A new benchmark and clean-room harness show frontier AI agents reach only 0.337 factual F1 when synthesizing conclusions from scientific evidence.
VeriFact: Enhancing long-form factuality evaluation with refined fact extraction and reference facts
3 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
3
Pith papers citing it
1
external citations · OpenAlex
years
2026 3verdicts
UNVERDICTED 3representative citing papers
LaaB improves LLM hallucination detection by mapping self-judgment labels back into neural feature space and using mutual learning under logical consistency constraints between responses and meta-judgments.
CURE trains LLMs to reason about uncertainty at the claim level via a structured protocol and multi-stage calibration, improving factual accuracy by up to 39.9% on biography generation while boosting calibration metrics.
citing papers explorer
-
Can AI Agents Synthesize Scientific Conclusions?
A new benchmark and clean-room harness show frontier AI agents reach only 0.337 factual F1 when synthesizing conclusions from scientific evidence.
-
Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments
LaaB improves LLM hallucination detection by mapping self-judgment labels back into neural feature space and using mutual learning under logical consistency constraints between responses and meta-judgments.
-
Think Through Uncertainty: Improving Long-Form Generation Factuality via Reasoning Calibration
CURE trains LLMs to reason about uncertainty at the claim level via a structured protocol and multi-stage calibration, improving factual accuracy by up to 39.9% on biography generation while boosting calibration metrics.