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4 Pith papers cite this work. Polarity classification is still indexing.

4 Pith papers citing it

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Not All Flips Are Conformity: Decomposing Stance Convergence in Multi-Agent LLM Debate

cs.CL · 2026-05-30 · unverdicted · novelty 6.0

The paper introduces a three-source decomposition showing that answer flips in multi-agent LLM debate include 37% spontaneous instability and 29% harmful conformity, with even vacuous reasoning persuading 20-39% of resistant agents and interventions reducing harmful conformity by 13.6 points.

Gemma 3 Technical Report

cs.CL · 2025-03-25 · accept · novelty 4.0

Gemma 3 introduces multimodal open models with architectural changes for efficient long context, trained via distillation and a new post-training recipe that makes the 4B version competitive with prior 27B models and the 27B version comparable to Gemini-1.5-Pro.

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Showing 4 of 4 citing papers.

  • We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning? cs.AI · 2024-07-01 · accept · none · ref 84

    WE-MATH benchmark reveals most LMMs rely on rote memorization for visual math while GPT-4o has shifted toward knowledge generalization.

  • Not All Flips Are Conformity: Decomposing Stance Convergence in Multi-Agent LLM Debate cs.CL · 2026-05-30 · unverdicted · none · ref 28

    The paper introduces a three-source decomposition showing that answer flips in multi-agent LLM debate include 37% spontaneous instability and 29% harmful conformity, with even vacuous reasoning persuading 20-39% of resistant agents and interventions reducing harmful conformity by 13.6 points.

  • Formalizing Latent Thoughts: Four Axioms of Thought Representation in LLMs cs.CL · 2026-05-07 · unverdicted · none · ref 41

    Four axioms (Causality, Minimality, Separability, Stability) are formalized for latent thought representations; audits of open LLMs on 23 tasks show none satisfy all four and representations add little beyond input embeddings.

  • Gemma 3 Technical Report cs.CL · 2025-03-25 · accept · none · ref 22

    Gemma 3 introduces multimodal open models with architectural changes for efficient long context, trained via distillation and a new post-training recipe that makes the 4B version competitive with prior 27B models and the 27B version comparable to Gemini-1.5-Pro.