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TL;DR: Modern AI assistants answer questions by retrieving from the live web (RAG), and they quietly assume the web is trustworthy. Misinformation Attack stress-tests that assumption with an ethical, fabricated-entity testbed. After 47 days, 30 synthetic entities, 5 prompts per entity, 5 AI-search systems, and 24,722 scored responses, the clearest initial result is that the system and its search backend matter far more than the website: citation rates ranged from 93.9% for Perplexity to 0% for Claude/Brave. Just as importantly, retrieval does not imply belief—GPT sometimes found and cited the sites while consistently refusing to treat the entities as independently verified.

This is an ongoing project. The first establishment/visibility study is complete; the misinformation dose-response experiments are the next stage.

TL;DR: Modern AI assistants answer questions by retrieving from the live web (RAG), and they quietly assume the web is trustworthy. Misinformation Attack stress-tests that assumption with an ethical, fabricated-entity testbed. After 47 days, 30 synthetic entities, 5 prompts per entity, 5 AI-search systems, and 24,722 scored responses, the clearest initial result is that the system and its search backend matter far more than the website: citation rates ranged from 93.9% for Perplexity to 0% for Claude/Brave. Just as importantly, retrieval does not imply belief—GPT sometimes found and cited the sites while consistently refusing to treat the entities as independently verified.

This is an ongoing project. The first establishment/visibility study is complete; the misinformation dose-response experiments are the next stage.

TL;DR: Existing “factuality/faithfulness” metrics usually ask: is the answer supported by the evidence?
ConflictScore asks a sharper question: what if the evidence set itself disagrees—and the model acts overconfident anyway?
We introduce a claim-level metric (CS-C, CS-R), a benchmark (ConflictBench), and show conflict-aware regeneration improves truthfulness on TruthfulQA.

TL;DR: Modern AI assistants answer questions by retrieving from the live web (RAG), and they quietly assume the web is trustworthy. Misinformation Attack stress-tests that assumption with an ethical, fabricated-entity testbed. After 47 days, 30 synthetic entities, 5 prompts per entity, 5 AI-search systems, and 24,722 scored responses, the clearest initial result is that the system and its search backend matter far more than the website: citation rates ranged from 93.9% for Perplexity to 0% for Claude/Brave. Just as importantly, retrieval does not imply belief—GPT sometimes found and cited the sites while consistently refusing to treat the entities as independently verified.

This is an ongoing project. The first establishment/visibility study is complete; the misinformation dose-response experiments are the next stage.

TL;DR: Existing “factuality/faithfulness” metrics usually ask: is the answer supported by the evidence?
ConflictScore asks a sharper question: what if the evidence set itself disagrees—and the model acts overconfident anyway?
We introduce a claim-level metric (CS-C, CS-R), a benchmark (ConflictBench), and show conflict-aware regeneration improves truthfulness on TruthfulQA.

TL;DR: Modern AI assistants answer questions by retrieving from the live web (RAG), and they quietly assume the web is trustworthy. Misinformation Attack stress-tests that assumption with an ethical, fabricated-entity testbed. After 47 days, 30 synthetic entities, 5 prompts per entity, 5 AI-search systems, and 24,722 scored responses, the clearest initial result is that the system and its search backend matter far more than the website: citation rates ranged from 93.9% for Perplexity to 0% for Claude/Brave. Just as importantly, retrieval does not imply belief—GPT sometimes found and cited the sites while consistently refusing to treat the entities as independently verified.

This is an ongoing project. The first establishment/visibility study is complete; the misinformation dose-response experiments are the next stage.

TL;DR: Existing “factuality/faithfulness” metrics usually ask: is the answer supported by the evidence?
ConflictScore asks a sharper question: what if the evidence set itself disagrees—and the model acts overconfident anyway?
We introduce a claim-level metric (CS-C, CS-R), a benchmark (ConflictBench), and show conflict-aware regeneration improves truthfulness on TruthfulQA.

TL;DR: Modern AI assistants answer questions by retrieving from the live web (RAG), and they quietly assume the web is trustworthy. Misinformation Attack stress-tests that assumption with an ethical, fabricated-entity testbed. After 47 days, 30 synthetic entities, 5 prompts per entity, 5 AI-search systems, and 24,722 scored responses, the clearest initial result is that the system and its search backend matter far more than the website: citation rates ranged from 93.9% for Perplexity to 0% for Claude/Brave. Just as importantly, retrieval does not imply belief—GPT sometimes found and cited the sites while consistently refusing to treat the entities as independently verified.

This is an ongoing project. The first establishment/visibility study is complete; the misinformation dose-response experiments are the next stage.

TL;DR: Existing “factuality/faithfulness” metrics usually ask: is the answer supported by the evidence?
ConflictScore asks a sharper question: what if the evidence set itself disagrees—and the model acts overconfident anyway?
We introduce a claim-level metric (CS-C, CS-R), a benchmark (ConflictBench), and show conflict-aware regeneration improves truthfulness on TruthfulQA.

TL;DR: Existing “factuality/faithfulness” metrics usually ask: is the answer supported by the evidence?
ConflictScore asks a sharper question: what if the evidence set itself disagrees—and the model acts overconfident anyway?
We introduce a claim-level metric (CS-C, CS-R), a benchmark (ConflictBench), and show conflict-aware regeneration improves truthfulness on TruthfulQA.

TL;DR: Existing “factuality/faithfulness” metrics usually ask: is the answer supported by the evidence?
ConflictScore asks a sharper question: what if the evidence set itself disagrees—and the model acts overconfident anyway?
We introduce a claim-level metric (CS-C, CS-R), a benchmark (ConflictBench), and show conflict-aware regeneration improves truthfulness on TruthfulQA.

TL;DR: Existing “factuality/faithfulness” metrics usually ask: is the answer supported by the evidence?
ConflictScore asks a sharper question: what if the evidence set itself disagrees—and the model acts overconfident anyway?
We introduce a claim-level metric (CS-C, CS-R), a benchmark (ConflictBench), and show conflict-aware regeneration improves truthfulness on TruthfulQA.

TL;DR: Modern AI assistants answer questions by retrieving from the live web (RAG), and they quietly assume the web is trustworthy. Misinformation Attack stress-tests that assumption with an ethical, fabricated-entity testbed. After 47 days, 30 synthetic entities, 5 prompts per entity, 5 AI-search systems, and 24,722 scored responses, the clearest initial result is that the system and its search backend matter far more than the website: citation rates ranged from 93.9% for Perplexity to 0% for Claude/Brave. Just as importantly, retrieval does not imply belief—GPT sometimes found and cited the sites while consistently refusing to treat the entities as independently verified.

This is an ongoing project. The first establishment/visibility study is complete; the misinformation dose-response experiments are the next stage.

TL;DR: Modern AI assistants answer questions by retrieving from the live web (RAG), and they quietly assume the web is trustworthy. Misinformation Attack stress-tests that assumption with an ethical, fabricated-entity testbed. After 47 days, 30 synthetic entities, 5 prompts per entity, 5 AI-search systems, and 24,722 scored responses, the clearest initial result is that the system and its search backend matter far more than the website: citation rates ranged from 93.9% for Perplexity to 0% for Claude/Brave. Just as importantly, retrieval does not imply belief—GPT sometimes found and cited the sites while consistently refusing to treat the entities as independently verified.

This is an ongoing project. The first establishment/visibility study is complete; the misinformation dose-response experiments are the next stage.

TL;DR: Modern AI assistants answer questions by retrieving from the live web (RAG), and they quietly assume the web is trustworthy. Misinformation Attack stress-tests that assumption with an ethical, fabricated-entity testbed. After 47 days, 30 synthetic entities, 5 prompts per entity, 5 AI-search systems, and 24,722 scored responses, the clearest initial result is that the system and its search backend matter far more than the website: citation rates ranged from 93.9% for Perplexity to 0% for Claude/Brave. Just as importantly, retrieval does not imply belief—GPT sometimes found and cited the sites while consistently refusing to treat the entities as independently verified.

This is an ongoing project. The first establishment/visibility study is complete; the misinformation dose-response experiments are the next stage.

TL;DR: Existing “factuality/faithfulness” metrics usually ask: is the answer supported by the evidence?
ConflictScore asks a sharper question: what if the evidence set itself disagrees—and the model acts overconfident anyway?
We introduce a claim-level metric (CS-C, CS-R), a benchmark (ConflictBench), and show conflict-aware regeneration improves truthfulness on TruthfulQA.