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20232026
most citedVoting or Consensus? Decision-Making in Multi-Agent Debate

13 citations · 68 across the 49 of their papers we have counts for

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Showing 2026 · cs.AIShow all

5 papers · 2 filters

cs.AI2026

AutoRecLab: Describe the Experiment, Get the Code!

Moritz Baumgart, Philipp Meister, Justus Krell +3

Empirical evaluation is central to recommender-systems (RecSys) research, but turning experimental designs into executable code remains a manual and error-prone task. We present Au…

cs.AI2026

Speak for Me: Giving LLMs the Situational Awareness to Participate in a Meeting

Muneeb Khan, Frederic Kirstein, Terry Ruas +1

In online meeting delegation, LLM agents fail to recognize when to speak. With no structured way to track stances, coverage, and floor, they miss the moments where they should cont…

cs.AI2026

Beyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal

Kia-Jüng Yang, Dominik Meier, Jiachen Zhao +2

Large reasoning models (LRMs) generate chain-of-thought (CoT) traces before producing final outputs, introducing a dynamic internal state that may complicate control mechanisms suc…

cs.AI2026

Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling

Florian Valentin Wunderlich, Lars Benedikt Kaesberg, Jan Philip Wahle +2

Advances in inference methods have enabled language models to improve their predictions without additional training. These methods often prioritize raw performance over cost-effect…

cs.AI2026

Mind the Gap Between Spatial Reasoning and Acting! Step-by-Step Evaluation of Agents With Spatial-Gym

Lars Benedikt Kaesberg, Tianyu Yang, Niklas Bauer +3

Spatial reasoning is central to navigation and robotics, yet measuring model capabilities on these tasks remains difficult. Existing benchmarks evaluate models in a one-shot settin…