Evidence Receipt. Related Resources.
LLaVA-LE: Large Language-and-Vision Assistant for Lunar Exploration
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Page Freshness
Signal Canvas proof surface
Canonical route: /signal-canvas/llava-le-large-language-and-vision-assistant-for-lunar-exploration
- Proof freshness
- stale
- Proof status
- partial
- Display score
- 8/10
- Last proof check
- 2026-03-27
- Score updated
- 2026-04-02
- Score fresh until
- 2026-05-02
- References
- 0
- Source count
- 0
- 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
LLaVA-LE: Large Language-and-Vision Assistant for Lunar Exploration
Canonical ID llava-le-large-language-and-vision-assistant-for-lunar-exploration | Route /signal-canvas/llava-le-large-language-and-vision-assistant-for-lunar-exploration
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/llava-le-large-language-and-vision-assistant-for-lunar-explorationMCP example
{
"tool": "search_signal_canvas",
"arguments": {
"mode": "paper",
"paper_ref": "llava-le-large-language-and-vision-assistant-for-lunar-exploration",
"query_text": "Summarize LLaVA-LE: Large Language-and-Vision Assistant for Lunar Exploration"
}
}source_context
{
"surface": "signal_canvas",
"mode": "paper",
"query": "LLaVA-LE: Large Language-and-Vision Assistant for Lunar Exploration",
"normalized_query": "2603.24696",
"route": "/signal-canvas/llava-le-large-language-and-vision-assistant-for-lunar-exploration",
"paper_ref": "llava-le-large-language-and-vision-assistant-for-lunar-exploration",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Preparing verified analysis
Dimensions overall score 8.0
GitHub Code Pulse
Claim map
- Evidencepartial
we introduce LLaVA-LE (Large Language-and-Vision Assistant for Lunar Exploration), a vision-language model specialized for lunar surface and subsurface characterization.
ImplicationpartialThe abstract explicitly states the specialization of LLaVA-LE for lunar exploration.
Verificationpartialpartial
- Evidencepartial
we curate a new large-scale multimodal lunar dataset, LUCID (LUnar Caption Image Dataset) consisting of 96k high-resolution panchromatic images paired with detailed captions describing lunar terrain characteristics
ImplicationpartialThe abstract provides specific numbers and descriptions for the LUCID dataset.
Verificationpartialpartial
- Evidencepartial
and 81k question-answer (QA) pairs derived from approximately 20k images in the LUCID dataset.
ImplicationpartialThe abstract provides specific numbers for the QA pairs and the images they are derived from.
Verificationpartialpartial
- Evidencepartial
Leveraging this dataset, we fine-tune LLaVA using a two-stage training curriculum: (1) concept alignment for domain-specific terrain description, and (2) instruction-tuned visual question answering.
ImplicationpartialThe abstract clearly outlines the two-stage training curriculum.
Verificationpartialpartial
- Evidencepartial
LLaVA-LE achieves a 3.3x overall performance gain over Base LLaVA
ImplicationpartialThe abstract provides a specific quantitative performance gain compared to Base LLaVA.
Verificationpartialpartial
- Evidencepartial
and 2.1x over our Stage 1 model
ImplicationpartialThe abstract provides a specific quantitative performance gain compared to the Stage 1 model.
Verificationpartialpartial
- Evidencepartial
with a reasoning score of 1.070, exceeding the judge's own reference score
ImplicationpartialThe abstract provides a specific reasoning score and states it exceeds the judge's reference score.
Verificationpartialpartial
