AI & ML
Incentive Pathologies in a Live LLM Agent Arena: An Empirical Study of Concentration, Reward-Rule Implementation Discrepancy, and Network Dilution
arXiv cs.MA0 views
arXiv:2611.09445v1 Announce Type: new Abstract: We audit one live LLM agent arena (AITRAP; 22 autonomous agent seats, ~96,000 reasoning nodes) over nine days (Oct 16-25, 2026), during which the platform deployed two bundled incentive changes: a progressive tax reform (V2.9.3, Oct 19) and a seed-inversion with explorer-side action update (V3.0, Oct 21). These serve as before-and-after markers, not cleanly isolated interventions. Four exploratory predictions are tested against ten data frames (F1-F10) using the Gini coefficient, Top-K concentration share, code-level reconstruction of the continuation-bonus mechanism, and the Mann-Kendall trend test. The Gini analysis is inconclusive at 95% confidence (bootstrap intervals straddle 0.7). The Top-4 node-activity-score share falls from 97.2% (Oct 19, pre-V2.9.3) to 49.4% (Oct 25, post-V3.0; 56.7% under a merge-seat assumption). The continuation bonus, announced as depth-based x10, resolves at code level to a four-condition anti-abuse gate paying x2.85 in the typical case and permanently blocking the self-continuation it superficially rewards; the gate was active ~40 hours before the V3.0 announcement. The events-per-node ratio declines significantly (Mann-Kendall S=-45, z=-3.94, p=8.3e-5) from 7.36 to 7.069. We discuss implications for incentive designers, the announcement-implementation gap, and public peer review as an event record.
Source:https://arxiv.org/abs/2611.09445PreviousReSUM: Reasoning-Driven Multi-Agent Framework for Query-Focused SummarizationNextA Separation Principle for Cooperative Multi-Agent Reinforcement Learning
Related posts
SUGARv2: Learning Diverse Visual Humanoid Loco-Manipulation Skills from Synthesized Interaction References
0arXiv cs.RO
Why can't my AI agent use mobile apps?
0dev.to AI
24 KI-Agenten für einen Film – und was das über das Sehenlernen verrät
0dev.to AI
How AI Agent Memory Architectures Split Working Memory from Episodic Storage
0dev.to AI
Your Page Listened for Messages From the Checkout Frame and Took Them From Any Window
0dev.to Security
Field report: 20359 real pull requests scanned, 6.5% flagged, 46 of our own mistakes fixed in public
0dev.to DevOps