“The success of an AI product depends on how intuitively users can interact with its capabilities”Towards Data Science Janna Lipenkova on AI strategy, AI products, and how domain knowledge can change the entire shape of an AI solution.
The post “The success of an AI product depends on how intuitively users can interact with its capabilities” appeared first on Towards Data Science.
Janna Lipenkova on AI strategy, AI products, and how domain knowledge can change the entire shape of an AI solution.
The post “The success of an AI product depends on how intuitively users can interact with its capabilities” appeared first on Towards Data Science. Read More
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
The post Anthropic details cyber espionage campaign orchestrated by AI appeared first on AI 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
The post Anthropic details cyber espionage campaign orchestrated by AI appeared first on AI News. Read More
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
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
Planning Agents on an Ego-Trip: Leveraging Hybrid Ego-Graph Ensembles for Improved Tool Retrieval in Enterprise Task Planningcs.AI updates on arXiv.org arXiv:2508.05888v2 Announce Type: replace
Abstract: Effective tool pre-selection via retrieval is essential for AI agents to select from a vast array of tools when identifying and planning actions in the context of complex user queries. Despite its central role in planning, this aspect remains underexplored in the literature. Traditional approaches rely primarily on similarities between user queries and tool descriptions, which significantly limits retrieval accuracy, specifically when handling multi-step user requests. To address these limitations, we propose a Knowledge Graph (KG)-based tool retrieval framework that captures the semantic relationships between tools and their functional dependencies. Our retrieval algorithm leverages ensembles of 1-hop ego tool graphs to model direct and indirect connections between tools, enabling more comprehensive and contextual tool selection for multi-step tasks. We evaluate our approach on a synthetically generated internal dataset across six defined user classes, extending previous work on coherent dialogue synthesis and tool retrieval benchmarks. Results demonstrate that our tool graph-based method achieves 91.85% tool coverage on the micro-average CompleteRecall metric, compared to 89.26% for re-ranked semantic-lexical hybrid retrieval, the strongest non-KG baseline in our experiments. These findings support our hypothesis that the structural information modeled in the graph provides complementary signals to pure similarity matching, particularly for queries requiring sequential tool composition.
arXiv:2508.05888v2 Announce Type: replace
Abstract: Effective tool pre-selection via retrieval is essential for AI agents to select from a vast array of tools when identifying and planning actions in the context of complex user queries. Despite its central role in planning, this aspect remains underexplored in the literature. Traditional approaches rely primarily on similarities between user queries and tool descriptions, which significantly limits retrieval accuracy, specifically when handling multi-step user requests. To address these limitations, we propose a Knowledge Graph (KG)-based tool retrieval framework that captures the semantic relationships between tools and their functional dependencies. Our retrieval algorithm leverages ensembles of 1-hop ego tool graphs to model direct and indirect connections between tools, enabling more comprehensive and contextual tool selection for multi-step tasks. We evaluate our approach on a synthetically generated internal dataset across six defined user classes, extending previous work on coherent dialogue synthesis and tool retrieval benchmarks. Results demonstrate that our tool graph-based method achieves 91.85% tool coverage on the micro-average CompleteRecall metric, compared to 89.26% for re-ranked semantic-lexical hybrid retrieval, the strongest non-KG baseline in our experiments. These findings support our hypothesis that the structural information modeled in the graph provides complementary signals to pure similarity matching, particularly for queries requiring sequential tool composition. Read More