Evidence Receipt. Related Resources.
Evidence Receipt. Related Resources.
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Canonical route: /signal-canvas/adareasoner-dynamic-tool-orchestration-for-iterative-visual-reasoning
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Canonical ID adareasoner-dynamic-tool-orchestration-for-iterative-visual-reasoning | Route /signal-canvas/adareasoner-dynamic-tool-orchestration-for-iterative-visual-reasoning
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/adareasoner-dynamic-tool-orchestration-for-iterative-visual-reasoningMCP example
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References: Pending verification
Proof: Verification pending
Freshness state: stale
Source paper: AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning
PDF: https://arxiv.org/pdf/2601.18631v1
Source count: Pending verification
Coverage: 33%
Last proof check: 2026-03-19T21:31:49.672Z
Signal Canvas receipt window
/buildability/adareasoner-dynamic-tool-orchestration-for-iterative-visual-reasoning
Subject: AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning
Verdict
Watch
Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.
Preparing verified analysis
Dimensions overall score 10.0
No public code linked for this paper yet.
improving the 7B base model by +24.9% on average
Implication not extracted yet.
partial
surpassing strong proprietary systems such as GPT-5 on multiple tasks, including VSP and Jigsaw
Implication not extracted yet.
partial
Tool-GRPO, a reinforcement learning algorithm that optimizes tool selection and sequencing based on end-task success
Implication not extracted yet.
partial
an adaptive learning mechanism that dynamically regulates tool usage
Implication not extracted yet.
partial
its performance is heavily dependent on the quality and relevance of the available tools
Implication not extracted yet.
partial
enabling coordination of multiple tools and generalization to unseen tools
Implication not extracted yet.
partial
it autonomously adopts beneficial tools, suppresses irrelevant ones, and adjusts tool usage frequency based on task demands
Implication not extracted yet.
partial
The complexity of orchestrating a wide variety of tools may lead to challenges in implementation and model training scalability
Implication not extracted yet.
partial
improving the 7B base model by +24.9% on average
Explicitly stated numeric result in the abstract and analysis.
partial
surpassing strong proprietary systems such as GPT-5 on multiple tasks, including VSP and Jigsaw.
Directly stated comparative result in the abstract.
partial
its performance is heavily dependent on the quality and relevance of the available tools.
Directly stated as a caveat in the analysis section.
partial
Tool-GRPO, a reinforcement learning algorithm that optimizes tool selection and sequencing based on end-task success
Explicitly named and described as a core component in both abstract and analysis.
partial
Related resources will appear here when this paper maps cleanly to topic, benchmark, or dataset surfaces.
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Mingyang Song
Fudan University
Haoyu Sun
Tongji University
Jiawei Gu
National University of Singapore
Linjie Li
University of Washington
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Receipt path
/buildability/adareasoner-dynamic-tool-orchestration-for-iterative-visual-reasoning
Paper ref
adareasoner-dynamic-tool-orchestration-for-iterative-visual-reasoning
arXiv id
2601.18631
Generated at
2026-03-19T21:31:49.672Z
Evidence freshness
stale
Last verification
2026-03-19T21:31:49.672Z
Sources
0
References
0
Coverage
33%
Lineage hash
07d88978701a585128841c9e4aca7e0168f0d93547e2ca907b3ab34a7670b94f
Canonical opportunity-kernel lineage hash.
External signature
unsigned_external
No founder, registry, pilot, or production-adoption signature is attached to this receipt.
Verification
not_verified
Verification is blocked until an external signature is provided.
Verification pending / evidence receipt incomplete
repo_url
references