arXiv subjects

Molood Arman

Publications and source records attributed to Molood Arman.

3 recordsLinked to original sources

arXiv2026

When Outputs Disperse, Does Epistemic Revision Follow? A Black-Box Diagnostic for Machine Collectives

Collective intelligence research treats disagreement as evidence of epistemic diversity: if agents express different views, the group should retain capacity to revise. In LLM collectives this proxy can break: agents can produce diverse-looking arguments while preserving the same conclusion. We operationalize dispersion-revision coupling: the degree to which an intervention that verifiably increases the dispersion of a collective's outputs in embedding space is accompanied by genuine revision of its epistemic stance rather than premise-preserving reformulation. The diagnostic is black-box: it operates on generated text alone and makes no claims about the internal representations of the generating models. Two channels are measured independently: an output channel, the Coherence Index (CI), verifies that the intervention changed output dispersion; an epistemic channel, per-turn stance annotation, measures whether the collective revised. We propose CI with the Meta-Predictive Clarity System (MPCS), which inserts a Re-Differentiation Protocol (RDP) when outputs over-converge, as a reusable method for estimating this coupling regime. We evaluate five-agent collectives from two configurations (gpt-4o-mini and gemini-2.5-flash; 310 paired episodes per condition). On gpt-4o-mini, conditional dissent improves false-premise recovery by +17.7 points (p<1e-6) while static persona diversity harms recovery (-8.1, p=.007). On gemini-2.5-flash, the same intervention at a comparable budget yields no gain (26.1% vs 27.1%, p=.84) despite a verified dispersion drop; the two treatment effects differ from each other (z=3.79, p<.001). Mechanism tagging shows Gemini preserves the false premise via intra-framework dissent: 94% of tagged post-RDP responses reformulate rather than concede (vs 24% on GPT). We recommend reporting per-intervention stance shift and premise-preservation rate alongside accuracy.

cs.AI↗

arXiv2026

MAPS: Modeling Co-Existing Subjective Perspectives and Shared Meaning in Multi-Agent Cognitive Dialogue

Human dialogue involves more than exchanging information; it also expresses beliefs, emotions, and subjective cognitive styles. Yet current AI dialogue systems often enforce semantic uniformity, sacrificing diversity and interpretability. We present MAPS (Multi-Agent Perspective Spaces), a novel framework that models dialogue between cognitively distinct agents through domain-weighted profiles, dynamic GRU-based memory, and interpretable token-level attention. MAPS enables agents to maintain individualized reasoning while progressively converging on shared meaning. Evaluations on EmpatheticDialogues, TopicalChat, and MultiWOZ show that MAPS supports semantic alignment without collapsing subjectivity. Our results demonstrate a path toward cognitively grounded, interpretable dialogue systems that balance expressiveness and coherence.

cs.CL↗

arXiv2026

Empathy as Predictive Misalignment Tolerance: A Co-Regulation Framework and the Regime Structure of Dialogue Repair

Empathy is most often theorized as resonance: a mirroring of another's present emotional or cognitive state. This synchronic framing has shaped artificial systems, where empathic behavior is defined as affect recognition and response alignment. We argue this is the wrong target for extended dialogue, where understanding unfolds over time through prediction, divergence, and repair. We reframe empathy as predictive misalignment tolerance: the capacity to anticipate and regulate divergence across time rather than collapse it. We formalize this as Interpretive Error Tolerance (IET), a dynamic-threshold heuristic that models empathy as maintaining a viable band of divergence between agents. We evaluate this framework with two computational probes under controlled noise. The IET update rule does not outperform fixed baselines. Instead, we find a robust regime-dependent structure: repair trades discriminative fidelity for gist preservation. At low noise, repair degrades retrieval accuracy; at high noise, it preserves gist meaning, revealing an interaction between noise level, repair, and evaluation metric. We interpret this structure through IET, suggesting that empathy in extended interaction is not eliminating divergence but regulating its dynamics. This motivates a shift in empathic AI design from convergence toward managing interpretive distance.

cs.HC↗