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Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering
Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering

This research paper argues that current methods for Uncertainty Quantification (UQ) in large language models are fundamentally flawed because they fu…

2 months ago

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Position: Agents Should Invoke External Tools ONLY When Epistemically Necessary
Position: Agents Should Invoke External Tools ONLY When Epistemically Necessary

This position paper discusess Theory of Agent (ToA), a framework that redefines large language model agents as decision-makers who must choose betwee…

2 months ago

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From conversations to mechanisms: aligning advertiser Incentives in ai-powered product recommendations
From conversations to mechanisms: aligning advertiser Incentives in ai-powered product recommendations

This research paper explores the development of efficient recommendation systems, such as AI shopping assistants, that manage multi-round interaction…

2 months ago

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Is one layer enough? Training a single transformer layer can match full-parameter RL training
Is one layer enough? Training a single transformer layer can match full-parameter RL training

This paper explores a surprising structural property of large language models: most reinforcement learning (RL) gains are concentrated in a very smal…

2 months ago

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RL Excursions during Pre-Training: Re-examining Policy Optimization for LLM training
RL Excursions during Pre-Training: Re-examining Policy Optimization for LLM training

This research investigates the effectiveness of integrating reinforcement learning (RL) earlier in the large language model training pipeline rather …

2 months ago

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Language Generation with Feedback: Queries and Mistakes
Language Generation with Feedback: Queries and Mistakes

This paper introduces a theoretical framework for language generation in the limit, exploring how machines can learn to produce valid, unseen strings…

2 months ago

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Quantifying Theoretical AI Alignment Guarantees: Receiver-Utility Bounds in Bayesian Persuasion
Quantifying Theoretical AI Alignment Guarantees: Receiver-Utility Bounds in Bayesian Persuasion

This research paper explores theoretical AI alignment through the lens of Bayesian persuasion, specifically examining how a misaligned AI agent might…

2 months, 1 week ago

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SPIRAL: Learning to search and aggregate
SPIRAL: Learning to search and aggregate

The Spiral framework addresses a limitation in current language model training where models are optimized for single-trace reasoning but fail to coor…

2 months, 1 week ago

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Qwen-AgentWorld: Language World Models for General Agents
Qwen-AgentWorld: Language World Models for General Agents

We discuss Qwen-AgentWorld, a pioneering suite of language world models designed to simulate complex digital environments for artificial intelligence…

2 months, 1 week ago

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When Does Trajectory-Level Supervision Permit Efficient Offline Reinforcement Learning?
When Does Trajectory-Level Supervision Permit Efficient Offline Reinforcement Learning?

This paper discusses a statistical framework for offline reinforcement learning using trajectory-level supervision, where only final outcomes or pref…

2 months, 1 week ago

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