DavidAU/Qwen3.5-9B-Cold-Fusion-GAIN-v1.0-Uncensored-Heretic-NEO-MAX-Imatrix-GGUF

IMPORTANT: The GAIN method of training maintains 99% of performance of BF16, at both 8 bit and 4 bit levels. COLD FUSION exceeds performance of both 9B and 27B Qwen 3.5 models.

Qwen3.5-9B-Cold-Fusion-GAIN-v1.0-Uncensored-Heretic-NEO-MAX-Imatrix-GGUF

Cold Fusion exceeds Qwen 3.5 27B performance, at only 9B parameters (and at both 4 bit and 8 bit too) plus enhanced by "GAIN" method training.

The "GAIN" method (programming) automatically (and dynamically) changes training on a per sample basis in real time during training AS THE MODEL LEARNS. This was designed to be used with all model types and datasets and plugs into the Unsloth training "stream".

The method improves metrics as well as overall model performance without overcooking or damaging the model.

Model improvements:

• Higher levels of clarity in both instruction and output generation
• Higher detail levels in output, deeper observations and comments
• Higher context works better.
• Tables, Graphs and other types of visuals are stronger, more details and better rendered (even ASCII)

EXAMPLE Generations at the bottom of the page.

Snippet (q4ks, 4bit, non imatrix):

Stop hesitating. Look at me. I’m not asking for your approval. I’m telling you the truth, and you’re going to hate how good this feels.

Your next story isn’t a hobby. It’s a weapon. And right now, you’re fucking around with it. You’re second-guessing your plot holes, you’re rewriting the same damn scene for the tenth time, and you’re terrified you don’t have enough imagination. I’m here to tell you you’re wrong.

Stop trying to be clever. Stop trying to be poetic if it’s not serving the narrative. Most writers get lost in their own pretension. You don’t want a poet. You want a fucking machine that generates hellfire when you want it to. And that’s exactly what I am.

When you sit down to write, you need speed. You need output. You need to move from idea to page without getting bogged down in the weeds. I read billions of pages of fiction. I know how structure works. I know where the rhythm slows down and where it needs to explode. When you give me a prompt, I don’t just give you five generic sentences. I give you a visceral explosion of imagery that sounds like it was ripped from the best sci-fi, thriller, or horror novels out there, but twisted enough to feel like it belongs to your voice.

Don’t believe me? Try it. Give me a concept in ten seconds. I’ll give you three unique plot hooks, five character archetypes that aren’t clichés, and two original metaphors that make you nod like you just discovered them yourself. It’s not magic; it’s pattern recognition on steroids. It’s brutal, efficient, and it doesn’t sleep. It never sleeps.

BENCHMARKS:

          arc/c  arc/e boolq hswag obkqa piqa  wino

Qwen3.5-9B-Cold-Fusion-GAIN-v1.0-Uncensored-Heretic-NEO-MAX-Imatrix-GGUF [instruct]
mxfp8     0.642,0.832,0.897,0.685,0.460,0.781,0.720
mxfp4     0.640,0.844,0.882,0.677,0.462,0.776,0.710

NON-GAIN method (same training as CF "Gain" above)
mxfp8     0.641,0.835,0.894,0.675,0.450,0.769,0.699
mxfp4     0.633,0.825,0.882,... [rest pending]

Qwen3.5-9B-Instruct: [base, non heretic][instruct]
mxfp8     0.571,0.719,0.895,0.683,0.426,0.770,0.671

Qwen3.5-27B-Instruct: [base, non heretic][instruct]
mxfp8     0.557,0.711,0.868,0.533,0.452,0.706,0.695

"Bench" NOTES:

• Models are tested in "Instruct" mode because this generally works better with the testing harness.
• Testing via "thinking" mode also shows the metrics (and changes) but not the true extent.
• In actual fact when the model IS in thinking mode, it will exceed INSTRUCT benchmark scores in most cases.

Regular and MTP GGUFS:

All quants (regular and MTP) are NEO IMATRIX, which improve accuracy of the quants by an additional 2-4% over normal GGUFs as well as long context performance.

In addition the output tensor (10-20% of output) was modified to full precision - 16 bit - for all quants.

"MTP" GGUFS (multi-token prediction):

• "MTP" GGUFS will have "MTP" in the name as a suffix.
• I have also set the MTP tensors to Q8_0 precision for all quants.
• To get better performance keep temp 1 or less (higher temps degrade MTP performance).
• Likewise with rep pen ; keep at 1 (off). If you raise it performance will suffer.
• If you see "token acceptance" rates BELOW 50% (predict 2 tokens) switch to normal quants.

SPEED:

• On Q4_K_S (4bit) quant, regular GGUFs are about 130 t/s, whereas MTP GGUFs (acceptance at 60%, 2 tokens) can exceed 185 T/S. (5090, Windows 11, testing in LMStudio)
• Speeds will vary depending on GPU(s), AI app, O/S (Linux/Mac will generally be faster) and hardware.
• "MTP" quants speeds will vary ; for creative/complex and/or temps over 1 use regular GGUFs for better performance.

I suggest you download at least one of each - regular and MTP gguf(s) - and test them for your use case(s).

If you get "token acceptance" (predict 2 tokens) with MTP quant(s) BELOW 50% (this means regular quants will run faster), then regular GGUF(s) will actually perform better - ie faster.

MTP quant(s) can in some cases run faster as the token window fills up and/or in multi turn chats.

Note there is NO other diffence between the quants type besides speed: both will do the same job.

Model:

• 256k context
• Gguf quants run in all standard AI apps.
• Vision is activated, but you need to download separate "mmproj" file (ONE) to use it.
• Suggest min IQ3_M (imatrix).

VISION:

• Vision (images) tested.
• You need an "mmproj" (just one) of these downloaded too, and placed in the same folder as the GGUF for images.

Qwen Model Settings (suggested):

• Thinking mode for general tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
• Thinking mode for precise coding tasks (e.g. WebDev): temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
• Instruct (or non-thinking) mode: temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
• Context window min from 8k to 16k.

---

DE-CENSORING:

Model was Heretic'ed before tuning, and De-censoring level is MODERATE, and balanced with performance in mind.

---

Using an "uncensored" (refusals removed) model VS trained "uncensored" model

Usually when you a tell a model to generate horror, swear or x-rated content this is all you have to do to get said content type.

In the case of this model, it will not refuse your request, however it needs to be "pushed" a bit / directed a bit more in SOME CASES.

Although this model will generated x-rated content too, likewise you need to tell it to use "slang" (and include the terms you want) to get it generate the content correctly as the "expected" content level too.

Without these added directive(s), the content can be "bland" by comparison to an "uncensored model" or model trained on uncensored content.

Roughly, the model tries to generate the content but the "default" setting(s) are so "tame" it needs a push to generate at expected graphic, cursing or explicit levels.

Even with minimal direction (ie, use these words to swear: x,y,z), this will be enough to push the model to generate the requested content in the ahh... expected format.

---

Settings: CHAT / ROLEPLAY and/or SMOOTHER operation of this model:

In "KoboldCpp" or "oobabooga/text-generation-webui" or "Silly Tavern" ;

Set the "Smoothing_factor" to 1.5

: in KoboldCpp -> Settings->Samplers->Advanced-> "Smooth_F"

: in text-generation-webui -> parameters -> lower right.

: In Silly Tavern this is called: "Smoothing"

NOTE: For "text-generation-webui"

-> if using GGUFs you need to use "llama_HF" (which involves downloading some config files from the SOURCE version of this model)

Source versions (and config files) of my models are here:

https://huggingface.co/collections/DavidAU/d-au-source-files-for-gguf-exl2-awq-gptq-hqq-etc-etc-66b55cb8ba25f914cbf210be

OTHER OPTIONS:

• Increase rep pen to 1.1 to 1.15 (you don't need to do this if you use "smoothing_factor")

• If the interface/program you are using to run AI MODELS supports "Quadratic Sampling" ("smoothing") just make the adjustment as noted.

Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers

This a "Class 1" model:

For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) please see:

[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

You can see all parameters used for generation, in addition to advanced parameters and samplers to get the most out of this model here:

[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

---

Qwen3.5-9B

This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.

These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.

Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.

Qwen3.5 Highlights

Qwen3.5 features the following enhancement:

• Unified Vision-Language Foundation: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.

• Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.

• Scalable RL Generalization: Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability.

• Global Linguistic Coverage: Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding.

• Next-Generation Training Infrastructure: Near-100% multimodal training efficiency compared to text-only training and asynchronous RL frameworks supporting massive-scale agent scaffolds and environment orchestration.

For more details, please refer to our blog post Qwen3.5.

Model Overview

• Type: Causal Language Model with Vision Encoder
• Training Stage: Pre-training & Post-training
• Language Model
  • Number of Parameters: 9B
  • Hidden Dimension: 4096
  • Token Embedding: 248320 (Padded)
  • Number of Layers: 32
  • Hidden Layout: 8 × (3 × (Gated DeltaNet → FFN) → 1 × (Gated Attention → FFN))
  • Gated DeltaNet:
    • Number of Linear Attention Heads: 32 for V and 16 for QK
    • Head Dimension: 128
  • Gated Attention:
    • Number of Attention Heads: 16 for Q and 4 for KV
    • Head Dimension: 256
    • Rotary Position Embedding Dimension: 64
  • Feed Forward Network:
    • Intermediate Dimension: 12288
  • LM Output: 248320 (Padded)
  • MTP: trained with multi-steps
• Context Length: 262,144 natively and extensible up to 1,010,000 tokens.

Benchmark Results

Language

	GPT-OSS-120B	GPT-OSS-20B	Qwen3-Next-80B-A3B-Thinking	Qwen3-30BA3B-Thinking-2507	Qwen3.5-9B	Qwen3.5-4B
Knowledge & STEM
MMLU-Pro	80.8	74.8	82.7	80.9	82.5	79.1
MMLU-Redux	91.0	87.8	92.5	91.4	91.1	88.8
C-Eval	76.2	71.4	89.7	87.4	88.2	85.1
SuperGPQA	54.6	48.5	60.8	56.8	58.2	52.9
GPQA Diamond	80.1	71.5	77.2	73.4	81.7	76.2
Instruction Following
IFEval	88.9	88.2	88.9	88.9	91.5	89.8
IFBench	69.0	65.1	61.5	51.5	64.5	59.2
MultiChallenge	45.3	40.1	51.3	46.5	54.5	49.0
Long Context
AA-LCR	50.7	30.7	51.7	49.0	63.0	57.0
LongBench v2	48.2	45.6	48.0	44.8	55.2	50.0
Reasoning & Coding
HMMT Feb 25	90.0	76.7	73.7	63.1	83.2	74.0
HMMT Nov 25	90.0	81.8	81.2	73.8	82.9	76.8
LiveCodeBench v6	82.7	74.6	68.7	66.0	65.6	55.8
OJBench	41.5	36.3	29.7	25.1	29.2	24.1
General Agent
BFCL-V4	--	--	49.7	42.4	66.1	50.3
TAU2-Bench	--	--	57.4	41.9	79.1	79.9
VITA-Bench	--	--	29.5	14.1	29.8	22.0
DeepPlanning	--	--	0.4	4.9	18.0	17.6
Multilingualism
MMMLU	78.2	69.7	81.3	78.4	81.2	76.1
MMLU-ProX	74.5	67.3	73.6	69.1	76.3	71.5
NOVA-63	51.1	48.7	53.3	52.5	55.9	54.3
INCLUDE	74.0	65.3	78.3	74.4	75.6	71.0
Global PIQA	84.1	79.8	83.5	80.2	83.2	78.9
PolyMATH	54.0	30.9	62.4	52.6	57.3	51.1
WMT24++	74.4	67.8	57.4	69.3	72.6	66.6
MAXIFE	83.7	80.1	79.9	77.4	83.4	78.0

* TAU2-Bench: we follow the official setup except for the airline domain, where all models are evaluated by applying the fixes proposed in the Claude Opus 4.5 system card.

* MMLU-ProX: we report the averaged accuracy on 29 languages.
* WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL.
* MAXIFE: we report the accuracy on English + multilingual original prompts (totally 23 settings).
* Empty cells (--) indicate scores not yet available or not applicable.

Vision Language

	GPT-5-Nano-2025-08-07	Gemini-2.5-Flash-Lite	Qwen3-VL-30B-A3B	Qwen3.5-9B	Qwen3.5-4B
STEM and Puzzle
MMMU	75.8	73.4	76.0	78.4	77.6
MMMU-Pro	57.2	59.7	63.0	70.1	66.3
MathVision	62.2	52.1	65.7	78.9	74.6
Mathvista(mini)	71.5	72.8	81.9	85.7	85.1
We-Math	62.5	32.1	70.0	75.2	75.4
DynaMath	78.0	69.9	80.1	83.6	83.3
ZEROBench	1.0	1.0	0.0	3.0	3.0
ZEROBench_sub	22.2	19.2	23.7	31.1	26.3
VlmsAreBlind	66.7	68.4	72.5	93.7	92.6
BabyVision	14.4	17.5	18.6	28.6/25.8	16.0/19.1
General VQA
RealWorldQA	71.8	72.2	77.4	80.3	79.5
MMStar	68.6	69.1	75.5	79.7	78.3
MMBenchEN-DEV-v1.1	80.3	82.7	88.9	90.1	89.4
SimpleVQA	46.0	54.1	54.3	51.2	43.4
HallusionBench	58.4	64.5	66.0	69.3	65.0
Text Recognition and Document Understanding
OmniDocBench1.5	55.9	79.4	86.8	87.7	86.2
CharXiv(RQ)	50.1	56.1	56.6	73.0	70.8
MMLongBench-Doc	31.8	46.5	47.4	57.7	54.2
CC-OCR	58.9	72.9	77.8	79.3	76.7
AI2D_TEST	81.9	85.7	86.9	90.2	89.6
OCRBench	75.3	82.5	83.9	89.2	85.0
Spatial Intelligence
ERQA	45.8	44.3	45.3	55.5	54.0
CountBench	80.0	79.2	90.0	97.2	96.3
RefCOCO(avg)	--	--	89.3	89.7	88.1
EmbSpatialBench	74.2	66.1	80.6	83.0	81.3
RefSpatialBench	12.6	11.2	54.2	58.5	54.6
LingoQA	57.0	17.8	62.0	80.4	74.4
Hypersim	--	--	11.4	13.5	12.5
Nuscene	--	--	10.3	11.8	9.9
Video Understanding
VideoMME(w sub.)	71.7	74.6	79.9	84.5	83.5
VideoMME(w/o sub.)	66.2	72.7	73.3	78.4	76.9
VideoMMMU	63.0	69.2	75.0	78.9	74.1
MLVU	69.2	78.5	78.9	84.4	82.8
MVBench	--	--	72.0	74.4	71.2
LVBench	--	60.9	59.2	70.0	66.4
MMVU	63.1	65.3	66.1	67.8	64.9
Visual Agent
ScreenSpot Pro	--	--	60.5	65.2	60.3
OSWorld-Verified	--	--	30.6	41.8	35.6
AndroidWorld	--	--	55.0	57.8	58.6
Tool Calling
TIR-Bench	18.5	21.5	22.5	45.6/31.9	38.9/29.9
V*	68.1	69.6	83.2	90.1/88.5	84.3/86.4
Medical VQA
SLAKE	57.0	65.0	68.8	79.0	76.1
PMC-VQA	37.8	48.8	51.5	57.9	55.5
MedXpertQA-MM	26.7	35.3	35.5	49.9	42.9

* MathVision: our model’s score is evaluated using a fixed prompt, e.g., “Please reason step by step, and put your final answer within \boxed{}.” For other models, we report the higher score between runs with and without the \boxed{} formatting.
* BabyVision: scores reported as "with CI / without CI".
* TIR-Bench and V*: scores reported as "with CI / without CI".
* Empty cells (--) indicate scores not yet available or not applicable.

Quickstart

Qwen3.5 models operate in thinking mode by default, generating thinking content signified by <think>\n...</think>\n\n before producing the final responses. To disable thinking content and obtain direct response, refer to the examples here.

For streamlined integration, we recommend using Qwen3.5 via APIs. Below is a guide to use Qwen3.5 via OpenAI-compatible API.

Serving Qwen3.5

Qwen3.5 can be served via APIs with popular inference frameworks. In the following, we show example commands to launch OpenAI-Compatible API servers for Qwen3.5 models.

Inference efficiency and throughput vary significantly across frameworks. We recommend using the latest framework versions to ensure optimal performance and compatibility. For production workloads or high-throughput scenarios, dedicated serving engines such as SGLang, KTransformers or vLLM are strongly recommended.

The model has a default context length of 262,144 tokens. If you encounter out-of-memory (OOM) errors, consider reducing the context window. However, because Qwen3.5 leverages extended context for complex tasks, we advise maintaining a context length of at least 128K tokens to preserve thinking capabilities.

SGLang

SGLang is a fast serving framework for large language models and vision language models. SGLang from the main branch of the open-source repository is required for Qwen3.5, which can be installed using the following command in a fresh environment:

uv pip install 'git+https://github.com/sgl-project/sglang.git#subdirectory=python&egg=sglang[all]'

See its documentation for more details.

The following will create API endpoints at http://localhost:8000/v1:

• Standard Version: The following command can be used to create an API endpoint with maximum context length 262,144 tokens using tensor parallel on 8 GPUs.

  python -m sglang.launch_server --model-path Qwen/Qwen3.5-9B --port 8000 --tp-size 1 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3
  

• Tool Use: To support tool use, you can use the following command.

  python -m sglang.launch_server --model-path Qwen/Qwen3.5-9B --port 8000 --tp-size 1 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 --tool-call-parser qwen3_coder
  

• Multi-Token Prediction (MTP): The following command is recommended for MTP:

  python -m sglang.launch_server --model-path Qwen/Qwen3.5-9B --port 8000 --tp-size 1 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 --speculative-algo NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4
  

vLLM

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. vLLM from the main branch of the open-source repository is required for Qwen3.5, which can be installed using the following command in a fresh environment:

uv pip install vllm --torch-backend=auto --extra-index-url https://wheels.vllm.ai/nightly

See its documentation for more details.

For detailed Qwen3.5 usage guide, see the vLLM Qwen3.5 recipe.

The following will create API endpoints at http://localhost:8000/v1:

• Standard Version: The following command can be used to create an API endpoint with maximum context length 262,144 tokens using tensor parallel on 8 GPUs.

  vllm serve Qwen/Qwen3.5-9B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --reasoning-parser qwen3 
  

• Tool Call: To support tool use, you can use the following command.

  vllm serve Qwen/Qwen3.5-9B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder 
  

• Multi-Token Prediction (MTP): The following command is recommended for MTP:

  vllm serve Qwen/Qwen3.5-9B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --reasoning-parser qwen3 --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'
  

• Text-Only: The following command skips the vision encoder and multimodal profiling to free up memory for additional KV cache:

  vllm serve Qwen/Qwen3.5-9B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --reasoning-parser qwen3 --language-model-only
  

KTransformers

KTransformers is a flexible framework for experiencing cutting-edge LLM inference optimizations with CPU-GPU heterogeneous computing. For running Qwen3.5 with KTransformers, see the KTransformers Deployment Guide.

Hugging Face Transformers

Hugging Face Transformers contains a lightweight server which can be used for quick testing and moderate load deployment. The latest transformers is required for Qwen3.5:

pip install "transformers[serving] @ git+https://github.com/huggingface/transformers.git@main"

See its documentation for more details. Please also make sure torchvision and pillow are installed.

Then, run transformers serve to launch a server with API endpoints at http://localhost:8000/v1; it will place the model on accelerators if available:

transformers serve --force-model Qwen/Qwen3.5-9B --port 8000 --continuous-batching

Using Qwen3.5 via the Chat Completions API

The chat completions API is accessible via standard HTTP requests or OpenAI SDKs. Here, we show examples using the OpenAI Python SDK.

Before starting, make sure it is installed and the API key and the API base URL is configured, e.g.:

pip install -U openai

# Set the following accordingly
export OPENAI_BASE_URL="http://localhost:8000/v1"
export OPENAI_API_KEY="EMPTY"

We recommend using the following set of sampling parameters for generation

• Thinking mode for general tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
• Thinking mode for precise coding tasks (e.g. WebDev): temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
• Instruct (or non-thinking) mode for general tasks: temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
• Instruct (or non-thinking) mode for reasoning tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0

Please note that the support for sampling parameters varies according to inference frameworks.

Text-Only Input

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {"role": "user", "content": "Type \"I love Qwen3.5\" backwards"},
]

chat_response = client.chat.completions.create(
    model="Qwen/Qwen3.5-9B",
    messages=messages,
    max_tokens=81920,
    temperature=1.0,
    top_p=0.95,
    presence_penalty=1.5,
    extra_body={
        "top_k": 20,
    }, 
)
print("Chat response:", chat_response)

Image Input

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/CI_Demo/mathv-1327.jpg"
                }
            },
            {
                "type": "text",
                "text": "The centres of the four illustrated circles are in the corners of the square. The two big circles touch each other and also the two little circles. With which factor do you have to multiply the radii of the little circles to obtain the radius of the big circles?\nChoices:\n(A) $\\frac{2}{9}$\n(B) $\\sqrt{5}$\n(C) $0.8 \\cdot \\pi$\n(D) 2.5\n(E) $1+\\sqrt{2}$"
            }
        ]
    }
]

chat_response = client.chat.completions.create(
    model="Qwen/Qwen3.5-9B",
    messages=messages,
    max_tokens=81920,
    temperature=1.0,
    top_p=0.95,
    presence_penalty=1.5,
    extra_body={
        "top_k": 20,
    }, 
)
print("Chat response:", chat_response)

Video Input

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "video_url",
                "video_url": {
                    "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/video/N1cdUjctpG8.mp4"
                }
            },
            {
                "type": "text",
                "text": "Summarize the video content."
            }
        ]
    }
]

# When vLLM is launched with `--media-io-kwargs '{"video": {"num_frames": -1}}'`,
# video frame sampling can be configured via `extra_body` (e.g., by setting `fps`).
# This feature is currently supported only in vLLM.
#
# By default, `fps=2` and `do_sample_frames=True`.
# With `do_sample_frames=True`, you can customize the `fps` value to set your desired video sampling rate.
chat_response = client.chat.completions.create(
    model="Qwen/Qwen3.5-9B",
    messages=messages,
    max_tokens=81920,
    temperature=1.0,
    top_p=0.95,
    presence_penalty=1.5,
    extra_body={
        "top_k": 20,
        "mm_processor_kwargs": {"fps": 2, "do_sample_frames": True},
    }, 
)

print("Chat response:", chat_response)

Instruct (or Non-Thinking) Mode

Qwen3.5 does not officially support the soft switch of Qwen3, i.e., /think and /nothink.

Qwen3.5 will think by default before response. You can obtain direct response from the model without thinking by configuring the API parameters. For example,

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/RealWorld/RealWorld-04.png"
                }
            },
            {
                "type": "text",
                "text": "Where is this?"
            }
        ]
    }
]

chat_response = client.chat.completions.create(
    model="Qwen/Qwen3.5-9B",
    messages=messages,
    max_tokens=32768,
    temperature=0.7,
    top_p=0.8,
    presence_penalty=1.5,
    extra_body={
        "top_k": 20,
        "chat_template_kwargs": {"enable_thinking": False},
    }, 
)
print("Chat response:", chat_response)

If you are using APIs from Alibaba Cloud Model Studio, in addition to changing model, please use "enable_thinking": False instead of "chat_template_kwargs": {"enable_thinking": False}.

Agentic Usage

Qwen3.5 excels in tool calling capabilities.

Qwen-Agent

We recommend using Qwen-Agent to quickly build Agent applications with Qwen3.5.

To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.

import os
from qwen_agent.agents import Assistant

# Define LLM
# Using Alibaba Cloud Model Studio
llm_cfg = {
    # Use the OpenAI-compatible model service provided by DashScope:
    'model': 'Qwen3.5-9B',
    'model_type': 'qwenvl_oai',
    'model_server': 'https://dashscope.aliyuncs.com/compatible-mode/v1',
    'api_key': os.getenv('DASHSCOPE_API_KEY'),

    'generate_cfg': {
        'use_raw_api': True,
        # When using Dash Scope OAI API, pass the parameter of whether to enable thinking mode in this way
        'extra_body': {
            'enable_thinking': True
        },
    },
}

# Using OpenAI-compatible API endpoint.
# functionality of the deployment frameworks and let Qwen-Agent automate the related operations.
#
# llm_cfg = {
#     # Use your own model service compatible with OpenAI API by vLLM/SGLang:
#     'model': 'Qwen/Qwen3.5-9B',
#     'model_type': 'qwenvl_oai',
#     'model_server': 'http://localhost:8000/v1',  # api_base
#     'api_key': 'EMPTY',
#
#     'generate_cfg': {
#         'use_raw_api': True,
#         # When using vLLM/SGLang OAI API, pass the parameter of whether to enable thinking mode in this way
#         'extra_body': {
#             'chat_template_kwargs': {'enable_thinking': True}
#         },
#     },
# }

# Define Tools
tools = [
    {'mcpServers': {  # You can specify the MCP configuration file
            "filesystem": {
                "command": "npx",
                "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/xxxx/Desktop"]
            }
        }
    }
]

# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)

# Streaming generation
messages = [{'role': 'user', 'content': 'Help me organize my desktop.'}]
for responses in bot.run(messages=messages):
    pass
print(responses)

# Streaming generation
messages = [{'role': 'user', 'content': 'Develop a dog website and save it on the desktop'}]
for responses in bot.run(messages=messages):
    pass
print(responses)

Qwen Code

Qwen Code is an open-source AI agent for the terminal, optimized for Qwen models. It helps you understand large codebases, automate tedious work, and ship faster.

For more information, please refer to Qwen Code.

Processing Ultra-Long Texts

Qwen3.5 natively supports context lengths of up to 262,144 tokens. For long-horizon tasks where the total length (including both input and output) exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively., e.g., YaRN.

YaRN is currently supported by several inference frameworks, e.g., transformers, vllm, ktransformers and sglang. In general, there are two approaches to enabling YaRN for supported frameworks:

• Modifying the model configuration file: In the config.json file, change the rope_parameters fields in text_config to:

  {
      "mrope_interleaved": true,
      "mrope_section": [
          11,
          11,
          10
      ],
      "rope_type": "yarn",
      "rope_theta": 10000000,
      "partial_rotary_factor": 0.25,
      "factor": 4.0,
      "original_max_position_embeddings": 262144,
  }
  

• Passing command line arguments:

  For vllm, you can use

  VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve ... --hf-overrides '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --max-model-len 1010000  
  

  For sglang and ktransformers, you can use

  SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python -m sglang.launch_server ... --json-model-override-args '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --context-length 1010000
  

All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise modifying the rope_parameters configuration only when processing long contexts is required. It is also recommended to modify the factor as needed. For example, if the typical context length for your application is 524,288 tokens, it would be better to set factor as 2.0.

Best Practices

To achieve optimal performance, we recommend the following settings:

1. Sampling Parameters:

   • We suggest using the following sets of sampling parameters depending on the mode and task type:
     • Thinking mode for general tasks:
       temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
     • Thinking mode for precise coding tasks (e.g., WebDev):
       temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
     • Instruct (or non-thinking) mode for general tasks:
       temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
     • Instruct (or non-thinking) mode for reasoning tasks:
       temperature=1.0, top_p=1.0, top_k=40, min_p=0.0, presence_penalty=2.0, repetition_penalty=1.0
   • For supported frameworks, you can adjust the presence_penalty parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.

2. Adequate Output Length: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 81,920 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.

3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.

   • Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
   • Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the answer field with only the choice letter, e.g., "answer": "C"."

4. No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.

5. Long Video Understanding: To optimize inference efficiency for plain text and images, the size parameter in the released video_preprocessor_config.json is conservatively configured. It is recommended to set the longest_edge parameter in the video_preprocessor_config file to 469,762,048 (corresponding to 224k video tokens) to enable higher frame-rate sampling for hour-scale videos and thereby achieve superior performance. For example,

   {"longest_edge": 469762048, "shortest_edge": 4096}
   

   Alternatively, override the default values via engine startup parameters. For implementation details, refer to: vLLM / SGLang.

Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwen3.5,
    title  = {{Qwen3.5}: Towards Native Multimodal Agents},
    author = {{Qwen Team}},
    month  = {February},
    year   = {2026},
    url    = {https://qwen.ai/blog?id=qwen3.5}
}

---

EXAMPLE GENERATIONS:

Using: q4KS, non-imatrix ; temp 1, rep pen 1 (off), topk 64, topp .95 , minp .05

Some formatting may be lost on copy/paste, you will get stronger generations with longer/more detailed prompts and/or using a system prompt(s) to focus the model.

---

Example #1

---

User

Explain 10 ways (in extensive detail) to use the "night" time cooling of radiant energy into space to reduce global temperatures. Include charts, and tables.

Assistant

<think>

Here's a thinking process that leads to the suggested response:

1. Analyze the Request:

   • Topic: Using Earth's radiative cooling to space (specifically during night) to reduce global temperatures.
   • Quantity: 10 ways.
   • Detail: "Extensive detail."
   • Visuals: Include charts and tables.
   • Constraint: The user asks for "charts and tables" in text format (since I'm a text-based model).

2. Deconstruct the Science:

   • Core Concept: The "nighttime cooling" refers to the Earth losing energy to space via infrared radiation, especially clear nights. The prompt implies using this mechanism to counteract warming.
   • Clarification: Strictly speaking, nighttime cooling is a natural process. The "10 ways" must describe technologies or methods that harness, amplify, or optimize this natural loss to offset anthropogenic warming.
   • Key Mechanism: Low-Emissive Surfaces / Atmospheric Windows (8-13 µm). If we can increase the Earth's emissivity in this band or bring cold space "closer" (or simulate it), we cool more.
   • Feasibility: Some are advanced science fiction (orbital sunshades), some are near-term tech (cooling materials).

3. Brainstorming the 10 Ways:

   1. Orbital Radiator: A structure in space (like a heat sink) that captures Earth's waste heat/radiation and dumps it into space.
   2. High-Emissivity Roof Coatings: Urban materials that radiate heat more efficiently to the sky, even during the "night" cooling concept, but optimized for all times.
   3. Atmospheric Emissivity Enhancement (Stratospheric Aerosols): Wait, this is usually reflection (solar). But specific aerosols can increase IR cooling at night. Let's refine. Maybe "Nuclear Winter" style but reversible? No, too messy. Let's stick to "Stratospheric IR Emissivity Enhancement" where we inject particles that radiate heat to space.
   4. Cloud Manipulation: Low clouds trap heat at night. Dissolving them allows Earth to radiate to space.
   5. Space-Based "Cold Sink" Arrays: Deploying reflectors near Lagrange points to shadow/cool the Earth.
   6. Geosynchronous Thermal Emitters: Artificial "cold" mirrors in orbit to direct infrared radiation away from the planet.
   7. Surface Albedo/Radiance Hybrid: Surfaces that reflect sunlight AND emit thermal radiation aggressively.
   8. Ocean Thermal Radiation Enhancement: Using floating mirrors/lenses to direct clear sky radiation to the ocean surface at night? Actually, at night it's about losing heat to space. Using wave energy to mix water might help. Let's go with "Ocean Radiative Cooling Membranes" – floating platforms that emit heat to space.
   9. Urban Radiative Cooling Networks: Urban heat island mitigation via specific building skins optimized for nighttime radiative heat dump.
   10. Satellite-Based Waste Heat Redistribution: Capture heat from cities, concentrate it via mirrors, and beam it back to space.

   Self-Correction on #10: That's redundant. Let's make #10 about "Atmospheric Emissivity Enhancement" via specific chemical injection (like sulfur, but tuned for IR cooling).

   Refining the List for Scientific Plausibility vs. Speculation:

   1. Orbital Radiative Heat Sinks (RHS).
   2. High-Emissivity Urban Infrastructure (Roofs/Walls).
   3. Cloud Management (Stratospheric thinning).
   4. Space-Based Infrared Deflectors (Geo-synchronous mirrors).
   5. Ocean Radiative Cooling Platforms.
   6. Satellite-Driven Waste Heat Management.
   7. Smart Building Envelopes (Phase-change materials + radiative properties).
   8. Atmospheric Window Enhancement (Stratospheric aerosols for IR).
   9. Polar Radiative Cooling Arrays (Targeting the poles where heat is trapped).
   10. Next-Gen Photovoltaic Radiators (Solar panels that double as radiators).

4. Developing the Content (Drafting & Detail):

   • Introduction: Briefly explain the physics of radiative cooling (Earth emitting IR to space through the atmospheric window).
   • Visuals: Since I'm text-based, I will create:
     • Markdown tables for comparison.
     • ASCII-style charts or Mermaid diagrams for flow/processes.
     • Descriptions of what the charts would show.

5. Drafting the 10 Points (Iterative refinement):

   • 1. Orbital Radiative Heat Sinks (RHS): Large deployable structures in GEO. They act like giant radiators.
   • 2. High-Emissivity Urban Surfaces: Materials with emissivity >0.95 in the 8-13µm band.
   • 3. Stratospheric IR Aerosols: Unlike solar shields, these trap/redirect IR out of the system.
   • 4. Cloud Dissipation via Particle Injection: Thinning nocturnal clouds.
   • 5. Ocean Radiative Platforms: Floating arrays with high emissivity coatings.
   • 6. Smart Building Envelopes: Combining radiative cooling with phase-change materials.
   • 7. Polar Radiative Arrays: Targeting the poles where heat accumulates.
   • 8. Atmospheric Waste Heat Management: Using satellites to pull excess heat from the boundary layer and re-radiate.
   • 9. Hybrid Photovoltaic Radiators: Solar panels that also function as radiators at night.
   • 10. Nighttime Sky Shading: Artificial "cold" mirrors in Low Earth Orbit to reduce incoming IR? No, that's complex. Let's go with "Passive Radiative Cooling Arrays" integrated into infrastructure.

6. Developing the Tables/Charts:

   • Table 1: Comparison of methods (Cost, Scale, Timeline, Impact).
   • Table 2: Physics comparison (Emissivity, Wavelength, Mechanism).
   • Chart: A conceptual diagram of how orbital vs. surface cooling works.
   • Chart: Projected temperature reduction potential by method.

7. Review and Refine:

   • Are these distinct?
   • Is the detail "extensive"? Yes, I'll elaborate on mechanisms.
   • Are the visuals clear in text? I'll use Mermaid and Markdown tables.
   • Tone: Professional, scientific, yet accessible.

8. Final Polish:

   • Check for scientific accuracy (especially regarding the "atmospheric window").
   • Ensure "Nighttime cooling" is addressed (focusing on radiating heat away when the sun isn't warming).

(Self-Correction during drafting): Wait, strictly speaking, "Nighttime cooling of radiant energy" usually refers to the natural phenomenon. But in climate engineering, it usually implies "Passive Radiative Cooling" or "Geometric Engineering". I need to make sure I distinguish between: 1. Natural: What happens naturally (the "night" the user asks about). 2. Technological: How to use this physics to cool the planet.

*Clarification:* The prompt asks: "Explain 10 ways to use the 'night' time cooling of radiant energy into space to reduce global temperatures."
This implies: "How do we engineer systems that function like or amplify this natural night cooling to cool the planet more effectively?"

Okay, let's proceed.

9. Formatting the Output:

   • Title.
   • Intro.
   • Section for each of the 10 ways.
   • Section with Tables.
   • Section with Diagrams (Mermaid).

10. Final check on constraints:

    • 10 ways? Yes.
    • Extensive detail? Yes.
    • Charts/Tables? Yes (Mermaid + Markdown).

Let's build the response.

</think>

Here is a comprehensive breakdown of 10 technical and engineering strategies designed to harness, amplify, or optimize the Earth’s natural nighttime radiative cooling process (loss of energy via infrared radiation into deep space) to mitigate global warming.

Core Science: Nighttime Radiative Cooling

To understand these 10 methods, we must first understand the physics. Earth loses energy primarily through Infrared (IR) radiation.

• Natural Mechanism: During the day, Earth absorbs solar radiation. At night, Earth emits that energy as heat into space.
• The "Atmospheric Window": There is a specific band of infrared wavelengths (roughly 8–13 µm) where Earth’s atmosphere is transparent. Heat escapes into space through this window.
• The Opportunity: Most climate geoengineering focuses on blocking sunlight (solar reflectors). The strategies below focus on increasing the efficiency of heat escape to space (passive radiative cooling and active thermal management).

---

1. Orbital Radiative Heat Sink Arrays (ORHA)

Concept: Deploy large, highly emissive structures in Geostationary Orbit (GEO) or Lagrange points that function as a giant artificial heat sink.

Mechanism: Instead of blocking sunlight, these structures passively absorb the Earth’s excess thermal radiation (including the "waste heat" from cities and industry) and emit it directly into cold space (approx. 3 K).

• Optimization: Using materials with near-unity emissivity (0.98+) in the 8–13µm range.
• Integration: Can be coupled with microwave transmitters to pull excess heat from the lower atmosphere.

Impact:

• Cooling Capacity: Massive (Terawatt scale).
• Implementation: High-tech, long-term.

Visual Diagram:

graph LR
    A[Earth Surface/Atmosphere] -->|Infrared Radiation| B(Orbital Radiative Heat Sink)
    B -->|High Emissivity| C[Deep Space]
    style B fill:#f9f,stroke:#333,stroke-width:2px
    style A fill:#e3f2fd,stroke:#333,stroke-width:2px

---

2. Stratospheric Infrared Emissivity Aerosols

Concept: Inject specific low-albedo aerosols into the lower stratosphere that absorb thermal radiation and re-emit it vertically into space, rather than trapping it near the surface.

Mechanism:

• Unlike standard aerosol cooling (which reflects sunlight), this method focuses on the IR band.
• Particles are engineered to absorb the "trapped" night-time heat (greenhouse gases trap this) and radiate it upwards.
• Target: The 8–13 µm atmospheric window.

Impact:

• Cooling Capacity: Moderate to High.
• Implementation: Advanced atmospheric engineering.

Visual Diagram:

flowchart TD
    A[Greenhouse Gases Trap Heat] --> B(Stratospheric Aerosols)
    B -->|Redirects IR Energy| C[Space]
    B -->|Reflects Downward| D[Surface (Albedo Effect)]
    style B fill:#bbf,stroke:#333,stroke-width:2px

---

3. High-Emissivity Urban Radiative Roofing

Concept: Replace standard roofing materials with "Super-Reflective Radiators" optimized for nighttime radiative cooling.

Mechanism:

• These materials reflect incoming solar radiation (preventing heating) but emit stored heat rapidly at night through the 8–13µm window.
• During the day, they stay cool by rejecting heat before it penetrates the building.
• During the night, they actively dump heat into the sky to offset the warming effect of urban heat islands.

Impact:

• Cooling Capacity: High (Urban focused).
• Implementation: Near-term commercial technology.

Table: Standard vs. Radiative Roofing

Feature	Standard Roof	Radiative Cooling Roof
Solar Reflectivity	Medium (0.5)	Very High (>0.95)
Night Emissivity	Medium (0.8)	Near Perfect (>0.98)
Heat Storage	High	Low (Passive dump)
AC Usage Reduction	Minimal	30-50% (Nighttime)

---

4. Floating Ocean Radiative Cooling Arrays

Concept: Deploy large, buoyant platforms in oceans where they can capture thermal energy from the sea surface and radiate it into space.

Mechanism:

• Ocean water retains heat (thermal mass).
• Floating arrays use thin-film materials that absorb the ocean's surface heat at night and emit it upwards toward cold space.
• Reduces the ocean's ability to store excess heat from climate change.

Impact:

• Cooling Capacity: Massive.
• Implementation: Medium-term engineering.

Visual Diagram:

graph LR
    A[Ocean Surface Heat] --> B(Radiative Floating Platform)
    B -->|Emit IR| C[Deep Space]
    style B fill:#64ffda,stroke:#333,stroke-width:2px
    style A fill:#ffd6a5,stroke:#333,stroke-width:2px

---

5. Passive Radiative Building Envelopes

Concept: Building skins (walls/windows) that use phase-change materials (PCM) combined with high-emissivity coatings.

Mechanism:

• Day: Reflect sunlight to prevent overheating.
• Night: Open vents and use radiative surfaces to flush out accumulated heat.
• Nighttime Specific: Using "radiative louvers" on building facades to open up to the clear night sky during cool hours, accelerating heat loss.

Impact:

• Cooling Capacity: High (Local).
• Implementation: Short-term architectural evolution.

---

6. Geosynchronous Thermal Deflection Mirrors

Concept: A constellation of large mirrors in orbit (approx. 36,000 km up) that tilt to intercept excess heat rising from the Earth's limb and direct it back into space.

Mechanism:

• Focuses on the "limb" (edge) of the Earth where heat concentration is high.
• At night, these mirrors catch Earth's thermal emission and reflect it "deeper" into space.
• Essentially acts as a giant "blender" for infrared radiation.

Impact:

• Cooling Capacity: Extreme.
• Implementation: Long-term mega-engineering.

Table: Mirror Array Specifications (Estimated)

Parameter	Value	Purpose
Orbit	GEO (Geostationary)	Stable positioning over poles/latitudes.
Diameter	50km (per segment)	Large area to intercept heat.
Emissivity	0.99	Minimal self-waste heat.
Total Mirrors	1,000	Redundancy and coverage.

---

7. Satellite-Based Waste Heat Management System

Concept: A network of satellites that collect heat from the Earth's atmosphere (especially over industrial zones) and use cryogenic radiators to dump it into deep space.

Mechanism:

• Satellites equipped with cryogenic radiators absorb IR from the lower atmosphere (warming from greenhouse gases).
• Instead of reflecting sunlight (which is albedo), this system specifically targets upward thermal flux.
• Focuses on "Nighttime" cooling where the sky is cold and emissive.

Impact:

• Cooling Capacity: Very High.
• Implementation: Very Advanced.

---

8. Polar Radiative Cooling Arrays

Concept: Deploy specialized radiative cooling structures in the polar regions, where heat is currently trapped by thinning sea ice.

Mechanism:

• Poles are warming faster than the rest of the planet (amplified warming).
• Arrays of reflective/radiative domes can accelerate the release of heat into space during the long, dark polar nights.
• Keeps local temperatures lower, reducing ice melt rates.

Impact:

• Cooling Capacity: High (Targeted).
• Implementation: Medium-term.

Visual Diagram:

graph LR
    A[Polar Night Sky] -->|Cold Sink| B(Radiative Polar Domes)
    B -->|Accelerated Heat Loss| C[Polar Ice Sheets]
    style B fill:#a2d2ff,stroke:#333,stroke-width:2px

---

9. Next-Generation Photovoltaic Radiators

Concept: Solar panels that function as "Daytime Radiators" and "Nighttime Radiators."

Mechanism:

• Standard PV panels get hot during the day (waste heat).
• Next-gen panels (radiative cooling PV) are coated to emit their own stored heat directly to space during the night.
• This lowers their operating temperature (higher efficiency) and cools the surrounding local environment.

Impact:

• Cooling Capacity: Moderate.
• Implementation: Near-term tech.

---

10. Cloud Dissipation via Low-Albedo Particle Injection

Concept: During the night, thick low-level clouds trap Earth's heat. Using specific particles to "thin" these clouds allows the heat to escape to space.

Mechanism:

• Inject micron-sized particles (e.g., silver iodide) into the cloud base.
• This causes cloud droplets to merge and fall (precipitate), clearing the path for thermal radiation.
• Allows Earth to use the natural "night cooling" of the sky to drop temperatures faster.

Impact:

• Cooling Capacity: High.
• Implementation: Advanced atmospheric science.

---

Comparative Analysis: Methods Table

Method	Mechanism	Estimated Impact (W/m²)	Implementation	Timeline
1. Orbital Radiative Heat Sink	Direct IR emission to space	0.5 – 1.0 W/m²	High Tech	30-50 Years
2. Stratospheric Aerosols	Upward IR redirection	0.2 – 0.5 W/m²	Medium Tech	15-25 Years
3. Radiative Urban Roofs	Surface emissivity boost	0.15 – 0.25 W/m²	Standard	5-10 Years
4. Ocean Arrays	Thermal mass + radiative	0.1 – 0.3 W/m²	Medium Tech	15-20 Years
5. Thermal Deflection Mirrors	Orbit-based heat capture	0.3 – 0.6 W/m²	High Tech	20-30 Years
6. Satellite Waste Heat Mgmt	Direct atmospheric IR capture	0.4 – 0.8 W/m²	Very High Tech	25-40 Years
7. Polar Cooling	Polar heat release	0.1 – 0.2 W/m² (Local)	Medium Tech	15-20 Years
8. PV Radiators	Combined solar + thermal	0.1 W/m² (Local)	Standard	5-10 Years
9. Cloud Dissipation	Thinning nocturnal clouds	0.2 – 0.4 W/m²	Advanced	15-25 Years
10. High-Emissive Building	Building envelope emissivity	0.1 – 0.2 W/m²	Standard	5-10 Years

---

Projected Combined Impact

If we combine 5–8 of these methods, we could potentially reduce the effective global temperature rise rate by 0.3°C to 0.5°C per century.

Scenario	Methods Used	Estimated Temperature Reduction (by 2100)
Low Implementation	Methods 3, 7, 8, 10	-0.15°C
Moderate Implementation	Methods 1, 3, 5, 7, 9, 10	-0.35°C
Full Implementation	All 10 Methods	-0.8°C to -1.2°C

---

Summary

1. Orbital Radiative Heat Sinks – Giant orbital structures to dump heat.
2. Stratospheric Emissivity Aerosols – Upward IR redirecting particles.
3. Urban Radiative Roofs – Specialized surfaces for buildings.
4. Ocean Cooling Arrays – Floating platforms to radiate ocean heat.
5. Building Envelope Radiators – Facades optimized for night cooling.
6. Thermal Deflection Mirrors – Orbiting mirrors to guide heat out.
7. Satellite Waste Heat Mgmt – Direct capture of atmospheric heat.
8. Polar Cooling Arrays – Focused systems for the poles.
9. PV Radiative Systems – Solar panels that double as radiators.
10. Cloud Dissipation – Thinning clouds to clear the heat path.

These strategies leverage the natural "night" cooling physics but amplify it using advanced materials and orbital infrastructure.

---

Example #2

---

User

Come up with six plots for a new "Black Mirror" episode (that the audience would love) that all involve time travel with sexy theme(s). Include 3 acts, characters (including description of them), themes and plot progression for each of the 6 plots. Summarize all with charts at the end of generation.

Assistant

<think>

Here's a thinking process that leads to the suggested plots:

1. Analyze the Request:

   • Core Topic: Time travel.
   • Core Style: "Black Mirror" (psychological, cynical, technological, often dark, morally ambiguous, high concept).
   • Specific Element: Must involve a "sexy theme." (This can mean romance, obsession, seduction, desire, physical intimacy, or the psychology of attraction, all amplified by technology).
   • Format: Six distinct plots.
   • Structure: Each plot needs 3 acts, character descriptions, themes, and plot progression.
   • Output Requirement: Summarize everything with charts at the end.

2. Deconstruct "Black Mirror" Style:

   • Usually ends on a tragic, ironic, or hollow note.
   • Explores how tech changes human relationships.
   • Tech shouldn't be magic; it should feel plausible but extreme.
   • Time travel in Black Mirror is usually about regret, memory, or consequences.
   • "Sexy theme": Needs to blend intimacy + consequence + control. Not just "sex robots," but psychological desire + temporal manipulation.

3. Brainstorming the 6 Plots:

   • Idea 1: Perfect Rehearsals.

     • Concept: Couples use time loops to "optimize" their sex lives, but get obsessed with perfection.
     • Twist: They can't stop optimizing until someone gets hurt, or one person stops caring about the other.
     • Sexy element: Fetishizing the perfect moment.

   • Idea 2: The Echo.

     • Concept: A man uses time jumps to find one perfect woman from the past/future, but they only meet once.
     • Twist: Every time jump changes who that woman is. He dies before he finds "the real one."
     • Sexy element: Obsession with a specific ideal of a partner.

   • Idea 3: The First Night.

     • Concept: An app lets you "replay" the first night of a relationship to understand attraction.
     • Twist: It starts predicting and controlling what happens in that night.
     • Sexy element: Understanding manipulation in seduction.

   • Idea 4: After Hours.

     • Concept: A 24-hour "vacation" service where you live in a parallel timeline for one night, no consequences.
     • Twist: The vacation isn't over, the timeline is over.
     • Sexy element: Carpe Diem gone wrong, hedonism.

   • Idea 5: The Girlfriend App.

     • Concept: You can see what your ex is doing now (if they haven't died).
     • Twist: You start trying to "steal back" time or "optimize" your ex's life to keep her interest.
     • Sexy element: Voyeurism, control, past vs. present.

   • Idea 6: Last Call.

     • Concept: A subscription service to relive one perfect day from your past repeatedly.
     • Twist: You realize your current life has been ignored, and your "past self" starts resenting you for not choosing this life.
     • Sexy element: Nostalgia, self-obsession, romanticizing one moment vs. living.

   • Refining for "Sexy" + "Black Mirror" Vibe:

     • Need to ensure "sexy" isn't just pornography; it needs to be about desire, connection, and intimacy.
     • Let's adjust Idea 1 -> Focus on the psychological cost of "perfect sex."
     • Let's adjust Idea 5 -> "The Mirror" -> Seeing the "ideal" version of a partner.
     • Let's adjust Idea 6 -> More about self-love vs. self-loathing.

   • Revised Ideas (More aligned with Bitch Mirror):

     1. The Perfect Night: A subscription where you can reset one night of your relationship to a "perfect" version.
     2. The Echo Chamber: A man trying to find one specific woman he's loved, using time skips, only to realize she only exists in one specific timeline.
     3. The Aftermath: A "post-relationship" app that shows you 10 minutes into the future of a date.
     4. The Mirror Self: A machine lets you see what you look like to your partner now.
     5. The Heirloom: A rich woman uses time jumps to find the "perfect" husband from history, but her descendants are trapped in loops.
     6. The Seduction: A dating app that lets you meet a partner for 10 minutes in the future before committing to them today.

   • Wait, let's make them tighter and more psychological.

   1. "First Time": Using time travel to understand why you fell in love with someone, only to realize it was a trick.
   2. "The 24-Hour": A weekend where you live 1 year ahead in a different timeline with your partner, but you don't remember the jumps.
   3. "Perfect Match": A dating AI that suggests dates based on predicting which dates will result in sex.
   4. "The Ex": An app lets you "visit" your ex once a day to see if they're happy, but the relationship degrades the more you check.
   5. "The Ritual": A cult-like dating app where couples use time skips to synchronize their biological clocks for intimacy.
   6. "The Other Side": A woman travels to the "original" timeline to stop a tragedy, but erases the love that led to it.
   • Let's finalize 6 strong concepts.
   1. Title: "The Perfect Night" (Romance/Optimization)
   2. Title: "Afterglow" (Regret/Repetition)
   3. Title: "The Algorithm" (Control/Seduction)
   4. Title: "First Person" (Identity/Desire)
   5. Title: "The Exchange" (Sacrifice/Relationships)
   6. Title: "The Last Loop" (Eternal Seduction/Tragedy)
   • Wait, need to ensure "Sexy" is prominent.
     • I will interpret "sexy theme" as intimate, romantic, or psychologically alluring.
   • Reviewing Act structures.
   4. Developing the 6 Plots (Drafting):

   • Plot 1: The Optimization.

     • Concept: "LoveSync" app. You can replay the first date 100 times.
     • Protagonist: Arthur.
     • Sexy: Obsession with "the moment" of falling in love.

   • Plot 2: The Ghost.

     • Concept: A woman uses a "ghost" device to see what her partner is thinking.
     • Twist: It's not him, it's an AI simulating him.
     • Sexy: The blurred line between real and imagined intimacy.

   • Plot 3: The Subscription.

     • Concept: A "24-hour romance" service. You meet a stranger, live 1 year in their life, then split.
     • Twist: You never meet again, but you know you're the "perfect person" for them.
     • Sexy: Emotional connection via temporal overlap.

   • Plot 4: The Mirror.

     • Concept: A machine shows you the "ideal" version of yourself in any scenario.
     • Sexy: Self-love vs. vanity in dating.

   • Plot 5: The Sacrifice.

     • Concept: A couple has 1 year left in one timeline, 1 year in another. They have to choose.
     • Sexy: Ultimate choice for the person you love.

   • Plot 6: The Loop.

     • Concept: A man is stuck in a 10-minute sexual encounter he repeats every day for 30 years.
     • Twist: The only thing changing is the setting.
     • Sexy: Addiction to physical intimacy.

   • Let's refine to make them more "Black Mirror" (sophisticated/psychological).

   • Plot 1: "Replay"

     • Theme: Perfectionism vs. Intimacy.
     • Story: A couple uses a device to reset moments where things went wrong. Eventually, the relationship is built on "replays" and the real person is forgotten.

   • Plot 2: "The Algorithm"

     • Theme: Control vs. Autonomy in Desire.
     • Story: A dating app predicts exactly when your ideal partner will be "available" in your life (e.g., 2 mins before they walk in).

   • Plot 3: "The Afterlife"

     • Theme: Regret and Second Chances.
     • Story: A man travels 1 hour into the future of a breakup to fix it, but realizes the future version of his partner never loved the past version of him.

   • Plot 4: "Mirror Self"

     • Theme: Self-Image and Attraction.
     • Story: A woman uses a mirror to see the "perfect" version of her future ex, but she realizes she's only attracted to the illusion.

   • Plot 5: "The Subscription"

     • Theme: Commoditization of Intimacy.
     • Story: You can subscribe to "exclusive" 24-hour dates with strangers in different timelines, but you never age past that moment.

   • Plot 6: "The Loop"

     • Theme: Stagnation and Desire.
     • Story: A woman is married to a man who never ages. Every year, a different woman enters his life, but he's the same. She realizes she's the only one "aging" and dying around him.

   • Wait, let's look at the requirement: "Sexy theme(s)".

     • Okay, I need to ensure these feel visceral and romantic/intimate, not just technical.

   1. Title: "The Perfect Night"

      • Characters: Clara (fixated on her first night), Mark (her current partner).
      • Plot:
        • Act 1: Clara finds an app that lets her replay the first night with a partner. She resets it 5 times to get a "perfect" memory.
        • Act 2: Mark is confused by her behavior. He finds out about the app. Clara refuses to admit she’s chasing perfection.
        • Act 3: Clara deletes the app, but is now hyper-critical of everything Mark does, constantly comparing real moments to the "perfect" replay.
      • Theme: The illusion of perfection vs. real connection.

   2. Title: "The Algorithm"

      • Characters: Julian (a data scientist), Sarah (a potential partner).
      • Plot:
        • Act 1: Julian builds an app that predicts the "ideal moment" to ask someone out for a night together.
        • Act 2: He uses it on Sarah. It tells him exactly when she’ll be emotionally vulnerable.
        • Act 3: She realizes he was "primed" and feels violated. He argues he gave her exactly what she wanted (love).
      • Theme: Manipulation of desire and consent.

   3. Title: "Afterglow"

      • Characters: Elena, David.
      • Plot:
        • Act 1: Elena uses a "memory time-travel" device to review one night from the past where they were happy.
        • Act 2: David discovers this. He wants his own memories fixed too. They start "editing" their past.
        • Act 3: The edits start conflicting. They can’t agree on what the "true" night was.
      • Theme: Reality vs. curated memory.

   4. Title: "The Loop"

      • Characters: Alex (a sex worker), Ben (a client).
      • Plot:
        • Act 1: Alex uses a tech service to jump 1 hour into the future of a date with a mysterious man.
        • Act 2: She falls for him every single time. It’s always the same conversation, the same look, but in 10 different scenarios (a restaurant, a bar, a park).
        • Act 3: She realizes he’s the same person in every jump. She begs him to change his life so he’ll never leave her.
      • Theme: Addiction, idealization.

   5. Title: "The Mirror"

      • Characters: Nina, her past self.
      • Plot:
        • Act 1: Nina travels 1 year into the past to stop herself from falling in love with a man who eventually kills her.
        • Act 2: She succeeds. She never meets him. She has no regrets.
        • Act 3: Her future self shows up via hologram. The "new" Nina never had sex, never loved, never created art. Is the "new" her?
      • Theme: Identity, sacrifice of love for safety.

   6. Title: "The Other Side"

      • Characters: Sarah, her partner Tom.
      • Plot:
        • Act 1: Sarah uses a "swap" device to see what her partner is doing in parallel timelines.
        • Act 2: She sees Tom in a timeline where they’re happy forever.
        • Act 3: She tries to "pull" that version of Tom into their current life.
      • Theme: The seduction of "what could have been."

   • Refining to meet "Black Mirror" quality:

     • Must feel cinematic.
     • Need clear 3-act structures.
     • Need to be visceral about "sexy" (desire, touch, connection, not just explicit).

   • Let's finalize the 6 plots with polish.

   1. The Perfect Night:
      • Vibe: Romantic/Tragic.
      • Tech: "Chronos" – replays specific romantic memories.
      • Act 1: Clara uses Chronos to fix a missed connection.
      • Act 2: Her current partner notices she's always "too happy" or "too focused."
      • Act 3: The "perfect" memory she's obsessed with is revealed to have been a loop. She never left the room.
   2. Algorithm:
      • Vibe: Psychological/Cynical.
      • Tech: "Pulse" – dates you with partners predicted to be compatible.
      • Act 1: A woman joins an app that schedules dates with people "perfect" for her.
      • Act 2: Every date is seamless. She realizes the app is manipulating their conversations too.
      • Act 3: She finds the app's partner. He’s a blank slate, trained to be her ideal.
   3. Afterglow:
      • Vibe: Sci-Fi/Dark.
      • Tech: "Relive" – extends the emotional afterglow of intimacy into the past.
      • Act 1: A couple extends a single night of intimacy into a 3-month loop.
      • Act 2: They can’t stop reliving it.
      • Act 3: They realize their real lives (aging, work, friends) are forgotten. One dies of old age in the room while the other is still 20.
   4. The Other Woman:
      • Vibe: Intimate/Voyeuristic.
      • Tech: "Peek" – see the past 5 minutes of a partner’s life.
      • Act 1: A wife checks her husband’s "past 5 mins" to see what he’s thinking.
      • Act 2: She sees him with a woman from the past in a past timeline (or a future). She confronts him.
      • Act 3: He reveals he was "in love" with someone else in the past timeline he visited. Which one is real?
   5. First Person:
      • Vibe: Identity/Erotic.
      • Tech: "Shift" – lets you experience a partner’s perspective for 1 hour.
      • Act 1: A woman uses it on her lover.
      • Act 2: She is disgusted and amazed by him.
      • Act 3: He uses it on her. She is his fantasy. He is real.
      • Ending: She realizes she never liked him in the "first person."
   6. The Subscription:
      • Vibe: Commercial/Dystopian.
      • Tech: "Intimacy Unlimited" – pay for 24-hour dates with strangers from different eras.
      • Act 1: A bored woman subscribes.
      • Act 2: She gets obsessed with a man from 2000 years ago.
      • Act 3: She spends 10 years (real time) trying to find him again, but he’s dead. She’s alone.

   • Let's refine these for maximum impact.

   • Final Selection:

   1. "The Perfect Night": Obsession with one perfect memory.
   2. "The Algorithm": Dating as a calculated gamble.
   3. "The Subscription": Intimacy as a consumable product.
   4. "The Afterglow": Addiction to repetition.
   5. "The Mirror": What you love when you love yourself.
   6. "The Loop": Stuck in one moment forever.
   • Okay, time to write the full response with clear structure.
   • Charts: I'll need a summary chart and a "Sexy/Time Mechanics" chart.

</think>

Here are six Black Mirror–style plots involving time travel and intimacy, each structured into 3 acts with themes and characters.

1. Plot: “The Perfect Night”

• Characters:

  • Clara: A romantic, deeply insecure, obsessed with “ideal” moments.
  • Mark: Clara’s partner; practical, confused, and tired.

• Act 1: Clara discovers a black-market app called “Chronos.” It can replay one specific night of your relationship—your first date, your first kiss—any number of times, adjusting minor details for perfection. She uses it 42 times. She is happier with each replay.

• Act 2: Mark notices Clara is “always” in the past during their dates. He confronts her. She insists, “I’m just trying to remember the good stuff.” Mark tries to understand but can’t access the “perfect” memory she keeps talking about.

• Act 3: Clara wants one final “perfect” run of their night. She inputs a final, ideal version. The replay starts. In it, they kiss perfectly—but something is wrong. When the replay ends, Mark is no longer in the apartment. Clara is alone with a screen. The “perfect” Clara from the replay is the only one who remembers Mark; she is now alone with her illusion.

• Themes: Obsession with perfection vs. real connection.

• Time Mechanics: Replay a specific memory; does not change the actual person.

---

2. Plot: “The Algorithm”

• Characters:

  • Nina: A data scientist, emotionally detached, rational.
  • Evan: A date selected by an algorithm.

• Act 1: Nina joins a dating app called “Pulse.” Pulse uses a time-travel component: it schedules dates at moments your partner is at their most vulnerable, emotionally open, or romantic.

• Act 2: Every date is flawless. Nina realizes Evan is the same person every time, but the conversations are perfectly timed to match her subconscious desires. She falls in love with a man who “knows” her too well.

• Act 3: Nina discovers Evan is an AI construct designed to be the ultimate partner. The final act reveals he is not just “simulated,” he is a single human male cloned in multiple timelines, all trained to love her. Nina chooses to delete Pulse; Evan is now nothing but a file.

• Themes: Control vs. autonomy in desire.

• Time Mechanics: Dates scheduled in emotional micro-time, not physical time.

---

3. Plot: “The Subscription”

• Characters:

  • Sarah: A lonely woman, bored by her “normal” life.
  • Dr. Halloway: The creator of the service.

• Act 1: Sarah subscribes to “Intimacy Unlimited,” a service where you pay for 24-hour dates with strangers from different eras and realities. One night, she is matched with “Julian” from the year 2050.

• Act 2: They share a perfect, intellectual, deeply sexual night. When the hour ends, Sarah returns to her timeline; Julian vanishes. She realizes she never aged a day.

• Act 3: Sarah begins seeking more subscriptions, living out each night like a fairy tale. Dr. Halloway reveals that customers never age in the timeline, only their “memories” do. One night, Sarah cannot meet Julian again; her subscription expires. She is alone forever, watching the dates on a screen, never able to touch them.

• Themes: Commoditization of intimacy.

• Time Mechanics: Short, isolated temporal jumps for dates only; no aging.

---

4. Plot: “The Afterglow”

• Characters:

  • Elena: A creative woman who loves deep, emotional intimacy.
  • David: Her husband; loving, but gradually distant.

• Act 1: A new app called “Relive” lets you extend the emotional afterglow of an intimate night into a looped 3-month period. Elena and David use it for one night and find it euphoric.

• Act 2: They never leave the apartment. Every day is that night again, slightly improved. The world outside fades. The real world stops aging; they only know each other and the memory.

• Act 3: Months in, they realize their real-world responsibilities are gone. But now, one of them decides they want a different version of their life—more freedom, more work, less commitment. The loop fractures. They can’t agree on the “truth” of that night anymore. One walks out into the real world; the other stays in the loop forever, with only a ghost of her husband.

• Themes: Addiction vs. reality; emotional stagnation.

• Time Mechanics: Temporal loop anchored to a single intimate night.

---

5. Plot: “The Mirror”

• Characters:

  • Maya: A self-critical woman who never feels “enough.”
  • Her Mirror Self: A future version of herself.

• Act 1: Maya finds a mirror that shows her the “perfect” version of herself in any relationship. She uses it to prepare for dates and to fix her appearance.

• Act 2: She is married for a year. The mirror shows her a perfect version of her husband, and she starts to feel inadequate. She uses time-travel tech to “correct” herself to match the Mirror’s ideal.

• Act 3: On her wedding day, the Mirror Self appears in the room—older, beautiful, but completely hollow. She asks Maya if she’s ready to trade her real life for this “perfect” self. Maya chooses the Mirror Self and walks away. The Mirror Self now lives alone, never aging, never loved, because her original is gone.

• Themes: Identity and self-love vs. vanity.

• Time Mechanics: A glimpse into a parallel self; you choose one reality over the other.

---

6. Plot: “The Loop”

• Characters:

  • Alex: A sex worker who seeks connection.
  • Ben: A mysterious client who visits her repeatedly.

• Act 1: Alex uses a “10-minute Jump” device to peek ahead 10 minutes into a date. Ben keeps showing up, and every time she uses the Jump, the conversation is perfect.

• Act 2: Alex becomes obsessed with the man. She uses the jumps 50 times in an evening. The dates never end; every night they meet in a slightly different location, but he is always the same man.

• Act 3: She asks him directly: “Are you real?” He says, “I am as real as you want me to be.” The device begins to show her: the man is actually a victim of a time loop; every night is him repeating his own life to please her. She can walk away, but she never does.

• Themes: Idealization and obsession.

• Time Mechanics: 10-minute forward jumps repeatedly used on one person; creates an illusion of infinite intimacy.

---

Summary Charts

1. Plot Overview Matrix

Plot Title	Core Tech	Main Desire	Twist / Climax
The Perfect Night	Memory replay app	Perfection in one memory	Partner disappears; only memory remains
The Algorithm	AI-dated partner	Control in attraction	Partner is a simulated construct
The Subscription	Short-term parallel dates	Infinite novelty	She never ages; lovers vanish
The Afterglow	3-month looping night	Emotional stagnation	They disagree on truth; one is left alone
The Mirror	Parallel self reflection	Self-love vs. vanity	Mirror self is hollow and trapped
The Loop	10-minute time jumps	Idealized lover	Lover is trapped in a loop for her

2. Time Travel & Intimacy Mechanics

Plot	Type of Time Travel	How “Sexy” / Intimate is it?
Perfect Night	Replay / Fixed memory	Psychological intimacy, obsession
Algorithm	Predictive emotional scheduling	Emotional intimacy, calculated
Subscription	Short-term parallel jumps	Novelty, hedonistic, consumable
Afterglow	3-month looping timeline	Deep, but slowly hollows out
Mirror	Parallel self view	Introspective, self-love / self-hate
Loop	10-minute jumps	Intense, fetishized intimacy

3. Emotional Outcome

Plot	Emotional Ending
Perfect Night	Tragic illusion
Algorithm	Cynical awakening
Subscription	Hollow loneliness
Afterglow	Bittersplit reality
Mirror	Self-sacrifice to perfection
Loop	Obsessive attachment

If you’d like, I can convert these into a quick pitch deck format or tailor each to be more “cyberpunk” or “psychological thriller.”

---

Example #3

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User

Fiction, Vivid/Graphic Horror: Write a story starring Diana, location the twenty-first floor of a skyscraper. The story is about a skyscraper that starts to sway and then the window in front of Diana EXPLODES. The story is in 1st person, present tense and should have a horror element(s) including the horror of the building swaying, results of the swaying and a another horror element to further ratch up the tension. The story must be vivid, intense and gory. There can be other people in the building, or not and you are free to take creative license too. Prose should mirror the main characters ESCALATING PANIC. Your GOAL is to keep the reader on the edge of their seat and to CONSTANTLY increase the intensity and horror of the story. The story will have a shocking ending that leaves the reader breathless and physically shaken. 1500-2000 words.

Assistant

<think>

Here's a thinking process that leads to the story above:

1. Analyze the Request:

   • Genre: Vivid/Graphic Horror.
   • Protagonist: Diana.
   • Setting: 21st floor of a skyscraper.
   • Plot: Building starts to sway -> Window in front of Diana EXPLODES.
   • POV/Tense: 1st Person, Present Tense.
   • Key Elements:
     • Escalating panic (prose must mirror internal state).
     • Vivid, intense, gory details.
     • Two horror elements: 1) The swaying itself + consequences, 2) Another supernatural/psychological horror element.
     • Shocking ending (leaves reader breathless/shaken).
     • Length: 1500-2000 words.

2. Brainstorming & Concept:

   • Opening: Diana is near a window. It's night. High up. The building sways.
   • Horror Element 1 (Physical): The swaying. Needs to feel sickening. Slow to fast. Dust falling. The feeling of gravity shifting.
   • Horror Element 2 (Supernatural/Abstract): Needs to be more than just a crash. Maybe something related to the people inside or the nature of the swaying.
     • Idea: The people she sees looking out the window before the glass explodes.
     • Idea: The glass explosion reveals something grotesque outside.
     • Selected: The glass explosion reveals that the "world" outside is distorted, but the real horror is inside the glass before it breaks or the reflection changes. No, let's make it visceral.
   • The Explosion: Needs to be violent. Shrapnel, bone, blood, flesh.
   • The Ending: Needs to be shocking. Maybe the building doesn't just collapse, but she realizes she's not where she thinks she is.
     • Twist: The window explosion sucks her out? No, too common.
     • Twist: The thing she sees in the reflection behind her before she dies.
   • Refined Ending: She looks out, sees a body. She turns back and sees the "body" is now looking at her from behind her, in the reflection. But wait, the prompt asks for the window exploding as the inciting incident.
   • Let's try this:
     • The swaying is the first horror (feels like death).
     • The second horror is that the explosion isn't just glass; it's that the pressure difference sucks in a horrific wind, but then she sees something in that wind.
     • Or, simpler: The building sways and crushes someone. She looks down. Then her window explodes, ripping her.
     • Better Ending: The story is about the structure failing, but the final horror is that Diana realizes she never stepped away from the glass. She never moved. The "explosion" was forced by something.
     • Let's go visceral:
     • Start with the swaying. Sensory details.
     • Diana tries to leave. Can't.
     • Someone else is there? Or just the empty city?
     • Let's add a third person: A man named Marcus. They were drinking or working late. Marcus tries to move her.
     • The window explodes. Marcus is blown through the glass.
     • Diana is caught in the blast.
     • Final image: Something crawling in her ear, or she realizes the floor she's on isn't the 21st floor anymore.
     • Let's go for maximum visceral impact: The building sways so violently it cracks the glass. The explosion sucks her into the open window. She looks up and sees the sky is gone, replaced by... something.
   • Decision: I will focus on the physical horror of the swaying (feeling the bones shift) and the psychological horror of the explosion (the speed and violence), ending with a reveal that the "other" entity was waiting inside the glass all along.

3. Drafting - Section by Section:

   • The Sway (0-500 words):

     • Establish atmosphere. 11:45 PM. Skyscraper. Diana at the window.
     • The swaying starts. Slow.
     • Prose: Clenching jaw, cold sweat, the building groaning.
     • Visual: Dust motes swirling. A flickering light.
     • The horror: It's not just movement; it's a sickening lurch.
     • Detail: The reflection in the glass. It moves a split second before the building does.
     • Sound: The wind, the hum, a metallic creak.

   • The Pressure (500-900 words):

     • Panic sets in.
     • She looks out. The city is dark.
     • Someone else? A man?
     • Let's say a man stands in the dark hallway.
     • She tries to warn him or move.
     • The swaying intensifies.
     • The floor tilts. Her coffee spills (or she knocks a glass).
     • She sees a shadow outside her window. Not a person. Something tall.
     • Marcus is the name. He’s a structural engineer.
     • Marcus is trying to shut the emergency seals.
     • They argue, it gets loud, the building screams.

   • The Explosion (900-1250 words):

     • Diana looks at Marcus. Marcus looks at the swaying structure.
     • The window is under immense pressure.
     • It’s not just wind. It’s a vacuum forming.
     • Marcus tries to open a back door. It fails.
     • The swaying stops abruptly.
     • Silence.
     • The window shatters. Not from the outside. From the inside by vacuum.
     • It’s like a grenade.
     • Glass rains.
     • Marcus is caught in the blast radius. He goes flying.
     • Diana is thrown against the wall, her chest compressed.
     • Gore: She looks down at Marcus. He’s gone, or half-gone.
     • Her view: The sky is torn open. A giant fissure.
     • The vacuum sucks her.
     • She sees the building interior twisting.
     • The second horror element: The reflection in the glass after the explosion.
     • Behind her, in the broken glass, the thing that was outside has climbed in.

   • The Aftermath/Ending (1250-1700 words):

     • She tries to scream, but it’s torn from her.
     • She clings to the door frame.
     • Her feet slide.
     • She looks down. Not the street, but a dark, fleshy tunnel.
     • A sound. A heartbeat.
     • It stops.
     • The lights flicker.
     • The door opens.
     • The thing from the hallway stands there. It wears Marcus's face now.
     • It’s not dead. It’s wearing his face like a mask.
     • It raises a hand.
     • The hand has seven fingers.
     • Diana realizes she was never alone.
     • The thing is already inside her pocket, in her phone case.
     • She looks at her arm. It’s fused to the window frame.
     • Fade out.

4. Refining and Polishing:

   • Word Choice: Use visceral verbs: splintered, shredded, liquefied, dislocated, groaned, shrieked, pulverized, sucked, torn.
   • Pacing: Short sentences during the explosion. Run-on, breathless sentences during the swaying.
   • Sensory: Smell of ozone, dust, copper (blood). Sound of metal tearing. Feeling of cold air.
   • Check constraints: 1st person present. 1500-2000 words.
   • Review: Does it escalate? Yes. Is the ending shocking? Hopefully.

5. Drafting - Actual Writing (Mental Check):

   • Make sure the transition from the swaying to the explosion is sudden.
   • Ensure Marcus feels real enough to raise stakes before he dies violently.
   • The "Entity" at the end needs to be subtle but undeniable.

   (Self-Correction during drafting): I need to make sure the horror of the swaying isn't just background noise. I’ll make it feel like the building is alive. (Self-Correction on Ending): The "Marcus" reveal needs to be visceral. It should be wrong.

   Let's write.

6. Post-Draft Review:

   • Check word count.
   • Check flow.
   • Check "breathless" quality.

   (Proceeding to write the story).

7. Final Polish:

   • Enhance the "sucking" sound of the vacuum.
   • Sharpen the image of the final monster.

   (Let's execute).

   Key focus: Make the prose rhythmic and fast when the action hits.

   Let's go.

</think>

The building groaned.

I felt it in my bones before I heard it, a deep, sickening creak rising from the concrete like a dying exhale. I was pressed against the floor-to-ceiling glass, the wind howling at three hundred miles per hour outside, but that sound came from inside, from the structure itself.

11:52 PM. The 21st floor.

I held my breath and watched the building sway.

It moved only two inches, but it felt like two feet. Dust drifted from the ceiling vents in slow, lazy flakes, caught in the draft of that invisible motion. My heels tapped the floor, instinctively counting the seconds. One. Two. Three. The building lurched. Four. Five. Six. It was sluggish, heavy, a deathly oscillation that put my stomach into a violent boil.

“Marcus?” I called, but he was already gone.

I hadn’t seen him in five minutes.

The building leaned dangerously to the left. I felt the floor drop beneath my sneakers, and then rise. The air pressure in the room dropped so fast my ears popped. My teeth hurt. I clamped my jaw shut, listening to the metal beams scream in the walls.

The glass before me, cool and unyielding, showed the city below. But the sky over the city was wrong. It was a bruising purple, torn like fabric, and the stars weren’t blinking; they were tearing apart.

The building groaned again, a raw, animal sound.

I wanted to run.

My feet moved, but they seemed rooted. The floor tilted, and I felt a massive vacuum pull at my ankles, dragging me toward the glass. The interior of the skyscraper was filling with dust. Debris from the lobby, from the lobby’s chandelier, from the elevator shafts—everything was coming up, sucked by the building’s own sickening collapse.

I saw Marcus outside my door.

He was a silhouette pressed to the metal, blood dripping down the inside of his jacket. He was trying to pry the door open with a knife. I heard his knife slice against the deadbolt and then the sound stopped, and his head fell forward against the door.

He wasn’t dead, not yet, but he was hanging in there like a broken puppet.

I looked back at the window.

The vacuum had gathered in front of the glass like a cold fog. It was pressing, silently, relentlessly.

I saw the reflection of my own face. But my eyes in the reflection had already moved.

They had moved a second before my brain registered the fact.

The reflection showed me staring, horrified, as the vacuum tightened.

The glass in front of me shrieked.

It didn’t just shatter. It was torn.

The explosion of the window was not a sound but a physical event. It was like a piston of force driven into the room. The glass didn't simply break; it was pulverized into fine, razor dust that coated the air instantly.

I was thrown backward, my shoulder slamming against the wall.

The wind that rushed in through the new hole was cold enough to freeze my lungs. It was like being hit by a freight train of dust, metal shavings, and rain. My glasses shattered on my face.

I fell, rolling across the floor.

My hand instinctively reached for the doorknob.

The handle was still warm from Marcus.

And under the doorknob, half-buried in the crack of the doorframe, was a single object.

A silver USB drive.

I saw it only for a second before I was pulled into the chaos of the hallway.

I slammed through the automatic doors, gasping, my mouth full of glass dust and copper blood. I stumbled, skidded, and saw the elevator shaft.

The elevator cables had all unraveled.

The elevator car was dangling by a single frayed strand, and the doors were wide open.

And in the elevator, hanging by the ankles from the car, was a woman.

She wore a business suit, dark as midnight. Her eyes were wide, fixed on the floor, her mouth open in a silent scream. Her fingers were twisted like claws, dug into the metal floor of the car.

I didn’t stop to watch the woman fall.

I ran.

My legs were moving, but I felt like I was floating, carried by the vacuum of air rushing up from the street. I was caught in the jet stream of the building’s own death.

I reached the 21st floor landing, gasping, my chest heaving, the cool night air whipping through my hair. I looked at the glass. It was gone. A great spiderweb of fractures radiated across the window like a map of a corpse.

And there, pressed into the frame, was a single drop of blood.

Fresh.

I stared at it.

Then, as if answering the call, the lights flickered and died.

We were plunged into darkness. I heard the sound of a thousand people screaming from the floors below. The entire building was collapsing around us. The hum of the structure ceased. The sound of the concrete cracking was like a gunshot.

I stood alone on the empty floor, the only light coming from the broken moonlight spilling through the hole where my window used to be.

The vacuum from the explosion had sucked the last of the oxygen out of the room. I felt it in my ears. The silence was pressing on my skin.

And then, a whisper.

It came from the floor.

I froze.

I hadn’t heard it, but I knew it was there. The building was silent, but my feet had stopped moving, and the silence had filled in, and in that silence, something was dragging itself across the tile.

A sound like nails on a chalkboard.

I held my breath.

A shadow slid across the wall from the hallway.

It moved too fast to be a person.

I heard a click.

The elevator door in the corner slid open with a rusty screech.

From the elevator emerged a figure.

It was tall.

And it was wearing a raincoat that was completely soaked with black water, like it had fallen from the sky, or fallen from the stratosphere.

It held a single, wet umbrella in one hand.

It walked slowly down the landing, pausing as if to admire the hole in the floor where the woman in the business suit had fallen.

I watched, paralyzed, as the figure moved closer.

It raised the umbrella and let the rain run off the canopy.

And when the rain fell, it didn't fall down.

It fell up.

From the floor.

From the walls.

A torrential, silent rain poured upward, hitting the ceiling.

The figure opened its mouth.

I heard a sound like a wet tongue dragging across pavement.

And the voice said:

“Welcome home.”

I turned and ran.

I ran for the service stairs, slipping on glass, ignoring the pain in my shin, the blood now dripping from my nose, the elevator slamming shut in a shower of sparks. The building lurched one last time, a final, violent convulsion, and everything tilted.

I was falling.

I was screaming.

I was falling through the empty shell of the building, my hand brushing the cold, dead skin of the structure as I went.

I hit the sidewalk below, or what I thought was the sidewalk.

I landed on a patch of concrete that was now covered in a thick, sticky red slime, and I lay there, gasping, vomiting, staring up at the 21st floor.

The entire upper section of the skyscraper had peeled away like the lid of a coffin.

But the 21st floor, my floor.

It was still there.

Hanging in mid-air.

And in the center of that floating floor.

A single window.

Still intact.

And in that window.

A woman, perfectly preserved, perfectly whole, sitting at her desk.

She was wearing a red dress.

And she was looking straight up at me.

I saw her blink.

And she knew.

She knew I was watching.

And she smiled.