ARM: Role-Conditioned Neuron Transplantation for Training-Free Generalist LLM Agent Merging

• URL: http://arxiv.org/abs/2601.07309v1
• Date: Mon, 12 Jan 2026 08:31:53 GMT
• Title: ARM: Role-Conditioned Neuron Transplantation for Training-Free Generalist LLM Agent Merging
• Authors: Zhuoka Feng, Kang Chen, Sihan Zhao, Kai Xiong, Yaoning Wang, Minshen Yu, Junjie Nian, Changyi Xiao, Yixin Cao, Yugang Jiang,
• Abstract summary: Agent-Role Merging (ARM) is an activation-guided, role-conditioned neuron transplantation method for model merging in LLM agents.<n>ARM improves existing merging methods from static natural language tasks to multi-turn agent scenarios.
• Score: 51.409102048965394
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Interactive large language model agents have advanced rapidly, but most remain specialized to a single environment and fail to adapt robustly to other environments. Model merging offers a training-free alternative by integrating multiple experts into a single model. In this paper, we propose Agent-Role Merging (ARM), an activation-guided, role-conditioned neuron transplantation method for model merging in LLM agents. ARM improves existing merging methods from static natural language tasks to multi-turn agent scenarios, and over the generalization ability across various interactive environments. This is achieved with a well designed 3-step framework: 1) constructing merged backbones, 2) selection based on its role-conditioned activation analysis, and 3) neuron transplantation for fine-grained refinements. Without gradient-based optimization, ARM improves cross-benchmark generalization while enjoying efficiency. Across diverse domains, the model obtained via ARM merging outperforms prior model merging methods and domain-specific expert models, while demonstrating strong out-of-domain generalization.

Related papers

• MARTI-MARS$^2$: Scaling Multi-Agent Self-Search via Reinforcement Learning for Code Generation [64.2621682259008]
  Multi-Agent Reinforced Training and Inference Framework with Self-Search Scaling (MARTI-MARS2)<n>We propose a Multi-Agent Reinforced Training and Inference Framework with Self-Search Scaling (MARTI-MARS2) to integrate policy learning with multi-agent tree search.<n>We show that MARTI-MARS2 achieves 77.7%, outperforming strong baselines like GPT-5.1 on challenging code generation benchmarks.
  arXiv  Detail & Related papers  (2026-02-08T07:28:44Z)
• MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning [53.37068897861388]
  MedSAM-Agent is a framework that reformulates interactive segmentation as a multi-step autonomous decision-making process.<n>We develop a two-stage training pipeline that integrates multi-turn, end-to-end outcome verification.<n>Experiments across 6 medical modalities and 21 datasets demonstrate that MedSAM-Agent achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-02-03T09:47:49Z)
• Merging Beyond: Streaming LLM Updates via Activation-Guided Rotations [55.047454145941366]
  Streaming Merging is an innovative model updating paradigm that conceptualizes merging as an iterative optimization process.<n> ARM is a strategy designed to approximate gradient descent dynamics.<n> ARM requires only early SFT checkpoints and, through iterative merging, surpasses the fully converged SFT model.
  arXiv  Detail & Related papers  (2026-02-03T08:15:57Z)
• Game-Theoretic Co-Evolution for LLM-Based Heuristic Discovery [37.96481049421407]
  Large language models (LLMs) have enabled rapid progress in automatic discovery.<n>We propose a game-theoretic framework that reframes discovery as a program level co-evolution between solver and instance generator.
  arXiv  Detail & Related papers  (2026-01-30T12:14:52Z)
• Training One Model to Master Cross-Level Agentic Actions via Reinforcement Learning [42.1534425503333]
  CrossAgent is a unified agentic model that masters heterogeneous action spaces and autonomously selects the most effective interface for each step of a trajectory.<n>Experiments on over 800 tasks in the open-world Minecraft environment demonstrate that CrossAgent achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-12-10T14:52:29Z)
• NExT-OMNI: Towards Any-to-Any Omnimodal Foundation Models with Discrete Flow Matching [64.10695425442164]
  We introduce NExT-OMNI, an open-source omnimodal foundation model that achieves unified modeling through discrete flow paradigms.<n>Trained on large-scale interleaved text, image, video, and audio data, NExT-OMNI delivers competitive performance on multimodal generation and understanding benchmarks.<n>To advance further research, we release training details, data protocols, and open-source both the code and model checkpoints.
  arXiv  Detail & Related papers  (2025-10-15T16:25:18Z)
• Sample-Efficient Online Learning in LM Agents via Hindsight Trajectory Rewriting [92.57796055887995]
  We introduce ECHO, a prompting framework that adapts hindsight experience replay from reinforcement learning for language model agents.<n> ECHO generates optimized trajectories for alternative goals that could have been achieved during failed attempts.<n>We evaluate ECHO on stateful versions of XMiniGrid, a text-based navigation and planning benchmark, and PeopleJoinQA, a collaborative information-gathering enterprise simulation.
  arXiv  Detail & Related papers  (2025-10-11T18:11:09Z)
• The Landscape of Agentic Reinforcement Learning for LLMs: A Survey [103.32591749156416]
  The emergence of agentic reinforcement learning (Agentic RL) marks a paradigm shift from conventional reinforcement learning applied to large language models (LLM RL)<n>This survey formalizes this conceptual shift by contrasting the degenerate single-step Markov Decision Processes (MDPs) of LLM-RL with the temporally extended, partially observable Markov decision processes (POMDPs) that define Agentic RL.
  arXiv  Detail & Related papers  (2025-09-02T17:46:26Z)
• MinionsLLM: a Task-adaptive Framework For The Training and Control of Multi-Agent Systems Through Natural Language [2.4171019220503402]
  MinionsLLM provides standardized interfaces for defining environments, agents, and behavioral primitives.<n>It integrates Large Language Models (LLMs) with Behavior Trees (BTs) and Formal Grammars to enable natural language control of multi-agent systems.
  arXiv  Detail & Related papers  (2025-08-01T13:10:29Z)
• JoyAgents-R1: Joint Evolution Dynamics for Versatile Multi-LLM Agents with Reinforcement Learning [6.81021875668872]
  We propose JoyAgents-R1, which first applies Group Relative Policy Optimization to the joint training of heterogeneous multi-agents.<n>We show that JoyAgents-R1 achieves performance comparable to that of larger LLMs while built on smaller open-source models.
  arXiv  Detail & Related papers  (2025-06-24T17:59:31Z)
• Revisiting Multi-Agent World Modeling from a Diffusion-Inspired Perspective [54.77404771454794]
  We develop a flexible and robust world model for Multi-Agent Reinforcement Learning (MARL) using diffusion models.<n>Our method, Diffusion-Inspired Multi-Agent world model (DIMA), achieves state-of-the-art performance across multiple multi-agent control benchmarks.
  arXiv  Detail & Related papers  (2025-05-27T09:11:38Z)
• Activation-Guided Consensus Merging for Large Language Models [25.68958388022476]
  We present textbfActivation-Guided textbfConsensus textbfMerging (textbfACM), a plug-and-play merging framework that determines layer-specific merging coefficients.<n>Experiments on Long-to-Short (L2S) and general merging tasks demonstrate that ACM consistently outperforms all baseline methods.
  arXiv  Detail & Related papers  (2025-05-20T07:04:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.