ScienceToStartup
TrendsTopicsSavedArticlesChangelogCareersAbout

113 Cherry St #92768

Seattle, WA 98104-2205

Backed by Research Labs
All systems operational

Product

  • Dashboard
  • Workspace
  • Build Loop
  • Research Map
  • Trends
  • Topics
  • Articles

Enterprise

  • TTO Dashboard
  • Scout Reports
  • RFP Marketplace
  • API

Resources

  • All Resources
  • Benchmark
  • Database
  • Dataset
  • Calculator
  • Glossary
  • State Reports
  • Industry Index
  • Directory
  • Templates
  • Alternatives
  • Changelog
  • FAQ
  • Docs

Company

  • About
  • Careers
  • For Media
  • Privacy Policy
  • Legal
  • Contact

Community

  • Open Source
  • Community
ScienceToStartup

Copyright © 2026 ScienceToStartup. All rights reserved.

Privacy Policy|Legal
  1. Home
  2. Signal Canvas
  3. Control Your LLM: Identify Good & Bad Neurons for Task Perfo
← Back to Paper

Control Your LLM: Identify Good & Bad Neurons for Task Performance

Fresh1d ago
Export BriefOpen in Build LoopConnect with Author
View PDF ↗
Viability
0.0/10

Compared to this week’s papers

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: pending

Distribution: unknown

Source paper: Identifying Good and Bad Neurons for Task-Level Controllable LLMs

PDF: https://arxiv.org/pdf/2601.04548v1

First buyer signal: unknown

Distribution channel: unknown

Starting…

Dimensions overall score 6.0

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Claim extraction is still pending for this paper. Check back after the next analysis run.

Competitive landscape

Competitor map is still being generated for this paper. Enable generation or check back soon.

Keep exploring

Builds On This
From Early Encoding to Late Suppression: Interpreting LLMs on Character Counting Tasks
Score 4.0down
Builds On This
Discovering Decoupled Functional Modules in Large Language Models
Score 2.0down
Builds On This
A Neuropsychologically Grounded Evaluation of LLM Cognitive Abilities
Score 5.0down
Builds On This
Beyond the Answer: Decoding the Behavior of LLMs as Scientific Reasoners
Score 3.0down
Builds On This
Mechanistic Interpretability of Cognitive Complexity in LLMs via Linear Probing using Bloom's Taxonomy
Score 5.0down
Builds On This
A Monosemantic Attribution Framework for Stable Interpretability in Clinical Neuroscience Large Language Models
Score 3.0down
Builds On This
Do Large Language Models Mentalize When They Teach?
Score 3.0down
Higher Viability
CNT: Safety-oriented Function Reuse across LLMs via Cross-Model Neuron Transfer
Score 7.0up

Startup potential card

Startup potential card preview
Share on XLinkedIn

Related Resources

  • How can LLM optimization be used to improve the efficiency of LLM fine-tuning?(question)
  • How do frameworks like OptiKIT democratize LLM optimization for non-expert teams?(question)
  • How can LLM optimization techniques contribute to more sustainable AI practices by reducing energy consumption?(question)

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Recommended Stack

PyTorchML Framework
FastAPIBackend
TensorFlowML Framework
JAXML Framework
KerasML Framework

Startup Essentials

Antigravity

AI Agent IDE

Render

Deploy Backend

Railway

Full-Stack Deploy

Supabase

Backend & Auth

Vercel

Deploy Frontend

Firebase

Google Backend

Hugging Face Hub

ML Model Hub

Banana.dev

GPU Inference

Estimated $10K - $14K over 6-10 weeks.

MVP Investment

$10K - $14K
6-10 weeks
Engineering
$8,000
GPU Compute
$800
LLM API Credits
$500
SaaS Stack
$300
Domain & Legal
$100

6mo ROI

0.5-1x

3yr ROI

6-15x

GPU-heavy products have higher costs but premium pricing. Expect break-even by 12mo, then 40%+ margins at scale.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Talent Scout

Find Builders

LLM experts on LinkedIn & GitHub

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.