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2,089 papers
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.
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.
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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.
It is argued that religious alignment in generative AI requires interpretive alignment: systems that disclose their limits, preserve plurality, and avoid simulating sacred authority and sycophantic personalization.
CrabOS elevates support for complex tasks with alternating human and AI leadership from bridge-dependent application-level solutions to native operating-system capabilities, which provide a new foundation for developing and running AI agents.
Course-based undergraduate research experiences (CUREs) broaden access to authentic scientific inquiry through responsive instructor support as research problems become increasingly complex. Generative artificial intelligence (GenAI) may extend this support by providing individualized assistance that can adapt as stude...
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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