Preprint

MASCOT: Towards Multi-Agent Socio-Collaborative Companion Systems
MASCOT: Towards Multi-Agent Socio-Collaborative Companion Systems

MASCOT is a multi-agent framework for socio-collaborative companions that uses bi-level optimization—persona-aware behavioral alignment and collaborative dialogue optimization—to counter persona collapse and social sycophancy, improving role consistency and reducing redundant dialogue.

Jul 1, 2026

UniSD: Towards a Unified Self-Distillation Framework for Large Language Models
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models

UniSD unifies the fragmented landscape of self-distillation for large language models, providing a principled framework that supports systematic comparison and new combinations across data, representation, and decoding levels.

May 30, 2026

TextReg: Mitigating Prompt Distributional Overfitting via Regularized Text-Space Optimization
TextReg: Mitigating Prompt Distributional Overfitting via Regularized Text-Space Optimization

TextReg introduces a regularized text-space optimization objective that mitigates prompt distributional overfitting, improving robustness across tasks, models, and evaluation distributions.

May 30, 2026

AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent
AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent

AgentArk distills the collaborative behavior of multi-agent systems into a single LLM agent, decomposing trajectories into role-conditioned skills and recovering most of the ensemble's performance at a fraction of the cost.

Feb 4, 2026

Consistency Should Be the Priority for Unified Multimodal Models

A position paper arguing that consistency across views, modalities, and prompts should be the priority research target for unified multimodal models.

Feb 3, 2026

Efficient Knowledge Probing of Large Language Models by Adapting Pre-trained Embeddings

An efficient approach to probing LLM knowledge that adapts pre-trained embeddings to query model knowledge with substantially reduced compute.

Feb 1, 2026

Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems
Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems

We propose a framework for developing topology-aware Multi-Agent Systems (MAS), emphasizing agent selection, structure profiling, and topology synthesis, to enhance coordination and efficiency in complex task.

Jul 4, 2025