9 papers
ToolAnchor: Anchoring Counterfactual Context to Boost Agentic Tool-use Capability
Weiting Liu, Jieyi Bi, Wanqi Zhou +4
The paper introduces ToolAnchor, a method that injects counterfactual contexts to help large language model agents overcome reliance on familiar tools and adapt to new toolsets wit…
A General Neural Backbone for Mixed-Integer Linear Optimization via Dual Attention
Peixin Huang, Yaoxin Wu, Yining Ma +3
Mixed-integer linear programming (MILP) is a foundational framework for combinatorial optimization across science and engineering, but remains hard to solve at scale due to NP-hard…
AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library
Minwei Kong, Ao Qu, Xiaotong Guo +12
Optimization modeling underlies critical decision-making across industries, yet remains difficult to automate: natural-language problem descriptions must be translated into precise…
Beyond Static Priors: Dynamic Neural Guidance for Large-Scale Ant Colony Optimization
Dat Thanh Tran, Van Khu Vu, Yining Ma
Neural-guided Ant Colony Optimization (ACO) suffers from a fundamental training-inference misalignment: policies are typically trained to generate static priors (e.g., heatmaps), y…
Rethinking Positional Encoding for Neural Vehicle Routing
Chuanbo Hua, Federico Berto, Andre Hottung +8
Transformer-based models have become the dominant paradigm for neural combinatorial optimization (NCO) of vehicle routing problems (VRPs), yet the role of positional encoding (PE)…
Internalizing Curriculum Judgment for LLM Reinforcement Fine-Tuning
Han Zheng, Yining Ma, Karthick Gunasekaran +4
In LLM Reinforcement Fine-Tuning (RFT), curriculum learning drives both efficiency and performance. Yet, current methods externalize curriculum judgment via handcrafted heuristics…