- Elo-Evolve: A Co-evolutionary Framework for Language Model Alignment
Jing Zhao, Ting Zhen, Junwei Bao, Hongfei Jiang, Yang Song · Feb 14, 2026 · Citations: 0
Pairwise Preference Automatic Metrics Multi Agent
Current alignment methods for Large Language Models (LLMs) rely on compressing vast amounts of human preference data into static, absolute reward functions, leading to data scarcity, noise sensitivity, and training instability.
- Build, Judge, Optimize: A Blueprint for Continuous Improvement of Multi-Agent Consumer Assistants
Alejandro Breen Herrera, Aayush Sheth, Steven G. Xu, Zhucheng Zhan, Charles Wright · Mar 3, 2026 · Citations: 0
Pairwise PreferenceRubric Rating Llm As JudgeSimulation Env Long Horizon
Conversational shopping assistants (CSAs) represent a compelling application of agentic AI, but moving from prototype to production reveals two underexplored challenges: how to evaluate multi-turn interactions and how to optimize tightly…
- Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition
Zheng Hui, Xiaokai Wei, Yexi Jiang, Kevin Gao, Chen Wang · Apr 26, 2025 · Citations: 0
Pairwise Preference Automatic Metrics Multi Agent
These domains typically involve fixed content and passive consumption, where user preferences can be matched by genre or theme.
- PrivAct: Internalizing Contextual Privacy Preservation via Multi-Agent Preference Training
Yuhan Cheng, Hancheng Ye, Hai Helen Li, Jingwei Sun, Yiran Chen · Feb 14, 2026 · Citations: 0
Pairwise Preference Automatic Metrics Multi Agent
We propose PrivAct, a contextual privacy-aware multi-agent learning framework that internalizes contextual privacy preservation directly into models' generation behavior for privacy-compliant agentic actions.
- Multi-Agent Comedy Club: Investigating Community Discussion Effects on LLM Humor Generation
Shiwei Hong, Lingyao Li, Ethan Z. Rong, Chenxinran Shen, Zhicong Lu · Feb 16, 2026 · Citations: 0
Pairwise PreferenceRubric Rating Multi Agent
Prior work has explored multi-turn interaction and feedback for LLM writing, but evaluations still largely center on prompts and localized feedback, leaving persistent public reception in online communities underexamined.
- Exploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in Planning
Guilhem Fouilhé, Rebecca Eifler, Antonin Poché, Sylvie Thiébaux, Nicholas Asher · Mar 2, 2026 · Citations: 0
Pairwise Preference Multi Agent
When automating plan generation for a real-world sequential decision problem, the goal is often not to replace the human planner, but to facilitate an iterative reasoning and elicitation process, where the human's role is to guide the AI…
- Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks
Kunihiro Miyazaki, Takanobu Kawahara, Stephen Roberts, Stefan Zohren · Feb 26, 2026 · Citations: 0
Pairwise Preference Multi Agent
While mainstream approaches deploy multi-agent systems mimicking analyst and manager roles, they often rely on abstract instructions that overlook the intricacies of real-world workflows, which can lead to degraded inference performance and…
- Decentralized Ranking Aggregation: Gossip Algorithms for Borda and Copeland Consensus
Anna Van Elst, Kerrian Le Caillec, Igor Colin, Stephan Clémençon · Feb 26, 2026 · Citations: 0
Pairwise Preference Multi Agent
The concept of ranking aggregation plays a central role in preference analysis, and numerous algorithms for calculating median rankings, often originating in social choice theory, have been documented in the literature, offering theoretical…
- The Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems
Xiaoze Liu, Ruowang Zhang, Weichen Yu, Siheng Xiong, Liu He · Feb 17, 2026 · Citations: 0
Pairwise Preference Multi Agent
Multi-Agent Systems (MAS) powered by Large Language Models have unlocked advanced collaborative reasoning, yet they remain shackled by the inefficiency of discrete text communication, which imposes significant runtime overhead and…
- CORE: Measuring Multi-Agent LLM Interaction Quality under Game-Theoretic Pressures
Punya Syon Pandey, Yongjin Yang, Jiarui Liu, Zhijing Jin · Aug 16, 2025 · Citations: 0
Pairwise Preference Multi Agent
Game-theoretic interactions between agents with Large Language Models (LLMs) have revealed many emergent capabilities, yet the linguistic diversity of these interactions has not been sufficiently quantified.
