ML/AI

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AI Level of Detail: Distance-Aware ML Model Precision Selection for Real-Time Human Motion Prediction in Games

Announce Type: new Abstract: Modern game engines spend significant compute animating NPCs with learned motion models. This paper proposes AI Level of Detail (AI LOD), a framework in which machine learning inference precision is adapted based on the distance between each NPC and the player camera. The core idea mirrors classical geometry LOD: substitute a cheaper approximation where the difference is imperceptible.

FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics

arXiv:2605.17373v2 Announce Type: replace Abstract: AI research agents accelerate ML research by automating hypothesis generation, experimentation, and empirical refinement. Existing agent strategies range from greedy hill-climbing to tree search and evolutionary optimization, yet which strategy choices drive performance remains unclear. Answering this question requires a benchmark that separates agent strategy (e.g., search topology) from execution infrastructure (e.g., code editor), so...

Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution

arXiv:2605.02640v2 Announce Type: replace Abstract: As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), are increasingly deployed in high-stakes domains, ensuring their trustworthiness has become a central challenge. However, the core trustworthy AI objectives, such as fairness, robustness, privacy, and explainability, are hard to achieve simultaneously, especially while preserving utility. This position paper argues that causality is...

Position: Prioritize Identifying Structure, Not Complex Models, for Scientific Discovery

Announce Type: cross Abstract: Modern Machine Learning (ML) and Artificial Intelligence (AI) models, especially large language models (LLMs), are increasingly used to generate scientific hypotheses and mechanistic explanations from observational data. This position paper argues that in the high-dimensional proxy regimes where modern ML excels, mechanistic learning is generically underdetermined: many incompatible mechanisms induce essentially the same observational relationships on the...

Position: State-of-the-Art Claims Require State-of-the-Art Evidence

arXiv:2605.17273v3 Announce Type: replace Abstract: State-of-the-Art (SOTA) claims pervade Artificial Intelligence (AI) and Machine Learning (ML) research. These claims rest on benchmark evaluations, where models are ranked by aggregate scores across tasks. Public benchmarks or leaderboards are the most visible instance, but the same structure appears in paper tables throughout the literature.

ARIADNE: AI-RAN Informed Link Adaptation in Digital Twin Network Environments

Announce Type: replace Abstract: Artificial Intelligence (AI)-powered Radio Access Network (RAN) networks have attracted significant attention from both industry and academia. Meanwhile, Digital Twins offer a safe playground for experimenting with AI/Machine Learning (ML)-based solutions for advanced AI-RAN research.

2-Step Agent: A Framework for the Interaction of a Decision Maker with AI Decision Support

arXiv:2602.21889v3 Announce Type: replace Abstract: Predictions from ML models support human decision making in several fields, including high-stakes ones such as healthcare and the judiciary. Yet, we still lack a clear understanding of how decision makers learn from ML-based decision support (ML-DS). In this paper, we introduce a general computational framework, the 2-Step Agent, to capture this process.

2-Step Agent: A Framework for the Interaction of a Decision Maker with AI Decision Support

arXiv:2602.21889v2 Announce Type: replace Abstract: Predictions from ML models support human decision making in several fields, including high-stakes ones such as healthcare and the judiciary. Yet, we still lack a clear understanding of how decision makers learn from ML-based decision support (ML-DS). In this paper, we introduce a general computational framework, the 2-Step Agent, to capture this process.

CS336: Language Modeling from Scratch

Course Staff Logistics - Lectures: Monday/Wednesday 3:00-4:20pm in Skilling Auditorium - Recordings: YouTube playlist - Office hours: - Percy Liang: Fridays 11am-12pm in Gates 366 - Tatsu Hashimoto: Tuesdays 11-12am in Gates 364 - Marcel Rød: Tuesdays 4:30-5:30pm in Gates 498, Wednesdays 4:30-5:30pm in Gates 415 - Herman Brunborg: Wednesdays 1:30-2:30pm, Fridays 1:30-2:30pm, location Gates 392 - Steven Cao: Mondays 4:30-5:30pm, Thursdays 9:30-10:30am, Gates 200 - Contact: Students should ask...