PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 4, 20260 citationsOpen Access

Eradicating Automation Bias: The Viveka Patha — Deterministic Directed Acyclic Graph Verification against the Satya Kosha for Absolute Truth Alignment in Large Language Models

View Full Paper
ASAmit Raj Sharma

Key Points

  • This work aims to develop a pre-generation intervention to ensure the alignment of outputs from large language models with absolute truth.
  • Introduced the Viveka Patha system using a Directed Acyclic Graph (DAG) for token verification.
  • Utilized a hard-coded truth repository, known as the Satya Kosha.
  • Tested the intervention on consumer-grade hardware, specifically the NVIDIA RTX 4050.
  • Achieved a 93.3% mathematical accuracy in ensuring output validity.
  • Obtained a 100% success rate in blocking adversarial prompting.
  • Established that absolute truth alignment is feasible on standard hardware.

Abstract

We present "Viveka Patha," a novel pre-generation intervention for Large Language Models that utilizes a Directed Acyclic Graph (DAG) to cross-reference every candidate output token against a hard-coded truth repository (Satya Kosha). Unlike probabilistic post-generation filters like RAG or RLHF, this system creates a deterministic barrier against hallucinations at the sampling layer. Tested on an NVIDIA RTX 4050 (6GB VRAM), we achieve a 93.3% mathematical accuracy and a 100% block rate against adversarial prompting, demonstrating that absolute truth alignment is computationally viable on consumer-grade hardware.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Amit Raj Sharma (2026) studied this question.

synapsesocial.com/papers/69a7cd6ed48f933b5eed9d20https://doi.org/10.5281/zenodo.18838442
Ask AI
Helpful
Bookmark
Share
View Full Paper