3 citations · 4 across the 13 of their papers we have counts for
6 papers · 1 filter
Spec2Game: Can LLMs Generate Complete Playable Games from Detailed Specifications?
Yixue Cai, Yuzhe Zhao, Hanxiang Chao +4
Generating an executable program does not necessarily mean that it correctly implements the behavioral requirements specified in natural language. To evaluate large language models…
From Knowing to Acting: Benchmarking Self-Awareness Capability of LLM Agents
Yifan Li, Shengbin Yue, Boyu Feng +6
The integration of external tools has transitioned LLM agents from passive responders to autonomous systems. However, current benchmarks prioritize execution success, neglecting se…
LC-ERD: Mining Latent Logic for Self-Evolving Reasoning via Consistency-Regulated Reward Decomposition
Yanyu Chen, Jiyue Jiang, Dianzhi Yu +8
The evolution of Large Language Model (LLM) reasoning is bottlenecked by the scarcity of high-quality process data. While self-alignment via endogenous rewards offers a solution, m…
Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security
Jinhu Qi, Muzhi Li, Jiahong Liu +9
Agentic AI systems -- Large Language Models (LLMs) augmented with planning, tool use, memory, and long-horizon interactions -- can execute complex tasks autonomously, but their mul…
Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion
Muzhi Li, Cehao Yang, Chengjin Xu +5
The Knowledge Graph Completion~(KGC) task aims to infer the missing entity from an incomplete triple. Existing embedding-based methods rely solely on triples in the KG, which is vu…
Context-aware Inductive Knowledge Graph Completion with Latent Type Constraints and Subgraph Reasoning
Muzhi Li, Cehao Yang, Chengjin Xu +5
Inductive knowledge graph completion (KGC) aims to predict missing triples with unseen entities. Recent works focus on modeling reasoning paths between the head and tail entity as…