1 citations · 1 across the 10 of their papers we have counts for
11 papers
$OneMillion-Bench: How Far are Language Agents from Human Experts?
Qianyu Yang, Yang Liu, Jiaqi Li +19
As language models (LMs) evolve from chat assistants to long-horizon agents capable of multi-step reasoning and tool use, existing benchmarks remain largely confined to structured…
NarrativeLoom: Enhancing Creative Storytelling through Multi-Persona Collaborative Improvisation
Yuxi Ma, Yongqian Peng, Fengyuan Yang +5
Large Language Models show promise for AI-assisted storytelling, yet current tools often generate predictable, unoriginal narratives. To address this limitation, we present Narrati…
The AI Hippocampus: How Far are We From Human Memory?
Zixia Jia, Jiaqi Li, Yipeng Kang +12
Memory plays a foundational role in augmenting the reasoning, adaptability, and contextual fidelity of modern Large Language Models and Multi-Modal LLMs. As these models transition…
BEDA: Belief Estimation as Probabilistic Constraints for Performing Strategic Dialogue Acts
Hengli Li, Zhaoxin Yu, Qi Shen +8
Strategic dialogue requires agents to execute distinct dialogue acts, for which belief estimation is essential. While prior work often estimates beliefs accurately, it lacks a prin…
Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia
Chandler Smith, Marwa Abdulhai, Manfred Diaz +83
Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with bo…
Native Parallel Reasoner: Reasoning in Parallelism via Self-Distilled Reinforcement Learning
Tong Wu, Yang Liu, Jun Bai +6
We introduce Native Parallel Reasoner (NPR), a teacher-free framework that enables Large Language Models (LLMs) to self-evolve genuine parallel reasoning capabilities. NPR transfor…
Make an Offer They Can't Refuse: Grounding Bayesian Persuasion in Real-World Dialogues without Pre-Commitment
Buwei He, Yang Liu, Zhaowei Zhang +6
Large language models (LLMs) still struggle with strategic persuasion, largely because existing approaches either neglect information asymmetry or rely on unrealistic pre-commitmen…
Adaptive Preference Optimization with Uncertainty-aware Utility Anchor
Xiaobo Wang, Zixia Jia, Jiaqi Li +2
Offline preference optimization methods are efficient for large language models (LLMs) alignment. Direct Preference optimization (DPO)-like learning, one of the most popular approa…
Understanding and Leveraging the Expert Specialization of Context Faithfulness in Mixture-of-Experts LLMs
Jun Bai, Minghao Tong, Yang Liu +2
Context faithfulness is essential for reliable reasoning in context-dependent scenarios. However, large language models often struggle to ground their outputs in the provided conte…
TongSearch-QR: Reinforced Query Reasoning for Retrieval
Xubo Qin, Jun Bai, Jiaqi Li +2
Traditional information retrieval (IR) methods excel at textual and semantic matching but struggle in reasoning-intensive retrieval tasks that require multi-hop inference or comple…
ReflectEvo: Improving Meta Introspection of Small LLMs by Learning Self-Reflection
Jiaqi Li, Xinyi Dong, Yang Liu +6
We present a novel pipeline, ReflectEvo, to demonstrate that small language models (SLMs) can enhance meta introspection through reflection learning. This process iteratively gener…