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Q-Learning with World Models
Q-Learning with World Models

The researchers introduce Q-Learning with World Models (QWM), a framework designed to enhance sample efficiency and performance in robotic reinforcem…

2 weeks ago

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Conformal Language Modeling via Posterior Sampling
Conformal Language Modeling via Posterior Sampling

This paper introduces Conformal Language Modeling via Posterior Sampling, a novel framework designed to reduce hallucinations in Large Language Model…

2 weeks, 2 days ago

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BoNVoyage: Learning Better Rewards without Ranking
BoNVoyage: Learning Better Rewards without Ranking

BoNVoyage is a novel training framework designed to improve reward models (RMs) used in reinforcement learning from human feedback. Traditional RMs o…

2 weeks, 3 days ago

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Demystifying Agent Skills: Why They Work—Until They Don’t
Demystifying Agent Skills: Why They Work—Until They Don’t

This research investigates the operational dynamics of agent skills, which are structured packages of procedural knowledge designed to help AI agents…

2 weeks, 5 days ago

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Jagged Judges: Epistemic Stability Under Silence, Pressure, and Persistence
Jagged Judges: Epistemic Stability Under Silence, Pressure, and Persistence

This paper introduces the Wiggle Framework, a novel diagnostic tool designed to evaluate the epistemic stability of Large Language Models when they a…

3 weeks ago

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Predicting Neural Scaling Laws without Training: A Data Manifold Oracle
Predicting Neural Scaling Laws without Training: A Data Manifold Oracle

This paper introduces the Data Manifold Oracle (DMO), a training-free framework designed to predict neural scaling laws by analyzing raw text through…

3 weeks ago

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Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing
Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing

This paper introduces a rigorous statistical framework for discovering human-interpretable insights from unstructured data, such as text, audio, and …

3 weeks, 4 days ago

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Overcoming the Incentive Collapse Paradox
Overcoming the Incentive Collapse Paradox

This paper introduces and addresses the incentive collapse paradox, a phenomenon where accuracy-based payments fail to motivate human effort as AI as…

3 weeks, 4 days ago

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Position: Modular Memory is the Key to Continual Learning Agents
Position: Modular Memory is the Key to Continual Learning Agents

This paper introduces a framework for modular memory as the essential solution for creating continual learning agents that adapt without forgetting. …

3 weeks, 5 days ago

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Harness RL is Meta-Learning: Training to Self-Improve at Test Time
Harness RL is Meta-Learning: Training to Self-Improve at Test Time

This paper introduces harness RL, a novel meta-learning framework designed to enable large language models to self-improve during test-time adaptatio…

4 weeks ago

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