Unverified50% confidenceDecision RuleExact time
When a certain category contains thousands of facts, the approach of making one LLM call per category is difficult to scale, and a more robust approach is to normalize facts into a structured schema and delegate the comparison logic to deterministic code.
1
Sources
50%
Confidence
Long-term
Relevance
10/7/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedHaving LLMs holistically reason about causal structure in a larger context (Rowwise or Full graph) tends to produce better results than making isolated pairwise judgments.73% similarUnverifiedLLM的知识以分布式方式编码于数十亿参数的权重矩阵中，而非以符号化的逻辑规则存储72% similarUnverifiedLLM在涉及精确数值计算、多步逻辑推导时容易产生形式正确但内容错误的幻觉输出71% similarUnverified涉及数字、事实和精确逻辑时不应盲目信任LLM的输出71% similarUnverifiedThe author believes that the 'Chain-of-Thought' output of LLMs is more likely a highly fitted reproduction of reasoning patterns in training data, rather than genuine logical deduction in the true sense.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/985941API
curl https://kongchang.com/api/v1/knowledge/claims/985941MCP
get_claim(id=985941)