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Palm Garden AI develops Coherence Guard relational decision layer for human-facing robots
Palm Garden AI is developing a hardware-agnostic software layer, including Coherence Guard, for human-robot interaction and relationships. The post Palm Garden AI develops Coherence Guard relational decision layer for human-facing robots appeared first on The Robot Report.
Neil Rimer thinks the AI money is coming back out
Neil Rimer, the venture capitalist who co-founded Index Ventures, predicts the historic wealth AI is generating in Silicon Valley will have to be redistributed, voluntarily or involuntarily.
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
Capital One releases VulnHunter, an open-source AI tool that finds software flaws before hackers do
Capital One on Thursday released VulnHunter, an open-source, agentic AI security tool that scans source code for exploitable vulnerabilities, maps out how an attacker would reach them, and proposes targeted fixes — all before a single line ships to production. The tool, built internally and now available on GitHub under an Apache 2.0 license, is one of the most ambitious attempts by a major financial institution to turn offensive AI capabilities into a public defensive resource.At a time when security teams are facing a rising tide of new AI threats, Capital One s decision to open-source the tool reflects an effort, according to CISO Chris Nims, to address an increasingly brief window before sophisticated, next-generation AI attack capabilities become affordable and accessible to virtually every adversary. Capital One is not simply releasing another vulnerability scanner. VulnHunter introduces what the company calls an attacker-first forward analysis — a workflow in which the tool begi
Intuit scrapped its own AI agent architecture twice in four months. At VB Transform 2026, its AI VP called that the fast path
Intuit was an early pioneer in the usage of agentic AI, but its path to success has hardly been a straight line.At VB Transform 2026, Intuit VP of AI Nhung Ho described how the company rebuilt its agent architecture twice in the span of about four months, first moving from a fleet of specialist agents to a central orchestration layer, then abandoning that layer for a skills and tools based system once the orchestrator itself started failing under its own complexity. The full second rebuild took 60 days, with a first working version in under 20.The failure mode that forced the second rewrite was specific. Agents in the orchestrated system passed results to each other in natural language, and each handoff lost context the next agent needed to act correctly. If you have 10 agents and they all are passing to each other, every time that pass happens, error compounds, Ho said.Why the orchestration layer broke downHo said the original push toward specialist agents came from a straightforward
Founder of Maximo discusses how robotics is accelerating solar construction
Maximo founder Deise Yumi Asami discusses the process of automating solar construction with robotics. The post Founder of Maximo discusses how robotics is accelerating solar construction appeared first on The Robot Report.
Undergrads’ weed-killing robot wins top prize
Andrew James (from left), Neil Morrison, Natalia Kurz and Michael Neiss work on a prototype of their weed-killing robot ahead of The Farm Robotics Challenge, which they won on May 21. By Holly Hartigan A team of Cornell undergraduates beat 95 other teams to take the grand prize at The Farm Robotics Challenge with their [ ]
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.