Args: model: the model to track decay: §4 — "decay factor of 0.9999"

Save a list of extracted items as individual files.

Check if extracted text is reasonable quality (not garbled).

§4 — Linear variance schedule. "We set the forward process variances to constants increasing linearly from β_1 = 10^-4 to β_T = 0.02."

[UNSPECIFIED] — Weight initialization not described in paper. Using Xavier uniform, a common choice for transformer models.

§3.2.3 — Create causal (autoregressive) mask for decoder self-attention. "We also modify the self-attention sub-layer in the decoder stack to

Build optimizer with parameter groups. §{{SECTION}} — "{{quote about optimizer}}"

Build learning rate scheduler. §{{SECTION}} — "{{quote about LR schedule}}"

Compute all evaluation metrics reported in the paper. §6, Table 2 — "We report BLEU scores" Args: hypotheses: list of predicted tran

Compute BLEU score using sacrebleu for reproducibility. §6, Table 2 — "BLEU" score on WMT 2014 EN-DE and EN-FR. The paper reports BLEU score

Compute FID score between generated samples and real data. §4 — "We report FID score... Our best results are FID: 3.17" Requires the pytorch

§3.2.3 — Create a causal (autoregressive) mask. "We also modify the self-attention sub-layer in the decoder stack to prevent positions from

Create a padding mask for source sequences. Args: tokens: (batch, seq_len) — token IDs pad_idx: the padding token index Retu

Download the PDF from arxiv.

Extract algorithm boxes from the paper. Looks for patterns like: Algorithm 1: Name ...algorithm body... (ends at next section h

Extract numbered equations. Looks for: - LaTeX equation environments: \\begin{equation}...\\end{equation} - Display math with numberi

Extract footnotes from the paper.

Extract tables from the paper text. Looks for: - Markdown tables (pipes) - Table captions (Table N: ...) - Structured text that

Extract text using pdfplumber (fallback).

Extract text using pymupdf4llm (preserves math notation as LaTeX).

Fetch HTML version from ar5iv (renders math as readable text).

Fetch paper metadata from the arxiv API.

Search for official code repositories linked to this paper. Checks two sources: 1. The paper text itself — GitHub/GitLab URLs, "code availabl

Args: x: {{description}} — shape: (batch, {{dims}}) Returns: {{description}} — shape: (batch, {{

Generate samples using Algorithm 2 — Sampling. §3.4 — "sampling from p_θ(x_{t-1} | x_t) = N(x_{t-1}; μ_θ(x_t, t), σ²_t I)" Args: mod

Return train and test transforms for CIFAR-10. §4 — "random horizontal flips during training" [FROM_OFFICIAL_CODE] — normalize to [-1, 1]

Create CIFAR-10 train and test dataloaders. §4 — CIFAR-10 unconditional generation at 32×32 §4 — "batch size 128" [FROM_OFFICIAL_CODE — paper

§5.3, Eq. 3 — Transformer learning rate schedule. "We used the Adam optimizer with β1 = 0.9, β2 = 0.98 and ε = 10^−9. We varied the learning

Identify section boundaries using heading patterns. Detects: - Markdown headings (# , ## , ### ) - Numbered headings (1. Introduction

Load training configuration.

Load a trained DDPM model from checkpoint. §4 — "we also report results with an exponential moving average" The EMA parameters typically prod

Extract arxiv ID from a URL or bare ID string. Handles: https://arxiv.org/abs/2106.09685 https://arxiv.org/pdf/2106.09685.pdf

Algorithm 2, lines 3-4 — Single reverse step: sample x_{t-1} from p_θ(x_{t-1} | x_t). "x_{t-1} = 1/√α_t * (x_t - β_t/√(1-α̅_t) * ε_θ(x_t, t)) + σ

§3, Eq. 4 — Forward process: sample x_t from q(x_t | x_0). "A notable property is that we can sample x_t at any arbitrary time step t in clo

Algorithm 2 — Full reverse sampling process. "Algorithm 2 Sampling 1: x_T ~ N(0, I) 2: for t = T, ..., 1 do 3: z ~ N(0, I) if t

Save generated samples as images. Args: samples: (N, C, H, W) in [0, 1] output_dir: Directory to save images prefix: File

§3.2.1, Eq. 1 — Attention(Q, K, V) = softmax(QK^T / √d_k) V "We compute the attention function on a set of queries simultaneously, packed to

Algorithm 1 — DDPM Training. Args: config_path: Path to YAML config file

Minimal training example with paper-specified optimizer and schedule.

Main training loop. {{Describe what this training loop does and which paper sections it follows.}}

Update EMA weights after each training step.

Load and preprocess a single sample. Returns: dict with keys: {{key_1}}: {{description}} — shape: {{shape

Load and preprocess a single sentence pair. Returns: dict with keys: "src": source token IDs — shape: (src_len,)

Args: data_dir: path to the dataset root directory split: one of "train", "val", "test"

Args: smoothing: §5.4 — ε_ls = 0.1 pad_idx: index of padding token (loss ignored for these positions)

Args: data_dir: path to the preprocessed data directory split: one of "train", "valid", "test" max_seq_le

Print architecture summary.

Load BPE-tokenized source and target sentences. TODO: Implement based on your tokenization format. Expected: each line is space-separ

Load sample paths/metadata for the given split. TODO: Implement based on the dataset structure.

Apply preprocessing as described in §{{SECTION}}. TODO: Implement the paper's preprocessing pipeline: {{list preprocessing st

Load EMA weights into model (for evaluation/sampling).

Pad sequences to the same length within a batch. §5.1 — "Each training batch contained a set of sentence pairs containing approximately 25000

Create combined causal + padding mask for decoder self-attention. Args: tgt: (batch, tgt_len) — target token IDs pad_idx: padding

§3.4, Eq. 14 — L_simple = E[||ε − ε_θ(x_t, t)||²] Args: noise_pred: (batch, C, H, W) — predicted noise ε_θ(x_t, t)

Args: t: (batch,) — integer timesteps Returns: (batch, embed_dim) — sinusoidal embedding

Args: x: (batch, in_channels, H, W) t_emb: (batch, time_embed_dim) Returns: (batch, out_channels

Args: x: (batch, channels, H, W) Returns: (batch, channels, H, W)

Predict noise ε_θ(x_t, t). Args: x: (batch, C, H, W) — noisy image x_t t: (batch,) — integer timesteps Retur

Compute label-smoothed cross-entropy loss. Args: logits: (batch, seq_len, vocab_size) — model output logits (NOT probabilities)

Args: query: (batch, seq_q, d_model) key: (batch, seq_k, d_model) value: (batch, seq_k, d_model)

Args: x: (batch, seq_len, d_model) Returns: (batch, seq_len, d_model)

Args: x: (batch, seq_len, d_model) — embedding output (already scaled by √d_model) Returns: (batch, seq_len,

Args: x: (batch, seq_len, d_model) src_mask: (batch, 1, 1, seq_len) — padding mask Returns: (batc

Args: x: (batch, tgt_len, d_model) — decoder input memory: (batch, src_len, d_model) — encoder output tgt

Args: x: (batch, src_len, d_model) — embedded + positionally-encoded source mask: (batch, 1, 1, src_len) — source pad

Args: x: (batch, tgt_len, d_model) — embedded + positionally-encoded target memory: (batch, src_len, d_model) — encod

Full encoder-decoder forward pass. Args: src: (batch, src_len) — source token IDs tgt: (batch, tgt_len) — target toke

Restore original model weights (after evaluation).