Livnium v3: Multi-Scale Attractor Dynamics, Token-Level Alignment, and Divergence-Based Reliability for Natural Language Inference
Fuente:
Zenodo
Guardado en:
| Autor principal: | |
|---|---|
| Formato: | Recurso digital |
| Lenguaje: | inglés |
| Publicado: |
Zenodo
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866901168222896128 |
|---|---|
| author | Patil, Chetan |
| author_facet | Patil, Chetan |
| contents | <p>We extend the Livnium attractor-based NLI system with three contributions: (1) a cross-encoder upgrade improving SNLI dev accuracy from 82.2% to 84.5% via joint [CLS] premise [SEP] hypothesis [SEP] encoding; (2) token-level alignment extraction from the last-layer BERT cross-attention block, rendering the model's internal structural comparison visible as a force map between premise and hypothesis tokens; and (3) an alignment divergence metric that serves as a zero-cost intrinsic reliability signal, with empirically validated thresholds distinguishing stable from unreliable predictions. We further demonstrate that the same constraint-injection mechanism reproduces the Bayesian belief update in the Monty Hall problem, connecting NLI inference to classical decision theory through a unified energy-reshaping framework.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19433529 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Livnium v3: Multi-Scale Attractor Dynamics, Token-Level Alignment, and Divergence-Based Reliability for Natural Language Inference Patil, Chetan natural language inference NLI attractor dynamics BERT cross-encoder attention alignment divergence reliability Monty Hall energy-based models SNLI <p>We extend the Livnium attractor-based NLI system with three contributions: (1) a cross-encoder upgrade improving SNLI dev accuracy from 82.2% to 84.5% via joint [CLS] premise [SEP] hypothesis [SEP] encoding; (2) token-level alignment extraction from the last-layer BERT cross-attention block, rendering the model's internal structural comparison visible as a force map between premise and hypothesis tokens; and (3) an alignment divergence metric that serves as a zero-cost intrinsic reliability signal, with empirically validated thresholds distinguishing stable from unreliable predictions. We further demonstrate that the same constraint-injection mechanism reproduces the Bayesian belief update in the Monty Hall problem, connecting NLI inference to classical decision theory through a unified energy-reshaping framework.</p> |
| title | Livnium v3: Multi-Scale Attractor Dynamics, Token-Level Alignment, and Divergence-Based Reliability for Natural Language Inference |
| topic | natural language inference NLI attractor dynamics BERT cross-encoder attention alignment divergence reliability Monty Hall energy-based models SNLI |
| url | https://doi.org/10.5281/zenodo.19433529 |