> ## Documentation Index
> Fetch the complete documentation index at: https://doc.blueapi.ir/llms.txt
> Use this file to discover all available pages before exploring further.

# EMEP Research Reference Cards

> Annotated bibliography of 18 papers underpinning EMEP merge strategies, evaluation, evolution, and PEFT methods. Each card links to a component and experiment ID.

EMEP is built on established research in model merging, evolutionary optimization, parameter-efficient fine-tuning, and benchmark integrity. This page provides structured reference cards for every foundational paper.

## Model Merging

### Model Soups

* **Authors**: Wortsman et al.
* **Year**: 2022
* **Research problem**: How to combine multiple fine-tuned models without additional training
* **Method**: Weight-space averaging of models fine-tuned with different hyperparameters; interpolation coefficients optimized on a validation set
* **Main finding**: Averaging weights of models fine-tuned in the same soup outperforms individual models and reduces hyperparameter sensitivity; works because weights in the same basin can be linearly combined
* **Limitations**: Requires models fine-tuned from the same initialization; does not address task interference
* **Relevance to EMEP**: Justifies MergeEngine weight averaging as a baseline strategy
* **Component affected**: [MergeEngine](/merge/merge-engine)
* **Experiment**: EXP-TBD-001

### Task Arithmetic

* **Authors**: Ilharco et al.
* **Year**: 2022
* **Research problem**: How to edit pre-trained models by adding or removing capabilities via arithmetic in weight space
* **Method**: Compute task vectors (fine-tuned minus pre-trained weights), then add or subtract scaled task vectors to the pre-trained base; scaling coefficients tuned on validation data
* **Main finding**: Task vectors encode semantic capabilities; arithmetic operations correspond to capability addition and removal; enables multi-task composition without joint training
* **Limitations**: Task interference causes degradation when combining many tasks; scaling is task-dependent
* **Relevance to EMEP**: Foundational merge strategy implemented in [TensorEngine](/merge/tensor-operations)
* **Component affected**: [MergeEngine](/merge/merge-engine), [math/task-arithmetic](/math/task-arithmetic)
* **Experiment**: EXP-TBD-002

### TIES-Merging

* **Authors**: Yadav et al.
* **Year**: 2023
* **Research problem**: How to reduce interference when merging multiple task-specific models
* **Method**: Three-step process: (1) Trim low-magnitude task vector elements, (2) Elect the majority sign for each parameter across task vectors, (3) Merge only parameters where signs agree, disjoint otherwise
* **Main finding**: Significant reduction in interference compared to naive averaging; preserves more individual task performance when merging many models
* **Limitations**: Sign election can discard valid updates; trimming threshold is hyperparameter-sensitive
* **Relevance to EMEP**: Primary advanced merge strategy for multi-model composition
* **Component affected**: [MergeEngine](/merge/merge-engine), [math/ties-math](/math/ties-math)
* **Experiment**: EXP-TBD-003

### DARE

* **Authors**: Yu et al.
* **Year**: 2023
* **Research problem**: How to merge task vectors while controlling capacity and reducing noise
* **Method**: Drop And REscale: randomly drop elements from task vectors with probability p, then rescale surviving elements by 1/(1-p); merge dropped vectors with base model
* **Main finding**: DARE outperforms magnitude-based trimming at high sparsity; random dropping preserves task performance better than low-magnitude removal
* **Limitations**: Optimal drop rate varies by task and model scale; requires calibration
* **Relevance to EMEP**: Sparsification strategy for noise reduction in merge pipelines
* **Component affected**: [MergeEngine](/merge/merge-engine), [math/dare-math](/math/dare-math)
* **Experiment**: EXP-TBD-004

### Evolutionary Model Merge

* **Authors**: Akiba et al.
* **Year**: 2024
* **Research problem**: How to automatically discover optimal merge recipes across diverse models
* **Method**: Evolutionary search over merge layer configurations, model combinations, and merge ratios; fitness evaluated on downstream tasks; uses NSGA-II for multi-objective optimization
* **Main finding**: Evolutionary search discovers merge combinations that outperform manual design; layer-wise merge ratios are more important than global ratios
* **Limitations**: Computationally expensive; requires large evaluation budget; search space grows combinatorially
* **Relevance to EMEP**: Direct justification for [EvolutionEngine](/evolution/evolution-engine) and genome encoding of merge recipes
* **Component affected**: [EvolutionEngine](/evolution/evolution-engine), [CandidateGenerator](/merge/merge-engine)
* **Experiment**: EXP-TBD-005

### Model Merging Survey

* **Authors**: Yang et al.
* **Year**: 2024
* **Research problem**: Systematic categorization of model merging techniques and their trade-offs
* **Method**: Taxonomy of merging methods by operation type (averaging, stitching, branching), application domain, and evaluation protocol
* **Main finding**: No single method dominates; choice depends on model similarity, task relatedness, and evaluation budget
* **Limitations**: Survey does not propose new methods; evaluation protocols vary across papers
* **Relevance to EMEP**: Informs [MergeStrategy](/merge/merge-strategies) catalog and strategy selection heuristics
* **Component affected**: [MergeStrategy](/merge/merge-strategies)
* **Experiment**: EXP-TBD-006

## Parameter-Efficient Fine-Tuning

### LoRA

* **Authors**: Hu et al.
* **Year**: 2021
* **Research problem**: How to fine-tune large models without updating all parameters
* **Method**: Low-Rank Adaptation: inject trainable rank-decomposition matrices into each layer; freeze pre-trained weights
* **Main finding**: LoRA achieves comparable accuracy to full fine-tuning with orders of magnitude fewer trainable parameters; rank can be tuned per task
* **Limitations**: Optimal rank is task-dependent; does not reduce inference memory without merging
* **Relevance to EMEP**: Optional fine-tuning path for producing task vectors before merge
* **Component affected**: [optional-finetuning](/finetuning/optional-finetuning), [lora](/finetuning/lora)
* **Experiment**: EXP-TBD-007

### QLoRA

* **Authors**: Dettmers et al.
* **Year**: 2023
* **Research problem**: How to fine-tune 65B parameter models on a single 48GB GPU
* **Method**: 4-bit NormalFloat quantization of base model weights with double quantization; paged optimizers; LoRA adapters for training
* **Main finding**: QLoRA matches 16-bit full fine-tuning and LoRA on downstream tasks; enables large model fine-tuning on consumer hardware
* **Limitations**: 4-bit quantization introduces small accuracy degradation; inference requires dequantization or adapter merging
* **Relevance to EMEP**: Enables optional fine-tuning in resource-constrained environments
* **Component affected**: [qlora](/finetuning/qlora)
* **Experiment**: EXP-TBD-008

### PEFT Survey

* **Authors**: Han et al.
* **Year**: 2024
* **Research problem**: Comprehensive review of parameter-efficient fine-tuning methods
* **Method**: Categorization by adaptation location, parameter type, and computational cost; empirical comparison across model scales
* **Main finding**: Adapter-based methods (LoRA, adapters, prefix tuning) offer the best accuracy-efficiency trade-off; prompt tuning underperforms on small datasets
* **Limitations**: Benchmark coverage is English-centric; long-context tasks underrepresented
* **Relevance to EMEP**: Guides optional fine-tuning strategy selection in [DatasetRegistry](/tracking/dataset-registry)
* **Component affected**: [peft](/finetuning/peft)
* **Experiment**: EXP-TBD-009

## Catastrophic Forgetting

### Catastrophic Forgetting (McCloskey & Cohen)

* **Authors**: McCloskey & Cohen
* **Year**: 1989
* **Research problem**: Why neural networks lose previously learned information when trained on new tasks
* **Method**: Controlled experiments on sequential learning in feedforward networks
* **Main finding**: New learning interferes with old representations; problem is fundamental to gradient-based optimization in shared parameter spaces
* **Limitations**: Studied on small networks; modern LLM scale was not accessible
* **Relevance to EMEP**: Motivates [regression-evaluation](/evaluation/regression-evaluation) and catastrophic forgetting controls
* **Component affected**: [catastrophic-forgetting-controls](/finetuning/catastrophic-forgetting-controls)
* **Experiment**: EXP-TBD-010

### Elastic Weight Consolidation (Kirkpatrick)

* **Authors**: Kirkpatrick et al.
* **Year**: 2017
* **Research problem**: How to protect important parameters during sequential learning
* **Method**: Compute Fisher information for each parameter; add penalty for changing parameters critical to previous tasks
* **Main finding**: EWC significantly reduces forgetting in sequential task learning; importance estimation is computationally expensive
* **Limitations**: Fisher information is approximate; does not scale cleanly to hundreds of tasks
* **Relevance to EMEP**: Candidate approach for merge-time forgetting mitigation
* **Component affected**: [catastrophic-forgetting-controls](/finetuning/catastrophic-forgetting-controls)
* **Experiment**: EXP-TBD-011

## Evolutionary Optimization

### NSGA-II

* **Authors**: Deb et al.
* **Year**: 2002
* **Research problem**: Efficient multi-objective optimization with Pareto-based selection
* **Method**: Non-dominated sorting with crowding distance; elitism via combined parent-offspring population; binary tournament selection
* **Main finding**: NSGA-II achieves diverse Pareto fronts with low computational cost; dominates predecessor NSGA on standard benchmarks
* **Limitations**: Performance degrades beyond 3-4 objectives; crowding distance becomes less discriminative
* **Relevance to EMEP**: Default multi-objective optimizer in [EvolutionEngine](/evolution/evolution-engine)
* **Component affected**: [multi-objective-optimization](/evolution/multi-objective-optimization)
* **Experiment**: EXP-TBD-012

### NSGA-III

* **Authors**: Deb & Jain
* **Year**: 2014
* **Research problem**: Scaling multi-objective optimization to many objectives (5+)
* **Method**: Reference point-based selection replaces crowding distance; uniform distribution of reference points ensures diversity
* **Main finding**: NSGA-III outperforms NSGA-II on many-objective problems; reference point specification requires domain knowledge
* **Limitations**: Reference point design is non-trivial; performance on 2-3 objectives is comparable to NSGA-II
* **Relevance to EMEP**: Alternative optimizer for experiments with more than four fitness objectives
* **Component affected**: [multi-objective-optimization](/evolution/multi-objective-optimization)
* **Experiment**: EXP-TBD-013

### CMA-ES

* **Authors**: Hansen
* **Year**: 2001
* **Research problem**: Derivative-free optimization for continuous, non-convex, ill-conditioned problems
* **Method**: Covariance Matrix Adaptation Evolution Strategy: adapts mutation distribution via covariance matrix update; uses rank-based selection
* **Main finding**: CMA-ES is state-of-the-art for black-box continuous optimization; sample efficiency is high for moderate dimensions
* **Limitations**: Computational cost scales with the square of dimension; not designed for discrete or combinatorial spaces
* **Relevance to EMEP**: Potential optimizer for continuous hyperparameter search (merge ratios, scaling coefficients)
* **Component affected**: [EvolutionEngine](/evolution/evolution-engine)
* **Experiment**: EXP-TBD-014

## Interpolation

### SLERP

* **Authors**: Shoemake
* **Year**: 1985
* **Research problem**: Smooth interpolation between orientations in 3D graphics
* **Method**: Spherical linear interpolation: interpolate along the great circle path on a unit sphere; preserves constant angular velocity
* **Main finding**: SLERP avoids the acceleration artifacts of linear interpolation; optimal for quaternion and rotation interpolation
* **Limitations**: Requires normalized inputs; undefined for antipodal vectors
* **Relevance to EMEP**: Applied to weight vector interpolation in [MergeEngine](/merge/merge-engine) to preserve angular relationships between model checkpoints
* **Component affected**: [math/slerp-math](/math/slerp-math)
* **Experiment**: EXP-TBD-015

## Evaluation

### LM Evaluation Harness

* **Authors**: Gao et al.
* **Year**: 2023
* **Research problem**: Standardized evaluation framework for few-shot and zero-shot LLM benchmarking
* **Method**: Unified API for task loading, prompt formatting, metric computation, and result aggregation; supports hundreds of tasks
* **Main finding**: Standardized evaluation reduces implementation variance; reproducible few-shot settings are critical for fair comparison
* **Limitations**: Task coverage is English-centric; custom tasks require boilerplate; contamination detection is manual
* **Relevance to EMEP**: [EvaluationEngine](/evaluation/framework) may integrate Harness tasks but maintains independent tracking and signing
* **Component affected**: [EvaluationEngine](/evaluation/framework)
* **Experiment**: EXP-TBD-016

### HELM

* **Authors**: Liang et al.
* **Year**: 2022
* **Research problem**: Holistic evaluation of language models across scenarios, metrics, and domains
* **Method**: Taxonomy of scenarios (QA, summarization, toxicity, etc.) with standardized metrics; multi-metric reporting instead of single leaderboard score
* **Main finding**: No model dominates all scenarios; trade-offs between accuracy, fairness, robustness, and efficiency are universal
* **Limitations**: Evaluation is expensive; scenario coverage still incomplete; some metrics are noisy
* **Relevance to EMEP**: Informs multi-objective fitness design and benchmark catalog structure
* **Component affected**: [benchmark-specification](/evaluation/benchmark-specification)
* **Experiment**: EXP-TBD-017

### Benchmark Contamination

* **Authors**: Sainz et al.
* **Year**: 2023
* **Research problem**: Detecting and measuring test data leakage into pre-training corpora
* **Method**: N-gram overlap analysis, template-based detection, and dynamic benchmark construction to identify contaminated examples
* **Main finding**: Contamination is widespread in popular benchmarks; even small overlap inflates scores significantly
* **Limitations**: Detection methods have false positives; dynamic benchmarks are expensive to construct
* **Relevance to EMEP**: Justifies strict [benchmark-integrity](/evaluation/benchmark-integrity) and Hidden Test Set isolation
* **Component affected**: [benchmark-integrity](/evaluation/benchmark-integrity)
* **Experiment**: EXP-TBD-018