Ant LingBot-Vision Deep Dive: Spatial-Native Vision Foundation Model — 1.1B Parameters Defeat 7B DINOv3 via Boundary Forcing, Robotic Vision Finally "Sees" the Real World

class RandomMaskingAnalyzer:
    """
    DINOv3 random masking strategy analysis
    
    Core problem: Random masking learns geometric structures
    only indirectly and inefficiently
    """
    
    def __init__(self):
        self.patch_types = {
            "flat": {"info_density": 0.1, "geometric_value": 0.0, "ratio": 0.6},
            "edge": {"info_density": 0.9, "geometric_value": 1.0, "ratio": 0.1},
            "texture": {"info_density": 0.5, "geometric_value": 0.3, "ratio": 0.2},
            "corner": {"info_density": 0.8, "geometric_value": 0.8, "ratio": 0.1},
        }
    
    def analyze_random_mask_efficiency(self, mask_ratio: float = 0.3) -> dict:
        total_patches = 100
        flat_patches = int(total_patches * self.patch_types["flat"]["ratio"])
        edge_patches = int(total_patches * self.patch_types["edge"]["ratio"])
        texture_patches = int(total_patches * self.patch_types["texture"]["ratio"])
        corner_patches = total_patches - flat_patches - edge_patches - texture_patches
        
        masked_count = int(total_patches * mask_ratio)
        expected_flat = flat_patches * mask_ratio
        expected_edge = edge_patches * mask_ratio
        expected_texture = texture_patches * mask_ratio
        expected_corner = corner_patches * mask_ratio
        
        geometric_gain = (
            expected_edge * self.patch_types["edge"]["geometric_value"] +
            expected_texture * self.patch_types["texture"]["geometric_value"] +
            expected_corner * self.patch_types["corner"]["geometric_value"]
        )
        
        boundary_forced_gain = (
            edge_patches * self.patch_types["edge"]["geometric_value"] +
            corner_patches * self.patch_types["corner"]["geometric_value"]
        )
        
        efficiency = geometric_gain / boundary_forced_gain * 100 if boundary_forced_gain > 0 else 0
        
        return {
            "random_mask_geometric_gain": geometric_gain,
            "boundary_forced_geometric_gain": boundary_forced_gain,
            "geometric_learning_efficiency_pct": efficiency,
        }

analyzer = RandomMaskingAnalyzer()
result = analyzer.analyze_random_mask_efficiency(0.3)
print(f"Random masking geometric gain: {result['random_mask_geometric_gain']:.1f}")
print(f"Boundary forced geometric gain: {result['boundary_forced_geometric_gain']:.1f}")
print(f"Geometric learning efficiency: {result['geometric_learning_efficiency_pct']:.1f}%")

package boundary

import (
	"math"
)

// BoundaryField represents the boundary field
// Converts continuous geometric values into discrete classification distributions
// to avoid regression collapse
type BoundaryField struct {
	DistanceClasses    int   // 24 discrete distance classes
	DirectionClasses   int   // 12 discrete direction classes
	
	DistanceLogits     [][]float64 // [H][W][24]
	DirectionLogits    [][]float64 // [H][W][12]
	
	DecodedDistances   [][]float64 // [H][W]
	DecodedDirections  [][]float64 // [H][W] (radians)
}

// NewBoundaryField creates a boundary field
func NewBoundaryField(height, width int) *BoundaryField {
	bf := &BoundaryField{
		DistanceClasses:  24,
		DirectionClasses: 12,
	}
	
	bf.DistanceLogits = make([][]float64, height)
	bf.DirectionLogits = make([][]float64, height)
	bf.DecodedDistances = make([][]float64, height)
	bf.DecodedDirections = make([][]float64, height)
	
	for i := 0; i < height; i++ {
		bf.DistanceLogits[i] = make([]float64, width*24)
		bf.DirectionLogits[i] = make([]float64, width*12)
		bf.DecodedDistances[i] = make([]float64, width)
		bf.DecodedDirections[i] = make([]float64, width)
	}
	
	return bf
}

// DecodeBoundary decodes continuous boundary field from classification distributions
func (bf *BoundaryField) DecodeBoundary(height, width int) {
	for y := 0; y < height; y++ {
		for x := 0; x < width; x++ {
			dStart := x * bf.DistanceClasses
			dEnd := dStart + bf.DistanceClasses
			dProbs := softmax(bf.DistanceLogits[y][dStart:dEnd])
			
			expectedDist := 0.0
			for k := 0; k < bf.DistanceClasses; k++ {
				distance := float64(k) / float64(bf.DistanceClasses-1)
				expectedDist += dProbs[k] * distance
			}
			bf.DecodedDistances[y][x] = expectedDist
			
			dirStart := x * bf.DirectionClasses
			dirEnd := dirStart + bf.DirectionClasses
			dirProbs := softmax(bf.DirectionLogits[y][dirStart:dirEnd])
			
			sinSum, cosSum := 0.0, 0.0
			for d := 0; d < bf.DirectionClasses; d++ {
				angle := 2.0 * math.Pi * float64(d) / float64(bf.DirectionClasses)
				sinSum += dirProbs[d] * math.Sin(angle)
				cosSum += dirProbs[d] * math.Cos(angle)
			}
			bf.DecodedDirections[y][x] = math.Atan2(sinSum, cosSum)
		}
	}
}

// BoundaryForcingLoss computes joint loss including semantic self-distillation
// and boundary field classification objectives
type BoundaryForcingLoss struct {
	SemanticWeight  float64 // α
	BoundaryWeight  float64 // β
}

func softmax(logits []float64) []float64 {
	maxVal := logits[0]
	for _, v := range logits {
		if v > maxVal {
			maxVal = v
		}
	}
	
	sum := 0.0
	result := make([]float64, len(logits))
	for i, v := range logits {
		result[i] = math.Exp(v - maxVal)
		sum += result[i]
	}
	for i := range result {
		result[i] /= sum
	}
	return result
}

func cosineSimilarity(a, b []float64) float64 {
	dot, normA, normB := 0.0, 0.0, 0.0
	for i := range a {
		dot += a[i] * b[i]
		normA += a[i] * a[i]
		normB += b[i] * b[i]
	}
	if normA == 0 || normB == 0 {
		return 0
	}
	return dot / (math.Sqrt(normA) * math.Sqrt(normB))
}

func crossEntropy(pred, target []float64) float64 {
	loss := 0.0
	for i := range pred {
		targetProb := math.Exp(target[i])
		loss -= targetProb * math.Log(pred[i]+1e-10)
	}
	return loss
}

class AContrarioValidator:
    """
    A-contrario validation: automatically filters false boundary segments
    
    Core idea:
    1. Define null hypothesis H0: observed segment is from random noise
    2. Compute NFA (Number of False Alarms) = E[occurrences in random noise]
    3. NFA < ε (typically ε=1): reject H0, recognize as real boundary
    """
    
    def __init__(self, epsilon: float = 1.0):
        self.epsilon = epsilon
    
    def validate_boundary(self, 
                        segment_pixels: list,
                        image_shape: tuple,
                        boundary_strength: float) -> dict:
        H, W = image_shape
        N = len(segment_pixels)
        N_test = H * W
        
        eps = self._compute_alignment_precision(segment_pixels)
        p = self._random_occurrence_probability(N, eps)
        nfa = N_test * p
        
        is_significant = nfa < self.epsilon and boundary_strength > 0.3
        
        return {
            "is_significant": is_significant,
            "nfa": nfa,
            "precision_pixels": eps,
            "segment_length": N,
            "boundary_strength": boundary_strength,
        }
    
    def _compute_alignment_precision(self, pixels: list) -> float:
        if len(pixels) < 3:
            return 1.0
        
        xs = [p[0] for p in pixels]
        ys = [p[1] for p in pixels]
        n = len(xs)
        
        sx = sum(xs)
        sy = sum(ys)
        sxx = sum(x * x for x in xs)
        sxy = sum(xs[i] * ys[i] for i in range(n))
        
        denom = n * sxx - sx * sx
        if denom == 0:
            return 1.0
        
        a = (n * sxy - sx * sy) / denom
        b = (sy - a * sx) / n
        
        residuals = [abs(ys[i] - (a * xs[i] + b)) for i in range(n)]
        rmse = (sum(r * r for r in residuals) / n) ** 0.5
        return rmse
    
    def _random_occurrence_probability(self, n: int, eps: float) -> float:
        import math
        area = math.pi * eps * eps
        log_prob = n * math.log(area) - sum(math.log(i) for i in range(1, n+1))
        return math.exp(log_prob)
    
    def batch_validate(self, all_segments: list, image_shape: tuple) -> tuple:
        validated = []
        rejected = []
        
        for segment in all_segments:
            result = self.validate_boundary(
                segment["pixels"], image_shape, segment["strength"]
            )
            if result["is_significant"]:
                validated.append({**segment, "nfa": result["nfa"]})
            else:
                rejected.append(segment)
        
        return validated, rejected

validator = AContrarioValidator(epsilon=1.0)

# Simulate a real boundary (20 pixels, high alignment)
true_boundary = {
    "pixels": [(x, 2*x + 3 + 0.1*(x%2)) for x in range(20)],
    "strength": 0.85,
}
result = validator.validate_boundary(
    true_boundary["pixels"], (224, 224), true_boundary["strength"]
)
print(f"Real boundary - Pass: {result['is_significant']}, NFA={result['nfa']:.6f}")

import random
noise_boundary = {
    "pixels": [(random.randint(0, 223), random.randint(0, 223)) for _ in range(20)],
    "strength": 0.15,
}
result2 = validator.validate_boundary(
    noise_boundary["pixels"], (224, 224), noise_boundary["strength"]
)
print(f"Noise boundary - Pass: {result2['is_significant']}, NFA={result2['nfa']:.2f}")

class CornerBootstrapAnalyzer:
    """
    Corner bootstrap analysis
    
    Even with random boundary field initialization, 
    corner-anchored decoding produces consistent segments
    """
    
    def __init__(self):
        self.corners = []
    
    def generate_random_boundary_field(self, height, width, num_corners=5):
        import random
        self.corners = [
            (random.randint(0, width-1), random.randint(0, height-1))
            for _ in range(num_corners)
        ]
        
        import math
        field = []
        for y in range(height):
            row = []
            for x in range(width):
                min_dist = min(
                    ((x - cx)**2 + (y - cy)**2)**0.5
                    for cx, cy in self.corners
                )
                value = random.uniform(0, 0.3) + math.exp(-min_dist/20)
                row.append(value)
            field.append(row)
        return field
    
    def measure_consistency(self, trials=5, height=100, width=100):
        import math
        all_segments = []
        
        for _ in range(trials):
            field = self.generate_random_boundary_field(height, width)
            segments = self.decode_line_segments(field, height, width)
            all_segments.append(segments)
        
        consistency_scores = []
        for i in range(trials):
            for j in range(i+1, trials):
                score = self._segment_overlap(
                    all_segments[i], all_segments[j], height, width
                )
                consistency_scores.append(score)
        
        return sum(consistency_scores) / len(consistency_scores) if consistency_scores else 0.0
    
    def decode_line_segments(self, field, height, width, threshold=0.5):
        suppressed = [[0.0]*width for _ in range(height)]
        for y in range(1, height-1):
            for x in range(1, width-1):
                if field[y][x] > threshold:
                    neighbors = [
                        field[y-1][x-1], field[y-1][x], field[y-1][x+1],
                        field[y][x-1], field[y][x+1],
                        field[y+1][x-1], field[y+1][x], field[y+1][x+1],
                    ]
                    if field[y][x] >= max(neighbors):
                        suppressed[y][x] = field[y][x]
        
        segments = []
        visited = set()
        for y in range(height):
            for x in range(width):
                if suppressed[y][x] > 0 and (x,y) not in visited:
                    segment = []
                    stack = [(x,y)]
                    while stack:
                        cx, cy = stack.pop()
                        if (cx,cy) in visited:
                            continue
                        visited.add((cx,cy))
                        segment.append((cx,cy))
                        for dy in [-1,0,1]:
                            for dx in [-1,0,1]:
                                nx, ny = cx+dx, cy+dy
                                if (0<=nx<width and 0<=ny<height and
                                    suppressed[ny][nx] > 0 and
                                    (nx,ny) not in visited):
                                    stack.append((nx,ny))
                    if len(segment) > 5:
                        segments.append(segment)
        return segments
    
    def _segment_overlap(self, segs1, segs2, height, width):
        mask1 = set(p for seg in segs1 for p in seg)
        mask2 = set(p for seg in segs2 for p in seg)
        if not mask1 and not mask2:
            return 1.0
        if not mask1 or not mask2:
            return 0.0
        return len(mask1 & mask2) / len(mask1 | mask2)

import math
bootstrap = CornerBootstrapAnalyzer()
consistency = bootstrap.measure_consistency(trials=5)
print(f"Random boundary field decoding consistency (5 trials): {consistency:.3f}")

package training

import (
	"sync"
)

// LingBotVisionTrainer implements masked boundary modeling training loop
type LingBotVisionTrainer struct {
	Teacher       *VisionTransformer
	Student       *VisionTransformer
	BoundaryDecoder *BoundaryFieldDecoder
	Validator     *AContrarioValidator
	Config        *TrainingConfig
}

type TrainingConfig struct {
	BatchSize         int     // 1024
	LearningRate      float64 // 1e-4
	MomentumTeacher   float64 // 0.996
	MaskRatio         float64 // 0.3
	BoundaryMaskRatio float64 // 0.15
	SemanticWeight    float64 // α = 1.0
	BoundaryWeight    float64 // β = 0.5
	ImageSize         int     // 224
	PatchSize         int     // 16
	NumEpochs         int     // 100
	NumWorkers        int     // 8
}

type VisionTransformer struct {
	EmbedDim    int     // 1536 (ViT-g)
	NumHeads    int     // 24
	NumLayers   int     // 40
	PatchEmbed  *PatchEmbedding
	Transformer *TransformerEncoder
}

type PatchEmbedding struct {
	KernelSize  int
	Stride      int
	Weights     [][]float64
}

type TransformerEncoder struct {
	Layers []*TransformerLayer
}

type TransformerLayer struct {
	SelfAttn *MultiHeadAttention
	FFN      *FeedForward
}

type MultiHeadAttention struct {
	NumHeads int
	HeadDim  int
	QProj, KProj, VProj, OProj [][]float64
}

type FeedForward struct {
	W1, W2 [][]float64
}

// TrainingStepResult holds per-step training metrics
type TrainingStepResult struct {
	Loss         float64
	StudentFeats [][][]float64
	TeacherFeats [][][]float64
}

// TrainStep executes a single training step
func (t *LingBotVisionTrainer) TrainStep(images [][][]float32) *TrainingStepResult {
	batchSize := len(images)
	
	studentFeatures := make([][][]float64, batchSize)
	for i, img := range images {
		studentFeatures[i] = t.Student.Forward(img)
	}
	
	boundaryFields := make([]*BoundaryField, batchSize)
	for i, img := range images {
		bf := t.BoundaryDecoder.Predict(img)
		t.Validator.ValidateBatch(bf)
		boundaryFields[i] = bf
	}
	
	maskedIndices := make([][]int, batchSize)
	for i, bf := range boundaryFields {
		maskedIndices[i] = t.generateBoundaryMask(bf)
	}
	
	teacherFeatures := make([][][]float64, batchSize)
	for i, img := range images {
		teacherFeatures[i] = t.Teacher.Forward(img)
	}
	
	totalLoss := 0.0
	for i := range images {
		semanticLoss := t.computeSemanticLoss(
			studentFeatures[i], teacherFeatures[i], maskedIndices[i],
		)
		boundaryLoss := t.computeBoundaryLoss(
			studentFeatures[i], boundaryFields[i], maskedIndices[i],
		)
		totalLoss += t.Config.SemanticWeight*semanticLoss +
			t.Config.BoundaryWeight*boundaryLoss
	}
	totalLoss /= float64(batchSize)
	
	t.momentumUpdateTeacher()
	
	return &TrainingStepResult{
		Loss:         totalLoss,
		StudentFeats: studentFeatures,
		TeacherFeats: teacherFeatures,
	}
}

// generateBoundaryMask creates boundary-forced masks
func (t *LingBotVisionTrainer) generateBoundaryMask(bf *BoundaryField) []int {
	numPatches := (t.Config.ImageSize / t.Config.PatchSize) *
		(t.Config.ImageSize / t.Config.PatchSize)
	
	patchBoundaryScores := make([]float64, numPatches)
	patchesPerRow := t.Config.ImageSize / t.Config.PatchSize
	
	for y := 0; y < t.Config.ImageSize; y++ {
		for x := 0; x < t.Config.ImageSize; x++ {
			if bf.DecodedDistances[y][x] > 0.1 {
				py := y / t.Config.PatchSize
				px := x / t.Config.PatchSize
				idx := py*patchesPerRow + px
				patchBoundaryScores[idx] += bf.DecodedDistances[y][x]
			}
		}
	}
	
	totalMasked := int(float64(numPatches) * t.Config.BoundaryMaskRatio)
	
	type scoredIdx struct {
		idx   int
		score float64
	}
	scored := make([]scoredIdx, numPatches)
	for i := 0; i < numPatches; i++ {
		scored[i] = scoredIdx{i, patchBoundaryScores[i]}
	}
	
	for i := 0; i < totalMasked && i < numPatches; i++ {
		maxIdx := i
		for j := i + 1; j < numPatches; j++ {
			if scored[j].score > scored[maxIdx].score {
				maxIdx = j
			}
		}
		scored[i], scored[maxIdx] = scored[maxIdx], scored[i]
	}
	
	result := make([]int, totalMasked)
	for i := 0; i < totalMasked; i++ {
		result[i] = scored[i].idx
	}
	return result
}

// momentumUpdateTeacher updates teacher model with EMA
func (t *LingBotVisionTrainer) momentumUpdateTeacher() {
	m := t.Config.MomentumTeacher
	t.Teacher.PatchEmbed.Weights = t.momentumUpdateMatrix(
		t.Teacher.PatchEmbed.Weights, t.Student.PatchEmbed.Weights, m,
	)
}

func (t *LingBotVisionTrainer) momentumUpdateMatrix(
	teacher, student [][]float64, m float64,
) [][]float64 {
	h := len(teacher)
	w := len(teacher[0])
	result := make([][]float64, h)
	for i := range result {
		result[i] = make([]float64, w)
		for j := range result[i] {
			result[i][j] = m*teacher[i][j] + (1-m)*student[i][j]
		}
	}
	return result
}

// TrainEpoch trains for one epoch
func (t *LingBotVisionTrainer) TrainEpoch(dataset []ImageBatch) float64 {
	var wg sync.WaitGroup
	losses := make([]float64, len(dataset))
	
	for i, batch := range dataset {
		wg.Add(1)
		go func(idx int, imgs [][][]float32) {
			defer wg.Done()
			result := t.TrainStep(imgs)
			losses[idx] = result.Loss
		}(i, batch.Images)
	}
	
	wg.Wait()
	
	totalLoss := 0.0
	for _, l := range losses {
		totalLoss += l
	}
	return totalLoss / float64(len(losses))
}

class LingBotDepth2:
    """
    LingBot-Depth 2.0 Depth Completion Pipeline
    
    Based on LingBot-Vision encoder + Sensor-Validity Masking
    """
    
    def __init__(self, vision_encoder, model_size: str = "ViT-g"):
        self.encoder = vision_encoder
        self.depth_decoder = DepthDecoder()
        self.sensor_validity = SensorValidityMask()
        
        self.config = {
            "ViT-g": {"params": 1.1e9, "embed_dim": 1536},
            "ViT-L": {"params": 0.3e9, "embed_dim": 1024},
            "ViT-B": {"params": 86e6, "embed_dim": 768},
            "ViT-S": {"params": 21e6, "embed_dim": 384},
        }[model_size]
    
    def depth_completion(self, rgb_image, sparse_depth, sensor_mask=None):
        # 1. Encode RGB
        features = self.encoder.encode(rgb_image)
        
        # 2. Fuse sparse depth
        depth_features = self._fuse_sparse_depth(sparse_depth, features)
        
        # 3. Sensor-Validity Masking
        if sensor_mask is None:
            sensor_mask = self.sensor_validity.estimate_validity(rgb_image, sparse_depth)
        
        # 4. Decode dense depth
        dense_depth = self.depth_decoder.decode(depth_features, sensor_mask)
        
        # 5. Post-process: boundary sharpening
        dense_depth = self._boundary_sharpen(dense_depth, rgb_image)
        
        return dense_depth
    
    def _fuse_sparse_depth(self, sparse_depth, features):
        H, W, D = features.shape
        depth_encoding = np.zeros((H, W, 64), dtype=np.float32)
        for y in range(H):
            for x in range(W):
                py = int(y * 14 + 7)
                px = int(x * 14 + 7)
                if py < sparse_depth.shape[0] and px < sparse_depth.shape[1]:
                    d = sparse_depth[py, px]
                    if d > 0:
                        depth_encoding[y, x, :32] = d
                        depth_encoding[y, x, 32:] = 1.0
        
        fused = np.concatenate([features, depth_encoding], axis=-1)
        projection = np.random.randn(D + 64, D).astype(np.float32) * 0.01
        return fused @ projection
    
    def _boundary_sharpen(self, depth, rgb):
        boundary_field = self.encoder.get_boundary_field(rgb)
        H, W = depth.shape
        result = depth.copy()
        
        for y in range(1, H-1):
            for x in range(1, W-1):
                if boundary_field[y, x] > 0.3:
                    continue
                else:
                    neighborhood = depth[y-1:y+2, x-1:x+2]
                    result[y, x] = np.median(neighborhood)
        
        return result


class SensorValidityMask:
    """
    Sensor-Validity Masking
    
    Core innovation: uses real sensor failure patterns instead of random masks
    Model learns the actual distribution of depth sensor failures during training
    """
    
    def estimate_validity(self, rgb, depth):
        H, W = depth.shape
        validity = np.ones((H, W), dtype=np.float32)
        
        validity[depth <= 0] = 0.0
        validity[np.isnan(depth)] = 0.0
        
        gray = np.mean(rgb, axis=-1)
        validity[gray > 240] *= 0.3
        
        from scipy.ndimage import variance
        texture = variance(gray, size=5)
        validity[texture < 10] *= 0.5
        
        distance_weight = np.exp(-np.arange(H) / (H * 0.3))
        validity *= distance_weight[:, np.newaxis]
        
        return validity


import numpy as np

class DepthDecoder:
    def __init__(self):
        self.upsample_layers = 4
        self.channels = [1536, 768, 384, 192, 1]
    
    def decode(self, features, sensor_mask):
        x = features
        for i in range(self.upsample_layers):
            x = np.repeat(np.repeat(x, 2, axis=0), 2, axis=1)
            in_c = self.channels[i]
            out_c = self.channels[i+1]
            proj = np.random.randn(in_c, out_c).astype(np.float32) * 0.01
            x = x @ proj
        
        depth = x.squeeze(-1)
        depth = depth * (1 - sensor_mask) + sensor_mask
        return depth

# Simulate depth completion
encoder = type('obj', (object,), {
    'encode': lambda self, x: np.random.randn(16, 16, 1536).astype(np.float32),
    'get_boundary_field': lambda self, x: np.random.rand(224, 224),
})()
depth_model = LingBotDepth2(encoder, "ViT-g")

rgb_demo = np.random.randn(224, 224, 3).astype(np.float32)
sparse_demo = np.random.rand(224, 224).astype(np.float32)
sparse_demo[sparse_demo < 0.3] = 0

result = depth_model.depth_completion(rgb_demo, sparse_demo)
print(f"Input sparse non-zero: {(sparse_demo > 0).sum()} / {224*224}")
print(f"Output dense: {result.shape}, completed: {(result > 0).sum()} / {224*224}")