ScienceToStartup
TrendsTopicsSavedArticlesChangelogCareersAbout

113 Cherry St #92768

Seattle, WA 98104-2205

Backed by Research Labs
All systems operational

Product

  • Dashboard
  • GitHub Velocity
  • 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. Market-Bench: Benchmarking Large Language Models on Economic
← Back to Paper

Market-Bench: Benchmarking Large Language Models on Economic and Trade Competition

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

Compared to this week’s papers

Evidence fresh

Evidence Receipt

Freshness: 2026-04-08T03:21:54.703314+00:00

Claims: 6

References: 0

Proof: unverified

Freshness: fresh

Source paper: Market-Bench: Benchmarking Large Language Models on Economic and Trade Competition

PDF: https://arxiv.org/pdf/2604.05523v1

Source count: 0

Coverage: 0%

Last proof check: 2026-04-08T03:21:54.703Z

Paper Conversation

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

Paper Mode

Market-Bench: Benchmarking Large Language Models on Economic and Trade Competition

Overall score: 8/10
Lineage: d62b9dd65ba8…
Cmd/Ctrl+K
Search the latest paper corpus with startup-focused AI synthesis.

Canonical Paper Receipt

Last verification: 2026-04-08T03:21:54.703Z

Freshness: fresh

Proof: unverified

Repo: missing

References: 0

Sources: 0

Coverage: 0%

Missingness
  • - paper_evidence_receipts.references_count
  • - paper_evidence_receipts.coverage
Unknowns
  • - Canonical evidence receipt has not been materialized 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.

Starting…

Dimensions overall score 8.0

GitHub Code Pulse

No public code linked for this paper yet.

Key claims

Strong 6Mixed 0Weak 0

Competitive landscape

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

Keep exploring

Builds On This
RetailBench: Evaluating Long-Horizon Autonomous Decision-Making and Strategy Stability of LLM Agents in Realistic Retail Environments
Score 4.0down
Builds On This
AgenticPay: A Multi-Agent LLM Negotiation System for Buyer-Seller Transactions
Score 6.0down
Builds On This
[Re] Benchmarking LLM Capabilities in Negotiation through Scoreable Games
Score 4.0down
Builds On This
MERIT Feedback Elicits Better Bargaining in LLM Negotiators
Score 7.0down
Builds On This
Benchmarking at the Edge of Comprehension
Score 5.0down
Builds On This
AgentNoiseBench: Benchmarking Robustness of Tool-Using LLM Agents Under Noisy Condition
Score 5.0down
Prior Work
ACE-Bench: Agent Configurable Evaluation with Scalable Horizons and Controllable Difficulty under Lightweight Environments
Score 8.0stable
Competing Approach
$\texttt{YC-Bench}$: Benchmarking AI Agents for Long-Term Planning and Consistent Execution
Score 7.0down

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

PyTorchML Framework
OpenAI APILLM API
Anthropic ClaudeLLM API
LangChainAgent Framework
CrewAIAgent 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

1-2x

3yr ROI

10-25x

Automation tools have long sales cycles but high retention. Expect $5K MRR by 6mo, accelerating to $500K+ ARR at 3yr as enterprises adopt.

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.