arXiv:2601.04235v1 Announce Type: new Abstract: Obtaining reliable feedback from the environment is a fundamental capability for intelligent agents to evaluate the correctness of their actions and to accumulate reusable knowledge. However, most existing approaches rely on predefined measurements or fixed reward signals, which limits their applicability in open-ended and dynamic environments where new actions may require previously unknown forms of feedback. To address these limitations, this paper proposes an Actively Feedback Getting model, in which an AI agent […]

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Hi everyone, I’m a beginner who is just starting with Python and slowly learning about Reinforcement Learning (RL). I have a basic doubt and wanted guidance from people already in the field: Is a PhD or Master’s degree mandatory to get a job in Reinforcement Learning? Are there industry roles where a Bachelor’s + strong skills/projects are enough? Which type of RL roles usually require PhD, and which don’t? I’m not aiming for research right now — more […]

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arXiv:2602.21386v1 Announce Type: new Abstract: Logic locking as a solution for semiconductor intellectual property (IP) confidentiality has received considerable attention in academia, but has yet to produce a viable solution to protect against known threats. In part due to a lack of rigor, logic locking defenses have been historically short-lived, which is an unacceptable risk for hardware-based security solutions for critical systems that may be fielded for decades. Researchers have worked to map the concept of cryptographic indistinguishability […]

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Remember when choosing a database was simple? You picked MySQL or PostgreSQL for transactional data, maybe added MongoDB if you needed flexibility, and called it a day. I remember a conversation with a colleague regarding sharding, a method for horizontal scaling in MongoDB. Those days are over. The database landscape is going through the biggest shift since the NoSQL movement of the 2010s. But this time, it’s not just about scale or flexibility. Two forces are reshaping everything: […]

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arXiv:2602.13203v1 Announce Type: new Abstract: Telecommunication networks experience complex failures such as fiber cuts, traffic overloads, and cascading outages. Existing monitoring and digital twin systems are largely reactive, detecting failures only after service degradation occurs. We propose Adversarial Network Imagination, a closed-loop framework that integrates a Causal Large Language Model (LLM), a Knowledge Graph, and a Digital Twin to proactively generate, simulate, and evaluate adversarial network failures. The Causal LLM produces structured failure scenarios grounded in network dependencies […]

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arXiv:2602.21265v1 Announce Type: new Abstract: We introduce ToolMATH, a math-grounded benchmark that evaluates tool-augmented language models in realistic multi-tool environments where the output depends on calling schema-specified tools and sustaining multi-step execution. It turns math problems into a controlled, correctness-checkable benchmark with tool sets, enabling systematic evaluation of model reliability under (1) large, overlapping tool catalogs and (2) the absence of the intended capability. ToolMATH provides actionable diagnostic evidence of failure modes in tool-augmented agents, helping identify the […]

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We are living through the “Autocomplete Era” of software engineering. Tools like GitHub Copilot and ChatGPT can generate a microservice boilerplate in seconds. They can write your SQL schemas, your REST controllers, and even your unit tests. But if you ask an AI to “design a payment system for high-frequency trading,” it will likely give you the same generic architecture it gives for a generic e-commerce app. AI is excellent at the “How” (implementation). It is frequently terrible […]

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