Cosine similarity measures how closely two vectors point in the same direction. It does not, by itself, tell a retrieval system when a record was created, whether it is still valid, or whether a newer record replaces it. If an answer depends on what is true now, a system needs temporal information alongside semantic similarity.
What cosine similarity measures—and what it leaves out
For vectors X and Y, cosine similarity is their dot product divided by the product of their magnitudes: K(X, Y) = <X, Y> / (||X|| × ||Y||). It measures vector direction, not a document’s age or validity. Scikit-learn notes that for L2-normalized data, cosine similarity is equivalent to the linear kernel: scikit-learn’s cosine similarity documentation.
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OpenAI’s embedding guide describes ranking documents by cosine similarity and explains that unit-normalized embeddings let a dot product calculate cosine similarity. For those embeddings, cosine similarity and Euclidean distance produce identical rankings. Those facts concern vector geometry; neither metric supplies timestamp metadata: OpenAI’s embeddings guide.
The distinction is not that cosine similarity is broken. It answers “Which vector is most similar to this query?” A timestamp, validity interval, event order, or supersession relationship answers a different question: “Which information applies now?” A system that needs both must preserve and use both kinds of information.
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Why a semantically close result can be stale
In an illustrative pediatric-therapy copilot example, Devansh Jaiswal describes a note from four months earlier about tolerating musical games ranking ahead of a note from 90 minutes earlier about an acute auditory crisis. The example’s stated cause is that semantic closeness won in a system whose embedding did not include timestamp information. It is an anecdote about system design, not clinical evidence; its examples contain no real patient data. Jaiswal summarizes the distinction: “Many real systems also need ‘what is most true right now?’ Those are different questions.”
The practical issue can arise in any application where facts change: a recent incident report, a current policy, or a newly updated project note may matter more than an older but semantically closer record. Whether it should rank first depends on the task. Recency can help, but it should not automatically override relevance.
Ways to make time part of retrieval
Temporal signals can be used at different points in a retrieval pipeline. The right choice depends on whether time should affect which candidates are found, their order, or whether an older record remains valid at all.
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|---|---|---|---|
| Temporal filtering or retrieval constraints | During initial retrieval, using timestamps or validity fields | Records outside a time window should not qualify | A strict filter can exclude relevant context if the time boundary is wrong. |
| Recency-aware reranking | After retrieval, combining similarity and a time-based score | Freshness should influence ordering without wholly replacing semantic relevance | It cannot surface a record that initial retrieval did not return. |
| Type-specific temporal behavior | During filtering or ranking, with different time rules for different record types | Different facts go stale at different rates | Requires sensible record types and task-specific tuning. |
| Explicit supersession or conflict handling | When records are stored, retrieved, or interpreted | A newer record explicitly replaces or contradicts an older one | A timestamp or decay score alone may not express the relationship. |
These are design options, not a controlled comparison with established performance results. A timestamp can be enough for some tasks; conflicting facts may require explicit links or rules that identify which record supersedes another.
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A candidate recency-aware reranking formula
One illustrative design combines semantic similarity with an exponentially decaying recency score:
score = alpha * similarity + (1 - alpha) * recency
recency = 0.5 ** (age / half_life)
Here, age is the record’s age and half_life is the period over which its recency score halves. The example proposed by Jaiswal uses a sample alpha of 0.6 and half-lives of 6 hours for acute events, 72 hours for sleep logs, and 90 days for durable protocols. These are illustrative author-provided values, not tested defaults, clinical advice, or values validated by a cited study.
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Before blending scores, check that similarity and recency are on compatible scales. If one has a much larger numerical range, it may dominate the combined result regardless of the intended weighting. The example’s formula is a candidate to evaluate, not a universal ranking rule.
How to evaluate a time-aware ranking
- Define what “current” means for the task. Decide whether the need is a time window, a freshness preference, validity dates, event order, or explicit replacement of an older fact.
- Keep temporal metadata with each record. Store the timestamp or validity information separately from the embedding. Where known, also represent record type and supersession or conflict relationships.
- Retrieve a sufficiently broad candidate pool. A reranker only reorders retrieved candidates. If the fresh record is missing from that set, reranking cannot bring it into view.
- Compare candidate settings on representative queries. Use queries with known-good answers to assess relevance and freshness together. Test the weighting and decay periods for the record types and consequences involved; do not assume the illustrative values will work for your system.
- Handle direct contradictions explicitly. If a new record is meant to replace an older one, encode that relationship or apply conflict logic instead of relying on a small recency-score advantage to resolve the disagreement.
When recency should—and should not—win
A newer record is not automatically better. An old, durable protocol may remain valid, while a new note may be incomplete, mistaken, or unrelated to the user’s question. Conversely, in a task where a recent event changes what action is appropriate, a ranking based only on semantic similarity can bury important current information.
Choose temporal behavior according to the type of fact and the cost of using stale information. Similarity helps retrieve relevant content; temporal metadata helps determine whether that content is current or valid. As Jaiswal puts it in the article’s takeaways, “Similarity is not validity.”
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