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 *.zip filter=lfs diff=lfs merge=lfs -text
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2501.00663v1.pdf

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+oid sha256:a65e4a7d02784df1a040b487127e6dd09fff4474e5caf94d93263af3d50cfbc2
+size 3657065

2504.13173v1.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:faceb861d46d65fd7098cbdb97aa400081c7b5cb7e048ac8d010f01537915ab2
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README.md

@@ -1,12 +1,72 @@
----
-title: Titans Miras Demo
-emoji: 🔥
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 6.2.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Titans Miras Demo
+emoji: 🧠
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 6.2.0
+app_file: app.py
+pinned: false
+---
+
+# 🧠 Titans + MIRAS: A Brain That Changes Itself While Thinking
+
+A minimal but faithful reimplementation of **Titans** (test-time learning) and **MIRAS** (associative memory framework) using open-source models on Hugging Face.
+
+## What is this?
+
+This demo showcases a neural architecture that can **learn and update its memory while generating responses** - a brain that literally changes itself while thinking!
+
+### Key Features
+
+- 🔄 **Test-time learning**: Memory updates during inference (not just training)
+- 🎯 **Retention gate**: Surprising/novel inputs are more memorable (inspired by human memory)
+- 💾 **Persistent memory**: State is saved across sessions
+- 🤖 **Fully OSS**: Uses distilgpt2 and runs entirely on Hugging Face
+
+## Architecture
+
+```
+User Input
+    ↓
+[Base LM: distilgpt2] → Hidden States (768-dim)
+    ↓
+[Key/Value Projections] → Memory Space (256-dim)
+    ↓
+[MIRAS Memory Module] ← Test-time Gradient Updates
+    ↓
+[Text Generation] → Response + Memory Stats
+```
+
+### Components
+
+1. **Base Language Model**: distilgpt2 (frozen, no training)
+2. **Projection Layers**: Map hidden states to memory space
+3. **MIRAS Memory**: Associative memory with learnable key→value mapping
+4. **Retention Gate**: Adjusts learning rate based on surprise (loss magnitude)
+5. **Memory Store**: Persists memory state to disk
+
+## How It Works
+
+1. Input text is processed through distilgpt2
+2. Last hidden state is projected to key/value pairs
+3. Memory predicts value from key
+4. Loss (prediction error) indicates surprise
+5. Higher surprise → higher retention → faster learning
+6. Memory updated via gradient descent (1e-3 base LR)
+7. Response generated and memory saved
+
+## References
+
+- **Titans**: [Learning to Memorize at Test Time](https://arxiv.org/abs/2501.00663)
+- **MIRAS**: [Framework for Associative Memory with Attentional Bias](https://arxiv.org/abs/2504.13173)
+
+## Running Locally
+
+```bash
+pip install -r requirements.txt
+python app.py
+```
+
+Built with ❤️ exploring the future of adaptive AI systems.
+

app.py

@@ -1,40 +1,204 @@
-import gradio as gr
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "distilgpt2"
-
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-model.eval()
-
-def chat(text):
-    inputs = tokenizer(text, return_tensors="pt")
-    outputs = model(
-        **inputs,
-        output_hidden_states=True
-    )
-
-    h_last = outputs.hidden_states[-1][:, -1]
-
-    k = key_proj(h_last)
-    v = value_proj(h_last)
-
-    pred = memory(k)
-    loss = ((pred - v) ** 2).mean()
-
-    loss.backward()
-
-    with torch.no_grad():
-        memory.W -= 1e-2 * memory.W.grad
-        memory.W.grad.zero_()
-
-    return f"Loss: {loss.item():.4f}"
-
-gr.Interface(
-    fn=chat,
-    inputs="text",
-    outputs="text",
-    title="Base LM (no memory yet)"
-).launch()
-
+"""
+Titans + MIRAS Demo: A Brain That Changes Itself While Thinking
+
+This implements a minimal version of Titans (test-time learning) and MIRAS
+(associative memory) using distilgpt2 running on Hugging Face.
+
+Key features:
+- Test-time learning: Memory updates while generating responses
+- Retention gate: Surprising events are more memorable
+- Persistent memory: Remembers across sessions
+"""
+
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from miras_memory import MIRASMemory
+from projections import KeyProjection, ValueProjection
+from memory_store import MemoryStore
+
+
+# ========== Configuration ==========
+MODEL_NAME = "distilgpt2"
+HIDDEN_DIM = 768  # distilgpt2 hidden dimension
+MEMORY_DIM = 256  # memory dimension
+LEARNING_RATE = 1e-3  # test-time learning rate
+MAX_NEW_TOKENS = 50  # max tokens to generate
+
+
+# ========== Initialize Components ==========
+print("🧠 Initializing Titans + MIRAS brain...")
+
+# Base language model
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+tokenizer.pad_token = tokenizer.eos_token
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+model.eval()
+
+# Memory system
+memory = MIRASMemory(memory_dim=MEMORY_DIM, init_scale=0.01)
+key_proj = KeyProjection(HIDDEN_DIM, MEMORY_DIM)
+value_proj = ValueProjection(HIDDEN_DIM, MEMORY_DIM)
+
+# Memory persistence
+store = MemoryStore(save_dir="memory")
+store.load(memory)
+
+print("✅ Brain initialized!")
+
+
+# ========== Core Logic ==========
+def chat(user_input, conversation_history):
+    """
+    Main chat function that:
+    1. Processes input through base LM
+    2. Updates memory via test-time learning
+    3. Generates response
+    4. Returns response + memory stats
+    """
+    if not user_input.strip():
+        return conversation_history, conversation_history
+    
+    # === Step 1: Extract hidden states from input ===
+    inputs = tokenizer(user_input, return_tensors="pt", padding=True)
+    
+    with torch.no_grad():
+        outputs = model(
+            **inputs,
+            output_hidden_states=True
+        )
+    
+    # Get last hidden state of the last token
+    h_last = outputs.hidden_states[-1][:, -1, :]  # (1, hidden_dim)
+    
+    # === Step 2: Test-time memory learning ===
+    with torch.enable_grad():
+        # Project to key/value space
+        k = key_proj(h_last)
+        v = value_proj(h_last)
+        
+        # Compute memory loss
+        loss = memory.compute_loss(k, v)
+        
+        # Get retention factor (higher for surprising events)
+        retention = memory.retention_gate(loss)
+        effective_lr = LEARNING_RATE * retention
+        
+        # Backprop and update memory
+        loss.backward()
+        
+        with torch.no_grad():
+            memory.W -= effective_lr * memory.W.grad
+            memory.W.grad.zero_()
+            
+            # Update stats
+            memory.update_stats(loss)
+    
+    # === Step 3: Generate response ===
+    with torch.no_grad():
+        output_ids = model.generate(
+            inputs['input_ids'],
+            max_new_tokens=MAX_NEW_TOKENS,
+            do_sample=True,
+            temperature=0.8,
+            top_p=0.9,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+    
+    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    
+    # Remove the input prompt from response
+    if response.startswith(user_input):
+        response = response[len(user_input):].strip()
+    
+    # === Step 4: Save memory ===
+    store.save(memory)
+    
+    # === Step 5: Format output ===
+    stats = memory.get_stats()
+    
+    memory_info = (
+        f"**Memory Update**: Loss={loss.item():.4f} | "
+        f"Retention={retention:.2f}x | "
+        f"Updates={stats['updates']} | "
+        f"Avg Loss={stats['avg_loss']:.4f}"
+    )
+    
+    # Build conversation
+    bot_message = f"{response}\n\n---\n*{memory_info}*"
+    
+    # Update conversation history
+    conversation_history.append((user_input, bot_message))
+    
+    return conversation_history, conversation_history
+
+
+def clear_conversation():
+    """Clear the conversation but keep memory."""
+    return [], []
+
+
+# ========== Gradio Interface ==========
+with gr.Blocks(title="Titans + MIRAS: Self-Modifying Brain") as demo:
+    gr.Markdown("""
+    # 🧠 Titans + MIRAS: A Brain That Changes Itself While Thinking
+    
+    This is a minimal implementation of **Titans** (test-time learning) and **MIRAS** (associative memory).
+    
+    **What makes this special:**
+    - 🔄 **Test-time learning**: The memory updates with every interaction
+    - 🎯 **Retention gate**: Surprising inputs are more memorable
+    - 💾 **Persistent memory**: Remembers across sessions
+    
+    **How it works:**
+    1. Your input is processed through distilgpt2
+    2. Hidden states are projected to memory key/value space
+    3. Memory learns via gradient descent (learning rate adjusted by surprise)
+    4. Model generates a response
+    5. Memory is saved to disk
+    
+    *Watch the memory loss decrease as it learns from your conversations!*
+    """)
+    
+    chatbot = gr.Chatbot(
+        label="Conversation",
+        height=400,
+    )
+    
+    state = gr.State([])
+    
+    with gr.Row():
+        msg = gr.Textbox(
+            label="Your Message",
+            placeholder="Type your message here...",
+            scale=4,
+        )
+        submit = gr.Button("Send", scale=1, variant="primary")
+    
+    with gr.Row():
+        clear = gr.Button("Clear Conversation (Keep Memory)")
+    
+    gr.Markdown("""
+    ### 📊 Memory Stats
+    - **Loss**: How well memory predicts values (lower = better)
+    - **Retention**: Learning rate multiplier (higher for surprising inputs)
+    - **Updates**: Total number of memory updates
+    - **Avg Loss**: Average loss across all updates
+    
+    ### 📚 References
+    - **Titans**: [arxiv.org/abs/2501.00663](https://arxiv.org/abs/2501.00663)
+    - **MIRAS**: [arxiv.org/abs/2504.13173](https://arxiv.org/abs/2504.13173)
+    """)
+    
+    # Event handlers
+    msg.submit(chat, [msg, state], [chatbot, state]).then(
+        lambda: "", None, msg
+    )
+    submit.click(chat, [msg, state], [chatbot, state]).then(
+        lambda: "", None, msg
+    )
+    clear.click(clear_conversation, None, [chatbot, state])
+
+print("🚀 Launching Gradio interface...")
+demo.launch()

memory_store.py

@@ -0,0 +1,83 @@
+"""
+Memory Persistence
+
+Handles saving and loading memory state to/from disk so the brain
+remembers across sessions.
+"""
+
+import torch
+import json
+import os
+from pathlib import Path
+from datetime import datetime
+
+
+class MemoryStore:
+    """Manages persistent storage of memory state."""
+    
+    def __init__(self, save_dir="memory"):
+        self.save_dir = Path(save_dir)
+        self.save_dir.mkdir(exist_ok=True)
+        self.memory_path = self.save_dir / "memory.pt"
+        self.metadata_path = self.save_dir / "metadata.json"
+    
+    def save(self, memory_module):
+        """
+        Save memory state to disk.
+        
+        Args:
+            memory_module: MIRASMemory instance
+        """
+        # Save memory weights
+        torch.save({
+            'W': memory_module.W.data,
+            'update_count': memory_module.update_count,
+            'total_loss': memory_module.total_loss,
+        }, self.memory_path)
+        
+        # Save metadata
+        metadata = {
+            'last_updated': datetime.now().isoformat(),
+            'memory_dim': memory_module.memory_dim,
+            'updates': memory_module.update_count.item(),
+            'avg_loss': (memory_module.total_loss / max(memory_module.update_count, 1)).item(),
+        }
+        
+        with open(self.metadata_path, 'w') as f:
+            json.dump(metadata, f, indent=2)
+        
+        print(f"💾 Memory saved: {memory_module.update_count.item()} updates")
+    
+    def load(self, memory_module):
+        """
+        Load memory state from disk.
+        
+        Args:
+            memory_module: MIRASMemory instance to load into
+            
+        Returns:
+            bool: True if loaded successfully, False otherwise
+        """
+        if not self.memory_path.exists():
+            print("🆕 No saved memory found. Starting fresh!")
+            return False
+        
+        try:
+            checkpoint = torch.load(self.memory_path)
+            memory_module.W.data = checkpoint['W']
+            memory_module.update_count = checkpoint['update_count']
+            memory_module.total_loss = checkpoint['total_loss']
+            
+            print(f"✅ Memory loaded: {memory_module.update_count.item()} updates")
+            return True
+        except Exception as e:
+            print(f"⚠️ Error loading memory: {e}. Starting fresh!")
+            return False
+    
+    def get_metadata(self):
+        """Get metadata about saved memory."""
+        if not self.metadata_path.exists():
+            return None
+        
+        with open(self.metadata_path, 'r') as f:
+            return json.load(f)

memory_test/memory.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:112699910bd87e5a20fb5ea40d87869fe3f3f987d70d6f45c2ec6b1cf8fca32a
+size 264152

memory_test/metadata.json

@@ -0,0 +1,6 @@
+{
+  "last_updated": "2025-12-20T19:57:11.523657",
+  "memory_dim": 256,
+  "updates": 0,
+  "avg_loss": 0.0
+}

miras_memory.py

@@ -0,0 +1,97 @@
+"""
+MIRAS-inspired Associative Memory Module
+
+Implements an associative memory that learns key-value mappings
+through attentional bias objective during test time.
+"""
+
+import torch
+import torch.nn as nn
+
+
+class MIRASMemory(nn.Module):
+    """
+    Associative memory module inspired by MIRAS framework.
+    
+    The memory learns to map keys to values using a simple linear projection
+    and updates itself during test time via gradient descent.
+    
+    Args:
+        memory_dim: Dimensionality of memory keys/values
+        init_scale: Scale for random weight initialization
+    """
+    
+    def __init__(self, memory_dim=256, init_scale=0.01):
+        super().__init__()
+        self.memory_dim = memory_dim
+        
+        # Memory matrix: maps keys to values
+        # W: (memory_dim, memory_dim)
+        self.W = nn.Parameter(
+            torch.randn(memory_dim, memory_dim) * init_scale
+        )
+        
+        # Track number of updates for retention gate
+        self.register_buffer('update_count', torch.tensor(0))
+        self.register_buffer('total_loss', torch.tensor(0.0))
+        
+    def forward(self, key):
+        """
+        Query memory with a key.
+        
+        Args:
+            key: (batch_size, memory_dim) tensor
+            
+        Returns:
+            predicted_value: (batch_size, memory_dim) tensor
+        """
+        # Simple linear mapping: pred_v = k @ W
+        predicted_value = key @ self.W
+        return predicted_value
+    
+    def compute_loss(self, key, value):
+        """
+        Compute attentional bias loss between predicted and true value.
+        
+        Args:
+            key: (batch_size, memory_dim)
+            value: (batch_size, memory_dim)
+            
+        Returns:
+            loss: scalar tensor
+        """
+        pred = self.forward(key)
+        loss = ((pred - value) ** 2).mean()
+        return loss
+    
+    def retention_gate(self, loss):
+        """
+        Simple retention gate: higher loss = more surprising = more memorable.
+        
+        Returns a scaling factor for the learning rate based on surprise.
+        High loss (surprising) gets higher weight.
+        
+        Args:
+            loss: scalar tensor
+            
+        Returns:
+            retention_factor: scalar in range [0.5, 2.0]
+        """
+        # Normalize loss to a retention factor
+        # If loss is high (surprising), learn more aggressively
+        retention_factor = torch.clamp(loss / 0.1, 0.5, 2.0)
+        return retention_factor.item()
+    
+    def update_stats(self, loss):
+        """Track memory statistics."""
+        self.update_count += 1
+        self.total_loss += loss.item()
+    
+    def get_stats(self):
+        """Get memory statistics."""
+        avg_loss = self.total_loss / max(self.update_count, 1)
+        return {
+            'updates': self.update_count.item(),
+            'avg_loss': avg_loss.item(),
+            'memory_size': self.W.numel()
+        }

projections.py

@@ -0,0 +1,54 @@
+"""
+Key and Value Projection Layers
+
+Maps hidden states from the base language model into memory-compatible
+representations for the MIRAS memory module.
+"""
+
+import torch.nn as nn
+
+
+class KeyProjection(nn.Module):
+    """
+    Projects hidden states to memory keys.
+    
+    Args:
+        hidden_dim: Dimension of LM hidden states (e.g., 768 for distilgpt2)
+        memory_dim: Dimension of memory keys (e.g., 256)
+    """
+    
+    def __init__(self, hidden_dim, memory_dim):
+        super().__init__()
+        self.projection = nn.Linear(hidden_dim, memory_dim, bias=False)
+        
+    def forward(self, hidden_state):
+        """
+        Args:
+            hidden_state: (batch_size, hidden_dim)
+        Returns:
+            key: (batch_size, memory_dim)
+        """
+        return self.projection(hidden_state)
+
+
+class ValueProjection(nn.Module):
+    """
+    Projects hidden states to memory values.
+    
+    Args:
+        hidden_dim: Dimension of LM hidden states (e.g., 768 for distilgpt2)
+        memory_dim: Dimension of memory values (e.g., 256)
+    """
+    
+    def __init__(self, hidden_dim, memory_dim):
+        super().__init__()
+        self.projection = nn.Linear(hidden_dim, memory_dim, bias=False)
+        
+    def forward(self, hidden_state):
+        """
+        Args:
+            hidden_state: (batch_size, hidden_dim)
+        Returns:
+            value: (batch_size, memory_dim)
+        """
+        return self.projection(hidden_state)

requirements.txt

@@ -1,3 +1,4 @@
-torch
-transformers
-gradio
+torch
+transformers
+gradio
+numpy

test_components.py

@@ -0,0 +1,80 @@
+"""
+Quick test script to verify Titans+MIRAS components
+"""
+
+import torch
+from miras_memory import MIRASMemory
+from projections import KeyProjection, ValueProjection
+from memory_store import MemoryStore
+
+print("=" * 50)
+print("Testing Titans + MIRAS Components")
+print("=" * 50)
+
+# Test 1: Memory Module
+print("\n✓ Test 1: Memory Module")
+memory = MIRASMemory(memory_dim=256, init_scale=0.01)
+key_test = torch.randn(1, 256)
+value_test = torch.randn(1, 256)
+
+pred = memory(key_test)
+print(f"  - Forward pass: {pred.shape}")
+
+loss = memory.compute_loss(key_test, value_test)
+print(f"  - Loss computation: {loss.item():.4f}")
+
+retention = memory.retention_gate(loss)
+print(f"  - Retention gate: {retention:.2f}x")
+
+stats = memory.get_stats()
+print(f"  - Stats: {stats}")
+
+# Test 2: Projections
+print("\n✓ Test 2: Projection Layers")
+key_proj = KeyProjection(768, 256)
+value_proj = ValueProjection(768, 256)
+
+hidden = torch.randn(1, 768)
+k = key_proj(hidden)
+v = value_proj(hidden)
+print(f"  - Key projection: {k.shape}")
+print(f"  - Value projection: {v.shape}")
+
+# Test 3: Memory Store
+print("\n✓ Test 3: Memory Persistence")
+store = MemoryStore(save_dir="memory_test")
+
+# Save
+store.save(memory)
+print(f"  - Memory saved")
+
+# Create new memory and load
+memory2 = MIRASMemory(memory_dim=256, init_scale=0.01)
+loaded = store.load(memory2)
+print(f"  - Memory loaded: {loaded}")
+
+# Test 4: Full Pipeline
+print("\n✓ Test 4: Full Test-Time Learning Pipeline")
+memory3 = MIRASMemory(memory_dim=256, init_scale=0.01)
+
+for i in range(5):
+    # Simulate learning
+    k = torch.randn(1, 256)
+    v = torch.randn(1, 256)
+    
+    loss = memory3.compute_loss(k, v)
+    retention = memory3.retention_gate(loss)
+    lr = 1e-3 * retention
+    
+    loss.backward()
+    with torch.no_grad():
+        memory3.W -= lr * memory3.W.grad
+        memory3.W.grad.zero_()
+        memory3.update_stats(loss)
+    
+    stats = memory3.get_stats()
+    print(f"  - Step {i+1}: Loss={loss.item():.4f}, Retention={retention:.2f}x, Avg={stats['avg_loss']:.4f}")
+
+print("\n" + "=" * 50)
+print("✅ ALL TESTS PASSED!")
+print("=" * 50)