arXiv AI
SuperAI: Merging Capabilities Across Heterogeneous Foundation Models
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arXiv Machine Learning
Jun 18
PACT: Preserving Anchored Cores in Task-vectors for Model Merging
arXiv:2606. 18627v1 Announce Type: new Abstract: Model merging has emerged as a training-free alternative to multi-task learning, aiming to combine multiple task-specific fine-tuned models into a single multi-task model.
arXiv AI
Oct 21
Mitigating Model Merging Conflicts via Knowledge-Aware Task Vector Purification
arXiv:2510.14697v2 Announce Type: replace Abstract: Model merging aims to integrate task-specific abilities from individually fine-tuned models into a single model without extra training. In recent m...
arXiv Computer Vision
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CRLoMTL: Conflict-Resolution through Low-rank matrices for Multi-Task Learning
CRLoMTL is a parameter‑efficient multi‑task learning framework that mitigates task interference by operating in a low‑rank parameter‑update space rather than gradient space. It learns task‑specific LoRA updates and fuses them through a shared‑subspace resolver based on singular value decomposition, producing a single deployment‑time update that can be folded into the backbone. Experiments on Swin Transformer backbones across PASCAL‑Context, NYUD‑v2, and CelebA show that CRLoMTL improves accuracy over single‑task baselines while training only a small fraction of the parameters.
arXiv AI
Oct 15
Data-Aware Model Merging in the Essential Subspace for Multi-Task Knowledge Aggregation
arXiv:2606.19164v4 Announce Type: replace-cross Abstract: Model merging enables training-free multi-task learning by aggregating knowledge from models fine-tuned from a shared checkpoint, but their t...
arXiv AI
Sep 15
The Universe of Universes: Benefit Yield Functions, Implosion Thresholds, and Infrastructure-Aware Optimization in Multi-LLM Systems
The paper introduces the Universe of Universes (UoU) framework, treating the ecosystem of major large language models as a structured retrieval corpus and proposing a compositional Automated Reasoning and Machine Learning architecture for cross-model retrieval‑augmented generation. It formally defines the Benefit Yield Function (BYF), measuring marginal performance gain per added model, and identifies an implosion threshold θ* where BYF becomes zero and ensemble performance degrades. The work highlights gaps in current LLM ensemble research, such as lack of performance analysis across full model universes, and connects these findings to implications for DoD AI acquisition policy and testing of AI‑enabled systems.
arXiv AI
Jun 18
Essential Subspace Merging for Multi-Task Learning
arXiv:2606. 19164v1 Announce Type: cross Abstract: Model merging aims to enable multi-task learning by integrating the capabilities of multiple models fine-tuned from the same pre-trained checkpoint into a single model.