Temporal Grounding

The process of identifying whether a user query requires time-bound information. Explicit queries contain dates like '2024 revenue,' while implicit queries like 'current CEO' require inferred timeframes. The system classifies intent into categories: recency-sensitive (latest news), point-in-time (specific date), interval-based (date ranges), or time-agnostic (static facts). Misclassification leads to retrieval of stale or irrelevant context.

The mechanism for extracting and normalizing publication dates from source documents. The system parses metadata fields like lastmod, published_time, and HTTP headers, then resolves ambiguous formats (e.g., '01/02/24') to a canonical UTC timestamp. Fallback heuristics include crawling date extraction and content-based temporal analysis when metadata is missing. Unresolved timestamps are flagged with a low Source Reliability Score.

A retrieval-stage filter that excludes documents outside the query's relevant time range before semantic search executes. The system applies a pre-filter based on resolved timestamps, dramatically reducing the candidate pool. For example, a query about 'last quarter' triggers a filter for documents published within the preceding 90 days. This prevents the embedding model from wasting compute on temporally irrelevant chunks.

A re-ranking strategy that boosts the relevance score of documents based on their temporal proximity to the query's target timeframe. A decay function—often exponential or gaussian—reduces the weight of older documents. The formula score_final = semantic_score * e^(-λ * age) ensures that for recency-sensitive queries, a slightly less semantically relevant but more current document can outrank an older, perfectly matching one.

A post-retrieval check that identifies conflicting facts across documents with different timestamps. When a newer source states 'CEO is Alice' and an older source states 'CEO is Bob,' the system flags the contradiction. Resolution logic applies temporal precedence—newer authoritative sources override older ones—and may trigger a Chain-of-Verification step to confirm the current fact before generating an answer.

The practice of explicitly including temporal qualifiers in generated answers to prevent misinterpretation. Instead of stating 'The population is 8 million,' the system outputs 'As of the 2023 census, the population was 8 million.' This inline temporal attribution makes the validity window explicit. For volatile facts, the system may append a freshness disclaimer: 'This figure may have changed since publication.'

The process of identifying and linking specific spans of generated text to the exact source documents or data records that support them. This creates a direct, auditable trail from output to evidence.

• Inline Citation: Inserts reference markers directly into the text span requiring support.
• Attribution-Aware Chunking: Preserves source metadata during document preprocessing for precise retrieval-time citation.
• Enables verifiable output for compliance and user trust.

The systematic logging of the origin, transformations, and movement of data used in AI generation. It creates an unbroken chain of custody from source to output.

• Data Lineage: A complete record of a dataset's lifecycle, including its origin and transformations.
• Blockchain Anchoring: Records a hash of provenance metadata on an immutable public ledger for independent verification.
• Critical for regulatory compliance and debugging hallucinations.

A set of techniques designed to reduce the generation of factually incorrect or unsupported content by language models.

• Retrieval-Augmented Generation (RAG): Conditions generation on retrieved external context.
• Constrained Decoding: Manipulates token probabilities to favor words supported by evidence.
• Chain-of-Verification (CoVe): The model self-corrects by drafting and answering independent verification questions.

A quantitative evaluation score measuring the degree to which a generated statement is logically entailed by and consistent with the provided source context.

• Uses Natural Language Inference (NLI) to determine entailment, contradiction, or neutrality.
• Grounded BERTScore: Adapts semantic similarity metrics to penalize tokens lacking contextual support.
• Independent of general world knowledge, focusing strictly on source alignment.

The process of validating generated factual statements by querying a structured knowledge graph to confirm the existence and correctness of subject-predicate-object triples.

• Entity Disambiguation: Resolves textual mentions to a single, unique identity in the knowledge base.
• Provides deterministic factual grounding against a curated semantic network.
• Complements probabilistic retrieval with structured, relational validation.

The process of aligning a model's predicted probability of correctness with its actual empirical likelihood of being correct.

• Ensures that a 90% confidence score is truly accurate 90% of the time.
• Prevents overconfident hallucinations by surfacing uncertainty.
• Essential for building trustworthy autonomous systems that know when to defer to human operators.