pulsatrix Directory Reference

Directory dependency graph for pulsatrix:

Directories
	viz

Files
	acquisition_functions.hpp
	Bayesian-Optimization acquisition functions over a GP posterior: Expected Improvement, Probability of Improvement, GP-Upper-Confidence-Bound (Snoek, Larochelle, Adams, "Practical Bayesian Optimization of Machine Learning Algorithms," NeurIPS 2012).
	activation_snapshot.hpp
	Self-contained, enumerable copy of one forward pass's cached activations.
	adam_optimizer.hpp
	Adam optimizer – operates uniformly across any Module's parameters().
	agent.hpp
	Abstract RL agent interface – the inference-time policy contract, act() only.
	aggregator_module.hpp
	Differentiable p-mean quantifier aggregator – Phase 1 Mission 1 of campaign_exai_dl_library_neuro_symbolic (Logic Tensor Networks' Real Logic: agg_p(x) = (mean(x^p))^(1/p), standing in for a fuzzy universal/existential quantifier over a batch of groundings).
	asha.hpp
	ASHA (Asynchronous Successive Halving Algorithm; Li, Jamieson, Rostamizadeh, Gonina, Hardt, Recht, Talwalkar, "A System for Massively Parallel Hyperparameter Tuning," MLSys 2020): unlike synchronous Successive Halving/Hyperband (which process one full rung across every candidate before any candidate advances to the next), ASHA promotes a candidate to the next rung as soon as it qualifies (its metric is in the top 1/eta fraction of every candidate that has ever completed that rung so far), never waiting on the rest of the current rung's population.
	assert.hpp
	PULSATRIX_ASSERT – debug-only invariant check for programmer errors, distinct from throw (used for caller-facing contract violations). See cpp_style_guide/context_style_project_conventions.md's assert-vs-throw table.
	attribution.hpp
	First-class explanation result type – values, method, and metadata together.
	audio_collate.hpp
	AudioPadCollate – zero-pads variable-length waveforms into one batch Tensor.
	audio_folder_dataset.hpp
	Directory-of-class-subfolders audio Dataset, mirroring ImageFolderDataset.
	audio_transforms.hpp
	Sample-level audio Transforms – linear-interpolation resampling.
	autograd.hpp
	Reverse-mode autodiff – walks a ComputationGraph backward, accumulating gradients via per-node backward functions supplied by the caller.
	avg_pool2d_module.hpp
	2D average pooling, non-overlapping windows (stride == kernel), no padding.
	batch_norm_fold.hpp
	Folds an eval-mode BatchNorm into the Conv2D before it for the duration of an LRP explanation – Zennit's BatchNorm canonizer (roadmap FND-5, lrp_issues #8).
	batch_norm_module.hpp
	Batch normalization (Ioffe & Szegedy, 2015) – per-channel statistics computed across the batch and spatial dimensions jointly, unlike GroupNormModule's per-(batch-row, group) statistics.
	bce_with_logits_loss.hpp
	Binary cross-entropy on raw logits – combined sigmoid + BCE, numerically stable.
	bounded_queue.hpp
	Fixed-capacity thread-safe blocking queue – the staged-pipeline backbone.
	calibration_loss.hpp
	Brier score – a proper-scoring-rule calibration loss (Brier, 1950).
	cartpole_env.hpp
	CartPole-v1 environment – classic cart-pole balancing physics, discrete actions.
	categorical_policy_agent.hpp
	Stochastic categorical (discrete-action) policy over a logit-producing Module.
	circuit_graph.hpp
	Self-contained circuit-graph artifact – scored nodes and weighted edges.
	cma_es.hpp
	A CMA-ES-*style* ask-tell evolutionary strategy (Hansen & Ostermeier's Covariance Matrix Adaptation Evolution Strategy) for continuous hyperparameter search: an adaptive mean, a per-dimension adaptive scale (separable/diagonal covariance, Ros & Hansen, "A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity," PPSN 2008), and an adaptive global step size.
	collate.hpp
	Batch assembly – Batch, CollateFn, DefaultCollate.
	computation_graph.hpp
	Owns and exposes graph structure – the interpretability substrate every explainer (Phase 2+) walks.
	conjunction_module.hpp
	Differentiable fuzzy conjunction (t-norm) – Phase 1 Mission 0 of campaign_exai_dl_library_neuro_symbolic (differentiable fuzzy-logic core, Logic Tensor Networks-shaped).
	continuous_cartpole_env.hpp
	Continuous-action variant of CartPoleEnv – identical physics, force is a fraction.
	conv2d_module.hpp
	2D convolution – implemented via im2col + DeviceBackend::gemm (no new backend primitive).
	cpu_backend.hpp
	CPU implementation of DeviceBackend – the first, reference DeviceBackend implementation.
	cross_entropy_loss.hpp
	Softmax + negative log-likelihood classification loss.
	crossover.hpp
	Genetic-algorithm crossover operators: one-point, two-point, uniform (generic sequence genotypes), and blend/BLX-alpha, simulated binary (SBX) (real-valued genotypes).
	csv_dataset.hpp
	Dataset over a CSV file's numeric feature/label columns – Phase 1's tabular reference case.
	csv_reader.hpp
	Minimal hand-rolled CSV parser – RFC-4180-ish, whole-file-at-once.
	cublas_check.hpp
	PULSATRIX_CUBLAS_CHECK – converts a cuBLAS call failure into a thrown C++ exception, parallel to PULSATRIX_CUDA_CHECK (cuda_check.hpp). Only compiled when PULSATRIX_ENABLE_CUDA is set.
	cuda_backend.hpp
	CUDA implementation of DeviceBackend. Only compiled when PULSATRIX_ENABLE_CUDA is set.
	cuda_check.hpp
	PULSATRIX_CUDA_CHECK – converts a CUDA runtime failure into a thrown C++ exception at the DeviceBackend boundary, per gpu_backend_programming/context_gpu_cuda_kernel_mechanics.md's Error Checking pattern. Only compiled when PULSATRIX_ENABLE_CUDA is set.
	data_loader.hpp
	Orchestrates sampling, fetch, and collation into batches.
	data_thread_pool.hpp
	Minimal generic thread pool for CPU-side data pipeline work.
	datalog_atom.hpp
	A Datalog atom: a predicate name plus a tuple of terms, no function symbols. Phase 3 Mission 0 of campaign_exai_dl_library_neuro_symbolic (Datalog core).
	datalog_dual_semiring.hpp
	Forward-mode-automatic-differentiation semiring (DualNumber<T>/DualSemiring<T>) – a second, differentiable real-valued instantiation of Mission 1's generic Semiring trait shape, carrying a value and its derivative w.r.t. one seeded scalar through every ⊕/⊗ the weighted engine performs. Phase 3 Mission 2 of campaign_exai_dl_library_neuro_symbolic (Neural-Predicate Integration).
	datalog_engine.hpp
	Bottom-up fixpoint evaluation (naive and semi-naive), boolean semiring only. Phase 3 Mission 0 of campaign_exai_dl_library_neuro_symbolic (Datalog core).
	datalog_fact_database.hpp
	A set of ground (fully-constant) Datalog atoms. Phase 3 Mission 0 of campaign_exai_dl_library_neuro_symbolic (Datalog core).
	datalog_lrp.hpp
	LRP-style relevance propagation for the real-valued (+, x) provenance-semiring Datalog circuit built in Mission 1/2 – Phase 3 Mission 3 of campaign_exai_dl_library_neuro_symbolic (LRP for the Datalog/Provenance-Semiring Circuit), extending Phase 1-2's fuzzy-logic LRP methodology (the weighted-sum/ epsilon-rule split for +, the bilinear split for x) to derived-fact weights instead of Module output tensors.
	datalog_rule.hpp
	A Datalog rule: head :- body1, body2, ..., range-restricted (safe) by construction. Phase 3 Mission 0 of campaign_exai_dl_library_neuro_symbolic (Datalog core).
	datalog_semiring.hpp
	Generic provenance-semiring abstraction (zero/one/add=(+)/mul=(x)) plus the boolean (trivial) and real-valued (+, x) concrete instantiations. Phase 3 Mission 1 of campaign_exai_dl_library_neuro_symbolic (Generic Provenance-Semiring Abstraction).
	datalog_term.hpp
	Function-symbol-free Datalog term – a constant or a variable, never a compound term. Phase 3 Mission 0 of campaign_exai_dl_library_neuro_symbolic (Datalog core).
	datalog_weighted_engine.hpp
	Semiring-parameterized bottom-up fixpoint evaluation – the weighted counterparts of Mission 0's naive_evaluate/semi_naive_evaluate. Phase 3 Mission 1 of campaign_exai_dl_library_neuro_symbolic (Generic Provenance-Semiring Abstraction).
	datalog_weighted_fact_database.hpp
	A map from ground Datalog atom to a semiring-typed weight. Phase 3 Mission 1 of campaign_exai_dl_library_neuro_symbolic (Generic Provenance-Semiring Abstraction).
	dataset.hpp
	Random-access dataset abstraction – Sample, Dataset (size()/get()).
	dataset_validator.hpp
	Generic per-field descriptive statistics + missingness/outlier detection over any Dataset.
	detailed_balance_loss.hpp
	GFlowNet Detailed Balance loss (Bengio et al., "GFlowNet Foundations", arXiv:2111.09266).
	determinism.hpp
	One global seed for everything that isn't given its own, and a deterministic mode that forbids nondeterministic computation (roadmap FND-7).
	device_backend.hpp
	Abstract interface isolating vendor-specific memory/compute operations from Tensor/ComputationGraph.
	disjunction_module.hpp
	Differentiable fuzzy disjunction (t-conorm) – Phase 1 Mission 0 of campaign_exai_dl_library_neuro_symbolic (differentiable fuzzy-logic core, Logic Tensor Networks-shaped).
	dqn_agent.hpp
	Epsilon-greedy DQN policy over an arbitrary Q-network Module.
	dqn_loss.hpp
	DQN's masked-MSE Bellman loss – squared error on the taken action only.
	dqn_target.hpp
	DQN Bellman target computation (vanilla + Double DQN) and target-network hard sync.
	dropout_module.hpp
	Inverted dropout – the first module whose forward behavior genuinely differs between training and inference (Module::is_training()).
	egan_mutation.hpp
	E-GAN's own three named "mutation" objectives (Wang et al. 2019, "Evolutionary Generative Adversarial Networks") plus its sample-quality/diversity fitness metric. In E-GAN, "mutation" means training a copy of a generator against a different loss function, not perturbing its weights directly – a structurally different kind of mutation than this campaign's own Phase 3 NEAT/ES operators, which do perturb weights.
	egan_training.hpp
	E-GAN's own full generational training loop (Wang et al. 2019): each generation, every population member independently attempts every given mutation objective (Phase 5 Mission 1) via a fresh weight-copy offspring, keeps whichever mutation scored the best combined fitness, then the shared discriminator trains on a real batch plus the (now-mutated) population's own pooled fake output (Phase 5 Mission 0).
	embedding_module.hpp
	Lookup-table (row-select) layer – y = W[index], batched (N, L) -> (N, L, embedding_dim).
	environment.hpp
	Abstract RL environment interface (gymnasium-shaped reset/step) + StepResult.
	evolution_strategies.hpp
	Evolution Strategies (Salimans et al. 2017, "Evolution Strategies as a Scalable Alternative to Reinforcement Learning"): a black-box, gradient-free optimizer over an arbitrary flat parameter vector, driven entirely by a scalar fitness function – zero inherent RL dependency, applicable to any fixed-topology network's flattened weight vector or any other real-valued parameterization.
	evolutionary_loop.hpp
	The shared six-step evolutionary-loop skeleton (evaluate -> select parents -> vary via crossover/mutation -> evaluate offspring -> survivor-select -> loop), generic over which survivor-selection policy (survivor_selection.hpp) and which offspring-production recipe (selection + crossover + mutation, composed by the caller) is used.
	explainer_context.hpp
	Stable interface every explainer gets, regardless of type (charter Part 2 SS2).
	explainer_stability.hpp
	Production API for an explainer's repeated-run stability audit metric (charter Phase 4).
	fixed_topology_xor_network.hpp
	A small, fixed-topology (2 input -> 2 hidden -> 1 output, both layers sigmoid) MLP whose weights are a flat parameter vector, plus a plain analytic fitness function scoring it against the four XOR patterns – the "fixed-topology network" and "plain analytic fitness function" this campaign's Evolution Strategies mission needs, deliberately using the same XOR benchmark and the same fitness convention (4.0 minus sum of squared error) as Mission 2's NEAT proof, for a direct same-problem cross-check between the two techniques.
	flatten_module.hpp
	Reshape-only Module – flattens every non-batch dim of a (N, ...) input to (N, flattened_features), for chaining Conv2DModule's batched (N,C,H,W) output into a LinearModule's batched (N, in_features) input.
	gae.hpp
	Generalized Advantage Estimation (Schulman et al. 2016) over a stored rollout.
	gaussian_process.hpp
	Gaussian-Process regression surrogate for Bayesian Optimization – the "gaussian bands" technique named at this campaign's own drafting (Snoek, Larochelle, Adams, "Practical Bayesian Optimization of Machine Learning Algorithms," NeurIPS 2012).
	generator_population.hpp
	E-GAN's own population-of-generators infrastructure (Wang et al. 2019, "Evolutionary Generative Adversarial Networks"): N independently-parameterized generator Modules, and the concrete mechanism for training one shared discriminator against fake samples pooled from the whole population – in E-GAN, the discriminator's own training distribution is the union of every population member's output, not one generator's alone, which is what gives the discriminator (and, in turn, each generator's own training signal) a harder, more diverse target than a single- generator GAN ever sees.
	gflownet_forward_policy.hpp
	Masked stochastic categorical policy over a logit-producing Module – P_F for GFlowNet training objectives.
	gflownet_trajectory.hpp
	Rolls out one full HyperGrid episode under a GFlowNetForwardPolicy.
	gp_bo.hpp
	The GP-BO trial loop: random-initialize, then repeatedly fit a GP to every trial observed so far and propose the next point by maximizing an acquisition function over random candidates in the unit hypercube.
	grad_cam.hpp
	Grad-CAM – gradient-weighted class activation mapping over the last conv layer (charter Part 1, Phase 2; theory: xai_context.aDNA's vision_gradcam.md).
	group_norm_module.hpp
	Group normalization (Wu & He, 2018) – rank-3 (C, H, W), matching Conv2DModule's convention, unlike RMSNormModule/LayerNormModule's rank-1 feature-vector scope.
	gru_module.hpp
	Single-layer GRU – gated recurrence with a reset-gated candidate.
	hip_backend.hpp
	HIP/ROCm implementation of DeviceBackend. Only compiled when PULSATRIX_ENABLE_HIP is set.
	hip_check.hpp
	PULSATRIX_HIP_CHECK – converts a HIP runtime failure into a thrown C++ exception at the DeviceBackend boundary, per gpu_backend_programming/context_gpu_cuda_kernel_mechanics.md's Error Checking pattern. Only compiled when PULSATRIX_ENABLE_HIP is set.
	hipblas_check.hpp
	PULSATRIX_HIPBLAS_CHECK – converts a hipBLAS call failure into a thrown C++ exception, parallel to PULSATRIX_HIP_CHECK (hip_check.hpp). Only compiled when PULSATRIX_ENABLE_HIP is set.
	host_guard.hpp
	PULSATRIX_REQUIRE_HOST – always-on guard for code paths that dereference Tensor::data() on the host.
	hpo_genotype.hpp
	Decodes a real-valued genotype (a unit-hypercube point, one gene per parameter, each in [0, 1]) into a full hyperparameter Configuration – the encoding this campaign's GA operators (crossover.hpp/mutation.hpp, all defined over std::vector<double>) evolve directly, decoded only when a genotype needs to be evaluated.
	hpo_sampling.hpp
	Grid search and random search over a SearchSpace: the two simplest, baseline hyperparameter-optimization algorithms, proving the SearchSpace/Trial abstraction's instantiate/train/read-back-metric loop end-to-end before Phase 2's GP-BO/TPE.
	hyperband.hpp
	Hyperband (Li, Jamieson, DeSalvo, Rostamizadeh, Talwalkar, JMLR 2018): runs several Successive Halving "brackets" with different (num_configs, initial_budget) trade-offs – covering the tension between "few configs trained long" and "many configs trained short, halved down" that a single Successive Halving run commits to in advance – and returns the best result across every bracket.
	hypergrid_env.hpp
	HyperGrid – the standard minimal GFlowNet correctness-check environment.
	image_decoder.hpp
	Decodes image files (PNG/JPEG/BMP/etc.) into Tensors via stb_image.
	image_folder_dataset.hpp
	Directory-of-class-subfolders image Dataset, mirroring torchvision's ImageFolder.
	image_transforms.hpp
	Sample-level image Transforms – resize, center-crop, normalize, horizontal flip.
	individual.hpp
	A genetic-algorithm candidate solution: a genotype paired with its fitness.
	integrated_gradients.hpp
	Integrated Gradients – baseline-interpolated gradient integral (charter Part 1, Phase 2; theory: xai_context.aDNA's vision_integrated_gradients.md).
	iterable_dataset.hpp
	Streaming dataset abstraction – reset()/next() for sources with no random access.
	kernel_shap.hpp
	KernelSHAP – model-agnostic Shapley value approximation via weighted linear regression (charter Part 1, Phase 3; theory: xai_context.aDNA's technique_shap.md).
	kl_divergence_loss.hpp
	VAE KL-divergence-to-standard-normal loss term.
	layer_norm_module.hpp
	Layer normalization (Ba et al., 2016) – mean-centered/scaled RMSNormModule sibling.
	learnable_scalar.hpp
	A single trainable float, updated by plain SGD – not a Tensor/Module parameter.
	lime.hpp
	LIME – local interpretable model-agnostic explanations (charter Part 1, Phase 3; theory: xai_context.aDNA's technique_lime.md).
	linear_algebra.hpp
	Small, dense linear-system solve – shared by weighted_linear_regression.hpp (Phase 3's LIME/KernelSHAP) and gaussian_process.hpp (this campaign's GP-BO surrogate).
	linear_module.hpp
	Dense/fully-connected layer – the reference Module implementation.
	linear_probe.hpp
	Linear probe – trains one linear classifier to decode a binary concept from a layer's activations, answering "is this concept linearly represented here?".
	lrp.hpp
	LRP – whole-model Layer-wise Relevance Propagation explainer.
	lrp_conservation.hpp
	Production API for LRP's conservation-delta audit metric (charter Phase 4).
	lrp_rule_config.hpp
	Selects which LRP rule variant a Module::propagate_relevance() call uses.
	lstm_module.hpp
	Single-layer LSTM – this codebase's first gated recurrent module.
	mamba_module.hpp
	Mamba/S6 selective-state-space recurrence with the MambaLRP relevance rule.
	matrix_decompositions.hpp
	Small dense decompositions on the host: symmetric eigensolver, power iteration, QR and SVD (roadmap FND-4).
	max_pool2d_module.hpp
	2D max pooling, non-overlapping windows (stride == kernel), no padding.
	metrics_sink.hpp
	Keeps monitoring/visualization tools out of the training core – same OCP/DIP pattern as DeviceBackend/ExplainerContext.
	mnist_classifier_example.hpp
	Real MNIST classifier: Conv2DModule -> ReluModule -> FlattenModule -> LinearModule -> CrossEntropyLoss, mirroring XorNetwork's training-loop pattern and grad_cam_mnist_demo.cpp's network shape exactly.
	mnist_dataset_adapter.hpp
	Adapts a pre-loaded MnistDataset onto the generic Dataset interface.
	mnist_loader.hpp
	Parses real MNIST IDX/ubyte files (fetched by tools/fetch_mnist.py) into Tensor images and integer labels.
	module.hpp
	Abstract base every layer subclasses – NVI forward(), pure-virtual LRP contract.
	mse_loss.hpp
	Mean squared error loss.
	multihead_attention_module.hpp
	Multi-head scaled dot-product attention – this codebase's first Module composed out of other real Modules, plus AttnLRP's Eq. 15 bilinear relevance rule.
	mutation.hpp
	Genetic-algorithm mutation operators: bit-flip (generic boolean genotypes) and Gaussian, polynomial (real-valued genotypes).
	neat_evolution.hpp
	NEAT's own generational evolutionary loop: fitness evaluation, speciation, fitness sharing, proportional offspring allocation, and mutation-only reproduction.
	neat_genome.hpp
	NEAT genome (Stanley & Miikkulainen, "Evolving Neural Networks through Augmenting Topologies," Evolutionary Computation 10(2), 2002): a connection-gene list with global historical markings (innovation numbers) plus the two structural mutations (add-connection, add-node) that grow topology from a minimal starting point.
	neat_phenotype.hpp
	Decodes a NEATGenome into an evaluable phenotype: a forward pass over the genome's own irregular connection graph.
	neat_speciation.hpp
	NEAT speciation (Stanley & Miikkulainen 2002): a compatibility-distance metric over two genomes' gene lists, population grouping by that metric, and fitness sharing – the mechanism that protects a structurally novel but not-yet-optimized genome from being immediately out-competed before its innovation has a chance to be refined.
	neat_xor_fitness.hpp
	XOR fitness function for NEAT – Stanley & Miikkulainen 2002's own validation task, chosen here for the same reason it was chosen there: XOR is the canonical not-linearly-separable problem, so a genome that solves it starting from a fully connected (no-hidden-node) minimal topology has genuinely grown new structure to do so, not merely tuned weights on an already-sufficient network shape.
	negation_module.hpp
	Standard fuzzy negation y = 1 - x – Phase 1 Mission 0 of campaign_exai_dl_library_neuro_symbolic (differentiable fuzzy-logic core).
	neuro_symbolic_datalog_bridge.hpp
	Wires a real neural predicate (LinearModule + sigmoid, reusing Phase 1 Mission 2's ToyKnowledgeBase pattern) into the weighted Datalog engine as one base fact's weight, on a small diamond-graph transitive-closure toy program (Mission 0/1's own ancestor shape). Computes the gradient of a derived query fact's weight w.r.t. the neural predicate's output (via forward-mode AD over the provenance semiring, see datalog_dual_semiring.hpp) and, transitively, the predicate's LinearModule parameters (via that module's own real backward(), mirroring ToyKnowledgeBase's hand-chained-Module::backward() precedent). Phase 3 Mission 2 of campaign_exai_dl_library_neuro_symbolic (Neural-Predicate Integration).
	neuro_symbolic_toy_kb.hpp
	Toy knowledge-base training demo – Phase 1 Mission 2 of campaign_exai_dl_library_neuro_symbolic (differentiable fuzzy-logic core's own correctness oracle: proves Missions 0-1's operators compose into a real, trainable Logic Tensor Network).
	node.hpp
	Computation graph node – op type, shape, optional label, parent/child edges.
	noise_schedule.hpp
	DDPM linear noise schedule: precomputed beta/alpha/alpha_bar, forward noising and reverse sampling steps.
	nsga2.hpp
	NSGA-II multi-objective survivor selection (Deb, Pratap, Agarwal, Meyarivan, "A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II," IEEE TEVC 6(2), 2002): fast non-dominated sorting, crowding distance, and truncation selection.
	op_type.hpp
	Closed set of operation categories every graph Node is tagged with.
	pbt.hpp
	Population Based Training (Jaderberg et al. 2017, "Population Based Training of Neural Networks"): a population of live, incrementally-trained trials periodically truncation-selected – the bottom fraction exploits (copies weights and hyperparameters from a uniformly-randomly-chosen top performer) then explores (perturbs the copied hyperparameters) – producing a hyperparameter schedule (different effective hyperparameters at different points in training) rather than a single fixed configuration.
	pbt_trial.hpp
	Extends the sibling HPO campaign's own ResumableTrial (successive_halving.hpp) with the weight/hyperparameter read-write capability Population Based Training's own exploit/explore step needs.
	pdp.hpp
	Partial Dependence Plot – a global marginal-effect technique, sweeping one feature's value while averaging the model's response over a background set (charter addition 2026-09-19, see charter Decisions Log).
	policy_gradient_loss.hpp
	REINFORCE's return-weighted negative log-likelihood loss over a batched rollout.
	polyak_update.hpp
	Soft (Polyak / exponential-moving-average) target-network update, as used by SAC.
	ppo_clipped_loss.hpp
	PPO's clipped surrogate objective (Schulman et al. 2017) over a batched rollout.
	relu_module.hpp
	ReLU activation – the second Module subclass, following LinearModule's pattern.
	reparameterize.hpp
	VAE reparameterization trick: z = mu + exp(log_sigma) * epsilon.
	replay_buffer.hpp
	Off-policy experience replay: fixed-capacity circular transition store + sampling.
	residual_module.hpp
	Generic skip-connection wrapper – Phase 4's only mission, the ResNet-style residual block generalized past a fixed Conv-BN-ReLU stack to any Module.
	retnet_module.hpp
	RetNet retention mechanism (recurrent mode), with an original derived LRP rule (no published rule exists for RetNet – see the class-level note).
	rms_norm_module.hpp
	RMS normalization layer (Zhang & Sennrich, 2019) – this codebase's first normalization Module, establishing the Tier 1 pattern for GroupNorm/LayerNorm.
	rnn_module.hpp
	Vanilla (Elman) recurrent layer – this codebase's first recurrent module.
	rollout_buffer.hpp
	On-policy trajectory storage: fill-once fixed-length rollout + discounted returns.
	rope_module.hpp
	Rotary Position Embedding – fixed per-position pair rotation, epsilon-rule LRP.
	rwkv_module.hpp
	RWKV-4 time-mixing (WKV linear-attention) recurrence, with an original derived LRP rule adapting MambaLRP's detach-the-gate technique to the WKV quotient (see the class-level note).
	safetensors.hpp
	Native safetensors reader and writer (roadmap IO-1): the one file format pulsatrix reads and writes for weights.
	saliency.hpp
	Saliency maps – raw gradient of a target output w.r.t. the input (charter Part 1, Phase 2).
	sampler.hpp
	Index-order abstraction for DataLoader – SequentialSampler, ShuffleSampler.
	satisfaction_loss.hpp
	Real-Logic-style knowledge-base satisfaction loss – Phase 1 Mission 1 of campaign_exai_dl_library_neuro_symbolic.
	search_space.hpp
	Typed hyperparameter search-space description: named parameters, each continuous, log-uniform, integer, or categorical.
	selection.hpp
	Genetic-algorithm selection operators (tournament, roulette/fitness-proportionate, linear-rank) over a population of Individual<Genotype, FitnessT>.
	sequential_module.hpp
	Model container – chains a sequence of existing Modules.
	sgd_optimizer.hpp
	Stochastic gradient descent – operates uniformly across any Module's parameters().
	shape.hpp
	Tensor dimension arithmetic – rank, element count, per-dimension access.
	sinusoidal_timestep_embedding.hpp
	Fixed sinusoidal encoding of a diffusion timestep, for conditioning a denoiser.
	softmax_module.hpp
	Rank-agnostic softmax over the last axis, with AttnLRP's Eq. 13 DTD relevance rule.
	sparse_autoencoder.hpp
	Sparse autoencoder – reconstructs an activation through an overcomplete, L1-penalized hidden layer, decomposing it into a larger, sparser basis.
	subtb_loss.hpp
	GFlowNet SubTB(lambda) loss (Madan et al., "Learning GFlowNets from partial episodes for improved convergence and stability", arXiv:2209.12782).
	successive_halving.hpp
	Successive Halving (the rung-based promotion mechanism underlying Hyperband and ASHA, Li, Jamieson, DeSalvo, Rostamizadeh, Talwalkar, "Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization," JMLR 2018): train a population of configurations for a small budget, keep the best fraction, repeat with a larger budget, until one survives.
	survivor_selection.hpp
	Survivor-selection policies for the evolutionary-loop skeleton (evolutionary_loop.hpp): generational replacement, (mu+lambda), (mu,lambda).
	swiglu_module.hpp
	SwiGLU gated feedforward block – second module composed from real LinearModule sub-objects, plus a diagonal specialization of AttnLRP's Eq. 15 bilinear rule.
	system_monitor.hpp
	Live CPU/GPU utilization, memory and temperature monitoring with a streaming log.
	system_monitor_detail.hpp
	SystemMonitor internals exposed for unit tests: the platform-source interface, the filesystem-rooted Linux parsers, and the log record encoders.
	tanh_gaussian_policy.hpp
	SAC's reparameterized, tanh-squashed Gaussian policy sampling (action + log-prob).
	tensor.hpp
	N-dimensional tensor – owns a buffer via DeviceBackend*, RAII (Rule of Five).
	text_collate.hpp
	PadCollate – right-pads variable-length token sequences into one batch Tensor.
	text_dataset.hpp
	Line-delimited corpus Dataset – tokenizes and indexes each line into a Tensor.
	tokenizer.hpp
	Minimal deterministic whitespace/punctuation word-level tokenizer.
	top_k.hpp
	Top-k selection along a tensor's last dimension, on any device (roadmap FND-3).
	tpe.hpp
	Tree-structured Parzen Estimator (Bergstra, Bardenet, Bengio, Kegl, "Algorithms for Hyper-Parameter Optimization," NeurIPS 2011) – a structurally distinct second surrogate family from GP-BO (gp_bo.hpp), handling mixed/categorical search spaces GP-BO's own vanilla kernel cannot (gp_bo.hpp restricts to Continuous/LogUniform; this file supports every ParameterKind).
	trajectory_balance_loss.hpp
	GFlowNet Trajectory Balance loss (Malkin et al. 2022, arXiv:2201.13259).
	transform.hpp
	Sample-level preprocessing abstraction – Transform, Compose, TransformDataset.
	transformer_block.hpp
	Pre-LN transformer block – Phase 3's literal exit-gate deliverable, third and last composition mission (RMSNorm x2, MultiHeadAttentionModule, SwiGLUModule, plus the resolved residual-split LRP rule).
	trial.hpp
	A single hyperparameter-optimization trial: the configuration tried, plus every metric value recorded against it.
	video_frame_directory_dataset.hpp
	Directory-of-pre-extracted-frames video Dataset (reduced-scope stub, no codec decode).
	video_transforms.hpp
	Frame-sampling Transform – selects a fixed number of evenly-spaced frames.
	vocabulary.hpp
	Token<->index lookup with a reserved <unk> fallback, plus a frequency-ranked builder.
	wav_reader.hpp
	Hand-rolled 16-bit PCM WAV decoder.
	weighted_linear_regression.hpp
	Weighted least squares via normal equations – the shared fitting primitive Phase 3's LIME and KernelSHAP explainers both reduce to.
	xor_training_example.hpp
	Phase 1's training-loop proof: Linear(2,4)->ReLU->Linear(4,1) learning XOR.