Making Every Tool Call Count: Necessary Tool-Evidence Path Rewards for Agentic Vision-Language Models
Modern vision-language models (VLMs) can directly answer many image-grounded questions, yet they often struggle with complex queries requiring…
Why am I seeing this Ranked on source trust — arXiv
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It clears the bar without leading strongly on any one factor. A middling score is not a claim that the story is important — only that nothing about it is weak.
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| Factor | Weight | Score | Contribution | Where it came from |
|---|---|---|---|---|
| Corroboration | 0.35 | 0.39 | +0.135 34% | 1 independent org on the story. Tier-3 aggregators never corroborate — they can show something is circulating, never that it is true. |
| Source trustleads | 0.25 | 0.85 | +0.212 53% | arXiv is the highest-trust source on this story and is first-party — the organisation announcing its own news. Trust is taken from the best source, not averaged. |
| Pickup rate | 0.20 | 0.00 | +0.000 0% | One counted organisation, so there is no spread to measure — nothing has picked this up to set a rate. |
| Freshness | 0.20 | 0.25 | +0.050 13% | Halves every 10 hours from the newest item on the story. This is the only factor that rewards a story for nothing more than being recent. |
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Auledas · CC BY 4.0 · Wikimedia Commons · illustrativeWhat happened
Modern vision-language models (VLMs) can directly answer many image-grounded questions, yet they often struggle with complex queries requiring fine-grained visual details or external knowledge. To acquire this missing evidence, agentic VLMs invoke tools such as image cropping, image search, and text search. However, existing training paradigms primarily evaluate tool-use based on final answer correctness, leaving evidence acquisition and utilization insufficiently supervised.
How this story arrived
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- 01 Arxivfirst-party first seen Making Every Tool Call Count: Necessary Tool-Evidence Path Rewards for Agentic Vision-Langua
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