# Access Sets Matter: Budgeting Expert Reads for Scalable Weight-Space Model Merging

arXiv: https://arxiv.org/abs/2605.29489 · HTML: https://mlsanity.com/paper/2605.29489/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 32 references that are in the map.
- First posted on arXiv 28 May 2026.

## Builds on (most cited first, 20 of 20)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time](https://mlsanity.com/paper/2203.05482/) (2022)
- [Instruction-Following Evaluation for Large Language Models](https://mlsanity.com/paper/2311.07911/) (2023)
- [DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://mlsanity.com/paper/1903.00161/) (2019)
- [TIES-Merging: Resolving Interference When Merging Models](https://mlsanity.com/paper/2306.01708/) (2023)
- [Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch](https://mlsanity.com/paper/2311.03099/) (2023)
- [Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities](https://mlsanity.com/paper/2408.07666/) (2024)
- [Not All Disagreement Is Learnable: Token Teachability in On-Policy Distillation](https://mlsanity.com/paper/2605.26844/) (2026)
- [Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models](https://mlsanity.com/paper/2407.06089/) (2024)
- [Activation-Informed Merging of Large Language Models](https://mlsanity.com/paper/2502.02421/) (2025)
- [Sens-Merging: Sensitivity-Guided Parameter Balancing for Merging Large Language Models](https://mlsanity.com/paper/2502.12420/) (2025)
- [InfiGFusion: Graph-on-Logits Distillation via Efficient Gromov-Wasserstein for Model Fusion](https://mlsanity.com/paper/2505.13893/) (2025)
- [Model Merging Scaling Laws in Large Language Models](https://mlsanity.com/paper/2509.24244/) (2025)
- [InfiFPO: Implicit Model Fusion via Preference Optimization in Large Language Models](https://mlsanity.com/paper/2505.13878/) (2025)
- [LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Merging](https://mlsanity.com/paper/2502.10749/) (2025)
- [Geometry Conflict: Explaining and Controlling Forgetting in LLM Continual Post-Training](https://mlsanity.com/paper/2605.09608/) (2026)
- [FeatCal: Feature Calibration for Post-Merging Models](https://mlsanity.com/paper/2605.13030/) (2026)
- [E-PMQ: Expert-Guided Post-Merge Quantization with Merged-Weight Anchoring](https://mlsanity.com/paper/2605.16882/) (2026)
- [Discovering Physical Directions in Weight Space: Composing Neural PDE Experts](https://mlsanity.com/paper/2605.14546/) (2026)

## Built on by (most cited first, 2 of 2)

- [Routing Drift Alone Does Not Diagnose Failure in Merged MoE LLMs](https://mlsanity.com/paper/2609.32821/) (2026)
- [SMAT: Simple and Efficient Merge-Aware Training](https://mlsanity.com/paper/2609.33437/) (2026)

## Abstract (arXiv)

Weight-space model merging is usually formulated as an algebraic operation on checkpoints, yet at LLM scale the limiting resource is often the set of expert weights that must be read. We introduce MergePipe, a budget-aware execution layer that casts LLM merging as an expert access-set problem: given a merge operator and a checkpoint family in a shared weight coordinate system, choose which expert delta blocks to access under an explicit I/O budget. MergePipe indexes parameter blocks, builds deterministic access plans, and executes the induced budgeted merge with replayable manifests. The plan is budget-sound by construction and recovers the full-read merge at full budget; for fixed-coefficient additive operators, the omitted-update error is bounded by the norm of omitted deltas. Across Qwen and Llama merging workloads, MergePipe reduces expert-read I/O by up to an order of magnitude and achieves up to 11× speedups. Representative budget sweeps show O(10-3) parameter deviation from full-read merges and no monotonic degradation on downstream benchmarks.