weitaikang/Weitaikang_method_paper

CueKFS — paper writing and rebuttal archive

This archive holds everything needed to write the CueKFS paper and defend it during rebuttal. It is a working archive, not a software release. The runnable code lives in the companion package author.

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1. What the project is, for a reader who has never seen it

The task. A long video can be hundreds or thousands of frames, but a vision-language model can only look at a handful. So a selector is given the video and a question, and must return k frames (here k = 8, 16 or 32). A separate model, the reader, then answers the question using only those frames. The selector never answers anything itself, and it is judged indirectly: by how accurate the reader becomes. This task is called keyframe selection (KFS).

What everyone else does. Score every candidate frame by how similar it is to the question, keep the top ones.

Why that breaks. A frame can be relevant without being literally similar to the question. Two concrete failures, both measured in Figure 1:

1. The question-level similarity signal is often empty. On the running example, the best-matching frame in a 102-frame video scores cos = 0.131, below SigLIP2's own match boundary of 0.147. Across Video-MME the raw question matches a median of 0.4% of frames, and for 45% of questions it matches none at all.
2. Decomposing the question into parts fixes the first problem but creates a second one: a part that is fixed before any evidence is seen can retrieve the wrong thing confidently, and no published selector has a path by which that evidence can get back to the part that produced it.

What CueKFS does. It is training-free and reframes the problem as matching frames against a set of dynamic visual cues. Three steps:

Step	Name	What happens
1	Decompose	A compact overview of the video is shown to a reasoning VLM, which splits the question into cues — short self-contained phrases, each describing one concrete visual element a single frame could show.
2	Cue-Wave-Guided Refinement	Every cue is scored against every frame, tracing a wave over time. Peaks and plateaus on that wave are the cue's own evidence. A reasoning VLM sees each cue next to its evidence and may keep, rewrite, split or drop it. Revised cues probe the video again. This is the core contribution.
3	Cue Budget Allocation	Cues are ranked by importance, shared evidence is de-duplicated, and the k frames are allocated across the surviving cues. Leftover slots are backfilled by farthest-point sampling.

How well it works. State of the art on three benchmarks (Video-MME, LongVideoBench, MLVU), three readers (gpt-5.5, Qwen2.5-VL-7B, InternVL3-8B) and three budgets, at a median of two reasoning-VLM calls per question. Headline: Video-MME with gpt-5.5, budget-average 82.70 against WFS-SB's 78.64 and uniform sampling's 77.41.

Every term above is defined again, more carefully, in §1 of 01_paper/SUBMISSION_MATERIALS.md.

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2. What is in this archive

00_source_of_truth/     where the narrative comes from
01_paper/               the manuscript, its source materials, and its figures
02_rebuttal/            evidence for questions reviewers are likely to ask
03_figure_toolkit/      the parts and scripts used to draw figures

00_source_of_truth — read this first

File	What it is
KFS_Final_Presentation.pdf	The project presentation. This defines the narrative. CueKFS is the first half; the second half is a different project (HeroFrame-Bench) and is out of scope here. Note the slides unfold the idea gradually, for an audience; the paper deliberately does not.
prompt_that_produced_the_materials_doc.txt	The instruction that generated 01_paper/SUBMISSION_MATERIALS.md, kept so it is clear what that document was asked to be.
project_one_pager.md	A short project summary.

01_paper

Path	What it is
SUBMISSION_MATERIALS.md	The main working document. Self-contained: glossary, abstract plan, introduction, related work, method, results, statistics, figure specifications, provenance for every number, limitations, to-do list. Written so someone with no prior context can read it top to bottom.
PAPER_GUIDE.md	Progress tracking and the writing plan.
POSITIONING_AGENTIC_QA.md	How this work is positioned against agentic video-QA systems.
manuscript/	The LaTeX source (cuekfs_iclr2027.tex), bibliography, ICLR style files, build script, and the current compiled PDF. Build droppings (.aux, .log, .fls, .synctex.gz) were removed.
figures/fig1..fig8/	One directory per figure. See below.
figures/_build/	The scripts that render the figures.
results_tables/	Where every number in the paper comes from. See below.

Figures. Each figN_*/ directory contains three things:

• full.png — the complete composed render. This is a reference for what the figure should say, not the artwork to paste into the paper.
• full.html — the same render as editable HTML.
• parts/*.svg — the individual components, to be rearranged and restyled by hand.

Figure	Parts
fig1_motivation	a_question_wave, b_cue_waves, c_frozen_vs_loop
fig2_pipeline	a_input, b_stage1_decompose, c_stage2_refine, d_stage3_allocate
fig3_step1_decompose	a_segmentation, b_cue_generation
fig4_step2_cwgr	a_wave_anatomy, b_batched_review, c_reprobe
fig5_step3_allocate	a_rank_dedup_allocate, b_final_frames
fig6_main_results	a_budget_average, b_margin_by_budget, c_k32_competitors
fig7_ablation	a_additive_ladder, b_cost_frontier
fig8_behavior	a_action_rates, b_effect_per_action, c_coverage, d_uncovered_control

results_tables/ — the provenance layer. When the paper states a number, this is where it was computed:

File	What it settles
downstream_reader_results.md	The authoritative main table. All three benchmarks × three readers × three budgets, for CueKFS, WFS-SB and uniform. Also records the denominator audit, which numbers changed and why, and which cells were deliberately left alone. Read the notes, not just the table.
ablation_results_v4.md	The full ablation programme.
abl_tables456.md	The deeper ablation and analysis tables.
experiments_ledger.md	Running ledger of what was run when.
split_analysis_videomme.md	Accuracy split by where the evidence sits in the video (single moment / multiple moments / whole video) and by whether the retrieval text has a concrete anchor. The where axis separates the methods; the anchor axis does not.
toolmerge_comparison.md, rawmax_results.md, h8_research.md	Side comparisons and probes.
analysis_summary.md, gpt55_taxonomy_summary.md, v46_audit_report.md	Analysis summaries and an independent audit.
system_design_v3.md, system_design_v4.md	The design record: what was tried, what was rejected, why. Useful when a reviewer asks "did you consider X".
reproduce_gpt55.md, experiment_commands_v4.md	The commands actually used.

02_rebuttal

Has its own README with a reviewer-question-to-file map. See 02_rebuttal/README.md.

03_figure_toolkit

The drawing kit: 42 reusable component PNGs in elements/, the related-work figure set in related_work_figs/, the Python builders in build_scripts/, and the rendered slide images in rendered_slides/. The node_modules directory of the original toolkit was dropped; reinstall with npm install if the HTML renderers are needed.

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3. Two things to be careful about

The authoritative numbers are in 01_paper/results_tables/downstream_reader_results.md, not in the raw run records. The records under 02_rebuttal/per_question_eval/ are what is physically on disk, and they cover the three benchmarks unevenly:

• Video-MME is complete and exact. videomme_jl_j7_seconds1p0_siglip0p98_k{8,16,32} is the paper's method and reproduces its numbers to the decimal (80.70 / 83.33 / 84.18 over 2699 questions). The reasoning VLM was gpt-5.5. Note that this run reused frozen cues from earlier in the hyper-parameter ladder and re-executed only step 3, which is what J7 tunes; the full provenance chain is documented in the companion package under release_artifacts/README.md.
• LongVideoBench and MLVU are not the paper's runs. What is on disk is abl_d2_batchreview_prop, a configuration that predates the J7 search. All 18 of its reader-by-budget cells land 0.3 to 3.5 points below the published numbers, and LongVideoBench additionally predates the fps-densify rerun. Those final runs were executed on separate compute and were not recoverable when this archive was built. Also note that mlvu × gpt5_5 × k16 on disk is a truncated file holding only two questions; ignore it.

So: analyses that need per-question ground truth are safe on Video-MME and should not be run on the LongVideoBench or MLVU records without accounting for the above.

Codenames. The codebase uses internal names that never appear in paper prose. j7 is the final configuration; d2 is the batched-review plus proportional-allocation variant; propsel is proportional selection. The mapping is in §10.4 of 01_paper/SUBMISSION_MATERIALS.md.

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4. Companion package

author holds the cleaned, runnable code: the method implementation, the configuration files, the baseline converters, and a step-by-step README. Nothing is duplicated between the two archives — code lives only there, figures and rebuttal evidence live only here.