LLMs get lost
in conversation.

The experiment is beautifully controlled. Take one fully-specified task — say, "write a SQL query" with all its constraints. Deliver it two ways. Concat: everything in a single prompt, the model sees the whole spec at once. Sharded: split the identical requirements into pieces and reveal one per turn, the way a real user drips out details as they think.

The information content is exactly the same. The only difference is timing. So any gap between concat and sharded isolates one thing: the cost of finding out as you go — which is how every real conversation and every agent session works.

Across 15 top LLMs and six task families, sharded delivery cost an average of ~39% in performance. But the headline isn't the average — it's which part moved. The paper decomposes a score into two pieces:

Aptitude barely fell — the ceiling is intact, the model still can do it. What exploded was unreliability: the gap between a model's best and worst attempts on the same sharded task roughly doubled. The same prompt, run twice, now lands in wildly different places. A capable model became an unpredictable one.

Why does drip-feeding break things? Because a model handed partial information tends to answer anyway. It fills the under-specified gaps with a guess and states a full solution — confident, complete, and possibly wrong on the parts it hadn't been told yet. That guess is now in the conversation history as its own words.

When a later turn adds a constraint that contradicts the guess, the model clings. It anchors on its earlier answer, patches around the edges instead of re-deriving, and builds the rest of the conversation on a cracked foundation. The paper's own summary is blunt: models make assumptions too early, and once wrong, "they get lost and do not recover."

You can capture the shape with one small model. Let p be the model's success rate when it sees the full spec at once (concat). In sharded mode, each turn where it commits on partial info is a chance to lock in a wrong assumption; call that per-turn trap probability q. Surviving a conversation means dodging the trap on every early turn:

One turn (turns=1) has no early-commitment trap, so sharded equals concat. Every extra turn multiplies in another (1−q) — geometric decay. That's why longer conversations get less reliable, not more.

It also explains the "aptitude intact, reliability gone" split: p (the ceiling) never changes; the whole loss lives in the compounding (1−q) factor, which is exactly a reliability term. The runnable model below plots the two curves and the widening gap.

The decay model points straight at the cures. Kill the early-commitment traps and the (1−q) factors disappear.

None of these make the model smarter. They change the delivery so its ceiling ability actually shows up — the same lever the experiment pulled to create the effect in the first place.

This is one of the most practically important negative results of the era, because every deployed chat and agent is multi-turn by default. Benchmarks that score single fully-specified prompts systematically overstate how models behave in the wild. The paper reframes a huge swath of "the model is unreliable" complaints as, more precisely, "the model is unreliable when we drip-feed it."

It also gives context engineering a crisp mandate: the highest-leverage thing you can do is keep the model's working context looking like a fresh, complete spec, not a long trail of half-formed turns. It pairs naturally with self-correction work like Reflexion — the difference being that recovery is hard once a model is lost, so the real win is preventing the early commitment rather than repairing it after.