FDA issues a Request for Information to solicit input on a proposed pilot program assessing how AI-enabled technologies could improve efficiency and decision-making in early-phase clinical trials. The pilot would be guided by principles aligned with the NIST AI Risk Management Framework.
The Department of Commerce/ITA invites proposals for industry-led pre-set consortia to be designated under the American AI Exports Program. Designated packages may receive priority government advocacy and related support, subject to applicable law.
FDA is extending the comment period for its Federal Register notice requesting input on a proposed AI-enabled pilot program for early-phase clinical trials. The extension is granted to allow additional time for public comments.
NIST announces the retitling of the Artificial Intelligence Safety Institute Consortium (AISIC) as the NIST Artificial Intelligence Consortium, revises its research scope, and reissues an invitation for organizations to submit letters of interest to collaborate under CRADAs.
The FCC adopted a Notice of Proposed Rulemaking (NPRM) to examine how to modernize and improve high-cost universal support mechanisms for future years, including support for broadband-capable networks.
Clinical reasoning over electronic health records (EHRs) is a fundamental yet challenging task in modern healthcare. While large language models (LLMs) offer a promising paradigm via in-context demonstrations that requires no task-specific parameter updates, existing methods for reasoning by patient analogy in EHR settings suffer from three core limitations: (1) Perspective Limitation, where data-
Actor-critic (AC) methods are a cornerstone of reinforcement learning (RL) but offer limited interpretability. Current explainable RL methods seldom use state attributions to assist training. Rather, they treat all state features equally, thereby neglecting the heterogeneous impacts of individual state dimensions on the reward. We propose RKHS-SHAP-based Advanced Actor-Critic (RSA2C), an attributi
LLMs are increasingly used for code generation, but their outputs often follow recurring templates that can induce predictable vulnerabilities. We study vulnerability persistence in LLM-generated software and introduce Feature--Security Table (FSTab) with two components. First, FSTab enables a black-box attack that predicts likely backend vulnerabilities from observable frontend features and knowl
In most existing AI humor research, humor was treated as either "present" or "not present." We explore the concept of humor as a social interaction with context and explanations. During this project, we defined a humor reasoning data object and developed a way to prompt LLMs to generate an explanation of humor effective for general population. We iterated from an earlier prompt to an improved prom
The general trace reconstruction problem seeks to recover an original sequence from its noisy copies independently corrupted by insertions, deletions, and substitutions. This problem arises in applications such as DNA data storage, a promising storage medium due to its high information density and longevity. However, errors introduced during DNA synthesis, storage, and sequencing require correctio
Late-stage capital remains concentrated in infrastructure and frontier AI, with public-market and undisclosed financing both appearing at scale. The cycle points to money favoring companies already operating at system level rather than broad seed dispersion.
Late-stage capital remains concentrated in infrastructure and frontier AI, with public-market and undisclosed financing both appearing at scale. The cycle points to money favoring companies already operating at system level rather than broad seed dispersion.
Late-stage capital remains concentrated in infrastructure and frontier AI, with public-market and undisclosed financing both appearing at scale. The cycle points to money favoring companies already operating at system level rather than broad seed dispersion.
The talent desk is thin, but it still flags formation and leadership changes rather than routine churn. The relevant signal is whether a named move adds operating capacity or marks a new company formation.
The talent desk is thin, but it still flags formation and leadership changes rather than routine churn. The relevant signal is whether a named move adds operating capacity or marks a new company formation.
The talent desk is thin, but it still flags formation and leadership changes rather than routine churn. The relevant signal is whether a named move adds operating capacity or marks a new company formation.
Benchmark activity is limited to one model’s scores across three tasks, so the main signal is coverage rather than movement. The desk matters here because even sparse entries can show where evaluation attention is being tracked.
Benchmark activity is limited to one model’s scores across three tasks, so the main signal is coverage rather than movement. The desk matters here because even sparse entries can show where evaluation attention is being tracked.
Benchmark activity is limited to one model’s scores across three tasks, so the main signal is coverage rather than movement. The desk matters here because even sparse entries can show where evaluation attention is being tracked.