Where to Find the Research Paper Detailing Needle 2: arXiv 2607.18363 Guide

The research paper detailing Needle 2 is available as arXiv pre‑print 2607.18363, titled “Needle 2: A 45M‑Parameter Foundation Tool‑Calling Model for Tiny Devices,” and is directly cited in the cactus‑compute/needle repository’s README.

The cactus‑compute/needle repository implements a 45‑million‑parameter foundation model optimized for tool‑calling on resource‑constrained devices. The research paper detailing Needle 2 provides the architectural blueprint, experimental validation, and performance benchmarks that underpin the Simple Attention Network used in the codebase.

Locating the Needle 2 Research Paper

The authoritative source for Needle 2’s design is the arXiv pre‑print hosted at the following URL:

• ArXiv 2607.18363: “Needle 2: A 45M‑Parameter Foundation Tool‑Calling Model for Tiny Devices”

According to the source code analysis, this citation appears in the repository’s README.md at line 21, serving as the primary reference for the model’s academic foundation. The paper contains the complete architecture description, ablation studies, and performance benchmarks that validate the quantization techniques and attention mechanisms implemented in the code.

Architecture Details from the Paper

The research paper details a Simple Attention Network architecture optimized for edge deployment. This design enables the 45M‑parameter model to execute tool‑calling operations with minimal latency while maintaining accuracy comparable to larger foundation models.

The architectural implementation resides in needle/model/architecture.py, which translates the paper’s mathematical specifications into the production inference engine. For execution logic, see needle/model/run.py, while quantization utilities that compress the model to the 14 MB footprint mentioned in the paper are located in needle/model/quantize.py.

Running Needle 2: Practical Implementation

After installing the package via pip install cactus-needle, you can interact with the model using the patterns described in the research paper.

Basic Tool‑Calling

The @needle.tool decorator registers Python functions as callable tools, matching the function‑calling protocol described in the paper:

# Quick start: run a simple tool‑calling query

import needle

@needle.tool
def get_weather(city: str):
    """Get the current weather for a city."""
    return {"city": city, "temp_c": 27, "sky": "clear"}

agent = needle.Needle(tools=[get_weather])
result = agent.run("What’s the weather like in Lagos right now?")
print(result["results"])

# → [{'city': 'Lagos', 'temp_c': 27, 'sky': 'clear'}]

Structured Data Extraction

The paper’s approach to structured output generation is implemented via Pydantic model binding:

# Structured extraction using a Pydantic model

from pydantic import BaseModel
import needle

class Invoice(BaseModel):
    vendor: str
    total: float
    due_date: str

text = "Invoice from Acme Corp, $1,200.00, due 2026-09-01"
invoice = needle.extract(text, Invoice)
print(invoice.vendor, invoice.total)   # → Acme Corp 1200.0

Loading Pre‑Packed Weights

To run the quantized model referenced in the benchmarks:

# Loading the pre‑packed 14 MB engine and running a query

agent = needle.Needle(weights="cactus-needle/needle2.cact", tools=[...])
response = agent.run("Summarize the latest news about AI.")
print(response["text"])

Mapping the Paper to Source Files

The cactus‑compute/needle repository contains several critical files that implement specific sections of the research paper:

• needle/model/architecture.py – Implements the Simple Attention Network architecture described in the paper’s methodology section.
• needle/model/run.py – Contains the inference engine that executes the forward pass detailed in the experimental evaluation.
• needle/model/quantize.py – Houses quantization utilities enabling the 14 MB deployment size cited in the paper’s efficiency benchmarks.
• needle/__init__.py – Exports the @needle.tool decorator and runtime environment that realizes the paper’s tool‑calling protocol.
• needle/cli.py – Provides command‑line access to the model weights and inference pipeline.
• needle/environments/smart_home.py – Example environment demonstrating the paper’s smart‑home tool‑calling scenarios.
• doc/apis.md – Documents the API contract consistent with the paper’s specification of input/output formats.
• doc/finetuning.md – Guide for fine‑tuning that references the hyperparameters and dataset preparation methods from the paper’s ablation studies.

Summary

• The research paper detailing Needle 2 is arXiv pre‑print 2607.18363, accessible at https://arxiv.org/abs/2607.18363.
• The paper is cited in README.md at line 21 of the cactus‑compute/needle repository.
• It describes a 45M‑parameter Simple Attention Network with full ablation studies and benchmarks.
• Core implementations live in needle/model/architecture.py, run.py, and quantize.py.
• Install via pip install cactus-needle to run the quantized 14 MB engine locally.

Frequently Asked Questions

What is the exact arXiv identifier for the Needle 2 research paper?

The research paper detailing Needle 2 is published under arXiv identifier 2607.18363 with the title “Needle 2: A 45M‑Parameter Foundation Tool‑Calling Model for Tiny Devices.” This identifier appears in the repository’s README and links directly to the PDF containing the full architectural specifications and experimental results.

Where does the repository cite the research paper?

The citation appears in the main README.md file at line 21, where the authors link to the arXiv abstract page. This reference anchors the codebase to its academic foundation, allowing developers to cross‑reference the implementation in needle/model/architecture.py with the mathematical descriptions in the paper.

What architecture does the Needle 2 paper describe?

The paper introduces the Simple Attention Network, a streamlined attention mechanism optimized for devices with severe memory and compute constraints. This architecture balances parameter efficiency (45 M total) with tool‑calling accuracy, using quantization strategies detailed in needle/model/quantize.py to achieve a 14 MB binary footprint.

How can I reproduce the experiments from the paper?

Reproduction requires installing the package (pip install cactus-needle), downloading the pre‑trained weights (cactus-needle/needle2.cact), and running the inference engine via needle/model/run.py. The doc/finetuning.md file provides hyperparameters and dataset preparation steps matching the ablation studies in the paper’s experimental section.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}