What is ACL25-AdaReTaKe AGENTS.md?

A set of instructions for reproducing the AdaReTaKe research paper's results from the provided code and exact software setup. The guide explains the repository layout, configuration, inference, and evaluation process.

It helps install the required versions, understand the model and cache code, run multi-GPU inference, and evaluate the results.

It gives a coding agent one agreed path through a research repository instead of leaving setup and execution steps scattered or implicit.

How iLearn-Lab/ACL25-AdaReTaKe configures Codex and OpenCode

Reuse

Borrowing it

Nothing to install: this file belongs to iLearn-Lab/ACL25-AdaReTaKe. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/iLearn-Lab/ACL25-AdaReTaKe/main/AGENTS.md

Clone the repo

git clone --depth 1 https://github.com/iLearn-Lab/ACL25-AdaReTaKe

Made for: Codex, OpenCode.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/instructions/ilearn-lab/acl25-adaretake/agents-md/github.svg)](https://agentmods.dev/instructions/ilearn-lab/acl25-adaretake/agents-md)

Your own site

<a href="https://agentmods.dev/instructions/ilearn-lab/acl25-adaretake/agents-md"><img src="https://agentmods.dev/badge/instructions/ilearn-lab/acl25-adaretake/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/instructions/ilearn-lab/acl25-adaretake/agents-md"><img src="https://agentmods.dev/badge/instructions/ilearn-lab/acl25-adaretake/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 2,221 This file is loaded in full into every session.

When invoked 2,221 The same file — it is already loaded in full.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.02221	$0.02221
Opus 5.5	$0.00888	$0.00888
Sonnet 5.5	$0.00444	$0.00444
Haiku 4.5	$0.00222	$0.00222

Measured 2mo ago against content hash b470861c7d65, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ACL25-AdaReTaKe AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

AGENTS.md · 224 lines

How it starts

The opening of the file, as written. The whole thing — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — AdaReTaKe Reproduction Guide for Coding Agents

This file tells any coding agent (Claude Code, Cursor, Copilot Workspace, etc.) how to fully reproduce the AdaReTaKe paper results from scratch. Follow the sections in order.

0. One-line Quickstart (tell the agent this)

Read AGENTS.md and reproduce the AdaReTaKe paper results end-to-end.

That single prompt is enough — everything the agent needs is in this file.

1. Repo Layout

retake/                     # Core library (monkey-patch + KV cache)
  qwen2_5_vl.py             # Forward override for Qwen2.5-VL; chunked prefill + temporal adaptation
  qwen2_vl.py               # Forward override for Qwen2-VL (same architecture, older model)
  llava_onevision.py         # Forward override for LLaVA-OneVision
  longvideo_cache.py         # KV cache compression (StandardVidLangKVCache)
  visual_compression.py      # Visual token compression utilities
  monkeypatch.py             # Patches HuggingFace model classes at import time
  infer_eval.py              # Multi-GPU inference + evaluation entry point
  dataset_utils.py           # Dataset loaders & eval metrics

configs/qwen2_5_vl/         # One YAML per benchmark (model + data + output settings)
  adaretake_qwen2-5-vl_mlvu.yaml
  adaretake_qwen2-5-vl_longvideobench.yaml
  adaretake_qwen2-5-vl_lvbench.yaml
  adaretake_qwen2-5-vl_videomme.yaml
  flexreduc_qwen2-5-vl_*_f1024*.yaml   # Ablation configs (1024-frame setting)

dataset/                    # Benchmark annotation JSONs (symlinked; not in git)
  mlvu/mlvu.json
  longvideobench/longvideobench_val.json
  lvbench/lvbench.json
  video_mme/video_mme.json

results/                    # Output directory (symlinked; not in git)
scripts/
  infer_eval.sh             # Wrapper script: bash scripts/infer_eval.sh <model_path> <config> <n_gpus> <fps>
docs/
  prepare_*.md              # Dataset download instructions per benchmark
  exps/                     # Experiment records

Read the full file on GitHub · 224 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 224 lines · 2,221 tokens per session scan A b470861c7d65

Subscribe to this mod's changes

ACL25-AdaReTaKe AGENTS.md is an instructions file published in the GitHub repository iLearn-Lab/ACL25-AdaReTaKe (91 stars, last pushed 6mo ago), licensed MIT. It adds 2,221 tokens to every session, about $0.0089 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Research Scientific

ACL25-AdaReTaKe AGENTS.md next to 6 instructions from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
ACL25-AdaReTaKe AGENTS.mdiLearn-Lab/ACL25-AdaReTaKe	Instructions file	91	6mo ago	MIT	A	2,221	Codex, OpenCode
vscode buildNext.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	9,719	GitHub Copilot
next.js AGENTS.mdvercel/next.js	Instructions file	143k	today	MIT	A	7,461	Codex, OpenCode
langchain AGENTS.mdlangchain-ai/langchain	Instructions file	149k	yesterday	MIT	A	4,680	Codex, OpenCode
vscode oss-third-party-notices.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	5,516	GitHub Copilot
vscode copilot-instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	3,093	GitHub Copilot
deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness	Instructions file	274k	2d ago	MIT	A	4,313	Codex, OpenCode

ACL25-AdaReTaKe: Instructions file for Codex