arXiv:2601.08012v1 Announce Type: new Abstract: Large language model (LLM)-based AI agents extend LLM capabilities by enabling access to tools such as data sources, APIs, search engines, code sandboxes, and even other agents. While this empowers agents to perform complex tasks, LLMs may invoke unintended tool interactions and introduce risks, such as leaking sensitive data or overwriting critical records, which are unacceptable in enterprise contexts. Current approaches to mitigate these risks, such as model-based safeguards, enhance agents’ reliability but […]

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On January 16, 2026, John Lee Dumas of Entrepreneurs on Fire hosted the 2026 Podcast Hall of Fame ceremony, which was a relatively intimate affair, unlike the elaborate red carpet events for movie stars and entertainers. A cash bar and appetizer station occupied one corner of the venue, nothing extravagant, but enough to make the space feel welcoming before the program began. The evening balanced live induction speeches with pre-recorded remarks from inductees who could not attend in person. The remote […]

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arXiv:2601.15360v1 Announce Type: new Abstract: Estimating Heterogeneous Treatment Effects (HTE) in industrial applications such as AdTech and healthcare presents a dual challenge: extreme class imbalance and heavy-tailed outcome distributions. While the X-Learner framework effectively addresses imbalance through cross-imputation, we demonstrate that it is fundamentally vulnerable to “Outlier Smearing” when reliant on Mean Squared Error (MSE) minimization. In this failure mode, the bias from a few extreme observations (“whales”) in the minority group is propagated to the entire majority […]

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arXiv:2607.00003v1 Announce Type: new Abstract: Personal Knowledge Graphs (PKGs) offer a privacy-preserving framework for modeling user preferences, yet constructing them from unstructured, decentralized conversational data remains a challenge. This paper bridges the gap between conversational “strings” and semantic “things” by presenting a reproducible pipeline for extracting structured user-preference triples using lightweight Large Language Models. We evaluate Qwen- and Gemma-based models on their ability to extract RDF-compliant triples linked to Wikidata identifiers from conversational data for PKG construction. Our […]

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arXiv:2602.03955v1 Announce Type: new Abstract: While large language model (LLM) multi-agent systems achieve superior reasoning performance through iterative debate, practical deployment is limited by their high computational cost and error propagation. This paper proposes AgentArk, a novel framework to distill multi-agent dynamics into the weights of a single model, effectively transforming explicit test-time interactions into implicit model capabilities. This equips a single agent with the intelligence of multi-agent systems while remaining computationally efficient. Specifically, we investigate three hierarchical […]

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Auditing AI Systems: A Practical Guide to Testing Models for Bias, Compliance, Security, and Explainability Photo by Stephen Dawson on Unsplash Why accuracy alone is not enough and how organizations can audit AI systems before regulators, attackers, or users expose the failures. Artificial intelligence systems are now embedded in decisions that affect people’s lives credit approvals, fraud detection, hiring, underwriting, and customer support automation. But while AI adoption has accelerated rapidly, governance frameworks have struggled to keep up. Most […]

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Zomato’s “Food Rescue” is essentially a race condition. Here’s how I built a headless monitor to win it. Every so often, Zomato throws a pop-up on your screen: a cancelled nearby order offered at 50% off. It’s gone in seconds — claimed by whoever is fast enough, or lucky enough to be staring at the right screen at the right time. I missed it one too many times. Not because I was slow — because I wasn’t even […]

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arXiv:2602.06062v1 Announce Type: new Abstract: The sixth-generation (6G) of wireless communication networks aims to leverage artificial intelligence tools for efficient and robust network optimization. This is especially the case since traditional optimization methods often face high computational complexity, motivating the use of deep learning (DL)-based optimization frameworks. In this context, this paper considers a multi-antenna base station (BS) serving multiple users simultaneously through transmit beamforming in downlink mode. To account for robustness, this work proposes an uncertainty-injected deep […]

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Controlling the false discovery rate (FDR) in high-dimensional variable selection requires balancing rigorous error control with statistical power. Existing methods with provable guarantees are often overly conservative, creating a persistent gap between the realized false discovery proportion (FDP) and the target FDR level. We introduce a learning-augmented enhancement of the T-Rex Selector framework that narrows this gap. Our approach replaces the analytical FDP estimator with a neural network trained solely on diverse synthetic datasets, enabling a substantially tighter […]

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