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Your AI Agent Passed Every Eval. Finance Still Killed It.
An AI agent passed every metric in the eval harness I published, then the CFO killed it — its successful resolutions cost more than the humans it replaced. The one metric that predicts whether an agent survives production, and how to measure it without a rebuild. The post Your AI Agent Passed Every Eval. Finance Still Killed It. appeared first on Towards Data Science.
Top 10 GitHub Repositories Trending in July 2026 (AI, ML & GenAI Edition)
If you ve spent any time on GitHub Trending this month, you ve probably noticed a pattern: it isn t research papers turning into repositories anymore, it s agents. Coding agents, pentesting agents, trading agents, and the infrastructure that ties them all together. We tracked star growth, momentum, and real-world impact to identify the ten repositories that mattered most [ ] The post Top 10 GitHub Repositories Trending in July 2026 (AI, ML GenAI Edition) appeared first on Analytics Vidhya.
Many Companies Use AI. Few Know How to Build an AI-Native Enterprise Data Platform.
A practical enterprise AI architecture with data agents, AI-powered QA, and AI governance. The post Many Companies Use AI. Few Know How to Build an AI-Native Enterprise Data Platform. appeared first on Towards Data Science.
Loop Engineering with Adaptive PDF Parsing: Start Cheap, Pay for a Heavier Parser Only When the Page Needs It
Enterprise Document Intelligence [Vol.1 #10A] - The escalation cascade and the free, deterministic checks that flag a failed parse before you pay for a deeper one The post Loop Engineering with Adaptive PDF Parsing: Start Cheap, Pay for a Heavier Parser Only When the Page Needs It appeared first on Towards Data Science.
HG-RAG: Hierarchy-Guided Retrieval-Augmented Generation for Structured Knowledge Graphs
arXiv:2607.14095v1 Announce Type: new Abstract: Retrieval Augmented Generation (RAG) has proven to be a widely successful process at improving the quality of outputs from a Large Language Model (LLM) for wider context. However, RAG systems typically retrieve context from flat document stores, which struggles when queries require hierarchical or relational reasoning across structured knowledge. I present HG-RAG (Hierarchy-Guided RAG), a framework that performs graph-traversal over a hierarchical knowledge graph to deliver structured context to a language model. My retrieval pipeline resolves a named entity anchor from the query, then expands context upward through parent nodes, laterally through relational neighbors, and downward through child nodes when needed. I evaluate HG-RAG against a dense retrieval baseline across three world scales (18-800 nodes) with four query types: local fact, hierarchical, neighborhood, and multi-hop. Results show HG-RAG consistently outperforms the flat ba
Intelligent Three Level Learning Architecture for Autonomous UAV Swarms in Search and Rescue
arXiv:2607.14093v1 Announce Type: new Abstract: This paper presents a novel three level hierarchical learning architecture for autonomous UAV swarms performing search and rescue operations. Unlike conventional approaches that apply a single learning paradigm across all hierarchy levels, the proposed architecture integrates three qualitatively different learning mechanisms corresponding to the biological hierarchy of reflexes, skills, and reasoning such as Hebbian neuroplasticity for individual agent adaptation, multi agent reinforcement learning with graph neural networks and behavior trees for tactical coordination, and model agnostic meta learning with BDI reasoning and a digital twin for strategic decision making. The architecture is formalized through twenty two architectural contracts organized across six components such as BDI, Behavior Trees, GNN, MARL, Neuroplasticity, Meta Learning that collectively provide six classes of formal guarantees such as safety, budget correctness, o
Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
arXiv:2607.14123v1 Announce Type: new Abstract: Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows. In practice, they are often generated and discarded without guiding meaningful action. This gap reflects foundational shortcomings: research has not yet established methodologies for integrating explanations into end-to-end, human-in-the-loop systems. This position paper argues that the machine learning community must pivot from ad-hoc XAI methods toward addressing foundational & structural challenges, including unclear problem formulations, underspecified evaluation objectives, and the absence of pipelines for explanation-driven feedback. We support this claim through an analysis of recent ICML, NeurIPS, and ICLR papers and a survey of XAI practitioners, revealing recurring issues that limit cumulative progress. We conclude by outlining a practical checklist designed to sh
CARPRT: Class-Aware Zero-Shot Prompt Reweighting for Black-Box Vision-Language Models
arXiv:2607.14125v1 Announce Type: new Abstract: Pre-trained vision-language models (VLMs) enable zero-shot image classification by computing the similarity score between an image and textual descriptions, typically formed by inserting a class label (e.g., "cat") into a prompt (e.g., "a photo of a"). Since the score for a given image-class pair is sensitive to the choice of prompt, existing studies ensemble multiple prompts using a weighting vector to aggregate scores across different prompts. Yet, in current strategies, the weighting vector assigned to each prompt is shared across all classes, implicitly assuming that prompts are conditionally independent of classes, which often does not hold in practice, as a prompt like "an aerial view of" might be apt for "airport" but ill-suited for "apple". To address this, we propose class-aware zero-shot prompt reweighting (CARPRT). This scoring scheme adjusts the weighting vector for each class label by capturing the class-specific relevance of
How to Connect MCP Servers with Claude (Claude Desktop and Claude Code)
Connecting MCP servers to Claude allows it to work with external tools, files, databases, repositories, and other systems instead of operating only within the chat window. The setup differs slightly between Claude Desktop and Claude Code, but both can be configured in just a few steps. In this article, you’ll learn how to connect MCP [ ] The post How to Connect MCP Servers with Claude (Claude Desktop and Claude Code) appeared first on Analytics Vidhya.
GPT-5.6 Sol vs Claude Fable 5: Benchmarks, Pricing & Hands-On
GPT-5.6 Sol and Claude Fable 5 are currently fighting for the frontier-model crown. Fable 5 holds a slight edge in general intelligence, while Sol hits back with stronger coding performance, faster execution and much lower pricing. In fact, GPT-5.6 Sol is priced closer to Claude Opus 4.8 than to Fable 5, which makes this comparison [ ] The post GPT-5.6 Sol vs Claude Fable 5: Benchmarks, Pricing Hands-On appeared first on Analytics Vidhya.