Papers with CES
Debate to Align: Reliable Entity Alignment through Two-Stage Multi-Agent Debate (2026.findings-acl)
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| Challenge: | Entity alignment (EA) aims to identify entities referring to the same real-world object across different knowledge graphs (KGs). |
| Approach: | They propose a reliable EA framework based on multi-agent debate that improves embedding quality and introduces a two-stage multi-role debate mechanism to enhance reliability. |
| Outcome: | The proposed framework improves embedding quality and the reasoning capability of LLMs while enabling more efficient debate-based reasoning. |
Activation Steering for Chain-of-Thought Compression (2026.findings-acl)
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| Challenge: | Large language models produce intermediate explanations, commonly referred to as chains of thought (CoTs), but the generated rationales are typically verbose, consuming many additional tokens, and thus degrading throughput and increasing inference energy consumption. |
| Approach: | They propose to generate concise reasoning traces by directly adjusting internal representations via activation steering. |
| Outcome: | The proposed method reduces generated token length by 69.4% across five reasoning benchmarks while maintaining accuracy. |