Select the failure patterns that show up specifically when a chatbot leans too hard on rolling summarization

**Rolling summarization** replaces older turns with a running prose digest. It saves tokens, but the compression is not free, and the things it drops follow a predictable shape. The first pattern is **numeric drift**. A user might say "my budget is 4,237 dollars" early on. When that turn rolls into the summary, the digest paraphrases it as "a budget around four thousand." Later replies use the round number, and the user notices the precise figure was lost. The second pattern is **rare-entity loss**. Named entities that appear only once (a specific product code, an obscure place name, a colleague's name mentioned in passing) tend not to survive a summary refresh, because the summarizer optimizes for the gist, not for low-frequency tokens. The third pattern is **broken pronoun chains**. If "she" referred to someone introduced ten turns ago, and the introduction got summarized away, the model now has "she" without a referent. The fourth pattern is **paraphrased code**. The summarizer treats a pasted code block as prose to summarize, not as a verbatim artifact to preserve, so the user's code becomes a description of code. Latency variability and faster first tokens are unrelated to summarization specifically; they happen for many reasons.

Rolling summarization compresses prior turns into a running digest. The compression is information-lossy, and the shape of that loss is predictable. Four failure modes are specifically diagnostic of over-reliance on this technique. The first is **numeric drift**. Exact values, dates, and identifiers tend to round or vanish across summary refreshes. A user-stated 4,237-dollar budget shows up in a later reply as "about four thousand," and the user has to repeat themselves. Prose summaries optimize for gist; gist does not include four-digit precision. The second is **rare-entity drop-out**. Named entities mentioned only once (a specific person, a less-common place, an obscure product code) are exactly the tokens a summarizer is most likely to elide. They contribute little to the gist of the conversation but are often the thing the user most cares about. The third is **broken antecedent chains**. Pronouns and definite references ("she," "the file," "that decision") only resolve against the turn that introduced the referent. When that turn is summarized away, the pronoun in the live tail becomes a dangling reference, and the model is forced to guess or hallucinate. The fourth is **verbatim artifact loss**. Code blocks, structured data, and exact-string payloads do not survive prose summarization. The summary becomes "the user pasted a Python script that does X," and the literal code is gone. If the user comes back to ask "can you modify the script I pasted earlier," the model can no longer see the script. Latency observations like faster first tokens or variable per-turn cost are not summarization-specific failure shapes; they happen for many reasons in any dynamic-context system.