ScienceToStartup
Product
Proof
DevelopersTrends
Resources
Company

113 Cherry St #92768

Seattle, WA 98104-2205

Backed by Research Labs

Product, Proof, and developer surfaces share one public navigation contract.

Product

  • Daily Dashboard
  • Signal Canvas
  • Build Loop
  • Evidence
  • Workspace
  • Terminal
  • Talent Layer
  • GitHub Velocity

Proof

  • Foresight
  • Proof Layer
  • Proof Homepage
  • Freshness Hub
  • Example Paper Page
  • Topic Proof Layer
  • Benchmark Scorecard
  • Public Dataset

Developers

  • Overview
  • Start Here
  • REST API
  • MCP Server
  • SDKs
  • Examples
  • Keys
  • Docs

Trends

  • Live Desk
  • Archive
  • Entities
  • Narratives
  • Topics
  • Methodology

Resources

  • All Resources
  • Benchmark
  • Dataset
  • Database
  • Glossary
  • Directory
  • Templates
  • Topics

Company

  • Company Hub
  • About
  • Articles
  • Changelog
  • Careers
  • Enterprise
  • Scout
  • RFPs
  • FAQ
  • Legal
  • Privacy
  • Contact
ScienceToStartup

Copyright © 2026 ScienceToStartup. All rights reserved.

Privacy|Legal
  1. Home
  2. Signal Canvas
  3. RePAIR: Interactive Machine Unlearning through Prompt-Aware
← Back to Paper

RePAIR: Interactive Machine Unlearning through Prompt-Aware Model Repair

Stale7d agoPending verification refs / 3 sources / Verification pending
Export BriefOpen in Build LoopConnect with Author
View PDF ↗
Viability
0.0/10

Compared to this week’s papers

Verification pending

Use This Via API or MCP

Use Signal Canvas as the narrative proof surface

Signal Canvas is the citation-first public layer for turning one paper into a structured commercialization narrative. Use it to hand off into REST, MCP, Build Loop, and launch-pack execution without losing source lineage.

Signal Canvas APIPaper Proof PageOpen Build LoopLaunch Pack Example

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/repair-interactive-machine-unlearning-through-prompt-aware-model-repair

stale
Proof freshness
stale
Proof status
unverified
Display score
6/10
Last proof check
2026-04-15
Score updated
2026-04-15
Score fresh until
2026-05-15
References
0
Source count
3
Coverage
50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

RePAIR: Interactive Machine Unlearning through Prompt-Aware Model Repair

Canonical ID repair-interactive-machine-unlearning-through-prompt-aware-model-repair | Route /signal-canvas/repair-interactive-machine-unlearning-through-prompt-aware-model-repair

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/repair-interactive-machine-unlearning-through-prompt-aware-model-repair

MCP example

{
  "tool": "search_signal_canvas",
  "arguments": {
    "mode": "paper",
    "paper_ref": "repair-interactive-machine-unlearning-through-prompt-aware-model-repair",
    "query_text": "Summarize RePAIR: Interactive Machine Unlearning through Prompt-Aware Model Repair"
  }
}

source_context

{
  "surface": "signal_canvas",
  "mode": "paper",
  "query": "RePAIR: Interactive Machine Unlearning through Prompt-Aware Model Repair",
  "normalized_query": "2604.12820",
  "route": "/signal-canvas/repair-interactive-machine-unlearning-through-prompt-aware-model-repair",
  "paper_ref": "repair-interactive-machine-unlearning-through-prompt-aware-model-repair",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 0

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: RePAIR: Interactive Machine Unlearning through Prompt-Aware Model Repair

PDF: https://arxiv.org/pdf/2604.12820v1

Source count: 3

Coverage: 50%

Last proof check: 2026-04-15T16:58:07.910Z

Paper Conversation

Citation-first answers with explicit evidence receipts, disagreement handling, commercialization framing, and next actions.

Paper Mode

RePAIR: Interactive Machine Unlearning through Prompt-Aware Model Repair

Overall score: 6/10
Lineage: 7407b44dc334…
Cmd/Ctrl+K
Search the latest paper corpus with startup-focused AI synthesis.

Canonical Paper Receipt

Last verification: 2026-04-15T16:58:07.910Z

Freshness: stale

Proof: unverified

Repo: missing

References: 0

Sources: 3

Coverage: 50%

Missingness
  • - repo_url
  • - references
  • - proof_status
Unknowns
  • - proof verification has not been recorded yet

Mode Notes

  • Corpus mode searches the research corpus broadly.
  • Paper mode pins trust state to the canonical paper kernel.
  • Workspace mode blends saved sources, prior evidence queries, and linked papers.

Preparing verified analysis

Dimensions overall score 6.0

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

No public claim map is available for this paper yet.

Author intelligence and commercialization panels stay hidden until the proof receipt is verified, cites at least 3 references, includes at least 2 sources, and clears 50% coverage. The paper narrative and citation surfaces remain public while verification is pending.

Keep exploring

Builds On This
MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models
Score 5.0down
Builds On This
The Unseen Threat: Residual Knowledge in Machine Unlearning under Perturbed Samples
Score 5.0down
Builds On This
Stake the Points: Structure-Faithful Instance Unlearning
Score 3.0down
Higher Viability
Rethinking Machine Unlearning: Models Designed to Forget via Key Deletion
Score 7.0up
Higher Viability
Towards Unveiling Vulnerabilities of Large Reasoning Models in Machine Unlearning
Score 7.0up
Higher Viability
From Anchors to Supervision: Memory-Graph Guided Corpus-Free Unlearning for Large Language Models
Score 7.0up
Higher Viability
The Unlearning Mirage: A Dynamic Framework for Evaluating LLM Unlearning
Score 8.0up
Higher Viability
TRU: Targeted Reverse Update for Efficient Multimodal Recommendation Unlearning
Score 7.0up

Startup potential card

Startup potential card preview
Share on XLinkedIn

Related Resources

Related resources will appear here when this paper maps cleanly to topic, benchmark, or dataset surfaces.

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

FastAPIBackend
PyTorchML Framework
TensorFlowML Framework
JAXML Framework
KerasML Framework

Startup Essentials

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

Antigravity

AI Agent IDE

MVP Investment

$9K - $12K
6-10 weeks
Engineering
$8,000
Cloud Hosting
$240
SaaS Stack
$300
Domain & Legal
$100

6mo ROI

2-4x

3yr ROI

10-20x

Lightweight AI tools can reach profitability quickly. At $500/mo average contract, 20 customers = $10K MRR by 6mo, 200+ by 3yr.

Talent Scout

J

Jagadeesh Rachapudi

P

Pranav Singh

R

Ritali Vatsi

P

Praful Hambarde

Find Similar Experts

AI experts on LinkedIn & GitHub