---
license: mit
tags:
- icml2026-repro
- paper-Elm4TdaXi0
- vision-language-models
- token-pruning
---

# SPLIT-VLM reproduction bundle

Reproduction of **SPLIT-VLM** (ICML 2026, OpenReview `Elm4TdaXi0`) — *Salience-Guided
Partitioning towards Local Coverage for Importance-Aware Token Dropping in Vision-Language Models*.

Contents:
- `scripts/split_prune.py` — faithful SPLIT implementation (temporal-shift importance, adaptive region budgets, diversity selection; Algorithm 1).
- `scripts/validate_mechanism.py` — Claim 3 mechanism check on the CLIP vision tower.
- `scripts/llava_split_eval.py` — LLaVA-1.5-7B token-dropping eval (POPE/TextVQA/ScienceQA) with SPLIT vs random/attention baselines.
- `scripts/aggregate.py`, `make_figures.py`, `make_results_fig.py` — aggregation + figures.
- `outputs/` — result JSONs, aggregate.{json,csv}, figures, mechanism validation.
- `paper_notes.md` — recovered method + target tables.

**Compute:** local Apple M1 Pro (MPS, fp16); reduced-scale subsets (POPE 150, TextVQA 60, ScienceQA 60).
HF Jobs (GPU) was blocked by lack of credits (402). See the Trackio logbook for the full write-up.

Reproduce: `python scripts/llava_split_eval.py --task pope --n 150 --budgets 192,128,64 --methods vanilla,split,random,attn`