ARES_POC (Autonomous Real-time Evaluation System Proof) — Research Verification Engine

ARES_POC is a mathematically grounded misinformation verification system.
Unlike LLM-based fact checkers, ARES computes truth using a deterministic evidence functional:

$$ Truth'(C) = \frac{\sum_{i=1}^{M} S(C,E_i)\cdot N(C,E_i)\cdot W(E_i)}{\sum_{i=1}^{M} S(C,E_i)} $$

This repository is the Proof-of-Concept implementation accompanying the research manuscript.

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What ARES Does

Given a claim C, ARES:

1. Retrieves evidence passages (R)
2. Selects Top-M semantically relevant passages (S)
3. Evaluates entailment / contradiction (N)
4. Applies source credibility priors (W)
5. Computes a reproducible truth score

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Quick Start

Install dependencies

pip install -r requirements.txt

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Web Interface Preview (ARES_UI)

ARES also includes a research-grade visualization interface for inspecting the mathematical verification process.

Overall Interface

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Verdict Dashboard

The verdict panel shows the final decision along with the computed Truth Score and Confidence derived from the functional.

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Mathematical Evidence Trace

Every evidence passage contributing to the verdict is shown with:

• Semantic Similarity (S(C,E))
• Entailment Score (N(C,E))
• Source Weight (W(E))
• Individual Contribution to Truth'

This ensures full transparency of the decision process.

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Run single claim verification

py main.py "The Earth orbits the Sun" --k 10 --m 5

Run benchmark evaluation

py evaluate.py --dataset liar --samples 20

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File → Mathematical Operator Mapping

File	Operator	Role
retriever.py	R(C)	Evidence retrieval
similarity.py	S(C,E)	Top-M ranking
entailment.py	N(C,E)	Logical validation
credibility.py	W(E)	Source prior
verifier.py	V(C)	Truth functional

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Determinism

ARES uses a local cache/ to ensure that the same claim with same parameters always produces the same truth score.

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Research Integrity

ARES does not generate answers.
It derives verdicts strictly from discovered evidence.

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License

MIT License