How to Crack Machine Learning System-Design InterviewsTowards Data Science A comprehensive guide into Meta, Apple, Reddit, Amazon, Google, and Snap ML design interviews
The post How to Crack Machine Learning System-Design Interviews appeared first on Towards Data Science.
A comprehensive guide into Meta, Apple, Reddit, Amazon, Google, and Snap ML design interviews
The post How to Crack Machine Learning System-Design Interviews appeared first on Towards Data Science. Read More
Building AI Automations with Google OpalKDnuggets Google Opal is a no-code, experimental tool from Google Labs. It is designed to enable users to build and share AI-powered micro-applications using natural language.
Google Opal is a no-code, experimental tool from Google Labs. It is designed to enable users to build and share AI-powered micro-applications using natural language. Read More
How to Design an Advanced Multi-Agent Reasoning System with spaCy Featuring Planning, Reflection, Memory, and Knowledge GraphsMarkTechPost In this tutorial, we build an advanced Agentic AI system using spaCy, designed to allow multiple intelligent agents to reason, collaborate, reflect, and learn from experience. We work through the entire pipeline step by step, observing how each agent processes tasks using planning, memory, communication, and semantic reasoning. By the end, we see how the
The post How to Design an Advanced Multi-Agent Reasoning System with spaCy Featuring Planning, Reflection, Memory, and Knowledge Graphs appeared first on MarkTechPost.
In this tutorial, we build an advanced Agentic AI system using spaCy, designed to allow multiple intelligent agents to reason, collaborate, reflect, and learn from experience. We work through the entire pipeline step by step, observing how each agent processes tasks using planning, memory, communication, and semantic reasoning. By the end, we see how the
The post How to Design an Advanced Multi-Agent Reasoning System with spaCy Featuring Planning, Reflection, Memory, and Knowledge Graphs appeared first on MarkTechPost. Read More
Comparing the Top 6 Agent-Native Rails for the Agentic Internet: MCP, A2A, AP2, ACP, x402, and KiteMarkTechPost As AI agents move from single-app copilots to autonomous systems that browse, transact, and coordinate with each other, a new infrastructure layer is emerging underneath them. This article compares six key “agent-native rails” — MCP, A2A, AP2, ACP, x402, and Kite — focusing on how they standardize tool access, inter-agent communication, payment authorization, and settlement,
The post Comparing the Top 6 Agent-Native Rails for the Agentic Internet: MCP, A2A, AP2, ACP, x402, and Kite appeared first on MarkTechPost.
As AI agents move from single-app copilots to autonomous systems that browse, transact, and coordinate with each other, a new infrastructure layer is emerging underneath them. This article compares six key “agent-native rails” — MCP, A2A, AP2, ACP, x402, and Kite — focusing on how they standardize tool access, inter-agent communication, payment authorization, and settlement,
The post Comparing the Top 6 Agent-Native Rails for the Agentic Internet: MCP, A2A, AP2, ACP, x402, and Kite appeared first on MarkTechPost. Read More
Anthropic details cyber espionage campaign orchestrated by AIAI News Security leaders face a new class of autonomous threat as Anthropic details the first cyber espionage campaign orchestrated by AI. In a report released this week, the company’s Threat Intelligence team outlined its disruption of a sophisticated operation by a Chinese state-sponsored group – an assessment made with high confidence – dubbed GTG-1002 and detected
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Security leaders face a new class of autonomous threat as Anthropic details the first cyber espionage campaign orchestrated by AI. In a report released this week, the company’s Threat Intelligence team outlined its disruption of a sophisticated operation by a Chinese state-sponsored group – an assessment made with high confidence – dubbed GTG-1002 and detected
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Meet SDialog: An Open-Source Python Toolkit for Building, Simulating, and Evaluating LLM-based Conversational Agents End-to-EndMarkTechPost How can developers reliably generate, control, and inspect large volumes of realistic dialogue data without building a custom simulation stack every time? Meet SDialog, an open sourced Python toolkit for synthetic dialogue generation, evaluation, and interpretability that targets the full conversational pipeline from agent definition to analysis. It standardizes how a Dialog is represented and
The post Meet SDialog: An Open-Source Python Toolkit for Building, Simulating, and Evaluating LLM-based Conversational Agents End-to-End appeared first on MarkTechPost.
How can developers reliably generate, control, and inspect large volumes of realistic dialogue data without building a custom simulation stack every time? Meet SDialog, an open sourced Python toolkit for synthetic dialogue generation, evaluation, and interpretability that targets the full conversational pipeline from agent definition to analysis. It standardizes how a Dialog is represented and
The post Meet SDialog: An Open-Source Python Toolkit for Building, Simulating, and Evaluating LLM-based Conversational Agents End-to-End appeared first on MarkTechPost. Read More
WOD-E2E: Waymo Open Dataset for End-to-End Driving in Challenging Long-tail Scenarioscs.AI updates on arXiv.org arXiv:2510.26125v3 Announce Type: replace-cross
Abstract: Vision-based end-to-end (E2E) driving has garnered significant interest in the research community due to its scalability and synergy with multimodal large language models (MLLMs). However, current E2E driving benchmarks primarily feature nominal scenarios, failing to adequately test the true potential of these systems. Furthermore, existing open-loop evaluation metrics often fall short in capturing the multi-modal nature of driving or effectively evaluating performance in long-tail scenarios. To address these gaps, we introduce the Waymo Open Dataset for End-to-End Driving (WOD-E2E). WOD-E2E contains 4,021 driving segments (approximately 12 hours), specifically curated for challenging long-tail scenarios that that are rare in daily life with an occurring frequency of less than 0.03%. Concretely, each segment in WOD-E2E includes the high-level routing information, ego states, and 360-degree camera views from 8 surrounding cameras. To evaluate the E2E driving performance on these long-tail situations, we propose a novel open-loop evaluation metric: Rater Feedback Score (RFS). Unlike conventional metrics that measure the distance between predicted way points and the logs, RFS measures how closely the predicted trajectory matches rater-annotated trajectory preference labels. We have released rater preference labels for all WOD-E2E validation set segments, while the held out test set labels have been used for the 2025 WOD-E2E Challenge. Through our work, we aim to foster state of the art research into generalizable, robust, and safe end-to-end autonomous driving agents capable of handling complex real-world situations.
arXiv:2510.26125v3 Announce Type: replace-cross
Abstract: Vision-based end-to-end (E2E) driving has garnered significant interest in the research community due to its scalability and synergy with multimodal large language models (MLLMs). However, current E2E driving benchmarks primarily feature nominal scenarios, failing to adequately test the true potential of these systems. Furthermore, existing open-loop evaluation metrics often fall short in capturing the multi-modal nature of driving or effectively evaluating performance in long-tail scenarios. To address these gaps, we introduce the Waymo Open Dataset for End-to-End Driving (WOD-E2E). WOD-E2E contains 4,021 driving segments (approximately 12 hours), specifically curated for challenging long-tail scenarios that that are rare in daily life with an occurring frequency of less than 0.03%. Concretely, each segment in WOD-E2E includes the high-level routing information, ego states, and 360-degree camera views from 8 surrounding cameras. To evaluate the E2E driving performance on these long-tail situations, we propose a novel open-loop evaluation metric: Rater Feedback Score (RFS). Unlike conventional metrics that measure the distance between predicted way points and the logs, RFS measures how closely the predicted trajectory matches rater-annotated trajectory preference labels. We have released rater preference labels for all WOD-E2E validation set segments, while the held out test set labels have been used for the 2025 WOD-E2E Challenge. Through our work, we aim to foster state of the art research into generalizable, robust, and safe end-to-end autonomous driving agents capable of handling complex real-world situations. Read More
AgentEvolver: Towards Efficient Self-Evolving Agent Systemcs.AI updates on arXiv.org arXiv:2511.10395v1 Announce Type: cross
Abstract: Autonomous agents powered by large language models (LLMs) have the potential to significantly enhance human productivity by reasoning, using tools, and executing complex tasks in diverse environments. However, current approaches to developing such agents remain costly and inefficient, as they typically require manually constructed task datasets and reinforcement learning (RL) pipelines with extensive random exploration. These limitations lead to prohibitively high data-construction costs, low exploration efficiency, and poor sample utilization. To address these challenges, we present AgentEvolver, a self-evolving agent system that leverages the semantic understanding and reasoning capabilities of LLMs to drive autonomous agent learning. AgentEvolver introduces three synergistic mechanisms: (i) self-questioning, which enables curiosity-driven task generation in novel environments, reducing dependence on handcrafted datasets; (ii) self-navigating, which improves exploration efficiency through experience reuse and hybrid policy guidance; and (iii) self-attributing, which enhances sample efficiency by assigning differentiated rewards to trajectory states and actions based on their contribution. By integrating these mechanisms into a unified framework, AgentEvolver enables scalable, cost-effective, and continual improvement of agent capabilities. Preliminary experiments indicate that AgentEvolver achieves more efficient exploration, better sample utilization, and faster adaptation compared to traditional RL-based baselines.
arXiv:2511.10395v1 Announce Type: cross
Abstract: Autonomous agents powered by large language models (LLMs) have the potential to significantly enhance human productivity by reasoning, using tools, and executing complex tasks in diverse environments. However, current approaches to developing such agents remain costly and inefficient, as they typically require manually constructed task datasets and reinforcement learning (RL) pipelines with extensive random exploration. These limitations lead to prohibitively high data-construction costs, low exploration efficiency, and poor sample utilization. To address these challenges, we present AgentEvolver, a self-evolving agent system that leverages the semantic understanding and reasoning capabilities of LLMs to drive autonomous agent learning. AgentEvolver introduces three synergistic mechanisms: (i) self-questioning, which enables curiosity-driven task generation in novel environments, reducing dependence on handcrafted datasets; (ii) self-navigating, which improves exploration efficiency through experience reuse and hybrid policy guidance; and (iii) self-attributing, which enhances sample efficiency by assigning differentiated rewards to trajectory states and actions based on their contribution. By integrating these mechanisms into a unified framework, AgentEvolver enables scalable, cost-effective, and continual improvement of agent capabilities. Preliminary experiments indicate that AgentEvolver achieves more efficient exploration, better sample utilization, and faster adaptation compared to traditional RL-based baselines. Read More
Proceedings of the Second International Workshop on Next-Generation Language Models for Knowledge Representation and Reasoning (NeLaMKRR 2025)cs.AI updates on arXiv.org arXiv:2511.09575v1 Announce Type: new
Abstract: Reasoning is an essential component of human intelligence in that it plays a fundamental role in our ability to think critically, support responsible decisions, and solve challenging problems. Traditionally, AI has addressed reasoning in the context of logic-based representations of knowledge. However, the recent leap forward in natural language processing, with the emergence of language models based on transformers, is hinting at the possibility that these models exhibit reasoning abilities, particularly as they grow in size and are trained on more and more data. Still, despite ongoing discussions about what reasoning is in language models, it is still not easy to articulate to what extent these models are actually capable of reasoning.
The goal of this workshop is to create a platform for researchers from different disciplines and/or AI perspectives to explore approaches and techniques with the aim to reconcile reasoning between language models using transformers and logic-based representations. The specific objectives include analysing the reasoning abilities of language models measured alongside KR methods, injecting KR-style reasoning abilities into language models (including by neuro-symbolic means), and formalising the kind of reasoning language models carry out. This exploration aims to uncover how language models can effectively integrate and leverage knowledge and reasoning with it, thus improving their application and utility in areas where precision and reliability are key requirements.
arXiv:2511.09575v1 Announce Type: new
Abstract: Reasoning is an essential component of human intelligence in that it plays a fundamental role in our ability to think critically, support responsible decisions, and solve challenging problems. Traditionally, AI has addressed reasoning in the context of logic-based representations of knowledge. However, the recent leap forward in natural language processing, with the emergence of language models based on transformers, is hinting at the possibility that these models exhibit reasoning abilities, particularly as they grow in size and are trained on more and more data. Still, despite ongoing discussions about what reasoning is in language models, it is still not easy to articulate to what extent these models are actually capable of reasoning.
The goal of this workshop is to create a platform for researchers from different disciplines and/or AI perspectives to explore approaches and techniques with the aim to reconcile reasoning between language models using transformers and logic-based representations. The specific objectives include analysing the reasoning abilities of language models measured alongside KR methods, injecting KR-style reasoning abilities into language models (including by neuro-symbolic means), and formalising the kind of reasoning language models carry out. This exploration aims to uncover how language models can effectively integrate and leverage knowledge and reasoning with it, thus improving their application and utility in areas where precision and reliability are key requirements. Read More
Visa builds AI commerce infrastructure for the Asia Pacific’s 2026 PilotAI News When Visa unveiled its Intelligent Commerce platform for Asia Pacific on November 12, it wasn’t just launching another payment feature—it was building AI commerce infrastructure to solve a crisis most merchants haven’t noticed yet: their websites are being flooded by AI agents, and there’s no reliable way to tell which ones are legitimate shoppers and which are malicious bots.
The post Visa builds AI commerce infrastructure for the Asia Pacific’s 2026 Pilot appeared first on AI News.
When Visa unveiled its Intelligent Commerce platform for Asia Pacific on November 12, it wasn’t just launching another payment feature—it was building AI commerce infrastructure to solve a crisis most merchants haven’t noticed yet: their websites are being flooded by AI agents, and there’s no reliable way to tell which ones are legitimate shoppers and which are malicious bots.
The post Visa builds AI commerce infrastructure for the Asia Pacific’s 2026 Pilot appeared first on AI News. Read More