AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,089 papers
By reframing speech BCIs as adaptive, user-centred systems, this work outlines the interdisciplinary priorities spanning speech neuroscience, neural engineering, machine learning, clinical practice, and neuroethics needed to move from proof-of-concept decoding toward reliable, expressive, and controllable communication...
Experimental results show that incorporating user traits can aid personalized prediction, as this work aims to shift the research paradigm from developing agents suited for the average user to designing systems tailored to specific individuals.
Accessible map systems either make a layout explorable by hand or convey information through speech; few combine both to support pre-travel spatial understanding for blind and low-vision (BLV) people. We present TouchingSpace: a system that retrieves map data for an outdoor place and renders its surroundings as bounded...
This work presents a targeted and traceable approach to investigating multi-agent LLM dialogue, applied to the VAST Challenge 2026 MC1 dataset, and finds that the KG structure enables users to identify actors worth investigating faster and summarizing only the region surrounding an actor of interest better supports per...
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FairAware, a fairness assessment tool co-designed with Human Resources domain experts, is presented, suggesting that fairness assessment tools for non-experts are usable for identifying biases but need built-in checks on understanding before stakeholders make higher-stakes decisions.
Surprisingly, like humans but to a greater extent, LLMs also demonstrated the same hypocrisy in condemning others who, like them, had deployed Bayes'rule.
Human intention can be modeled as a latent internal state that modulates how sensory information is evaluated and translated into action in human-machine systems. However, most existing brain-computer interfaces (BCIs) rely on control signals tightly coupled to externally imposed stimulation and do not explicitly infer...
SensWear is presented, an open, modular, and AI-ready wearable platform that decouples embodiment, sensing, data interfaces, and learning and case studies show arterial PPG waveform capture and competitive heart-rate accuracy while preserving inspectable raw data for reproducible closed-loop experiments.
The research identifies key design parameters for an LLM-driven multi-agent simulation system, offering valuable insights for creating more effective and scalable anti-bullying tools that could significantly reduce bullying in schools.
SPECTRA is proposed, a lightweight autonomous request framework for edge-cloud GUI grounding, comprising Saliency-Guided Targeted Perturbation and Efficient Cognitive Trajectory Analysis, which conducts a visual cognitive stress test and quantifies the topological divergence of the agent's high-dimensional cognitive tr...
This work introduces a temporal-graph-learning (TGL) controller that learns these predictions jointly and supplies both outputs in one forward pass, and shows that a small graph model can handle both decisions and improve the language agents it controls.
This work provides a general theoretical characterization of the problem of learning to assign prediction tasks to one agent from a set of available agents, including human decision-makers and AI models and develops a framework of sequential explore-exploit policy-learning algorithms that seek to maximize overall perfo...
AI agents can generate code at a scale that’s hard for humans to review line-by-line. PDD Kit helps ensure the code is doing what the developer intended by creating formal specifications and testing against them. It converts developer intent into a Verus specification, proves AI-generated code meets it for every input, and uses tests where proofs aren't practical. Developers review intent, not every line. The post Formally verifying agent-written full-stack web apps with PDD Kit appeared first o…
Data Mining & Modeling
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
During the School of Humanities, Arts, and Social Sciences’ 75th Anniversary Symposium, President Sally Kornbluth said “MIT has never needed your particular insight and guidance more than we do now.”
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
“X-Hinges” helps design and 3D print items like lamps and gloves with an interactive twist: automatically embedded sensors that perceive and adaptively respond to human contact.
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