All episodes

Actor-Critic without Actor: Critic-Guided Denoising for RL

DELTA-Code: How Does RL Unlock and Transfer New Programming Algorithms in LLMs?

Linear Transformers Implicitly Discover Unified Numerical Algorithms

Regularizing Extrapolation in Causal Inference

DoubleGen - Debiased Generative Modeling of Counterfactuals

What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT

Compute as Teacher: Turning Inference Compute Into Reference-Free Supervision

Learning without training: The implicit dynamics of in-context learning

Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model

Open Problems in Mechanistic Interpretability

Maestro: Joint Graph & Config Optimization for Reliable AI Agents

Thought Anchors: Which LLM Reasoning Steps Matter?

RL's Razor: Why Online RL Forgets Less

Why Language Models Hallucinate

ALFA: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning

Sample Efficient Preference Alignment in LLMs via Active Exploration

Adventures in Demand Analysis Using AI

Memento: Fine-tuning LLM Agents without Fine-tuning LLMs

On the Theoretical Limitations of Embedding-Based Retrieval

Performance Prediction for Large Systems via Text-to-Text Regression

Demystifying the Visual Quality Paradox in Multimodal Large Language Models

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Compute-Optimal Scaling for Value-Based Deep RL

LLM-based Conversational Recommendation Agents with Collaborative Verbalized Experience

Signal and Noise: Evaluating Language Model Benchmarks

Breaking Feedback Loops in Recommender Systems with Causal Inference

RAG is Dead, Context Engineering is King: Building Reliable AI Systems

A Survey of Personalization: From RAG to Agent

Facilitating the Adoption of Causal Infer-ence Methods Through LLM-Empowered Co-Pilot

Performance Prediction for Large Systems via Text-to-Text Regression

Sample More to Think Less: Group Filtered Policy Optimization for Concise Reasoning

DINOv3: Vision Models for Self-Supervised Learning

Agent Lightning: Training Any AI Agents with Reinforcement Learning

Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier

From Model Weights to Agent Workflows: Charting the New Frontier of Optimization in Large Language Models

Is Chain-of-Thought Reasoning a Mirage?

Agentic Web: Weaving the Next Web with AI Agents

The Assimilation-Accommodation Gap in LLM Intelligence

The Minimalist AI Kernel: A New Frontier in Reasoning

Statistical Rigor for Interpretable AI

Full-Stack Alignment: Co-Aligning AI and Institutions with Thick Models of Value

A foundation model to predict and capture human cognition

Generative Recommendation with Semantic IDs: A Practitioner’s Handbook

Hierarchical Reasoning Model

Test-time Offline Reinforcement Learning on Goal-related Experience

Interpreting Chain of Thought: A Walkthrough and Discussion

The wall confronting large language models

COLLABLLM: LLMs From Passive to Collaborative

A decade's battle on dataset bias: are we there yet?

GEPA: Generative Feedback for AI System Optimization

From AI-Curious to AI-First: Engineering Production AI Systems

Context Engineering: Beyond Simple Prompting to LLM Architecture

Agentic Misalignment: LLMs as Insider Threats

Small Language Models: Future of Agentic AI

Learning without training: The implicit dynamics of in-context learning

Inverse Scaling in Test-Time Compute

LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra

Microsoft's Blueprint: AI, Quantum, and the Agentic Future

Zuckerberg's AI Vision Analyzed

Inside Claude: Scaling, Agency, and Interpretability

Personalized language modeling from personalized human feedback

Position: Empowering Time Series Reasoning with Multimodal LLMs

An empirical risk minimization approach for offline inverse RL and Dynamic Discrete Choice models

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities

The Invisible Leash: Why RLVR May Not Escape Its Origin

Language Model Personalization via Reward Factorization

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective

Soft Best-of-n Sampling for Model Alignment

On Temporal Credit Assignment and Data-Efficient Reinforcement Learning

Bradley–Terry and Multi-Objective Reward Modeling Are Complementary

Probing Foundation Models for World Models

GenAI-Powered Statistical Inference (with Unstructured Data)

Interpretable Reward Modeling with Active Concept Bottlenecks

PrefillOnly: An Inference Engine for Prefill-only Workloads in Large Language Model Applications

A Collectivist, Economic Perspective on AI

Textual Bayes: Quantifying Uncertainty in LLM-Based Systems

The Winner's Curse in Data-Driven Decisions

SPIRAL: Self-Play for Reasoning Through Zero-Sum Games

Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence

Aligning Learning and Endogenous Decision-Making

Reliable Statistical Inference with Synthetic Data from Large Language Models

Multi-Turn Reinforcement Learning from Human Preference Feedback

Provably Learning from Language Feedback

Markets with Heterogeneous Agents: Dynamics and Survival of Bayesian vs. No-Regret Learners

Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation

Causal Abstraction with Lossy Representations

The Winner's Curse in Data-Driven Decisions

Embodied AI Agents: Modeling the World

Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence

What Has a Foundation Model Found? Inductive Bias Reveals World Models

Language Bottleneck Models: A Framework for Interpretable Knowledge Tracing and Beyond

Learning to Explore: An In-Context Learning Approach for Pure Exploration

Human-AI Matching: The Limits of Algorithmic Search

Uncertainty Quantification Needs Reassessment for Large-language Model Agents

Bayesian Meta-Reasoning for Robust LLM Generalization

General Intelligence Requires Reward-based Pretraining

Deep Learning is Not So Mysterious or Different

AI Agents Need Authenticated Delegation

Probabilistic Modelling is Sufficient for Causal Inference