Xuan-Jing Huang

A Token-Level Analysis of Sampled-Token Reverse-KL On-Policy Distillation

On-policy distillation (OPD) supervises a student on its own trajectories with token-level signals from a frozen teacher, yet how a sampled loss allocates updates across tokens remains poorly understood. We analyze the gradient of the per-token K2 estimator of reverse KL with respect to the student logits. The $\ell_1$...

MuseCritic: Learning Multi-Aspect Song Rewards through Natural-Language Aesthetic Critiques

Long-form song generation models continue to improve in duration, structural integrity, and acoustic complexity, making reliable aesthetic rewards increasingly important for aligning these models with human preferences. However, reward models for complete songs remain limited, and existing evaluators typically predict...

SPIEval: Evaluating Large Language Models as Mobile Assistants over Scattered Personal Information

Large language models (LLMs) are increasingly deployed as mobile assistants, where a key challenge is leveraging personal information scattered across multiple applications (apps) to complete user instructions. However, due to the lack of dedicated benchmarks, their capabilities remain poorly understood. To address thi...

MathCritique: Enhancing LLM Reasoning via Critique Models with Test-Time and Training-Time Supervision

Interpretability in the Era of Large Language Models: Mechanistic Methodology, Empirical Practices, and Applications

The rapid evolution of Large Language Models (LLMs) has brought unprecedented capabilities across reasoning, coding, and multimodal tasks. However, as performance scales, their opaque ''black-box'' nature raises a critical challenge: How can we trace the origins of emergent intelligence, and more importantly, how can w...

EntangleCodec: A Unified Discrete Audio Tokenizer via Semantic-Acoustic Entanglement

EntangleCodec is proposed, a unified discrete audio tokenizer that learns caption-aligned semantic-acoustic representations before quantization that achieves reconstruction quality competitive with specialized codecs, outperforms all codec-based baselines on audio understanding, and supports both TTS and TTA generation...

JFTA-Bench: Evaluate LLM's Ability of Tracking and Analyzing Malfunctions Using Fault Trees

A novel textual representation of fault trees is proposed, and a benchmark for multi-turn dialogue systems that emphasizes robust interaction in complex environments is constructed, evaluating a model's ability to assist in malfunction localization.

CAFE: Self-Improving Search Agents Need Co-Evolving Feedback

CAFE (Coupled Agent--Feedback Evolution), a framework in which a shared-parameter model alternates between search-agent and critic roles, is introduced, suggesting that a self-improving search agent needs feedback that co-evolves with the policy it guides.

Agents in the Large: Perception-Centered Architecture for Persistent Agents

Pera describes a persistent agent organized around perception and control components that continually perceive service-relevant signals from episodic task executions, internal context, and changes in the surrounding environment, and use these signals to construct lifecycle tasks.

Counteracting the Matthew Effect in Self-Improvement of LVLMs through Head-Tail Re-balancing

To mitigate a critical imbalance during the exploration-and-learning process, this work approaches head-tail re-balance during the exploration-and-learning process from two perspectives: distribution-reshaping and trajectory-resampling.

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments

AgentGym2 is presented, a new evaluation framework with task instances grounded in real-world end-to-end working demands that measures agents'ability to execute end-to-end procedures, discover tools via exploration, compose tools for unseen tasks, and remain robust to noisy and underspecified information.

Interpretability in the Era of Large Language Models: Mechanistic Methodology, Empirical Practices, and Applications

This tutorial provides a comprehensive, end-to-end view of LLM interpretability, transitioning from microscopic neural analysis to macroscopic application and deployment, and explores how these interpretability paradigms scale and inspire the design of frontier architectures, agentic systems, and thinking models.