arXiv: cs.AI preprintsInternational11 November 2026
Low-Margin Branch Points in Deterministic LLM Deliberation
This is an official announcement record
Firsthand records what arXiv: cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2611.00546v2 Announce Type: replace Abstract: Deterministic multi-agent LLM deliberation can exactly re- play a fixed transcript, yet exact replay alone does not test local resistance to controlled internal interventions. Because each response becomes context for later agents, a changed greedy token can redirect a transcript-mediated execution. We present a white-box audit of this local failure mode in a five-agent committee under greedy decoding. We prospectively select low- and high-margin token positions, require exact no-op re- play, inject norm-relative residual displacements, and m
Read the official announcement
Opens arxiv.org
More from arXiv: cs.AI preprints
- Auditable Human-Agent Teams for Autonomous X-ray Experimentation11 November 2026
- EarthRecord: From Pixels to Land Change with Multimodal Agents over Four Decades of Satellite Imagery11 November 2026
- Train on prefill, monitor at decode: Using sparse autoencoders for topic monitoring11 November 2026
- LandslideFM: A Foundation Model Towards Unified Landslide Perception and Understanding11 November 2026
- Tracing Psychometric Inference in Large Language Models11 November 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.