AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,707 papers
The vision is to leverage the capabilities of multiple GPT agents to contribute to SE tasks and to propose an initial road map for future work, arguing that multiple G PT agents can perform creative and demanding tasks far beyond coding and debugging.
This research highlights the heightened threats to data integrity and stakeholder trust in these evolving ecosystems through an intensive examination of the literature, initiating a pioneering discourse emphasizing fostering a foundation for developing secure and trustworthy Liquid AI environments.
Carbon-Aware Governance Gates (CAGG), an architectural extension that embeds carbon budgets, energy provenance, and sustainability-aware validation orchestration into human-AI governance layers, is proposed.
Experiments on the MER2026-EmoPrefer Challenge dataset and the error-augmented dataset demonstrate that EAPO improves emotion preference prediction and enhances the robustness of MLLM judges when evaluating fluent descriptions that conflict with the video's multimodal emotional evidence.
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This work proposes a two-stage framework to assess the severity of false claims during disasters, and investigates false claim severity assessment as a human-AI alignment problem, evaluating whether models can reproduce human judgments under a shared evaluation rubric rather than merely predicting severity labels.
The study reveals a gap between reviewers'intentions and actual reviewing behaviour, highlighting the need for software companies to revisit their AI policies to better support developers in reviewing LLM-generated code.
An observational study of 20,574 coding-agent sessions from 1,639 repositories across IDE and CLI workflows operationalizes misalignment as a breakdown made visible through developer pushback, and annotates each episode along four axes: form, cause, cost, and resolution.
This paper introduces a practical and unified approach to real-time adaptive experiments that can encompass these scenarios across textual, visual, and audio tasks and describes an adaptive treatment assignment strategy that identifies the optimal treatment up to three times as accurately as a fixed design.
TailorCoPilot demonstrates a viable pathway to capture practice-based expertise, operationalizing it to support both generative AI advancements and human apprenticeship.
SemanticSlider3D is presented, a technique for continuous semantic attribute editing of 3D objects that requires no per-attribute training and supported decision-making in 3D prototyping and was perceived as a valuable addition to existing workflows.
This work pauses a reflection-support agent when it would normally redirect the conversation, surface its observation, and asks users to interpret the pattern and decide how to proceed, and derives a taxonomy of nine interpretation categories and shows that similar reflective states can call for substantially different...
This work introduces RAGE-Vis, a Relation-Aware Generative Editing interface for natural language-based chart editing, and demonstrates the applicability of RAGE-Vis in complex editing tasks, including style adjustment, data extension, order rearrangement, legend layout, and color mapping.
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.
A Blog post by H company on Hugging Face
The PKG Center for Social Impact expands Code.Tulsa experiential learning program.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
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