paper_notes.md

# SPLIT-VLM — Reproduction Notes

**Paper:** SPLIT-VLM: Salience-Guided Partitioning towards Local Coverage for Importance-Aware Token Dropping in Vision-Language Models
**Authors:** Seungil Lee, Gilha Lee, Hyun Kim (Seoul National University of Science and Technology; KETI)
**Venue:** ICML 2026 (Poster). OpenReview id `Elm4TdaXi0` — https://openreview.net/forum?id=Elm4TdaXi0
**Public arXiv/GitHub:** none. PDF is OpenReview-challenge-gated; text extracted via authenticated browser + pdf.js.

## Claims to verify
1. On LLaVA-1.5-7B image understanding, SPLIT preserves **>99%** avg relative accuracy at **192** vision tokens and **~92.8%** at **64** tokens.
2. SPLIT consistently outperforms SOTA token-dropping methods on image & video benchmarks.
3. Token importance is estimated via **temporal shifts of hidden states across ViT layers**, eschewing attention scores/biases.

## Method (Algorithm 1) — training-free, batch size 1

Input: visual token set X={x_j}, partition into K disjoint regions X_k, total budget B, layer set L.

### 1. Temporal-shift importance (Sec 4.1)
For a token x with ViT hidden state h_ℓ(x) at layer ℓ:
- Relative change rate: **Δ_ℓ(x) = ‖h_ℓ(x) − h_{ℓ−1}(x)‖₂ / ‖h_ℓ(x)‖₂**   (Eq. 5, normalized so high-norm tokens are not overemphasized)
- Token importance: **I(x) = (1/|L|) Σ_{ℓ∈L} Δ_ℓ(x)**   (Eq. 6)
- Region importance: **I(X_k) = (1/|X_k|) Σ_{x∈X_k} I(x)**
- Best L = **all ViT layers** (ablation Fig 6b / Table 16). Middle layers best among single groups; layer 0 hurts.

### 2. Adaptive region budget (Eq. 7) — hybrid uniform + importance
- **B̃_k = B/K + B · I(X_k) / Σ_j I(X_j)**, rounded to nearest integer.
- K = **16 regions** for LLaVA-1.5-7B (24×24 patch grid → 4×4 regions of 6×6 patches). Ablation: 16 most stable; 6×6 helps only at large budgets, hurts at 64.

### 3. Diversity-score selection (Sec 4.2)
- Normalize each token embedding x_i; self-similarity **S_ij = ⟨x_i/‖x_i‖, x_j/‖x_j‖⟩** (Eq. 8, cosine over all N tokens).
- **μ_i = mean_j S_ij**, **σ_i = std_j S_ij** (Eq. 9).
- **Diversity score D(i) = λ·σ_i − μ_i**, **λ = 0.5** (fixed, no tuning) (Eq. 10).
- Within each region k, select the **B̃_k tokens with largest D**.
- Final reduced set R̂ = ∪_k R̂_k.

Note: importance/budget use ViT hidden states (temporal shift); the diversity self-similarity is computed on the token embeddings (post-encoder patch tokens fed to projector). Two complementary signals — ablation Table 7 confirms Temporal-Shift-for-allocation + Diversity-for-selection is best.

## Config for LLaVA-1.5-7B
- Vision encoder: CLIP ViT-L/14-336 → 336×336 image → 24×24 = **576 patch tokens**.
- Token budgets B ∈ {192 (↓66.7%), 128 (↓77.8%), 64 (↓88.9%)}; baseline = 576.
- Regions K=16 (4×4). Temporal shift over all 24 ViT layers. λ=0.5.
- Pruning applied at the ViT output / before the LLM (ViT-side), batch size 1, no training.
- Env in paper: H100, Python 3.12, PyTorch 2.7.0, CUDA 12.2.

## Target: Table 1 (LLaVA-1.5-7B, 10 image tasks). Avg = mean over tasks of score_pruned/score_vanilla.
Vanilla 576 (100%): GQA 61.22 | MMB 63.14 | MMB-CN 54.81 | MME 1477.65 | POPE 85.41 | SQA 68.12 | VQAtext 48.67 | VQAv2 77.41 | VizWiz 53.81 | OCRBench 205

| Method | budget | GQA | MMB | MME | POPE | SQA | VQAtext | VQAv2 | Avg |
|---|---|---|---|---|---|---|---|---|---|
| SPLIT | 192 | 59.5 | 62.6 | 1479.5 | 85.6 | 68.1 | 46.9 | 76.1 | **99.3%** |
| DART  | 192 | 59.2 | 62.7 | 1477.8 | 84.2 | 67.9 | 46.7 | 76.2 | 99.0% |
| SPLIT | 128 | 58.8 | 62.5 | 1455.2 | 85.5 | 68.4 | 45.8 | 75.5 | **97.8%** |
| SPLIT | 64  | 59.0 | 60.1 | 1375.5 | 84.5 | 68.6 | 41.5 | 73.2 | **92.8%** |
| DivPrune | 64 | 58.4 | 58.6 | 1362.1 | 83.7 | 67.3 | 40.8 | 72.4 | 91.7% |
| FastV | 64 | 53.1 | 50.1 | 1292.8 | 78.5 | 65.5 | 39.5 | 70.3 | 86.6% |

Baselines compared: MustDrop, FastV, PDrop, HiRED, SparseVLM, DivPrune, DART, GreedyPrune (+ VisPruner/VisionZip/CDPruner in Table 6 ablation).

## Reproduction strategy (given constraints)
- Full faithful implementation of SPLIT on `llava-hf/llava-1.5-7b-hf`.
- Evaluate relative accuracy at 576/192/128/64 on standard benchmarks; compare SPLIT vs baselines (random drop, FastV-style attention drop) — verifies Claims 1 & 3, partial Claim 2.
- Substantive run intended for HF GPU Job; **HF Jobs blocked by 402 (no credits)** as of run — fall back to scaled local run and document scale honestly.