All episodes

Probabilistic Modelling is Sufficient for Causal Inference

Not All Explanations for Deep Learning Phenomena Are Equally Valuable

e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs

Extrapolation by Association: Length Generalization Transfer in Transformers

Uncovering Causal Hierarchies in Language Model Capabilities

Generalization or Hallucination? Understanding Out-of-Context Reasoning in Transformers

Improving Treatment Effect Estimation with LLM-Based Data Augmentation

LLM Numerical Prediction Without Auto-Regression

Why in-context learning models are good few-shot learners?

Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina∗

The Logic of Machines: The AI Reasoning Debate

Layer by Layer: Uncovering Hidden Representations in Language Models

Causal Attribution Analysis for Continuous Outcomes

Training a Generally Curious Agent

Estimation of Treatment Effects Under Nonstationarity via Truncated Difference-in-Q’s

Strategy Coopetition Explains the Emergence and Transience of In-Context Learning

Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs

Agentic Supernet for Multi-agent Architecture Search

Sample Complexity and Representation Ability of Test-time Scaling Paradigms

Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators

LLMs Get Lost In Multi-Turn Conversation

PromptPex: Automatic Test Generation for Prompts

General Agents Need World Models

The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models

Decisions With Algorithms

Adapting, fast and slow: Causal Approach to Few-Shot Sequence Learning

Conformal Arbitrage for LLM Objective Balancing

Simulation-Based Inference for Adaptive Experiments

Agents as Tool-Use Decision-Makers

Quantitative Judges for Large Language Models

Self-Challenging Language Model Agents

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

How Bidirectionality Helps Language Models Learn Better via Dynamic Bottleneck Estimation

A Closer Look at Bias and Chain-of-Thought Faithfulness of Large (Vision) Language Models

Simplifying Bayesian Optimization Via In-Context Direct Optimum Sampling

Bayesian Teaching Enables Probabilistic Reasoning in Large Language Models

IPO: Interpretable Prompt Optimization for Vision-Language Models

Evolutionary Prompt Optimization discovers emergent multimodal reasoning strategies

Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know?

Diffusion Guidance Is a Controllable Policy Improvement Operator

Alita: Generalist Agent With Self-Evolution

A Snapshot of Influence: A Local Data Attribution Framework for Online Reinforcement Learning

Learning Compositional Functions with Transformers from Easy-to-Hard Data

Preference Learning with Response Time

Accelerating RL for LLM Reasoning with Optimal Advantage Regression

Algorithms for reliable decision-making need causal reasoning

Belief Attribution as Mental Explanation: The Role of Accuracy, Informativity, and Causality

Distances for Markov chains from sample streams

When and Why LLMs Fail to Reason Globally

IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis

No Free Lunch: Non-Asymptotic Analysis of Prediction-Powered Inference

Accelerating RL for LLM Reasoning with Optimal Advantage Regression

Statistical Inference for Online Algorithms

Prismatic Synthesis for Diverse LLM Reasoning Data

Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents

The Agentic Economy

Statistics for Large Language Models

Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search

Beyond Markovian: Reflective Exploration via Bayes-Adaptive RL for LLM Reasoning

Planning without Search: Refining Frontier LLMs with Offline Goal-Conditioned RL

Value-Guided Search for Efficient Chain-of-Thought Reasoning

Shallow Preference Signals: Large Language model aligns even better without truncated data?

Gaming Tool Preferences in Agentic LLMs

Partner Modelling Emerges in Recurrent Agents (But Only When It Matters)

LLM Populations Form Social Conventions and Collective Bias

LLM Generated Persona is a Promise with a Catch

Large Language Models for Digital Twin Simulation

From RL Distillation to Autonomous LLM Agents

Prompting, Auto-Prompting, and Human-AI Communication

Textual Gradients for LLM Optimization

Large Language Models as Markov Chains

Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation

Selective induction heads: how transformers select causal structures in context

The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains

How Transformers Learn Causal Structure with Gradient Descent

Planning anything with rigor: general-purpose zero-shot planning with llm-based formalized programming

Automated Design of Agentic Systems

What’s the Magic Word? A Control Theory of LLM Prompting

BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling

RL with KL penalties is better viewed as Bayesian inference

Asymptotics of Language Model Alignment

Qwen 2.5, RL, and Random Rewards

Theoretical guarantees on the best-of-n alignment policy

Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models

Improved Techniques for Training Score-Based Generative Models

Your Pre-trained LLM is Secretly an Unsupervised Confidence Calibrator

AlphaEvolve: A coding agent for scientific and algorithmic discovery

Harnessing the Universal Geometry of Embeddings

Goal Inference using Reward-Producing Programs in a Novel Physics Environment

Trial-Error-Explain In-Context Learning for Personalized Text Generation

Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Test-Time Reinforcement Learning (TTRL)

Interpreting Emergent Planning in Model-Free Reinforcement Learning

Agentic Reward Modeling_Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward Systems

Beyond Reward Hacking: Causal Rewards for Large LanguageModel Alignment

Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Highlighting What Matters: Promptable Embeddings for Attribute-Focused Image Retrieval

UFT: Unifying Supervised and Reinforcement Fine-Tuning

Understanding High-Dimensional Bayesian Optimization

Inference time alignment in continuous space