Answer Aggregation

The process of identifying and merging semantically identical information retrieved from multiple parallel reasoning paths. This prevents redundant statements from dominating the final output.

• Semantic Similarity Scoring: Uses cosine similarity between embeddings to cluster equivalent facts.
• Lexical Overlap Analysis: Employs n-gram matching (e.g., ROUGE-L) as a fast pre-filter.
• Canonicalization: Maps varied phrasings to a single, authoritative representation before synthesis.

The mechanism for arbitrating between contradictory pieces of evidence retrieved from different sources. The aggregator must decide which information to trust and which to discard.

• Source Authority Weighting: Prioritizes evidence from high-trust domains or peer-reviewed corpora.
• Recency Heuristics: Favors temporally newer information when staleness is a conflict factor.
• Majority Voting: Selects the claim supported by the largest number of independent retrieval paths.

The structural arrangement of aggregated facts into a narrative that flows logically. This transforms a bag of facts into a reasoned argument.

• Temporal Sequencing: Orders events chronologically when a timeline is implied.
• Causal Linking: Arranges premises before conclusions to mimic deductive reasoning.
• General-to-Specific Structuring: Presents high-level summaries before granular details to improve readability.

The consolidation of citations from multiple evidence paths into a clean, non-redundant reference list. This ensures the final answer is verifiable without overwhelming the user.

• Citation Span Grouping: Merges overlapping text spans that share the same source.
• Reference Normalization: Standardizes URLs and document IDs to prevent duplicate entries.
• Confidence-Weighted Attribution: Assigns higher prominence to citations that contributed most heavily to the final answer.

A post-generation validation step that verifies the aggregated answer against the original retrieved evidence. This catches fabrications introduced during the synthesis phase.

• Natural Language Inference (NLI): Uses a separate model to check if the generated claim is entailed by the source text.
• Atomic Fact Decomposition: Breaks the final answer into individual claims and verifies each one independently.
• Self-Refinement Loops: Feeds the aggregated answer back into the model with a verification prompt to identify unsupported statements.

A generation strategy that actively penalizes the language model for repeating information already stated in the aggregated response. This is crucial for maintaining conciseness.

• N-gram Blocking: Prevents the repetition of specific word sequences within the output.
• Coverage Mechanisms: Tracks which source facts have been used and discourages revisiting them.
• Diversity-Ranked Beam Search: Selects candidate sequences that maximize the union of distinct facts covered.