# Endocot Internal Chain Of Thought > Enable step-by-step reasoning in diffusion models through iterative latent state refinement. Condition diffusion on evolving thought states across multiple reasoning steps, grounded with textual supervision to prevent drift. - Skill: `adu2021/endocot-internal-chain-of-thought` (Agent Skill) - Install (CLI): `npx skillmds@latest add adu2021/endocot-internal-chain-of-thought` - Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/endocot-internal-chain-of-thought/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: adu2021 (https://skillmd.com/u/adu2021) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/adu2021/endocot-internal-chain-of-thought --- # Technique: Endogenous Chain-of-Thought via Iterative Latent Refinement Diffusion models generate images by denoising—but they commit to solutions early in this process. EndoCoT enables intermediate reasoning by allowing the conditioning signal to evolve across steps. Rather than static guidance, the framework iteratively refines hidden states representing thoughts, each conditioned on the previous step's reasoning, then uses these refined states to guide generation. This "endogenous" approach contrasts with external chain-of-thought: reasoning happens *within* the diffusion process via latent dynamics, enabling truly integrated visual-linguistic reasoning. ## Core Concept EndoCoT combines three mechanisms: 1. **Iterative Thought Guidance**: Refine hidden states across multiple reasoning steps before denoising 2. **Terminal Thought Grounding**: Align final reasoning state with explicit textual reference using semantic loss 3. **Progressive Training**: First supervise intermediate steps, then optimize final output quality This enables dynamic, evolving conditioning that guides diffusion through a reasoning trajectory. ## Architecture Overview - **MLLM backbone**: Language model for thought generation - **Hidden state buffer**: Evolving reasoning representations across steps - **Thought refinement network**: Updates states based on prior reasoning - **Text reference encoder**: Grounds final thoughts in language - **DiT decoder**: Diffusion transformer conditioned on thought states - **Progressive loss scheduler**: Balance reasoning supervision vs output quality ## Implementation Steps ### Step 1: Iterative Hidden State Refinement Refine latent reasoning states across multiple steps before using for generation. ```python import torch import torch.nn as nn class IterativeThoughtRefiner(nn.Module): def __init__(self, hidden_dim=768, num_reasoning_steps=4): super().__init__() self.hidden_dim = hidden_dim self.num_reasoning_steps = num_reasoning_steps # Thought evolution network self.refiner_layers = nn.ModuleList([ nn.Sequential( nn.Linear(hidden_dim, hidden_dim * 2), nn.ReLU(), nn.Linear(hidden_dim * 2, hidden_dim) ) for _ in range(num_reasoning_steps) ]) # Attention for reasoning integration self.attention_layers = nn.ModuleList([ nn.MultiheadAttention(hidden_dim, num_heads=8, batch_first=True) for _ in range(num_reasoning_steps) ]) def forward(self, initial_thought, reasoning_context): """ Iteratively refine thought representations. initial_thought: (batch, hidden_dim) initial hidden state reasoning_context: (batch, seq_len, hidden_dim) context from MLLM """ thought_trajectory = [initial_thought] current_thought = initial_thought for step in range(self.num_reasoning_steps): # Refine using MLP refined = self.refiner_layers[step](current_thought) # Integrate context via attention attended, _ = self.attention_layers[step]( refined.unsqueeze(1), # (batch, 1, hidden_dim) reasoning_context, # (batch, seq_len, hidden_dim) reasoning_context ) # Update thought current_thought = refined + attended.squeeze(1) thought_trajectory.append(current_thought) return torch.stack(thought_trajectory, dim=1) # (batch, steps+1, hidden_dim) ``` ### Step 2: Terminal Thought Grounding with Text Supervision Anchor final reasoning state to explicit textual reference to prevent drift. ```python class TerminalThoughtGrounder(nn.Module): def __init__(self, hidden_dim=768, text_encoder=None): super().__init__() self.hidden_dim = hidden_dim self.text_encoder = text_encoder # Projection for alignment self.thought_projector = nn.Linear(hidden_dim, hidden_dim) self.text_projector = nn.Linear(hidden_dim, hidden_dim) def ground_with_text(self, final_thought, reference_text): """ Align final thought with textual reference using semantic loss. final_thought: (batch, hidden_dim) reference_text: str or tokenized reference """ # Encode reference text if isinstance(reference_text, str): reference_embedding = self.text_encoder.encode(reference_text) else: reference_embedding = self.text_encoder(reference_text) # Project both to common space thought_proj = self.thought_projector(final_thought) text_proj = self.text_projector(reference_embedding) # Semantic L2 alignment loss alignment_loss = torch.nn.functional.mse_loss(thought_proj, text_proj) return alignment_loss def forward(self, thought_trajectory, reference_text): """ Compute grounding loss for final thought. thought_trajectory: (batch, steps, hidden_dim) reference_text: reference for grounding """ final_thought = thought_trajectory[:, -1, :] grounding_loss = self.ground_with_text(final_thought, reference_text) return grounding_loss ``` ### Step 3: Conditional Diffusion with Evolving Thought Use thought trajectory to condition diffusion generation at each step. ```python class DiffusionTransformerWithEvolvedThoughts(nn.Module): def __init__(self, dit_model, hidden_dim=768): super().__init__() self.dit = dit_model self.hidden_dim = hidden_dim # Project thoughts to condition dimension self.thought_to_condition = nn.Linear(hidden_dim, dit_model.cond_dim) def forward( self, noise, timestep, thought_trajectory, instruction_tokens=None ): """ Run one diffusion step with evolved thought conditioning. noise: (batch, channels, height, width) timestep: int or (batch,) thought_trajectory: (batch, steps, hidden_dim) instruction_tokens: optional additional conditioning """ batch_size = noise.shape[0] # Select appropriate thought based on diffusion progress # (early steps use earlier thoughts, late steps use refined thoughts) progress = timestep / 1000 # Normalized to [0, 1] step_idx = min( int(progress * thought_trajectory.shape[1]), thought_trajectory.shape[1] - 1 ) current_thought = thought_trajectory[:, step_idx, :] # (batch, hidden_dim) # Project to condition conditioning = self.thought_to_condition(current_thought) # (batch, cond_dim) # Optional: concatenate with instruction tokens if instruction_tokens is not None: conditioning = torch.cat([conditioning, instruction_tokens], dim=-1) # Denoising step model_output = self.dit( noise, timestep, c=conditioning ) return model_output ``` ### Step 4: Progressive Two-Stage Training First stage: supervise all intermediate reasoning steps. Second stage: optimize only final output. ```python def train_endocot( model, data_loader, text_encoder, num_epochs=10, stage1_epochs=5 ): """ Two-stage training: reasoning -> output quality. """ optimizer = torch.optim.Adam(model.parameters(), lr=1e-4) for epoch in range(num_epochs): is_stage1 = epoch < stage1_epochs for batch_idx, (images, prompts, texts) in enumerate(data_loader): # Forward pass image_noise = torch.randn_like(images) # Get initial thought prompt_embedding = text_encoder.encode(prompts) # Refine thoughts thought_refiner = model.thought_refiner thought_trajectory = thought_refiner( prompt_embedding, text_encoder.encode(texts).unsqueeze(1) ) if is_stage1: # Stage 1: Supervise all intermediate thoughts total_loss = 0 for step_idx in range(thought_trajectory.shape[1]): thought = thought_trajectory[:, step_idx, :] # Generate with this thought outputs = model.dit_with_thoughts( image_noise, torch.tensor([500]), # Midpoint diffusion step thought.unsqueeze(1) ) # Supervise against ground truth step_loss = torch.nn.functional.mse_loss(outputs, images) total_loss += step_loss # Also supervise terminal grounding grounding_loss = model.grounder.ground_with_text( thought_trajectory[:, -1, :], texts ) total_loss += 0.1 * grounding_loss else: # Stage 2: Optimize final output quality only # Freeze intermediate supervision thought_trajectory = thought_trajectory.detach() final_thought = thought_trajectory[:, -1, :] # Full generation with final thought outputs = model.dit_with_thoughts( image_noise, torch.tensor([0]), # Final step final_thought.unsqueeze(1) ) total_loss = torch.nn.functional.mse_loss(outputs, images) optimizer.zero_grad() total_loss.backward() optimizer.step() if batch_idx % 100 == 0: stage = "Stage1" if is_stage1 else "Stage2" print(f"Epoch {epoch} [{stage}]: Loss = {total_loss.item():.4f}") return model ``` ### Step 5: Integration with Prompt Engineering Demonstrate full workflow with reasoning prompts. ```python def generate_with_endogenous_reasoning( model, text_encoder, initial_prompt, reasoning_steps=4, num_diffusion_steps=50 ): """ Full generation pipeline with endogenous reasoning. """ # Step 1: Generate reasoning trajectory prompt_embedding = text_encoder.encode(initial_prompt) thought_refiner = model.thought_refiner thought_trajectory = thought_refiner( prompt_embedding, torch.zeros(1, 1, 768) # Placeholder context ) # Step 2: Prepare diffusion noise = torch.randn(1, 4, 64, 64) # Latent space noise dit_model = model.dit_with_thoughts # Step 3: Iterative diffusion with evolving thoughts for diffusion_step in range(num_diffusion_steps): timestep = (num_diffusion_steps - diffusion_step) / num_diffusion_steps # Update thought based on diffusion progress if diffusion_step % (num_diffusion_steps // reasoning_steps) == 0: # Optionally re-refine thought mid-generation pass # Denoising step noise = dit_model( noise, int(timestep * 1000), thought_trajectory ) # Step 4: Decode from latent space image = model.vae.decode(noise) return image ``` ## Practical Guidance **When to Use:** - Multi-step reasoning required for generation (e.g., "draw a car then add wheels") - Tasks benefiting from intermediate planning before generation - Scenarios where output quality depends on reasoning coherence - Fine-grained control over generation trajectory **When NOT to Use:** - Simple prompt-to-image tasks without reasoning requirements - Extreme latency constraints (iterative refinement adds overhead) - Tasks where early commitment to solution is beneficial **Hyperparameter Tuning:** - **num_reasoning_steps**: 2-6; more enables finer reasoning - **stage1_epochs vs stage2_epochs**: 50-50 split typical; adjust based on reasoning difficulty - **grounding_loss weight**: 0.05-0.2; stronger grounding prevents drift - **thought refinement MLP hidden dim**: Match backbone hidden dimension **Common Pitfalls:** - Insufficient grounding loss causing thought drift - Over-weighting intermediate supervision (stage 1), hurting final quality - Thought trajectory too rigid (insufficient refinement network capacity) - Misalignment between thought dimensionality and DIT conditioning ## Reference [EndoCoT paper on arXiv](https://arxiv.org/abs/2603.12252)