9 papers
Revisiting the Generalization of Neural Graph Edit Distance Models
Zhouyang Liu, Ning Liu, Yixin Chen +2
Neural approaches to Graph Edit Distance (GED) have achieved strong results under standard within-dataset evaluation, but much less is known about how well these models transfer ac…
Eluna: An Agentic LLM System for Automating Warehouse Operations with Reasoning and Task Execution
Ning Liu, P Aditya Sreekar, Kalle Kujanpää +11
Warehouse operations are governed by Standard Operating Procedures (SOPs) that encode complex, multi-system decision logic, which must be executed reliably under strict time constr…
Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems
Kalle Kujanpää, Ning Liu, Shahnawaz Alam +4
Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request. We replace this inference-time coding loop with an ag…
LLMs as a Jury: Cross-Model Consensus Can Outperform Process Reward Models for LLM Reasoning
Ning Liu
Selecting the correct answer from a pool of candidate reasoning chains is the engine of test-time scaling, yet the standard selectors each carry a cost: self-consistency inherits t…
Track, Rank, Crack: Epistemic Working Memory Scales Multi-Hop Reasoning in Language Agents
Ning Liu
Language agents that interleave reasoning and tool use degrade sharply as reasoning chains lengthen, even when each individual step is easy. We trace this to context dilution: an a…
Beyond Pairs: Your Language Model is Secretly Optimizing a Preference Graph
Ning Liu, Chuanneng Sun, Kristina Klinkner +1
Direct Preference Optimization (DPO) aligns language models using pairwise preference comparisons, offering a simple and effective alternative to Reinforcement Learning (RL) from h…
Hierarchy-Aware Neural Subgraph Matching with Enhanced Similarity Measure
Zhouyang Liu, Ning Liu, Yixin Chen +3
Subgraph matching is challenging as it necessitates time-consuming combinatorial searches. Recent Graph Neural Network (GNN)-based approaches address this issue by employing GNN en…
Graph2Region: Efficient Graph Similarity Learning with Structure and Scale Restoration
Zhouyang Liu, Yixin Chen, Ning Liu +2
Graph similarity is critical in graph-related tasks such as graph retrieval, where metrics like maximum common subgraph (MCS) and graph edit distance (GED) are commonly used. Howev…
Rethinking Flexible Graph Similarity Computation: One-step Alignment with Global Guidance
Zhouyang Liu, Ning Liu, Yixin Chen +3
Graph Edit Distance (GED) is a widely used measure of graph similarity, valued for its flexibility in encoding domain knowledge through operation costs. However, existing learning-…