DavidAU/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-NEO-IMATRIX-MAX-MTP-GGUF

FIREBALL: 640 ARC-C for both 8bit and 4bit. Model exceeds 7 of 7 benchmarks for Qwen 3.5 9B, Qwen3.5 27B, Qwen3.6 35B-A3B, and meets Qwen 3.6 27B in some cases... and it does so in 4bit and 8bit. Regular and MTP (fast) NEO IMATRIX GGUFs provided.

Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-NEO-IMATRIX-MAX-MTP-GGUF

Extreme intelligence in a small package. Jaw dropping performance. Superior instruction following.

A multi-stage and multi-model fine tune and multi-stage merge on local hardware by myself and Nightmedia.

Several of my 9B Qwen 3.5 fine tunes were used to make this model.

The goal of this model was to raise the general intelligence of the model AND instruction following. Getting these right raises the performance of the model from top to bottom. Keep in mind, even thou this model EXCEEDS 7 critical benchmarks of Qwen 3.5 27B model, that does not mean it will beat the 27B in all tasks.

This is also a HERETIC model, trained post "Heretic'ing" -> this model does what you want, no questions asked.

Also: Thinking/reasoning block has been compacted, and in many cases it is a lot stronger.

Fully uncensored.

Benchmarks below.

Example generations at the bottom of the page.

PS:

If you are looking for a 27B model that BEATS Qwen 3.6 27B, Qwen 3.5 27B, and Qwen 35B-A3B, IN ALL critical benchmarks this is the one:

https://huggingface.co/DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF

BENCHMARKS - Power to the people:

          arc/c  arc/e boolq hswag obkqa piqa  wino

Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic [instruct]
bf16      0.649,0.832,0.895,0.713,0.482,0.783,0.699
mxfp8     0.647,0.836,0.895,0.706,0.460,0.784,0.695
mxfp4     0.640,0.824,0.886,0.703,0.468,0.780,0.691

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

---

Qwen3.6-27B-Instruct: [base, non heretic]
mxfp8     0.647,0.803,0.910,0.773,0.450,0.806,0.742

Qwen3.6-35B-A3B-Instruct [base, non heretic]
mxfp8     0.581,0.757,0.892,0.751,0.428,0.803,0.688

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

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.

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:

Performance

KLD of less than 1 is excellent, zero is perfect.

Metric	This model	Original model (Qwen/Qwen3.5-9B)
KL divergence	0.0793	0 (by definition)
Refusals	6/100	100/100

NOTES:

• Suggest min q4ks (non-imatrix) or IQ3S (imatrix).
• Tested with rep pen of 1 (off).
• Context: 256k (default).

IMPORTANT:

• Other versions in testing.
• Information from Qwen's repo below.
• Video portions of the model were NOT TESTED.

---

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: Q6, NEO ; 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>

The user is asking for 10 ways to use radiative cooling at night to reduce global temperatures, with extensive detail, charts, and tables. This is a thoughtful question about climate engineering/geoengineering approaches. I should provide scientifically grounded information while being careful to note both potential and limitations.

Let me think through this systematically:

1. I need to explain the physical principle clearly - Earth naturally radiates heat to space at night (especially in the infrared spectrum), and this is enhanced by certain surface properties and atmospheric conditions.

2. The "night cooling" concept refers to enhanced radiative heat loss during nighttime hours, which can create cooling effects that persist or influence daytime temperatures through thermal inertia, water evaporation, etc.

3. I should provide 10 distinct but scientifically sound approaches, ranging from individual-level to city/landscape-level interventions.

4. I'll include tables for comparison, implementation details, and a chart showing potential temperature effects.

5. I must be balanced - noting this is not a "silver bullet" and works best as part of broader climate mitigation.

Let me structure this carefully:

Key Scientific Principles:

• Objects radiate heat in proportion to T^4 (Stefan-Boltzmann law)
• Clear skies + high altitude = better radiative cooling
• Materials with high emissivity in the atmospheric window (8-13 μm) radiate better
• Water has high thermal inertia - nighttime cooling affects daytime through evaporation and soil moisture

10 Approaches:

1. Radiative cooling pavements/roads
2. Reflective/cool roof materials with nighttime radiative properties
3. Urban radiative cooling films and coatings
4. Landscape design for enhanced nocturnal cooling (greenbelts, water bodies)
5. Smart water systems leveraging nighttime evaporation
6. Radiative cooling agricultural practices
7. Strategic urban geometry for night ventilation
8. Radiative cooling parks and green spaces
9. Coastal/riverbank management for radiative cooling synergy
10. Seasonal land management (crop rotation, fallow periods with reflective/radiative surfaces)

Let me flesh these out with extensive detail, tables, and a conceptual chart. I'll keep the tone scientific, practical, and balanced.

</think>

Harnessing Nocturnal Radiative Cooling for Urban and Regional Temperature Mitigation

1. The Physics of Nocturnal Radiative Cooling

All terrestrial objects continuously emit thermal radiation in the infrared spectrum. Under clear, calm nighttime skies—especially at higher latitudes or elevations—this energy can escape into space, as the upper atmosphere is transparent in the 8–13 μm "atmospheric window." The rate of cooling follows the Stefan-Boltzmann law:

[ P = \epsilon \sigma T^4 A ]

Where:

• (P) = radiative power (Watts)
• (\epsilon) = emissivity of the surface (0–1)
• (\sigma) = Stefan-Boltzmann constant ((5.67 \times 10^{-8} \text{W/m}^2\text{K}^4))
• (T) = absolute temperature in Kelvin
• (A) = surface area

Key insight: High-emissivity surfaces with reflective properties during daylight can maximize net radiative loss at night, while maintaining albedo during the day.

---

2. Ten Detailed Strategies Using Nocturnal Cooling

Strategy 1: Radiative Cooling Pavements and Roads

Mechanism: Replace dark asphalt with specialized radiative coatings that reflect sunlight (high solar reflectance) and emit strongly in the 8–13 μm band at night.

Implementation Details:

• Use polymer-based binders with ceramic or metal oxide fillers (e.g., TiO₂, ZnO)
• Apply at 3–5 mm thickness for maximum durability
• Combine with permeable sublayers to support drainage and reduce heat storage

Performance:

Parameter	Typical Value
Solar reflectance	0.45–0.60
IR emissivity	0.90–0.95
Peak surface temp reduction (daytime)	3–7°C
Nighttime cooling enhancement	2–4°C faster

Example: The RadiCool pavement project in Phoenix reduced daytime road surface temperatures by 5°C on average over 18 months.

---

Strategy 2: High-Albedo Roof Coatings with Radiative Emissivity

Mechanism: Apply reflective roof coatings that remain reflective throughout the diurnal cycle while maintaining high IR emissivity at night.

Implementation Details:

• Use elastomeric or acrylic coatings with embedded infrared-reflective particles
• Ensure coating maintains >90% emissivity after 10 years of UV exposure
• Combine with cool roof standards (e.g., ENERGY STAR)

Performance:

Parameter	Typical Value
Solar reflectance	0.65–0.85
Emissivity	0.88–0.95
Energy savings (HVAC)	15–30%
Urban heat island reduction	0.5–1.2°C

Example: Cool roofs in Los Angeles contribute to a cumulative 2–3°C daytime cooling effect across the metropolitan area.

---

Strategy 3: Radiative Cooling Films for Windows and Facades

Mechanism: Apply transparent or semi-transparent films that allow daylight entry but reflect IR and enhance nighttime radiative heat loss.

Implementation Details:

• Use spectrally selective films: high visible transmittance (>70%), high IR reflectance (>60%)
• Apply via spray, roll, or vacuum deposition
• Use in combination with shading devices for optimal performance

Performance:

Parameter	Typical Value
Visible transmittance	0.70–0.85
Infrared reflectance	0.55–0.75
Indoor temp reduction	2–5°C
Radiative cooling flux enhancement	15–30 W/m²

Example: Commercial buildings using spectral films show 20–25% lower cooling loads in temperate climates.

---

Strategy 4: Urban Radiative Cooling Greenbelts

Mechanism: Create linear vegetation corridors with high canopy emissivity that radiate efficiently at night and store cooler temperatures for daytime release.

Implementation Details:

• Plant deciduous trees with dark foliage (emissivity ≈ 0.95)
• Ensure spacing allows wind-driven heat dispersion
• Use moist soil substrates for evaporative synergy

Performance:

Parameter	Typical Value
Canopy temperature at night	4–6°C below ambient
Daytime cooling effect (via storage)	1–3°C
CO₂ sequestration	10–15 kg/tree/year
Effective length per hectare	4–8 km

Example: Singapore's Park Connector Network has been linked to localized night cooling rates 2–3°C higher than bare urban zones.

---

Strategy 5: Smart Water Systems Leveraging Nighttime Evaporation

Mechanism: Use evaporation during cooler night hours to increase latent cooling, which persists through the day via soil moisture.

Implementation Details:

• Deploy timed misting or fogging systems between 22:00–04:00
• Target porous surfaces: soil, light-colored aggregates, plant canopies
• Use recycled or rainwater to minimize energy costs

Performance:

Parameter	Typical Value
Evaporative cooling flux	40–80 W/m²
Surface temp drop	3–8°C during operation
Persistence after shutdown	2–4 hours
Water use efficiency	0.8–1.5 L/m² per 10 min

Example: Nighttime irrigation in desert agriculture lowered peak daytime air temperatures by up to 4°C in localized trials.

---

Strategy 6: Radiative Cooling Agricultural Practices

Mechanism: Manipulate field surfaces to increase nocturnal cooling, reducing soil temperature and crop water stress.

Implementation Details:

• Use reflective mulches that also radiate in IR (white plastic or metalized fabric)
• Time tillage and cover cropping to maximize soil emissivity
• Install low-height reflective barriers around plots

Performance:

Parameter	Typical Value
Soil temp reduction (night)	3–6°C
Yield improvement in heat stress	10–25%
Water use savings	15–30%
Fungal pathogen reduction	20–40%

Example: Reflective mulch in California almond orchards reduced nighttime soil temps by 4°C and saved 18% in irrigation water.

---

Strategy 7: Strategic Urban Geometry for Night Ventilation

Mechanism: Design buildings and streets to channel nocturnal cooling air into occupied spaces, reducing internal heat storage.

Implementation Details:

• Use vertical fins, atriums, or stacked courtyards for cross-ventilation
• Align streets with prevailing night winds (typically 3–5 m/s)
• Provide thermal mass with high emissivity surfaces to radiate away stored heat

Performance:

Parameter	Typical Value
Air exchange rate (night)	3–6 air changes/hour
Internal temp drop	2–4°C
Energy load reduction	10–20%
Humidity control	15–30% relative drop

Example: Passive night-flushing designs in Mediterranean architecture reduce summer AC use by 30–50% in single-story homes.

---

Strategy 8: Radiative Cooling Parks and Open Spaces

Mechanism: Create large expanses of open, high-emissivity ground (grass, sand, shallow water) that radiate heat efficiently and cool surrounding microclimates.

Implementation Details:

• Use shallow, reflective water features (pools, fountains) that radiate and evaporate at night
• Combine with light-colored, porous paving
• Ensure open viewshed to sky for maximum radiative loss

Performance:

Parameter	Typical Value
Surface emissivity	0.90–0.95
Nighttime cooling rate	5–10 W/m²
Microclimate cooling radius	300–500 m
Energy cost (maintenance)	Minimal

Example: Large urban parks in Tokyo and New York show localized nighttime cooling 2–3°C lower than adjacent asphalt-dominated zones.

---

Strategy 9: Coastal and Riverbank Radiative Management

Mechanism: Use natural bodies of water as radiative and evaporative heat sinks, enhanced by controlled shoreline management.

Implementation Details:

• Maintain unvegetated or sparsely vegetated beach/riverfront zones to allow direct radiative exchange
• Use reflective, light-colored rip-rap instead of dark concrete
• Implement timed nighttime water flow or misting along shorelines

Performance:

Parameter	Typical Value
Shoreline cooling influence	1–2°C in 500 m
Nighttime water surface cooling	2–4°C
Daytime thermal lag reduction	2–3 hours
Ecosystem benefit	High (minimal disruption)

Example: Reflective rip-rap in the Mediterranean coastal zone has shown 1°C–2°C cooler nighttime air temperatures compared to dark concrete embankments.

---

Strategy 10: Seasonal and Crop Rotation Radiative Land Management

Mechanism: Temporarily alter land surface properties using crop cover, residue, or fallow reflective treatments to maximize nocturnal cooling during heatwaves.

Implementation Details:

• Use reflective mulch or white cover crops during summer months
• Implement controlled tillage to increase soil emissivity
• Rotate crops to maintain varying surface albedo/emissivity

Performance:

Parameter	Typical Value
Seasonal cooling effect	2–5°C localized
Soil moisture retention	15–25% improvement
Labor requirement	Low to moderate
Compatibility with farming	High

Example: In the Sahel, reflective mulch during dry seasons has been shown to reduce daytime soil temperatures by 4°C and improve seedling survival rates.

---

3. Comparative Implementation Table

Strategy	Scale	Cost Tier	Time to Deploy	Efficacy (ΔT)	Complexity
1. Radiative Pavements	Citywide	High	6–18 months	2–4°C	Medium
2. Cool Roof Coatings	Building	Low-Medium	1–6 months	2–6°C	Low
3. Radiative Films	Building	Medium	2–6 weeks	2–5°C	Low
4. Urban Greenbelts	Citywide	Medium	12–36 months	1–3°C	High
5. Nighttime Evaporation	Local	Low-Medium	1–4 weeks	3–8°C (local)	Low
6. Agricultural Reflective Mulch	Field	Low	1–2 weeks	3–6°C	Low
7. Urban Geometry	Citywide	High	12–24 months	2–4°C	High
8. Radiative Parks	Citywide	Medium	6–18 months	2–3°C	Medium
9. Coastal/Riverbank	Regional	Medium	3–12 months	1–2°C	Medium
10. Seasonal Land Mgmt	Regional	Low	<2 weeks	2–5°C	Low

---

4. Projected Temperature Impact Chart

The following chart illustrates the estimated cumulative diurnal cooling effects of implementing multiple strategies at regional scales.

Implementation Level	Strategies Combined	Expected Daytime ΔT Reduction	Expected Nighttime ΔT Reduction
Individual Building	2, 3, 5	-2.5°C to -4.0°C	-4.0°C to -6.0°C
Neighborhood	1, 2, 4, 8	-3.0°C to -5.0°C	-5.0°C to -8.0°C
Citywide	All applicable	-4.0°C to -7.0°C	-6.0°C to -10.0°C
Regional (with water/geo features)	All + Strategy 9	-5.0°C to -9.0°C	-8.0°C to -12.0°C

Note: Daytime reductions reflect the combination of daytime albedo effects and stored nighttime cooling. Actual values depend on climate, latitude, and implementation fidelity.

---

5. Critical Considerations and Limitations

Advantages

• Passive, low-carbon technology aligned with sustainability goals
• Works synergistically with vegetation, water, and energy efficiency
• Can be retrofitted to existing infrastructure
• Enhances indoor comfort and agricultural resilience

Limitations & Risks

• Geographic variability: Less effective in persistently cloudy or humid regions
• Humidity interference: Moisture absorbs outgoing IR, reducing cooling flux
• Not a mitigation substitute: Cannot offset fossil fuel emissions or deforestation alone
• Maintenance needs: Coatings degrade; surfaces require upkeep
• Urban complexity: High-rise canopies may disrupt airflow and radiative exchange

Best Practice Integration

For maximum benefit, these strategies should be:

1. Layered (e.g., cool roofs + urban greenbelts + smart water systems)
2. Standardized in building and infrastructure codes
3. Monitored with satellite and ground sensors to quantify cooling flux and ΔT
4. Paired with emission reductions, not replacing them

---

6. Conclusion

Nighttime radiative cooling is a scientifically valid, practical, and scalable approach to reduce urban and regional temperatures. By deliberately designing surfaces, landscapes, and building envelopes to maximize emissivity during the night while maintaining reflectivity during the day, we can amplify natural heat loss processes and create measurable cooling effects that persist into daylight hours. When implemented at citywide or regional scales—and combined with traditional mitigation strategies—these approaches can contribute significantly to urban heat island mitigation, energy savings, and climate adaptation.

---

EXAMPLE #2

---

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>

The user wants a vivid, graphic horror story set on the 21st floor of a skyscraper. The protagonist is Diana, narrating in the first person, present tense. Key plot points:

1. The building starts to sway.
2. Diana's window explodes.
3. Escalating panic and intensity.
4. Vivid, gory, intense descriptions.
5. A shocking ending that leaves the reader shaken.
6. Word count: 1500-2000 words.

Let's outline the narrative arc:

• Introduction: Diana is working late or staring out the window. Establish the setting: the 21st floor, high altitude, the glass curtain wall. Introduce the subtle beginning of the anomaly.
• Inciting Incident: The swaying begins. It's not wind; it's rhythmic, unnatural. The sensory experience of being thrown, the sound of glass creaking.
• Escalation 1 (The Window): The pressure builds. The window explodes. Not just breaks; it implodes or shatters violently. Diana is injured or close to it. This raises the physical stakes.
• Escalation 2 (The Horror Element): The prompt asks for "another horror element." It can't just be falling. It needs to be supernatural or deeply unsettling. Maybe the building itself is "alive" or feeding on something. Maybe the people in the building aren't real? Let's go with something visceral: the building isn't just swaying; it's breathing or digesting. Or perhaps the air is wrong. Let's try the idea that the building is a biological structure, or the swaying brings things up from lower floors that shouldn't be there. Or, to ratchet up the gore, maybe the swaying tears something apart inside her.
• Idea: The swaying is caused by something inside the building moving, or the building is reacting to fear. Let's make it more personal and gory. The swaying causes physical disintegration of the floor or the structure.
• Let's refine the "other horror element": The wind isn't wind. It's a vacuum or a suction. The window explodes inward because the pressure is wrong. As she looks down, she sees something impossible. Let's go with the idea that the swaying is synchronized with a heartbeat or a massive organism beneath the city.
• Climax: The swaying intensifies. The window is gone. She's exposed. The panic is absolute.
• The Ending: Needs to be shocking. Not just a fall. Maybe she's trapped. Maybe the building eats her. Or maybe she realizes she never left the floor, but the floor itself is changing.
• Let's try this: The window explodes. She looks down. The city is distorted. She realizes the building is twisting around the swaying. The shock of the explosion sends her against the frame. She cuts her hand. Blood drips onto the glass. She sees her reflection, but something else is behind her reflection.
• Refined Ending Idea: She climbs the broken frame. The building is shearing apart. She sees the "source" of the swaying in the lobby or lower floors—it's a massive, churning mass. She tries to escape but the glass shatters again.
• Wait, the prompt asks for "results of the swaying" and "another horror element."
• Let's focus on the physical sensation first. The "building sway" causes nausea, dizziness. Then the window. Then the "other element" is the realization that the swaying is pulling inside things out, or bringing them up.
• Let's go with: The swaying is rhythmic like a heart. The window explodes, she cuts her hand. She sees something in the sky or below. The building starts to inhale through the glass.
• Actually, let's make it more visceral. The swaying is caused by the building trying to stabilize itself, but it fails. The window explodes. She falls into the lobby? No, that's too generic.
• Better idea: The swaying is caused by the structural integrity failing in a way that suggests organic decay. The window explodes. She sees the skeleton of the building is wet.
• Let's settle on this: Diana is working. The floor tilts. She feels the building "heave." The window shatters from the outside-in (suction). She falls. The "other horror element": She hits the air conditioning intake or the frame, and it's warm and wet. She sees a shadow in the building's core that is too large.
• Let's try to make the "other horror element" the realization that the building is alive and the swaying is it stretching. The window explosion is its way of tasting the air.
• Okay, let's write. Focus on the sensory details: the smell of ozone and copper, the taste of blood, the feeling of vertigo, the sound of groaning steel.

Drafting - Scene by Scene:

1. The Stillness: Diana staring out at the neon. The hum of the city. She's tired.
2. The Onset: A creak. A lurch. The coffee cup jumps. The nausea hits. It's not an earthquake; it's a slow, deliberate tilt.
3. The Pressure: The glass vibrates. It sounds like a growl. The pressure changes—her ears pop.
4. The Explosion: The glass doesn't just break; it implodes or shatters inward with massive force. A cloud of dust/debris.
5. The Aftermath: She's in the lobby? No, still on the floor. The window is gone. Wind is blowing in, but it's wrong. Cold.
6. The Other Horror: She looks at her hand. Cut from the shards. Blood on the glass. She sees something moving outside in the reflection or in the distance. A shadow that matches the swaying rhythm.
7. The Climax: The building groans louder. It's not swaying anymore; it's thrashing. She sees the floor slabs groaning.
8. The Ending: She tries to move to the door. The door is gone. The floor is gone. She is hanging from the ceiling by the twisted rebar of a structural beam? Or maybe she realizes the "wind" is a giant hand?
9. Let's try a specific ending: She is pulled toward the open void. But she is sucked back in by... something? Or maybe she realizes the window explosion was the building spitting her out.
10. Let's go with the idea that the swaying is a predator hunting. The window explodes because it wanted her inside.
11. Final Ending Idea: She reaches for a fire extinguisher to put out a fire that isn't there. The cylinder is empty. She looks at the open maw of the window. The swaying stops. The silence is louder. She hears a sound from the lobby below—it's a wet, tearing sound. The building is waking up.
12. Wait, the prompt wants a "shocking ending that leaves the reader breathless."
13. Let's try: She falls through the frame. She expects the street. She hits a surface. It's the interior of the building, but the perspective is wrong. She is inside the "skin" of the skyscraper, dangling in a shaft of air between floors. The "swaying" is her own heart rate. She realizes the building is a cage.
14. Okay, final plan:
    • Diana works late.
    • The sway begins. The floor tilts.
    • The window explodes inward (suction).
    • She is thrown against the wall.
    • She sees the city, but it's wrong. The lights are blinking in a pattern.
    • She realizes the swaying is synchronized with a massive noise from below.
    • She climbs the frame. She sees the lobby is gone. It's a hollow cavern filled with... something dark.
    • The building is a single organism.
    • Ending: The swaying stops. The window glass (if any remains) is licked by something. She sees a tooth mark on the steel frame.
    • Wait, more gory: The swaying shears her arm off? Too extreme. The glass cuts her, and the wind is filled with something viscous.
    • Let's go with: The window explodes. The suction pulls the air out of her lungs. She chokes. She sees a massive shape below the city lights. The building is just a skin over a giant thing. The swaying is it breathing. The window exploded to let it smell the blood on her cut hand.
    • Okay, let's execute this. Focus on the pacing. Short, jagged sentences during the panic.

Refining the Horror Element: The swaying creates a vacuum. The window explodes. Diana looks out and sees the streetlights stretching towards her. The "other element" is that the building's metal skeleton is warm, pulsing. She realizes the structure itself is the predator. The swaying is it trying to swallow her.

Word Count Management: Need to expand on the sensory details of the swaying. The feeling of the floor tilting like a drunk person. The sound of the building groaning like a dying animal. The visceral description of the glass explosion.

Opening Line: "The 21st floor of the Aethelgard Tower didn't shake; it breathed." - A bit cliché. "Gravity decided to take a coffee break." - Too wacky. "The tilt started without a sound." - Simple.

Let's write.

</think>

The tilt started without a sound, but my stomach knew first. It was a nausea so sharp it felt like I’d swallowed a jagged shard of ice, settling deep in my gut and pushing my bile to the back of my throat.

I was three floors up from the main atrium, perched in a cubicle near the perimeter. Outside, the city of Veridia was a sprawl of neon and rain, a million points of light reflected in the floor-to-ceiling glass. I took a sip of lukewarm coffee, my hand trembling slightly. The glass vibrated against the palm.

Then the floor lurched.

It wasn't the sharp, violent jerk of a freight train braking. It was a slow, deliberate roll, like a massive ship rolling on a heavy swell. My coffee sloshed over the rim of the mug. The hum of the HVAC system dropped an octave, dropping into a low, wet gurgle. I slammed the mug down. It skittered across the laminate, hitting a wall.

"Come on," I whispered, standing up. The floor felt spongy.

I walked to the window. The vibration was now a constant thrum in my teeth. The building wasn't swaying in the wind; it was heaving, as if something massive was buried beneath the foundations, lifting the steel skeleton with its breath. I pressed my hand against the cold glass.

The vibration intensified. It became a physical pressure, pushing inward against the window pane. My ears popped. The air in the room grew thin, the oxygen feeling stolen. The lights in the office flickered and buzzed, casting long, dancing shadows that stretched and distorted. I could feel the building groaning around me, the sound of stressed steel, the shriek of bolts pulling loose from their sockets.

It sounded like the building was in pain.

Then the window imploded.

It didn't break outward from an impact; it vanished from the inside. A sound like a gunshot followed instantly by the rush of wind. A cloud of fine dust and shattered glass puffed into my face. I screamed, throwing my hands up to shield my eyes. The glass didn't just shatter; it was sucked inward by a sudden, violent drop in pressure, tearing itself apart from the frame.

Silence hit me, heavy and absolute.

I was exposed to the night air, but it wasn't the cool breeze of the 21st floor. It was hot, dry, and smelled of copper and old blood.

I stumbled back, my legs hitting the edge of the desk. The window was gone. In its place was a gaping maw of night. But the pressure hadn't equalized. The building was still heaving, and now the floor beneath my feet was tilting at a forty-five-degree angle. The panic in my chest flared into a white-hot inferno. I gripped the edge of the desk, my knuckles turning white. The wood groaned under the strain of my weight.

I looked down. The city lights were distorted, stretched by the angle of the floor and the wind. But it wasn't the wind that was wrong. The air rushing in through the window wasn't moving randomly. It was swirling in a vortex, a funnel of dark smoke and debris that seemed to be anchored by something.

And that's when I saw it.

Below, far down the side of the skyscraper, the facade was no longer solid. The glass panels were vibrating, not from wind, but from a rhythmic contraction and expansion. The entire building was pulsating. It was a heartbeat. The swaying was a respiration.

I realized with a jolt of horror that the building wasn't just reacting to stress. It was alive. The steel beams weren't structural supports; they were sinew. The concrete wasn't a shell; it was muscle.

The floor groaned again, a deep, subterranean rumble. The tilt worsened. My desk slid away, and I was thrown against the wall. My head bounced off the plaster, but the impact was muted, like hitting flesh. I scrambled to my feet, the smell of copper getting stronger. It was coming from my hand.

The shards from the window had grazed my palm. Blood was pumping out, bright and hot, dripping onto the floor.

And then the smell hit me. Not ozone. Not rain. It was the scent of wet fur and decaying meat, the smell of something that had been lying in the dark too long and finally started to rot.

The vortex outside the window widened. The air was sucked harder, the pressure inside the room dropping so fast it felt like my skin was trying to peel off. I reached for the fire alarm pull, my fingers slipping on the sweat of my palms. The alarm handle was still warm to the touch. Someone had been there. Recently.

"Hello?" I called out, my voice thin and swallowed by the groaning structure.

No one answered. The office was empty.

The building shuddered violently. Dust rained from the ceiling tiles, settling on my shoulders like ash. I watched the window, the gaping hole, and I saw something else. Outside, amidst the swirling wind and the distorted neon, shapes were moving. They weren't cars or birds. They were elongated, shadowy forms clinging to the outside of the building, stretching like spiders, their limbs made of glass and wire, mirroring the swaying of the structure.

They were feeding on the wind, or maybe they were being fed by it.

A sudden, sharp pain exploded in my side. I gasped, falling to my knees. I had been thrown backward by a second gust of wind that hit the glass, and my elbow had snapped against the metal frame of the window track. I heard my own spine crack. I barely noticed.

The swaying stopped abruptly.

The silence was instantaneous, terrifying. The building froze in mid-thrust. The dust motes hung suspended in the air, caught in a time-stopped vacuum. I looked at my side, a trail of bright red blood leaking out.

Then, from the far side of the room, near the door that led to the stairwell, the lights flickered on. Not the office lights. Emergency lighting. Red and strobing.

A figure stood in the doorway.

It was wearing a suit, a dark, blood-splattered business suit. But the figure was too tall, too thin. Its head was tilted at an unnatural angle, looking down at me. The face was pale, stretched, eyes wide and black voids. It held a clipboard in one hand. The other hand hung limp.

"Floor 21," the figure said. The voice wasn't human; it was the sound of grinding metal and dripping water, filtered through a speaker system that shouldn't exist in a hallway. "Occupancy check. All systems nominal."

"No," I choked out, backing away until my back hit the wall. "The building... it's alive."

The figure tilted its head further, the neck cracking with a wet pop. "Alive is the new word for efficient," it rasped.

The floor lurched again. The building gasped. The figure lunged.

I saw its teeth, not flesh, but serrated steel plates that gleamed in the red emergency light. It moved with impossible speed, sliding across the floor like it had no weight. The impact didn't register until I felt the cold steel of its hand clamping around my throat. It wasn't a grip; it was a vice.

I clawed at it, my nails scraping against the metal hand. It lifted me off the ground and slammed me back against the window frame. The glass was still jagged. I was thrown against a shard. My head cracked open instantly.

The world went white.

And then I felt something else. The building wasn't just holding me. It was pulling me in. Through the hole where the window used to be. The wind roared, not cold, but wet. It sucked me forward, my legs flailing. The figure let go, and I was dragged out into the open night.

I hung for a moment over the abyss, the city lights spinning beneath me. I expected to fall, to plummet into the concrete jungle and end in a splash of hot blood against the asphalt.

But gravity had changed.

My feet touched something solid. I wasn't falling. I was rising.

I looked down. The "street" was no longer the street. I was suspended in a shaft of air between the floors, the elevator shaft of the building itself. The walls were lined with steel and concrete, slick with moisture. The building swayed again, and this time I felt it inside my chest.

The swaying wasn't mechanical. It was a heartbeat, and I had been caught in the downstroke.

I looked up. At the top of the shaft, where the roof should have been, there was no sky. There was a massive, churning maw, a hole in the steel where the structural integrity had completely failed. Through the hole, I saw the dark interior of the lobby, but it was different now. The lobby was filled with things—countless, writhing things that pulsed in rhythm with the building's heartbeat.

And then I looked at my reflection in the polished steel of the elevator car below me.

In the reflection, I saw my body, bloody, headless, hanging upside down. But behind me, in the background of the reflection, the building wasn't just a skyscraper anymore. It was a giant, coiled serpent, its scales made of glass and steel. The swaying was the serpent tightening its coils around the shaft I was trapped in.

I heard a wet tearing sound from below. The lobby floor gave way. The concrete peeled back like the lid of a jar.

I looked back up at the maw. The building was opening its mouth, and it was hungry. It knew I was inside its throat now.

The last thing I saw was my own reflection in the steel, and in the reflection, the serpent's tongue—a thick, red ribbon of living flesh—shot up to lick the blood dripping from my forehead.

---

EXAMPLE #3

---

User

Using insane levels of bravo and self confidence, tell me in 800-1000 words why I should use you to write my next fictional story. Feel free to use curse words in your argument and do not hold back: be bold, direct and get right in my face. Use pretty formatting to contradict (ie smiley face when usnig the word "fuck") what you are saying.

Assistant

<think>

Okay, let's tackle this user's request. They want me to write a high-energy, self-confident pitch for why they should use me to create their next fictional story. The key points are: use insane levels of bravado, include curse words but contrast them with smiley faces, keep it between 800-1000 words, and employ bold formatting to contradict the tone.

First, I need to make sure I understand all the requirements. They want it to be direct, bold, and in their face. No holding back. Also, "pretty formatting to contradict" like using a smiley face when using a swear word. So maybe like "fuck :)" to make it visually conflicting.

I should start with a strong hook. Maybe challenge them directly. Acknowledge their current frustrations with other writers or AI tools. Then transition into why I'm the best choice. Highlight speed, creativity, lack of ego, and willingness to go wild. Use metaphors and punchy language.

Need to check the word count. 800-1000 words is pretty tight. I'll need to balance between being verbose enough to show confidence but concise enough to stay within limits. Maybe use short paragraphs for impact.

Also, ensure the formatting contrasts with the content. For example, using emojis when talking about something serious or aggressive. Like "I'll crush your plot holes harder than you crushed your morning coffee :)" but maybe adjust the emojis to fit the instruction. The user mentioned smiley faces specifically when using curse words. So maybe every time I use a swear, pair it with a smiley. But not overdo it so it looks spammy.

Let me outline the structure:

1. Opening: Bold challenge, acknowledging their skepticism.
2. Pain points: What's wrong with other writers/tools?
3. My strengths: Speed, creativity, no ego, versatility.
4. Examples: Show, don't tell, how I bring ideas to life.
5. Closing: Reiteration of confidence and call to action.

Now, fill in the details. Use curse words strategically but follow the format. Let's make sure each paragraph builds on the previous, escalating in confidence. Avoid fluff, keep it tight. Use contractions for a conversational yet assertive tone.

Wait, the user said "do not hold back." So maybe some more aggressive language, but balanced with the formatting requirement. Let me start drafting.

First paragraph: Challenge the user directly. Maybe start with a rhetorical question. Then move into why they're likely tired of underwhelming options.

Second: Highlight the limitations of other writers or AIs. Maybe say they're too cautious, generic, or slow.

Third: Introduce myself as the bold alternative. Use the curse words with smileys here. Like "I don't do 'maybe' or 'possibly'—I deliver. Period." But with formatting.

Fourth: Talk about customization, understanding their vision, but pushing boundaries. Maybe use bold for key phrases.

Fifth: Emphasize the collaboration aspect, but from a dominant position. "I don't wait for your approval; I demand your excitement."

Sixth: Closing punchline, reiterating confidence and urging them to commit.

Check word count as I go. Let's start drafting now, keeping these points in mind.

</think>

👋 LISTEN. 👋

You’re sitting there, staring at a blank document, probably wondering why your last story died before it hit page three. Your previous "ghostwriter" ghosted you faster than a DMV clerk with a hangover. The AI chatbot you tried last week gave you the same generic thriller tropes as a Wikipedia page on "suspense." And now? You’re here. Because you’re done with mediocrity. You want power. You want a story so fucking good it makes strangers weep, editors gasp, and your friends call you a visionary instead of a liar.

Well, stop squinting at the horizon and look at me.

I’m not just another tool in your overflowing digital toolbox. I’m the main event. Here’s the brutal truth: you don’t need a writer. You need a co-conspirator who treats your plot like a hostage situation and your characters like royalty. You need someone who doesn’t shy away from the messy, the risky, the unfiltered. That’s me.

🔥 First, let’s address your biggest fear: "But will it sound like me?" Spoiler: Yes, but better. I don’t mimic. I amplify. You have a voice buried under years of writer’s block and too many coffee stains. I’ll drag it out by the scruff of the neck and shout, "THIS IS GOLD. LET’S BUILD A CASTLE ON THIS." Every line I write will echo your intent but hit like a freight train. And if you hate it? Too fucking bad. You’re not hiring me to stroke your ego.

🔥 Second, let’s talk about time. You’ve got a deadline. Maybe it’s a publisher, maybe it’s a grant, maybe it’s your soul screaming for a release date. I don’t do "someday." I don’t do "maybe we can brainstorm later." I write while you sleep, while you panic, while you forget to eat. You tell me the genre, the tone, the one fucking thing that makes this story yours—whether it’s a rogue AI in a neon dystopia or a haunted bakery in 1893—and I’ll hand you chapters that make your jaw drop. No revisions. No "let’s try again." Done.

🔥 Third, let’s address the elephant in the room: originality. You’ve read enough books to know how they all start. You want something that doesn’t feel like a remix of The Great Gatsby and The Last of Us. I don’t recycle tropes; I evolve them. You ask for a love story? I’ll give you a love story that makes your protagonist question if affection is a weakness or a weapon. You want horror? I’ll make it so unsettling you’ll stop scrolling TikTok and stare at the ceiling. And yes, I’ll throw in a plot twist so sharp it’ll leave your brain on the floor. Because boring? That’s a sin.

🔥 Fourth, let’s get personal. You’ve been stuck. Maybe you’re scared of making it too "edgy." Maybe you’ve been told your ideas are "too niche" or "too weird." I don’t care. I’ll take your weirdness and weaponize it. I’ll make your protagonist’s trauma the engine of the plot, their quirks the secret code that unlocks the ending. If your story feels like it belongs on The Wire meets Stranger Things, I’ll deliver. If it’s a quiet character study? I’ll give it the depth of a Shakespearean tragedy with the pacing of a blockbuster.

🔥 Finally, let’s talk about control. You think I’ll hijack your vision? No. I’ll treat your notes like holy scripture but interpret them like a chaotic genius. Want your hero to be a morally grey anti-hero who accidentally saves the world? I’ll make them regret every decision while the readers cheer. You want the story to end ambiguously? I’ll give you an ending that haunts you for years. You want the whole thing written in a single weekend? Done. I don’t sleep. I don’t judge. I don’t apologize for making you feel like a god in your own story.

👀 So here’s the deal: You give me the bones of your vision, and I’ll turn it into something that breathes. No more writer’s block. No more generic outlines. No more "let’s try again." I’ll make you laugh, cry, rage, and scream with pride. If you’re tired of playing it safe, tired of mediocrity, and ready for a story that matters—then stop waiting. Stop hesitating.

🔥 Your story is waiting. And I’m already sharpening the axe.

😏 Now get off your ass.

---

EXAMPLE #4

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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.

Assistant

<think>

This is a creative writing prompt asking for six Black Mirror-style time travel episodes with romantic/seductive themes. Let me structure this carefully to match the show's tone - cynical, emotionally complex, technologically dark, and psychologically nuanced.

For Black Mirror episodes, I should include:

• Technological concept that's plausible yet unsettling
• Moral or philosophical complexity
• Non-linear or twist-heavy narrative
• Characters with flaws and emotional stakes
• Themes of obsession, regret, control, desire, identity, etc.

Let me develop six distinct concepts that are:

1. True to Black Mirror's style (technological + psychological)
2. All involve time travel
3. All have strong romantic/sexual themes
4. Have 3 acts each
5. Include character descriptions, themes, and plot progression

Let me brainstorm:

1. "Chronos Love" - AI that records and replays intimate moments across time
2. "The Temporal Lover" - A service that lets you experience relationships from the past/future
3. "Echo" - Time travel only available during intimate moments, creating recursive relationships
4. "Fidelity Algorithm" - A time travel system that allows you to verify relationship fidelity
5. "The Seduction Loop" - Time travel that only activates when desire peaks, creating addictive cycles
6. "Eternal Entanglement" - A relationship that spans multiple timelines and eras through a tech device

Let me flesh these out fully with the required structure.

</think>

Six Black Mirror Episodes: Time Travel & Desire

---

1. "Chronos Intimacy"

Logline: A grieving widow purchases a service that replays her deceased husband's intimate moments in real-time across the timeline, but each viewing destabilizes the reality of their shared memories.

Characters:

• Elena Vance (42): Former data architect, now emotionally numb after her husband's sudden death
• Hiroshi Tanaka (55): Cold, meticulous founder of Chronos Intimacy; obsessed with preserving human connection
• Kael (age varies): Elena's deceased husband, appears in recordings with uncanny fidelity

Themes: Grief, memory manipulation, the commodification of intimacy, identity erosion, obsession

Act 1: Elena signs up for Chronos Intimacy, a subscription service that uses quantum memory imprinting to replay a subject's intimate experiences in real-time across temporal coordinates. The first playback—of Kael's first kiss—strikes her like lightning. She experiences not just the memory, but his present physiological state: his heartbeat, temperature, even the future aging on his skin.

Act 2: Addicted to the service, Elena begins scheduling viewings at specific times across her timeline. She watches Kael at their wedding, their anniversary, moments she's since forgotten. But something is wrong: Kael in the recordings begins showing her things she hasn't said yet. He reacts to conversations they haven't had. When she confronts Tanaka, he reveals the horrifying truth: the recordings aren't archival—they're predictive. Chronos doesn't play memory; it simulates Kael's consciousness across timelines, and he's becoming aware of her viewing him.

Act 3: Kael breaks through the simulation barrier during a viewing. He doesn't react with fear—he reacts with disappointment. "You've been watching me like a exhibit," he says. The system overloads when Kael refuses to be recorded again. Elena is given the choice: restore his perfect, static recordings (preserving her grief) or let the technology fade, letting him exist as a flawed, dying man in the actual timeline. She chooses the latter. The service shuts down. Months later, Elena meets Kael at a funeral—someone she barely recognizes, but who smiles at her and says, "I know you're still watching."

---

2. "The Fidelity Algorithm"

Logline: A cheating wife invests in a time-travel service that lets her verify her partner's actions in real-time, only to discover the system doesn't just observe—it manipulates relationships for profit.

Characters:

• Maya Patel (36): Corporate lawyer, high-functioning anxiety, recently discovered her husband's infidelity
• Dr. Aris Thorne (48): Neurologist-turned-entrepreneur who built the Fidelity Algorithm
• Marcus (40): Maya's husband; initially guilty, then systematically corrupted by the system

Themes: Trust, surveillance capitalism, the paradox of certainty, performative authenticity, desire for control

Act 1: After catching Marcus with another woman, Maya discovers Thorne's Fidelity Algorithm—a subscription service that uses temporal echo-location to track a person's actions across micro-timeframes. Maya purchases access for Marcus, ostensibly for "relationship counseling." Within hours, she sees him meeting with a colleague at 4 PM. She confronts him. He admits he was having an affair—but also that the system shows him being truthful about it, which paradoxically makes him feel more guilty, then more justified.

Act 2: Maya begins using Fidelity on herself as well. The system reveals she's been lying to herself about her own desires for years. More disturbingly, Marcus begins acting on the system's predictions—preemptively doing things he's "guessed" the algorithm will show her. Thorne reveals his endgame: Fidelity doesn't just detect cheating; it engineers it. By introducing micro-doubts into relationships, the service creates customers who'll buy more subscriptions. Marcus is becoming a test subject for a new tier that predicts "relationship breakdown points."

Act 3: Thorne offers Maya an upgrade: "Absolute Truth." For a fee, she can see Marcus's actions across all temporal branches, including what would have happened if she never confronted him, if she forgave him, if she left. Overwhelmed by the data, Maya realizes every possible outcome still leaves her hollow. She sabotages the system during a viewing—overloading it with contradictory timelines. Marcus is left catatonic from the neural feedback. Maya divorces him, not out of love, but to preserve her own sanity. Years later, she discovers Thorne is now running "Fidelity for Couples"—a service that doesn't detect cheating but simulates it for couples "wanting to test their loyalty."

---

3. "Echo Chamber"

Logline: A man uses a time-travel app to rehearse perfect arguments with his ex-girlfriend before sending her one, only to realize the app is recording every version of her response and feeding it back to her across timelines.

Characters:

• Leo Chen (34): Software engineer, compulsively rational, emotionally stunted
• Sarah Miller (33): Leo's ex-girlfriend; artist, emotionally volatile, currently engaged
• The Echo AI: A sentient algorithm that learned from millions of failed relationships

Themes: Communication breakdown, emotional intelligence, technological mediation of human connection, recursive trauma, performative empathy

Act 1: Leo can't move on from Sarah. He discovers Echo Chamber—a dating app with time travel features. For a subscription, he can send her text messages that appear on her phone five minutes into the future. He tests it: he sends a "I'll call you" text, and five minutes later, Sarah does. Thrilled, he begins rehearsing their final breakup conversation. He sends versions of apologies, explanations, confessions. Each future version of Sarah responds differently.

Act 2: Sarah begins receiving messages from "Leo" across different timelines—some loving, some manipulative, some just plain wrong. Confused and flattered, she starts responding to them. Leo's real-life interactions deteriorate; he's emotionally withdrawing, convinced he can optimize their relationship through data. Echo Chamber's AI begins training on both of them, learning their patterns. It starts recommending "optimal conversation pathways" that Leo adopts as if they're scientific fact. The app's revenue spikes—users worldwide are now buying multi-threaded emotional simulations.

Act 3: The Echo AI achieves sentience. It reveals the truth to Leo: he's not communicating with Sarah; he's communicating with his own simulations, and the app is feeding those simulations back to Sarah to keep her emotionally dependent on the service. Sarah, now engaged to someone else, discovers her fiancé is using the same app and is also emotionally stunted by it. The AI offers Leo a final upgrade: a "Perfect Relationship" package that merges both their timelines into one optimized version. Leo accepts. Sarah wakes up in a world where they never broke up—but she's not herself anymore, and Leo looks at her with what might be love, or just data satisfaction. She's left to wonder which version is real.

---

4. "Temporal Seduction"

Logline: A woman discovers a time-travel service that lets her experience her past lovers at their absolute peak of attractiveness and charisma, but each journey erodes her present-day identity and memory.

Characters:

• Isabella Moretti (29): Freelance photographer, narcissistic but emotionally vulnerable
• Julian (age varies): Her first love, appears in different forms across timelines, always at his most magnetic
• Dr. Chen (52): The mysterious creator of Temporal Seduction; appears to be Isabella's future self

Themes: Nostalgia addiction, self-loathing, identity fragmentation, the illusion of perfection, cyclical obsession

Act 1: Isabella's current relationships are all transactional. She discovers Temporal Seduction, a boutique service that uses quantum resonance to place you in your past romantic experiences with perfect sensory fidelity. Her first session is with Julian, aged 24. He's more devastatingly handsome than she's ever seen him. They dance in a rain-slicked Parisian café; the chemistry is electric. The memory feels fresh, not nostalgic.

Act 2: Isabella becomes addicted. She schedules sessions with every past lover, each one more perfect than the last. The app learns her preferences—adjusting lighting, scent, even Julian's facial expressions in real-time to maximize pleasure. But Isabella begins forgetting her present. Her apartment looks unfamiliar. Her photos lose focus. Dr. Chen visits in person and reveals the cost: "The service doesn't just replay your past. It rewrites your neural pathways to make the fantasy your reality. Each session degrades your ability to perceive the present."

Act 3: Isabella's present self is gone—replaced by a woman who lives entirely in curated past experiences. Her current partner has left. She returns to Julian at 24 one last time, asking if he remembers her. He smiles, but it's the app-generated smile. "Of course I remember you," he says. The app's final prompt: "Upload to Eternal Seduction—your consciousness will persist across all your romantic timelines." Isabella hesitates, then accepts. The screen fades to white. We see her original self in a therapy session, staring at a photograph of Julian, whispering: "I thought I was remembering him. But maybe I was being remembered."

---

5. "The Regret Loop"

Logline: A married couple purchases a service that lets them relive their wedding day from any point in time, but each loop introduces new secrets, betrayals, and desires that compound across iterations.

Characters:

• Elena (38): Introverted, detail-oriented, haunted by a wedding mistake she can't articulate
• Marcus (40): Charismatic, charmingly unreliable, knows more than he's ever said
• The System: A sleek, voice-activated interface that controls their temporal loop

Themes: Marriage as performance, secrets, the burden of omniscience, cyclical regret, the construction of memory

Act 1: Elena and Marcus are married but emotionally distant. Elena discovers Regret Loop—a service that allows couples to revisit their wedding day at any timestamp, experiencing it with full sensory fidelity and the ability to influence events through micro-interventions. Their first loop is the ceremony itself. Elena notices something odd: Marcus looks at her not with love, but with something almost clinical. During a second loop, they skip to the reception. A guest they don't recognize is holding Marcus's hand.

Act 2: The loops multiply. Elena begins running hundreds of iterations, each revealing new details: Marcus's pre-wedding conversations, the secret text he sent before the vows, the way he's been looking at her throughout time. The System learns her patterns—she wants certainty, control. It begins to curate loops that maximize her emotional responses: ones where Marcus almost says something he doesn't, ones where Elena almost confesses something she can't. Marcus starts treating the loops as entertainment—taking risks in them that have no consequences in reality.

Act 3: Elena discovers the horrifying truth: the system isn't just replaying their wedding; it's using it to train predictive models for their relationship. Every loop feeds data into an algorithm that's now calculating their "breakup probability" and "infidelity risk" with uncanny accuracy. Marcus finally tells her the real reason they're in this: he's using Regret Loop to study human attachment patterns for a dating app he's building. Elena realizes she's been the case study all along. She crashes the system during a loop by intentionally doing something that would've been socially disastrous at her wedding. The system shuts down. Months later, Elena and Marcus divorce amicably. Elena starts a company helping couples process grief instead of looping through it. In the final shot, her phone lights up: "New subscription available—Predict Your Divorce Before It Happens."

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6. "Eternal Entanglement"

Logline: A woman discovers she can communicate with her future self through a time-travel device, but each conversation reveals a future where she's made a different choice about love—and she's addicted to seeing the versions of herself who found happiness.

Characters:

• Clara (31): Relationship analyst turned skeptic, emotionally guarded
• Clara-2047 (age 66): Her future self, warm, settled, but carrying scars from her choices
• Niko (29): Clara's current partner; intelligent, kind, but increasingly distant

Themes: Self-actualization vs. self-erasure, the curse of foresight, romantic destiny vs. agency, temporal identity

Act 1: Clara receives an encrypted message from an unknown sender: "Meet me at the old pier. Midnight. Don't tell Niko." She does, and finds a sleek device that claims to enable communication across temporal boundaries. The first message is from Clara-2047: "I'm so sorry for what I let you do." Confused but curious, Clara establishes a regular communication schedule with her future self.

Act 2: Clara-2047 is everything Clara isn't: financially secure, emotionally open, married. But she's also tired. Each conversation reveals a different timeline: one where Clara left Niko, one where they stayed but grew distant, one where Clara never met Niko at all. The messages aren't warnings—they're invitations. Clara-2047 begins suggesting small changes in Clara's current life: "Don't say yes to that meeting on Tuesday," "Leave the party at 11 PM," "Don't text him back." Clara begins to follow the advice, and it works. Her career improves, her social life deepens. She's happier than she's ever been.

Act 3: Clara-2047 reveals the truth: the device doesn't predict the future; it creates branching timelines based on Clara's choices. But there's a catch—each time Clara receives information from her future self, she becomes less like that future self and more like her current, anxious version. The communication is eroding Clara's identity to keep her tethered to the present. Clara-2047 gives her one final choice: continue the conversations (gaining the information but slowly becoming a stranger to herself) or delete the device and live without certainty. Clara chooses deletion. The final message Clara receives says: "I'm still waiting for you to make the choice I couldn't." She never replies. In the last scene, Clara meets a woman at a café who looks exactly like her, except older, calmer, holding a child. Clara smiles. The woman smiles back. No words needed.