KCVANGUARD 2026 Final Project

NH-LoRA

A future-aware continual learning method that treats LoRA capacity as a structural decision, not a fixed adapter budget.

Rehearsal-Free CIL
Frozen ViT
Dynamic Rank
Sparse Routing
Why NH-LoRA

Adapter structure becomes part of the learning problem.

NH-LoRA is framed around the stability-plasticity pressure in rehearsal-free continual learning.

Continual learning without replay

Class-incremental systems must absorb new classes without revisiting old task data, making stability and plasticity hard to balance.

Adapter capacity is a moving target

A fixed low-rank budget can be too rigid when task novelty, conflict, and retention pressure change over time.

Structure becomes part of learning

NH-LoRA models when to reuse shared memory, expand task slots, open new capacity, and consolidate stable updates.

Architecture

Four moving parts, one structural control loop.

The method separates reusable memory, task-specific plasticity, per-instance routing, and future-aware planning.

Slow plasticity

Shared Core LoRA

Reusable cross-task low-rank memory for stable knowledge that should survive across tasks.

Fast plasticity

Expandable Task Slot Bank

Task-specific slots that can grow when the planner sees novelty or conflict evidence.

Conditional routing

Instance Router

Sparse per-sample slot activation to reduce inter-slot interference at inference time.

Structural control

Horizon Planner

A future-aware controller that predicts novelty, conflict, rank budget, and consolidation signals.

Benchmark teaser

Reported logs are shaped into an explorer.

Numbers below come from the migrated project logs and remain presented as reported artifacts, not final paper claims.

Custom Dataset

2

Ready for final benchmark values.

Deployment reported top average

71.20%

Paired with reported forgetting metric.

Tracked benchmark groups

3

CIFAR-100, ImageNet-A, and Custom dataset for Deployment.

NH-LoRA architecture diagram preview