To operate robustly in open-world environments, autonomous agents should be able to infer the behavior of unfamiliar systems through interaction alone, even in the absence of documentation. However, existing tool-use benchmarks expose semantic tool schemas in...
VQV Signal
RESEARCH
SOURCE-BACKED
TECHNICAL
ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step
To operate robustly in open-world environments, autonomous agents should be able to infer the behavior of unfamiliar systems through interaction alone, even in the absence of documentation. However, existing tool-use benchmarks expose semantic tool schemas in...
To operate robustly in open-world environments, autonomous agents should be able to infer the behavior of unfamiliar systems through interaction alone, even in the absence of documentation. However, existing tool-use benchmarks expose semantic tool schemas in static environments, allowing agents to rely on prior...
VQV organizes public signals from inspectable sources. It does not independently verify the underlying report.
Signal Strength 95%
Technical label SOURCE-BACKED
Public Interest 34
Category RESEARCH
Reader Depth TECHNICAL
Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.
Public Interest components
Recognizable Entity Score 0
Practical Impact Score 20
Novelty Interest Score 94
Consequence Score 34
Curiosity Score 16
Shareability Score 49
Topics
VQV surfaced this signal because it is recent, relevant to AI Agents, connected to arXiv.
No login, cookies, social SDKs, or automatic posting.