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. CoPE-VideoLM: Codec Primitives For Efficient Video Language
← Back to Paper

CoPE-VideoLM: Codec Primitives For Efficient Video Language Models

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: 100

Proof: pending

Distribution: unknown

Source paper: CoPE-VideoLM: Codec Primitives For Efficient Video Language Models

PDF: https://arxiv.org/pdf/2602.13191v1

First buyer signal: unknown

Distribution channel: unknown

Starting…

Dimensions overall score 7.0

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
Unified Spatio-Temporal Token Scoring for Efficient Video VLMs
Score 3.0down
Prior Work
Unified Spatiotemporal Token Compression for Video-LLMs at Ultra-Low Retention
Score 7.0stable
Prior Work
Dynamic Token Compression for Efficient Video Understanding through Reinforcement Learning
Score 7.0stable
Prior Work
Query-Conditioned Evidential Keyframe Sampling for MLLM-Based Long-Form Video Understanding
Score 7.0stable
Prior Work
Video-CoE: Reinforcing Video Event Prediction via Chain of Events
Score 7.0stable
Prior Work
Geometric Transformation-Embedded Mamba for Learned Video Compression
Score 7.0stable
Higher Viability
Learning Transferable Temporal Primitives for Video Reasoning via Synthetic Videos
Score 8.0up
Higher Viability
Contribution-aware Token Compression for Efficient Video Understanding via Reinforcement Learning
Score 8.0up

Startup potential card

Startup potential card preview
Share on XLinkedIn

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

OpenCVComputer Vision
Ultralytics YOLOComputer Vision
Stability AIGenerative AI
PyTorchML Framework
RoboflowComputer Vision

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

S

Sayan Deb Sarkar

Stanford University

R

Rémi Pautrat

Microsoft Spatial AI Lab

O

Ondrej Miksik

Microsoft Spatial AI Lab

M

Marc Pollefeys

ETH Zurich

Find Similar Experts

Video experts on LinkedIn & GitHub