AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,707 papers
FocusGen is presented, an interactive system that introduces external perspectives into visual design exploration through a "virtual focus group" of simulated persona agents, and is positioned as a divergence scaffold for early-stage ideation rather than a substitute for audience research.
Graphionale is developed as a testbed for empirically studying argument-map-style rationale visualization, and its findings contribute empirical design knowledge about when and how graphical rationales support human decision making, and inform the next-generation reasoning-aware AI interfaces.
A closer look at a broader set of terms that can express desirable results around visualization research, and untangle how these words are understood in the community in relation to other similar terms.
This research characterizes deceptive patterns as a sociotechnical phenomenon, integrating human and technical dimensions while offering a self-explainable interactive catalog to raise awareness among users and designers.
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This study explores and evaluates the ability of LLMs to follow and enhance human mental trajectories during semantic memory search and demonstrates that an LLM's abilities to track and predict human memory trajectories in this task exceed those of other humans.
Sycophancy co-occurs with degraded judged truthfulness (rho=0.40), a coupling that strengthens across generations, and a single direct instruction outperforms an elaborate reasoning protocol in seven of eight variants.
This work introduces personalized product search (PPS), a testbed for agentic personalization under raw queries and diverse histories, and constructs Agent Personalized Benchmark (APeB) from action logs, pairing underspecified intents with rich histories and user-viewed candidate items.
This paradigm shift from misuse to misclassification is not semantic: it offers educators a clear perspective on what to look for, what to assess, and what to intervene on.
Gen-TAS, a knowledge-grounded LLM framework for user-specific FPGA-GPP task allocation, is proposed by combining task-graph analysis with RAG, which grounds LLM reasoning in historical implementation knowledge and generates multiple explainable strategies tailored to the specified objectives.
An AI-Ass Sensemaking Model for Collaborative Discussions is proposed, which captures not only a discussion's arguments but also the norms and participants behind it, along with the context that gives each meaning.
The UIC-AIHealth4All system for ArchEHR-QA 2026, a shared task on grounded question answering from electronic health records, is described and an answer-first pipeline in which the model generates candidate answers citing specific note sentences is proposed, exploiting the asymmetry between judging relevance in the abs...
Through a controlled design that separates emotion from conversational context, it is shown that emotional context increases LLM sycophancy even in top-tier flagship models.
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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