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cs.AI2026
Adaptive Influence Graphs for Failure Attribution in Multi-Agent Systems
Yarden Bakish, Amir Dudai, Roy Ganz +4
Multi-agent LLM systems are increasingly deployed in real-world applications, where failures can be costly and difficult to localize. Despite growing efforts to automate failure at…
cs.AI2026
The Handoff Tax: Continuing Non-Native Trajectories in LLM Agents
Roy Ganz, Mor Shpigel Nacson, Adi Kalyanpur +1
Coding agents perform long-running tasks spanning dozens of model calls, tool uses, and code edits. As these runs unfold, users face a practical cost-quality trade-off: escalating…
cs.AI2026
DREAM: Deep Research Evaluation with Agentic Metrics
Elad Ben Avraham, Changhao Li, Ron Dorfman +8
Deep Research Agents generate analyst-grade reports, yet evaluating them remains challenging due to the absence of a single ground truth and the multidimensional nature of research…