uncha-hyperbolic-compositional-alignment

name	uncha-hyperbolic-compositional-alignment
title	UNCHA: Uncertainty-Guided Compositional Alignment in Hyperbolic VLMs
version	0.0.3
engine	skillxiv-v0.0.3-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2603.22042
keywords	["Hyperbolic VLM","Entailment Loss","Uncertainty Calibration","Compositional Alignment","Part-to-Whole Semantics"]
description	Swap fixed entailment loss with uncertainty-guided piecewise-continuous formulation to improve part-to-whole compositional alignment in hyperbolic vision-language models by +3.0-3.1% on zero-shot classification. Use when training hyperbolic VLMs on part-object relationships and need better multi-object compositional understanding.
category	Component Innovation

What This Skill Does

Replace the fixed entailment loss in hyperbolic vision-language models with an uncertainty-calibrated piecewise-continuous formulation that adaptively modulates contrastive temperatures. This improves compositional alignment by +3.0 percentage points on ImageNet and +7.74 pp on multi-object representation tasks.

The Component Swap

The old HyCoCLIP entailment loss becomes zero after achieving alignment, preventing further refinement:

# Old approach: fixed entailment loss saturates
loss_old = max(0, phi(part, whole) - eta * omega(part))

The new UNCHA approach replaces this with three coordinated modifications. First, add a piecewise-continuous component that prevents saturation:

# New: piecewise-continuous entailment with adaptive temperature
loss_ent_base = max(0, phi(part, whole) - eta * omega(part))
loss_ent_refined = loss_ent_base + alpha * phi(part, whole)  # Non-zero gradient after alignment

# Uncertainty calibration modulates loss weight by estimated uncertainty
u_part = model.predict_uncertainty(part)
loss_ent_calibrated = loss_ent_refined * torch.exp(-u_part) + u_part

Second, add an entropy regularization term to prevent degenerate uncertainty estimates:

# Entropy regularization on uncertainty predictions
u_normalized = torch.softmax(uncertainties, dim=0)
entropy_reg = -torch.sum(u_normalized * torch.log(u_normalized + 1e-8))
loss_entropy = entropy_reg  # Encourages non-trivial uncertainty

Third, modulate the contrastive temperature by uncertainty to soften supervision for uncertain pairs:

# Adaptive temperature based on uncertainty (for softmax in contrastive loss)
temperature_default = 0.07
temperature_adaptive = torch.exp(uncertainty / 2.0) * temperature_default

Performance Impact

Zero-shot classification (ViT-B/16):

• ImageNet: +3.0 percentage points (45.8% → 48.8%)
• CIFAR-100: +3.1 pp (60.1% → 63.2%)

Multi-object compositional representation (3 objects):