February 2026

This paper addresses the limitations of traditional protocol anomaly detection methods in handling dynamic state changes and unstructured behaviors. A deep protocol anomaly detection algorithm based on status code sequence modeling is proposed. The method uses the status codes returned during protocol communication as the core input. A state embedding layer is employed to transform discrete status codes into continuous vector representations. A gated recurrent unit (GRU) is then used to capture temporal dependencies and behavior patterns within […]

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Hi! I submitted 6 hours ago, before the deadline, however I still have my paper in state “Intention-to-submit registered”. Just wanted to confirm this is the expected behaviour, it’s the first paper I am submitting to this conference. Thanks! submitted by /u/KingPowa [link] [comments]

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Recent empirical work shows that large language models struggle to self-correct reasoning without external feedback. We propose a possible explanation: correlated error between generator and evaluator. When both components share failure modes, self-evaluation may provide weak evidence of correctness, and repeated self-critique may amplify confidence without adding information. We formalize this with two information-theoretic bounds. We then describe a practical architecture pairing high-entropy proposal generation with low-entropy external selection. This suggests an alternative to extended chain-of-thought in a […]

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The increasing adoption of large language models for simulating human behavior offers a promising new paradigm for social science research. However, a critical limitation persists: current LLM persona generation methods prioritize individual narrative richness over accurate population-level alignment of demographic and psychological traits, thereby compromising scientific validity. To address this, we introduce Deep Iterative Persona Alignment (DIPA), a novel framework that systematically generates LLM personas exhibiting high fidelity to real human psychological distributions. DIPA integrates powerful narrative generation […]

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Generative models have gained significant traction in offline reinforcement learning (RL) due to their ability to model complex trajectory distributions. However, existing generation-based approaches still struggle with long-horizon tasks characterized by sparse rewards. Some hierarchical generation methods have been developed to mitigate this issue by decomposing the original problem into shorter-horizon subproblems using one policy and generating detailed actions with another. While effective, these methods often overlook the multi-scale temporal structure inherent in trajectories, resulting in suboptimal performance. […]

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Part 1: Why Agentic Engineering Isn’t Vibe Coding A friend recently posted about AI skills using The Matrix analogy: Trinity doesn’t learn to fly a helicopter — Tank uploads a precise, verified program directly into her mind. She steps into the cockpit and flies. That analogy is perfect. But it exposes a fundamental misunderstanding about how these systems actually get built. Trinity doesn’t vibe her way into flying. Tank uploads a precise, verified program. That’s the difference between […]

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The way we interact with project management tools is changing. Instead of clicking through endless menus and dashboards, what if you could simply ask your AI assistant to create sprints, track time, or generate team reports? That’s exactly what the GitScrum MCP Server enables. Built on the Model Context Protocol, this open-source server connects AI assistants like Claude, GitHub Copilot, and Cursor directly to your GitScrum workspace. It’s not just another integration—it’s a complete operational layer that gives […]

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Modern applications often struggle to scale, not because of weak infrastructure, but because decision-making intelligence is added only after systems are already under load. Auto-scaling, DevOps pipelines, and cloud elasticity help, but they are largely reactive by design. As user demand grows, engineering teams are forced to chase latency spikes, error rates, and infrastructure bottlenecks after they appear, turning scalability into a constant firefight. This is where AI-native solutions fundamentally change how scalability is engineered. By embedding intelligence directly into […]

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Most AI apps send raw user input straight to OpenAI with zero filtering. I got tired of seeing it and built Sentinel Protocol, an open source local proxy with 81 security engines that scans every LLM request and response. It blocks PII, catches prompt injections, detects hallucinated URLs in model output, and handles MCP poisoning for AI agents. One command to start. Change one line in your SDK. 52,069 lines of code, 9 dependencies, MIT license. No cloud, […]

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Learn how CLAUDE.md, AGENTS.md, and AI memory files work. Covers file hierarchy, auto-memory, @imports, and which files you actually need for your setup.

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