Quantization
TokenRhythm/NeoHorse-1-4B was quantized to FP8 with ModelOpt v0.46.1 on my Blackwell-series GPU, using the v6_conversations.json calibration dataset for agentic work. I didn't see any noticable difference in quality with SGLang v0.5.19.
Usage
On SGLang v0.5.19, with a Blackwell-series GPU, the following command worked for me:
sglang serve \
--model Codeflux/NeoHorse-1-4B-FP8 \
--context-length 262144 \
--reasoning-parser qwen3 \
--tool-call-parser qwen3_coder \
--host 0.0.0.0 \
--port 8033 \
<div align="center">
<h1>NeoHorse-1-4B</h1>
<p><b>Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness.</b></p>
</div>
<div align="center">
<a href="https://github.com/TokenRhythm/NeoHorse"><img alt="GitHub" src="https://img.shields.io/badge/GitHub-NeoHorse-181717?logo=github&logoColor=white"></a>
<a href="https://www.modelscope.cn/models/TokenRhythm/NeoHorse-1-4B"><img alt="ModelScope" src="https://img.shields.io/badge/ModelScope-Models-624AFF?logo=modelscope&logoColor=white"></a>
<a href="https://huggingface.co/TokenRhythm"><img alt="Hugging Face" src="https://img.shields.io/badge/Hugging%20Face-Models-FFD21E?logo=huggingface&logoColor=000000"></a>
<a href="https://tokenrhythm.ai/"><img alt="Company" src="https://img.shields.io/badge/Company-TokenRhythm-F97316?logo=homeassistant&logoColor=white"></a>
<a href="https://x.com/opensquilla"><img alt="Twitter / X" src="https://img.shields.io/badge/Twitter%20%2F%20X-OpenSquilla-111827?logo=x&logoColor=white"></a>
<a href="https://www.apache.org/licenses/LICENSE-2.0"><img alt="License: Apache-2.0" src="https://img.shields.io/badge/License-Apache--2.0-64748B"></a>
</div>
<p align="center">
<a href="https://arxiv.org/abs/2609.08183"><b>Technical Report</b></a>
</p>
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NeoHorse-1-4B is a 4B causal language model and an initial prototype on the path toward recursive self-improvement (RSI). It is post-trained from Qwen3.5-4B for text-based agent harnesses, tool use, coding, and instruction following.
Derived from Qwen/Qwen3.5-4B and fine-tuned by TokenRhythm. This release contains language-model weights only and is repackaged for text-only inference. Vision weights are not included. Repackaging changes configuration and tensor key names, without changing the fine-tuned tensor values.
<p align="center">
<a href="https://huggingface.co/TokenRhythm/NeoHorse-1-4B/resolve/main/4B_head_fig.jpg">
<img src="https://huggingface.co/TokenRhythm/NeoHorse-1-4B/resolve/main/4B_head_fig.jpg" alt="NeoHorse-1-4B evaluation results" width="100%">
</a>
</p>
Highlights
- Path toward RSI: the routing harness assigns tasks to a heterogeneous model pool, records tool interactions and outcomes, estimates capability demand, and uses capability-level feedback to shape the next training mixture. Updated models can return to the harness, closing a prototype evaluation–selection–update loop; extending this loop across successive iterations is the next step toward RSI.
- Agentic post-training framework: the associated research explores routing-guided curriculum SFT and routing-guided on-policy distillation to turn execution trajectories into training signal while preserving execution and harness context around each response.
- Data quality: exact and near-duplicate removal, evaluation decontamination, structural validation, six-dimensional semantic evaluation, and subscene-level Scene/Goal/Outcome labeling.
- Broad gains: 64.87 macro average across ten benchmarks versus 58.94 for Qwen3.5-4B (+5.93).
Model Details
<div style="width:100%;max-width:none;margin:16px 0;padding:0;overflow-x:auto">
<table class="vl-table model-table" width="100%" style="display:table;width:100%;min-width:100%;table-layout:fixed;border-collapse:collapse;font-size:13px">
<thead><tr>
<th style="padding:9px 10px;text-align:left;border-bottom:2px solid #f97316;color:#c2410c;background:rgba(249,115,22,.10)">Property</th>
<th style="padding:9px 10px;text-align:left;border-bottom:2px solid #f97316;color:#c2410c;background:rgba(249,115,22,.10)">Value</th>
</tr></thead><tbody>
<tr>
<td style="padding:9px 10px;border-bottom:1px solid rgba(249,115,22,.16);font-weight:600">Model family</td>
<td style="padding:9px 10px;border-bottom:1px solid rgba(249,115,22,.16)">NeoHorse Agent-Native Causal Language Model</td>
</tr>
<tr>
<td style="padding:9px 10px;border-bottom:1px solid rgba(249,115,22,.16);font-weight:600">Parameters</td>
<td style="padding:9px 10px;border-bottom:1px solid rgba(249,115,22,.16)">Approximately <strong>4B</strong></td>
</tr>
<tr>
<td style="padding:9px 10px;border-bottom:1px solid rgba(249,115,22,.16);font-weight:600">Base model</td>
<td style="padding:9px 10px;border-bottom:1px solid rgba(249,115,22,.16)"><a href="https://huggingface.co/Qwen/Qwen3.5-4B">Qwen3.5-4B</a></td>
</tr>
<tr>
<td style="padding:9px 10px;border-bottom:1px solid rgba(249,115,22,.16);font-weight:600">Post-training</td>
<td style="padding:9px 10px;border-bottom:1px solid rgba(249,115,22,.16)">Routing-guided agentic post-training</td>
</tr>
<tr>
<td style="padding:9px 10px;border-bottom:1px solid rgba(249,115,22,.16);font-weight:600">Interface</td>
<td style="padding:9px 10px;border-bottom:1px solid rgba(249,115,22,.16)">Text input and text output</td>
</tr>
<tr>
<td style="padding:9px 10px;border-bottom:1px solid rgba(249,115,22,.16);font-weight:600">Context length</td>
<td style="padding:9px 10px;border-bottom:1px solid rgba(249,115,22,.16)">262,144 natively and extensible up to 1,010,000 tokens.</td>
</tr>
<tr>
<td style="padding:9px 10px;border-bottom:1px solid rgba(249,115,22,.16);font-weight:600">Weight format / precision</td>
<td style="padding:9px 10px;border-bottom:1px solid rgba(249,115,22,.16)">Safetensors / BF16</td>
</tr>
</tbody></table>
</div>
Evaluation
The 4B track compares NeoHorse-1-4B with five representative open-weight models. Results are grouped by capability in the table below. Higher is better; Δ is NeoHorse-1-4B minus Qwen3.5-4B. Bold marks the best available result; <ins>underlining</ins> marks the second-best.
<div style="overflow-x:auto">
<table class="vl-table" width="100%" style="display:table;width:100%;min-width:100%;border-collapse:collapse;table-layout:fixed;font-size:13px">
<thead><tr>
<th style="padding:9px 8px;text-align:center;border-bottom:2px solid #f97316;color:#c2410c;background:rgba(249,115,22,.10);text-align:left">Benchmark</th>
<th style="padding:9px 8px;text-align:center;border-bottom:2px solid #f97316;color:#c2410c;background:rgba(249,115,22,.10)">Qwen3.5-4B</th>
<th style="padding:9px 8px;text-align:center;border-bottom:2px solid #f97316;color:#c2410c;background:rgba(249,115,22,.10)">Gemma-4-E4B-it</th>
<th style="padding:9px 8px;text-align:center;border-bottom:2px solid #f97316;color:#c2410c;background:rgba(249,115,22,.10)">Nanbeige-4.2-3B</th>
<th style="padding:9px 8px;text-align:center;border-bottom:2px solid #f97316;color:#c2410c;background:rgba(249,115,22,.10)">Agents-A1-4B</th>
<th style="padding:9px 8px;text-align:center;border-bottom:2px solid #f97316;color:#c2410c;background:rgba(249,115,22,.10)">Spark-X2.5-4B</th>
<th style="padding:9px 8px;text-align:center;border-bottom:2px solid #f97316;color:#c2410c;background:rgba(249,115,22,.10);background:rgba(249,115,22,.18)">NeoHorse-1-4B</th>
<th style="padding:9px 8px;text-align:center;border-bottom:2px solid #f97316;color:#c2410c;background:rgba(249,115,22,.10);background:rgba(249,115,22,.18)">Δ vs Qwen3.5-4B</th>
</tr></thead><tbody>
<tr><td class="benchmark-capability" colspan="8" style="padding:10px 8px;font-weight:700;color:#c2410c;background:rgba(249,115,22,.10);border-top:2px solid #f97316">🤖 Agentic</td></tr>
<tr style="border-bottom:1px solid rgba(128,128,128,.16)">
<td class="benchmark-cell" style="padding:8px;font-weight:600"><div class="benchmark-name">QwenClawBench</div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">38.47</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">22.98</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">40.66</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">43.16</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value"><ins>43.52</ins></span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value"><strong>44.68</strong></span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value">+6.21</span></div></td>
</tr>
<tr style="border-bottom:1px solid rgba(128,128,128,.16)">
<td class="benchmark-cell" style="padding:8px;font-weight:600"><div class="benchmark-name">WorkBuddy Bench</div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">24.62</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">11.65</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">21.03</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value"><ins>33.37</ins></span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">26.47</span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value"><strong>34.41</strong></span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value">+9.79</span></div></td>
</tr>
<tr style="border-bottom:1px solid rgba(128,128,128,.16)">
<td class="benchmark-cell" style="padding:8px;font-weight:600"><div class="benchmark-name">PinchBench</div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">71.19</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">47.60</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">66.78</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value"><ins>75.07</ins></span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">62.37</span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value"><strong>77.33</strong></span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value">+6.14</span></div></td>
</tr>
<tr style="border-bottom:1px solid rgba(128,128,128,.16)">
<td class="benchmark-cell" style="padding:8px;font-weight:600"><div class="benchmark-name">VitaBench</div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">21.50</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">5.00</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">31.50</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value"><strong>39.25</strong></span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value"><ins>37.00</ins></span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value">32.00</span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value">+10.50</span></div></td>
</tr>
<tr style="border-bottom:1px solid rgba(128,128,128,.16)">
<td class="benchmark-cell" style="padding:8px;font-weight:600"><div class="benchmark-name">BFCL v4</div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">61.02</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">47.18</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value"><strong>67.28</strong></span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">46.60</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value"><ins>63.71</ins></span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value">61.79</span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value">+0.77</span></div></td>
</tr>
<tr style="border-bottom:1px solid rgba(128,128,128,.16)">
<td class="benchmark-cell" style="padding:8px;font-weight:600"><div class="benchmark-name">tau2-Bench</div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">84.29</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">43.60</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value"><ins>85.08</ins></span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">81.00</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">77.72</span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value"><strong>88.46</strong></span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value">+4.17</span></div></td>
</tr>
<tr><td class="benchmark-capability" colspan="8" style="padding:10px 8px;font-weight:700;color:#c2410c;background:rgba(249,115,22,.10);border-top:2px solid #f97316">💻 Coding</td></tr>
<tr style="border-bottom:1px solid rgba(128,128,128,.16)">
<td class="benchmark-cell" style="padding:8px;font-weight:600"><div class="benchmark-name">HumanEval</div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">87.20</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">84.76</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value"><strong>98.78</strong></span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">92.68</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">92.07</span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value"><ins>96.95</ins></span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value">+9.75</span></div></td>
</tr>
<tr style="border-bottom:1px solid rgba(128,128,128,.16)">
<td class="benchmark-cell" style="padding:8px;font-weight:600"><div class="benchmark-name">LiveCodeBench v6</div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">53.71</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">52.00</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value"><strong>72.50<sup>*</sup></strong></span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">56.57</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">54.86</span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value"><ins>59.43</ins></span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value">+5.72</span></div></td>
</tr>
<tr><td class="benchmark-capability" colspan="8" style="padding:10px 8px;font-weight:700;color:#c2410c;background:rgba(249,115,22,.10);border-top:2px solid #f97316">📚 Instruction Following</td></tr>
<tr style="border-bottom:1px solid rgba(128,128,128,.16)">
<td class="benchmark-cell" style="padding:8px;font-weight:600"><div class="benchmark-name">IFBench</div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">60.33</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">40.00</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">55.00</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">63.33</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value"><strong>73.33</strong></span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value"><ins>65.33</ins></span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value">+5.00</span></div></td>
</tr>
<tr style="border-bottom:1px solid rgba(128,128,128,.16)">
<td class="benchmark-cell" style="padding:8px;font-weight:600"><div class="benchmark-name">IFEval</div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">87.06</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">74.68</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">84.47</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">83.55</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value"><strong>91.13</strong></span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value"><ins>88.35</ins></span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value">+1.29</span></div></td>
</tr>
<tr><td class="benchmark-capability" colspan="8" style="padding:10px 8px;font-weight:700;color:#c2410c;background:rgba(249,115,22,.10);border-top:2px solid #f97316">📊 Overall</td></tr>
<tr style="border-bottom:1px solid rgba(128,128,128,.16)">
<td class="benchmark-cell" style="padding:8px;font-weight:600"><div class="benchmark-name">Ten-benchmark average</div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">58.94</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">42.95</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value"><ins>62.31</ins></span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">61.46</span></div></td>
<td style="padding:8px;text-align:center"><div class="metric-stack"><span class="metric-value">62.22</span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value"><strong>64.87</strong></span></div></td>
<td style="padding:8px;text-align:center;background:rgba(249,115,22,.16);font-weight:700"><div class="metric-stack"><span class="metric-value">+5.93</span></div></td>
</tr>
</tbody></table>
</div>
* Nanbeige-4.2-3B LiveCodeBench v6 result is reported in the corresponding model's official blog post or technical report.
Reported protocol: SGLang v0.5.17 · temperature=1.0 · top_p=0.95 · top_k=20 · min_p=0.0 · presence_penalty=1.5 · repetition_penalty=1.0 · thinking mode enabled with enable_thinking=true and force_nonempty_content=true. QwenClawBench, WorkBuddy Bench, and tau2-Bench use three runs; PinchBench and VitaBench use one run; the remaining benchmarks follow their official protocols. VitaBench uses the DeepSeek-V4-Flash simulator and judge.
Deployment
The examples below are for self-hosted deployment from a downloaded local checkpoint.
Local checkpoint path
The examples below assume the checkpoint has already been downloaded to local disk. Set MODEL_PATH to the directory containing config.json, tokenizer files, and model weights.
MODEL_PATH="/path/to/NeoHorse-1-4B"
The OpenAI-compatible requests below use the server's --served-model-name (for example, neohorse-1-4b), not the filesystem path.
SGLang
The technical report uses SGLang v0.5.17.
pip install "sglang==0.5.17"
MODEL_PATH="/path/to/NeoHorse-1-4B"
python3 -m sglang.launch_server \
--model-path "$MODEL_PATH" \
--served-model-name neohorse-1-4b \
--host 0.0.0.0 \
--port 30000 \
--context-length 262144 \
--reasoning-parser qwen3 \
--tool-call-parser qwen3_coder
Send an OpenAI-compatible request after the server starts:
curl http://localhost:30000/v1/chat/completions \
-H 'Content-Type: application/json' \
-d '{"model":"neohorse-1-4b","messages":[{"role":"user","content":"Write a Python function that returns the first n Fibonacci numbers."}],"max_tokens":512}'
vLLM
pip install -U vllm
MODEL_PATH="/path/to/NeoHorse-1-4B"
vllm serve "$MODEL_PATH" \
--served-model-name neohorse-1-4b \
--host 0.0.0.0 \
--port 8000 \
--max-model-len 262144 \
--reasoning-parser qwen3 \
--enable-auto-tool-choice \
--tool-call-parser qwen3_coder
The server exposes an OpenAI-compatible /v1/chat/completions endpoint. Send a request after the server starts:
curl http://localhost:8000/v1/chat/completions \
-H 'Content-Type: application/json' \
-d '{"model":"neohorse-1-4b","messages":[{"role":"user","content":"Write a Python function that returns the first n Fibonacci numbers."}],"max_tokens":512}'
The examples use the configured 262,144-token context limit. Actual capacity depends on GPU memory and serving settings; reduce the context limit if needed.
License
NeoHorse-1-4B is released under the Apache License 2.0.
The upstream model is Qwen/Qwen3.5-4B. Its original copyright notice, Copyright 2026 Alibaba Cloud, is retained in the license file. TokenRhythm has modified the model through fine-tuning and repackaging for text-only inference. Modification notices are included in this model card and the released configuration, weight index, and Safetensors metadata.
Citation
@misc{neohorse2026,
title = {NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness},
author = {NeoHorse Team},
year = {2026},
howpublished = {arXiv preprint},
eprint = {2609.08183},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2609.08183}
}
For questions or issue reports, use the NeoHorse project repository.
