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From Matching Models to Recruiting Agents: A Systematized Narrative Review of AI Recruitment Systems, Evaluation, and Governance
arXiv:2609.04286v1 Announce Type: new Abstract: Artificial intelligence in recruitment has shifted the object being automated from profile pairs and ranked lists to multi-stage workflows that retrieve evidence, compare candidates, and support or execute actions. This systematized narrative review traces that development from bilateral retrieval and behavioral ranking through neural person--job matching, large language model (LLM) components, and tool-using recruiting agents. Using a purposive search and coding protocol updated through 23 July 2026, plus targeted updates through 2 September 2026, we organize 40 representative works with supporting industrial and legal sources. This synthesis is not a prevalence estimate. We analyze three coupled transitions: from similarity to reciprocal suitability, from a model to a compound workflow, and from offline prediction to evidence- and productivity-aligned evaluation. Across document understanding, retrieval, ranking, assessment, interviewin
FailureSpot: Label-Efficient Timestamp-Level Failure Detection for Vision-Language-Action Models
arXiv:2609.04277v1 Announce Type: new Abstract: Vision-language-action (VLA) policies have shown strong potential for general-purpose robotic manipulation, but they can still fail unpredictably during long-horizon execution, making reliable failure detection essential for safe deployment. Existing methods either rely on visual models that typically detect failures only after erroneous actions have occurred, or use lightweight proactive detectors trained on VLA internal representations. However, these proactive methods are often supervised with trajectory-level labels, causing normal pre-failure behavior in unsuccessful trajectories to be incorrectly labeled as failure. This supervision mismatch introduces label noise and limits both trajectory-level detection accuracy and precise timestamp-level failure localization. In this work, we study fine-grained timestamp-level VLA failure detection while addressing the cost of dense annotation. We propose a data-efficient framework that first l
ProToMEx: Rapid, Interpretable Explanations via Structured Representations
arXiv:2609.04265v1 Announce Type: new Abstract: Existing post-hoc explainers for machine learning classifiers primarily focus on feature attribution, assigning importance scores to individual features. While valuable, this approach struggles to articulate the complex, combinatorial patterns that often drive a model's decision-making process. To overcome this limitation, we introduce ProToMEx, a new paradigm for explainability that leverages Probabilistic Topic Models (PTMs). Our model-agnostic framework learns latent ''topics'' that represent distinct, high-level reasons for a classification, moving beyond simple feature importance to reveal underlying semantic structures. ProToMEx naturally provides both global explanations of a model's overall behaviour and local explanations that can disentangle multiple co-existing reasons for a specific prediction. We demonstrate empirically that ProToMEx not only produces explanations of comparable fidelity to popular methods like SHAP and LIME b
VLA-Precision: Asymmetric Co-Bootstrapping for Efficient Real-World Online RL of Vision-Language-Action Models
arXiv:2609.04355v1 Announce Type: new Abstract: Pretrained vision-language-action (VLA) models enable broad manipulation but remain unreliable in tasks demanding precision and repeatability. Applying real-world online reinforcement learning (RL) to VLA post-training enables autonomous trial-and-error improvement beyond demonstrations alone, but exposes two bottlenecks: 1) unreliable value signals can induce policy drift; 2) large-VLA overhead constrains throughput and sample efficiency. To address these challenges, we present VLA-Precision, an efficient real-world online RL framework featuring the Asymmetric Co-Bootstrapping (ACoB) algorithm and the ACoB-Stream architecture. Specifically, ACoB establishes asymmetric co-bootstrapping across timescales: early intervention-guided behavioral learning rapidly improves policy performance while enhancing online experience quality. As autonomous experience accumulates, global return propagation and local preference ranking progressively calibr
The Open-Source Ontology Iceberg: Ontology Is Serious Business
I love open source. We have released several open-source projects ourselves.Continue reading on Medium »
The Moment Your AI Agent Becomes a Security Vulnerability
There is a moment when an AI agent stops being a chatbot.Continue reading on AI Engineering Collective »
Does the Future of Robotics Really Have to Look Human?
Our homes explain why we imagine robots like ourselves. They may also help us imagine something else.Continue reading on Medium »
I Learned That Reliable AI Applications Need Engineering Discipline More Than Clever Prompts
My AI prototypes became impressive surprisingly quickly. Making them dependable was the part that forced me to start thinking like an Continue reading on Generative AI »
Ponytail: Lazy Senior Engineer Skill
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The AI Question Is Changing From “Which Model Is Best?” to “Which Model Is Worth Paying For?”
Why Open AI Models Are Becoming Corporate America s New Cost StrategyContinue reading on Medium »
AI canvas, Synthetic self, The prompt is the product
Weekly curated resources for designers thinkers and makers.Continue reading on UX Collective »
Seattle Times and Newsday sue OpenAI and Microsoft for infringement
The OpenAI logo is displayed on a smartphone screen placed on a reflective surface on which the company's logo is projected in Creteil, France, on September 4, 2026, as OpenAI began rolling out GPT-6 Astra, its most advanced model to date. (Photo by Samuel Boivin/NurPhoto via Getty Images) | NurPhoto via Getty Images The Seattle Times and Newsday are just the latest plaintiffs to take OpenAI to court, alleging copyright infringement. The two outlets say the company used their journalism as training data for its AI models without permission and often reproduces passages from their reporting in response to user queries. This is similar to lawsuits filed by The New York Times, Ziff Davis, Merriam-Webster, and Encyclopedia Britannica against OpenAI. The Seattle Times and Newsday also named Microsoft as a defendant in the suit, since Copilot is built on OpenAI's technology. The publishers join a list of nearly 400 local newspapers that recently sued the two … Read the full story at The Verg