real-world deployment challenges. Read More
