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2,378 papers
AI agents are making it easier for the public to interact with government, such as by helping them apply for benefits, understand complex policies, and make their opinions heard. Although improving service accessibility is beneficial, any resulting surges in demand could strain unprepared government services. We term s...
Specifying a penalty can turn a legal obligation into a cost-benefit calculation that favors violation. We show that this enforcement information paradox occurs in AI agents. Most AI safety evaluations test whether models fail; we ask why, using compliance theory from law and economics as a diagnostic. We evaluate twel...
Overwhelmed courts in the United States review millions of default judgments each year. Unfortunately, such manual reviews are time-consuming and prone to error. In an audit of 188 debt collection cases granted default judgment by the Superior Court of Los Angeles, we find that 4% contained major defects that should ha...
As large language models gain tool access and are deployed as autonomous agents capable of editing records, executing transactions, and modifying infrastructure, we still evaluate them based on the sole metric of task completion. We argue that this evaluation focus constitutes a systematic design failure. Benchmark sco...
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Large language models (LLMs) have made fluent essay writing, code drafting, and quiz answering instantly available to students at every level, from secondary school through graduate study. Many educators do not object to LLM use per se; what they need to detect is the case in which a student pastes the assignment promp...
Abstract Recent research has shown that large language models (LLMs) exhibit rule-based insensitivity to contingencies, a functional analog of a well-documented phenomenon in human behavior. The present study extended these findings by examining whether tracking inductions can attenuate this insensitivity. Six frontier...
While computer systems that allow users to interact through conversational natural language (i.e., chatbots) have existed for many years, various types of applications advertising AI companionship (e.g., Character AI, Replika) have proliferated in recent years due to advancements in large language models. To better und...
Human uplift studies, or studies that measure the effects of AI access on human performance via randomized controlled trials (RCTs) or similar methodologies, increasingly inform frontier AI governance and deployment decisions. While RCT methods are robust in other fields, their interaction with the distinctive properti...
Adversary emulation is an essential procedure for cybersecurity assessments such as evaluating an organization’s security posture or facilitating structured training and research in dedicated environments. To allow for systematic and time-efficient assessments, several approaches from academia and industry have worked...
Large language models (LLMs) differ from previous technologies in functioning as collaborative "thought partners'' that can engage fluidly in natural language across a range of tasks and increasingly act as autonomous agents on users' behalf. As they come to shape consequential decisions from healthcare to personal adv...
This thesis asks how information enters prices at intraday horizons, and it approaches the question through three essays that move progressively from the reaction of prices to news toward the mechanism by which that reaction is produced. The first essay studies the intraday processing of firm-specific ESG news for Dow...
Archived source functions, recorded GPT-4o transformations, paired static metrics and scripts for remeasurement of 910 analysed pairs and a 100-function prompt ablation. Behavioural equivalence was not tested. This deposit contains research data and analysis code; the unpublished manuscript is not included. The manuscr...
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
We’re giving developers, enterprises, scientists, and consumers access to Gemini 4 Argon, our latest frontier model.
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