CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation | Paper Citation Record

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This paper cites Task vector quantization for memory-efficient model merging,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Task vector quantization for memory-efficient model merging,

Reference 1

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Observation 6dd877a2-3143-48c7-b13d-2d501a9e10c5 · outbound

This paper cites Parameter competition balancing for model merging,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Parameter competition balancing for model merging,

Reference 2

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This paper cites Toward Effective Model Merging in Semantic Segmen- tation,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Toward Effective Model Merging in Semantic Segmen- tation,

Reference 3

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This paper cites Deep Model Fusion: A Survey,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Deep Model Fusion: A Survey,

Reference 4

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Observation e9fb9e96-9b7f-4162-9e37-5da3a2bb6b25 · outbound

This paper cites Dataless Knowledge Fusion by Merging Weights of Language Models,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Dataless Knowledge Fusion by Merging Weights of Language Models,

Reference 5

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This paper cites Evolutionary optimization of model merging recipes,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Evolutionary optimization of model merging recipes,

Reference 6

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This paper cites Editing models with task arithmetic,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Editing models with task arithmetic,

Reference 7

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This paper cites Adamerging: Adaptive model merging for multi- task learning,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Adamerging: Adaptive model merging for multi- task learning,

Reference 8

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This paper cites Whoever started the interference should end it: Guiding data-free model merging via task vectors,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Whoever started the interference should end it: Guiding data-free model merging via task vectors,

Reference 9

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This paper cites Channel merging: Preserving specialization for merged experts,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Channel merging: Preserving specialization for merged experts,

Reference 10

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This paper cites Mergebench: A benchmark for merging domain-specialized LLMs,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Mergebench: A benchmark for merging domain-specialized LLMs,

Reference 11

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This paper cites Language models are super mario: Absorbing abilities from homologous models as a free lunch,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Language models are super mario: Absorbing abilities from homologous models as a free lunch,

Reference 12

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This paper cites TIES- Merging: Resolving Interference When Merging Models,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation TIES- Merging: Resolving Interference When Merging Models,

Reference 13

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This paper cites Model breadcrumbs: Scaling multi- task model merging with sparse masks,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Model breadcrumbs: Scaling multi- task model merging with sparse masks,

Reference 14

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This paper cites Localize-and-Stitch: Efficient Model Merging via Sparse Task Arithmetic,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Localize-and-Stitch: Efficient Model Merging via Sparse Task Arithmetic,

Reference 15

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This paper cites Pruning and quantization for deep neural network acceleration: A survey,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Pruning and quantization for deep neural network acceleration: A survey,

Reference 16

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This paper cites Knowledge composition using task vectors with learned anisotropic scaling,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Knowledge composition using task vectors with learned anisotropic scaling,

Reference 17

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This paper cites Learn to merge: Meta-learning for adaptive multi-task model merging,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Learn to merge: Meta-learning for adaptive multi-task model merging,

Reference 18

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This paper cites Averaging weights leads to wider optima and better generalization,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Averaging weights leads to wider optima and better generalization,

Reference 19

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This paper cites Model soups: averaging weights of multiple fine- tuned models improves accuracy without increasing inference time,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Model soups: averaging weights of multiple fine- tuned models improves accuracy without increasing inference time,

Reference 20

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This paper cites Merging Models with Fisher- Weighted Averaging,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Merging Models with Fisher- Weighted Averaging,

Reference 21

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Multi-Task Model Merging via Adaptive Weight Disentanglement

Reference 22

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Modeling multi-task model merging as adaptive projective gradient descent,

Reference 23

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Representation surgery in model merging with proba- bilistic modeling,

Reference 24

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This paper cites Localizing task information for improved model merging and com- pression,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Localizing task information for improved model merging and com- pression,

Reference 25

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Dropout: a simple way to prevent neural networks from overfit- ting,

Reference 26

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation BERT busters: Outlier dimensions that disrupt transformers,

Reference 27

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Outlier Dimen- sions that Disrupt Transformers are Driven by Frequency,

Reference 28

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Outlier weighed layerwise sparsity (OWL): a missing secret sauce for pruning LLMs to high sparsity,

Reference 29

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Learning n:m fine-grained structured sparse neural networks from scratch,

Reference 30

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Structured Pruning Learns Compact and Accurate Models,

Reference 31

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Completely derandomized self- adaptation in evolution strategies,

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Open llm leaderboard,

Reference 33

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Mistral 7B

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Hellaswag: Can a machine really finish your sentence?

Reference 36

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation TruthfulQA: Measuring How Models Mimic Human Falsehoods,

Reference 38

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Winogrande: An adversarial winograd schema challenge at scale,

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Training Verifiers to Solve Math Word Problems

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Open llm leaderboard v2,

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Qwen2.5 Technical Report

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Instruction-Following Evaluation for Large Language Models

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Measuring Mathematical Problem Solving With the MATH Dataset

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 46

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning

Reference 47

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation A framework for few-shot language model evaluation,

Reference 48

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This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 49

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Language models are unsupervised multitask learn- ers,

Reference 50

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Glue: A multi-task benchmark and analysis platform for natural lan- guage understanding,

Reference 51

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Neural network accept- ability judgments,

Reference 52

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation The repeval 2017 shared task: Multi-genre natural language inference with sentence representations,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:15:10.829691Z

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This paper cites Automatically constructing a corpus of sen- tential paraphrases,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Automatically constructing a corpus of sen- tential paraphrases,

Reference 54

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation The pascal recognising textual entailment challenge,

Reference 55

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation The second pascal recognising textual entailment challenge,

Reference 56

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This paper cites The third pascal recognizing textual entailment challenge,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation The third pascal recognizing textual entailment challenge,

Reference 57

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This paper cites The fifth pascal recognizing textual entailment challenge.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation The fifth pascal recognizing textual entailment challenge

Reference 58

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This paper cites Recursive deep models for semantic compositionality over a sentiment treebank,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Recursive deep models for semantic compositionality over a sentiment treebank,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:15:10.783030Z

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Race: Large-scale reading comprehension dataset from examinations,

Reference 60

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Observation 6073d17a-f7a1-4149-af05-235cbaac1d54 · outbound

This paper cites SQuAD: 100,000+ Questions for Machine Comprehension of Text.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation SQuAD: 100,000+ Questions for Machine Comprehension of Text

Reference 61

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This paper cites FusionBench: A Unified Library and Comprehensive Benchmark for Deep Model Fusion,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation FusionBench: A Unified Library and Comprehensive Benchmark for Deep Model Fusion,

Reference 62

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This paper cites raids-lab/crater,.

CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation raids-lab/crater,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:15:10.753732Z

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Observation b82bc893-8092-410d-b2c9-1e461c81420d · outbound

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Beyond Task Vectors: Selective Task Arithmetic Based on Importance Metrics

Reference 64

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Observation afade97b-e1f6-4daf-b251-2769d61c251b · outbound

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CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation Unresolved cited work

Reference 2018

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