Specialist Model Merging Frameworks

• Specialist model merging frameworks are techniques that integrate expert models fine-tuned for distinct tasks into a unified model, enhancing both specialization and generalization.
• Key methodologies include task vector arithmetic, sparsification, subspace boosting, and optimization-based strategies, which reduce computational costs and mitigate parameter conflicts.
• These frameworks enable privacy-preserving, scalable deployments across diverse domains such as vision, language, and healthcare while addressing catastrophic forgetting.

Specialist Model Merging Frameworks

Specialist model merging frameworks seek to integrate the strengths of multiple expert models—each fine-tuned for distinct tasks or domains—into a single unified model that leverages both generalization and specialization. These frameworks have risen to prominence as alternatives to joint multi-task or mixture training, offering improved memory efficiency, data privacy, and compositional flexibility. Recent research has produced a diverse ecosystem including training-free methods, optimization-based approaches, architecture-aware paradigms, and multi-objective search. Their deployment spans vision, language, multimodal, and cross-institutional healthcare models.

1. Foundations and Rationale

Traditional model deployment often relies on ensembles or domain-specific fine-tuning, incurring heavy computational and memory costs, especially when scaling to many domains (Zhang et al., 18 Dec 2024). Model merging leverages the parameter-space proximity of specialist models, typically derived from a shared pretrained checkpoint, to recombine expertise without further retraining. Key motivations include:

• Generalist–specialist unification (e.g., Segment Anything Model (SAM) and MedSAM for medical segmentation (Yang et al., 14 Aug 2025))
• Efficient memory and serving cost reduction—merged models store a single parameter set but maintain multi-domain capabilities (Zhang et al., 18 Dec 2024, 2505.10833)
• Privacy-preserving consolidation—enabling cross-institutional merges without sharing patient data (Yang et al., 14 Aug 2025, Timilsina et al., 17 Nov 2025)
• Mitigation of catastrophic forgetting and distributional shift—increasing robustness to out-of-distribution tasks (Yang et al., 14 Aug 2025)

The fundamental challenge is to balance retention of expert knowledge with cross-task generalization, while controlling destructive interference from conflicting parameter updates.

2. Core Merging Methodologies

2.1 Task Vector Arithmetic and Linear Interpolation

The most prevalent parameter-space merging strategy involves computing task vectors $\tau_i = \theta_i - \theta_0$ from experts $\theta_i$ and base $\theta_0$, then linearly recombining:

$$\theta_{\rm merged} = \theta_0 + \sum_{i=1}^n \lambda_i \tau_i$$

where $\lambda_i$ are scalar coefficients per expert/task (Yang et al., 14 Aug 2025, 2505.10833). Uniform averaging is the lowest-variance choice; task-weighted coefficients are tuned on surrogate validation data for performance boosts.

2.2 Sparsification and Conflict Mitigation

As the number of experts increases, parameter conflicts amplify, resulting in performance decline. Sparsification-based methods reduce overlap:

• TIES-Merging: Prunes low-magnitude or sign-conflicting deltas before merging, preserving only coherent task directions (Zhang et al., 18 Dec 2024, 2505.10833).
• DARE: Randomly masks and rescales task vectors to diversify the merged update (2505.10833).
• Consensus Task Arithmetic: Uses per-task magnitude thresholds and consensus masks to localize parameters critical for multi-task retention (2505.10833).

2.3 Subspace Management and Rank Preservation

High-dimensional merging across many experts suffers from “rank collapse,” whereby all task vectors occupy a low-dimensional subspace, stifling diversity (Skorobogat et al., 19 Jun 2025). Subspace Boosting reinflates the tail singular values of the merged vector’s SVD, restoring stable rank and achieving large gains as the expert pool size grows ($+10$–$15$ pp on vision tasks).

Higher-Order Generalized SVD (HO-GSVD) further quantifies pairwise expert alignment, allowing subset selection to minimize interference (Skorobogat et al., 19 Jun 2025).

2.4 Optimization-Based and Data-Guided Strategies

Several frameworks recast merging as constrained or multi-objective optimization:

• Black-box Zero-Order Layer-Wise Optimization: MedSAMix employs Bayesian optimization (SMAC) over layer groupings and merge hyperparameters, searching for configurations that maximize segmentation performance (Yang et al., 14 Aug 2025).
• Adaptive Projective Gradient Descent (APGD): Formulates the task performance gap as a data-free objective and projects all updates to the orthogonal complement of the shared subspace, balancing retention and adaptation (Wei et al., 2 Jan 2025).
• PSO-Merging: Particle Swarm Optimization supports gradient-free, data-informed search and scales efficiently to large models (Zhang et al., 27 Aug 2025).

2.5 Architecture-Aware and Heterogeneous Model Merging

When merging experts of differing architectures (layer depth, width, head permutation), bespoke methods are needed:

• Hierarchical Cosine-OT-LERP: Aligns attention heads via optimal transport and merges blocks by cosine-weighted interpolation, eliminating permutation variance (Timilsina et al., 17 Nov 2025).
• Elastic Neuron Zipping and Layer Alignment: Training-free Heterogeneous Model Merging aligns deeper layer structures to shallow ones using CKA, then merges neurons by greedy similarity projection (Xu et al., 29 Dec 2024).
• Channel Merging: Clusters channels with high cross-expert similarity and merges within clusters, preserving specialization at reduced storage cost (Zhang et al., 18 Dec 2024).

2.6 Functional Anchors and Representation-Based Approaches

Functional Dual Anchors (FDAs) build synthetic anchor inputs whose parameter gradients recreate task vector directions. This functional merge aligns models at the gradient level in input–representation space, offering robustness and complementarity with parameter merging (Shi et al., 24 Oct 2025).

SE-Merging enhances static merges by dynamically adjusting merging coefficients per sample, based on proximity in representation space, achieving more faithful task reconstruction without retraining (Chen et al., 22 Jun 2025).

3. Multi-Objective and Automated Merging Strategies

Certain frameworks explicitly seek Pareto-optimality across tasks rather than a single composite score:

• Multi-objective layer-wise merging (MedSAMix-M): Uses ParEGO scalarization to cover the Pareto frontier, calibrating weight interpolations to maximize either domain-specific accuracy or generalization (Yang et al., 14 Aug 2025).
• Automated search frameworks: SMAC-style multi-fidelity optimization methods support fine-grained layer group and fusion method selection (LFS, DIS), balancing evaluation cost with effective trade-off discovery (Su et al., 6 Feb 2025).

4. Practical Deployment and Empirical Insights

Recent benchmarks such as MergeBench (2505.10833) and OptMerge (Wei et al., 26 May 2025) provide systematic evaluation of merging frameworks across:

• LLMs (Llama, Gemma, Mistral), multimodal models (InternVL, Qwen2-VL)
• Domains: mathematics, code, instruction-following, multilingual, safety, vision, healthcare
• Methods: averaging, sparsification, subspace boosting, data-free optimization, router-based, expert lookup

Findings include:

• Tuning merging coefficients and introducing sparsification boosts retention, especially on larger base models (90%+ multi-task recovery at 8–9B scale).
• Simple averaging or task arithmetic is robust for closely related expert models; more complex methods (pruning, OT alignment) are advantageous for highly divergent or heterogeneous domains (Timilsina et al., 17 Nov 2025, Xu et al., 29 Dec 2024).
• Modular, automated search unlocks super-additive gains via layer-wise adaptation and optimizer-guided recipes, delivering up to +6.86 pts average multi-task improvement (Su et al., 6 Feb 2025).
• Subspace boosting restores diversity as expert pool size grows, preventing rank collapse and performance degradation (Skorobogat et al., 19 Jun 2025).
• Functional anchor methods can robustly adapt merged models to reach flatter optima and higher joint accuracy than parameter-space merges (Shi et al., 24 Oct 2025).
• In domain-specific scenarios (e.g. medical image segmentation or deepfake detection), frameworks exploiting shared structure (MedSAMix, R$^2$M) can mitigate bias and catastrophic forgetting, outperforming joint multi-task baselines (Yang et al., 14 Aug 2025, Park et al., 29 Sep 2025).

5. Limitations, Open Questions, and Future Directions

Current specialist merging frameworks face several constraints:

• Architectural compatibility: Most frameworks require expert models to share the same backbone and initialization; true heterogeneous merging remains challenging (Xu et al., 29 Dec 2024).
• Hyperparameter sensitivity: Trade-off parameters (e.g. $\lambda$ in task arithmetic, sparsity ratios) can cause drastic swings in performance and require careful tuning (Ueda et al., 4 Nov 2025).
• Scaling: Evaluation costs scale with the number of experts and fusion methods; automated search reduces burden but may misallocate resources with poor surrogates (Su et al., 6 Feb 2025).
• Interference: Adding many domains leads to destructive interaction and performance collapse, particularly in tri-domain or higher merges (Ueda et al., 4 Nov 2025).
• Lack of theoretical bounds: Most methods rely on empirical validation rather than principled guarantees regarding multi-task retention or transfer.

Emergent directions include:

• Integration with federated, privacy-preserving learning across institutions (hybrid-foundation or telemedicine scenarios) (Yang et al., 14 Aug 2025, Timilsina et al., 17 Nov 2025)
• Modular expert composition via adapters or plug-and-play routers, instead of full parameter merges (Pari et al., 4 Nov 2024, Zhang et al., 18 Dec 2024)
• Data-free optimization leveraging multi-objective Bayesian search, functional anchoring, and gradient-projection regularization (Yang et al., 14 Aug 2025, Wei et al., 2 Jan 2025, Shi et al., 24 Oct 2025)
• Subspace- and spectrum-aware merging ensures retention of diversity as specialist pools grow (Skorobogat et al., 19 Jun 2025)
• Benchmarks for reasoning-focused and cross-modality MLLM merges (Wei et al., 26 May 2025)

6. Comparative Results and Implementation Guidance

The table below surveys selected frameworks and their main traits (all metrics verbatim from cited papers):

Framework	Core Principle	Empirical Gains
MedSAMix (Yang et al., 14 Aug 2025)	Layer-wise optimizer, multi-objective	+6.67% Dice (single), +4.37% (multi-task)
Channel Merging (Zhang et al., 18 Dec 2024)	Channel-wise clustering, router	<1% drop vs unmerged, uses 53% of ensemble params
Subspace Boosting (Skorobogat et al., 19 Jun 2025)	SVD-spectrum rank restoration	+10–15pp acc. (viT-B, viT-L vision)
Hierarchical Cosine-OT (Timilsina et al., 17 Nov 2025)	Attention head OT alignment	Up to 45.80% QA accuracy on MedQA
AMM (Yin et al., 10 Oct 2025)	Weight adaptation, projection regular.	+1.1–1.4pt over WUDI baselines (reasoning)
OptMerge (Wei et al., 26 May 2025)	Low-rank task vector denoising	+2.48% avg. vs. WUDI, 67% acc. on AVQA/MUSIC
R$^2$M (Park et al., 29 Sep 2025)	Shared real axis + norm-matched fake	AUC 0.988 (in-dist), 0.774 (unseen transfer)
PSO-Merging (Zhang et al., 27 Aug 2025)	Data-driven, gradient-free search	+1.7–4.7pt over baseline, rapid convergence
Expert Merging++ (Zhang et al., 30 Sep 2025)	Unsupervised coefficient learning, chunking	Surpass Mixture Training in MLLM benchmarks
FREE-Merging (Zheng et al., 25 Nov 2024)	Fourier high-pass filtering + experts	93.7% acc. (vision), within 0.5% of MTL

Implementation best practices include leveraging architectural and task-vector similarity to guide method selection, using cross-validation or small calibration sets for coefficient tuning, integrating sparsification or boosting as the expert pool grows, and adopting optimization or functional-matching methods for highly divergent specialist pools.

7. Conceptual and Future Implications

Specialist model merging frameworks are critical for building scalable, adaptable, and privacy-preserving AI systems. They move beyond static ensembling or averaging and introduce mathematically rigorous tools for balancing specialization with generalization. Frameworks such as MedSAMix and R$^2$M demonstrate the possibility of domain-aware bias mitigation, while innovations like subspace boosting and functional anchors pave the way for merging ever-larger pools without loss of diversity. Heterogeneous architecture integration (elastic zipping, permutation alignment) and automated multi-fidelity search unlock new application spaces, from federated medical models to multimodal omnilingual agents. Continued development will hinge on scalable benchmarking, open-source toolchains, and principled merging-theory foundations.