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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