arxiv: 2605.06105 · v1 · submitted 2026-05-07 · 💻 cs.AI

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Shallow Prefill, Deep Decoding: Efficient Long-Context Inference via Layer-Asymmetric KV Visibility

Jungsuk Oh , Hyeseo Jeon , Hyunjune Ji , Kyongmin Kong , Jay-Yoon Lee

Authors on Pith no claims yet

Pith reviewed 2026-05-08 10:29 UTC · model grok-4.3

classification 💻 cs.AI

keywords long-context inferenceKV cachelayer asymmetryprefill optimizationefficient decodingmemory reductionLlama-3.1autoregressive generation

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The pith

Prompt tokens restricted to lower layers during prefill preserve nearly full model quality while cutting long-context inference costs by a quarter.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a phase-asymmetric policy for KV states in decoder-only models that materializes non-anchor prompt tokens only in lower layers during prefill while keeping all decode-phase tokens full-depth across every layer. In experiments on Llama-3.1-8B, this approach with a single beginning-of-sequence anchor reaches 51.2 average score on OLMES benchmarks versus 51.4 for the full model, yet improves time-to-first-token by 33 percent, time-per-output-token by 22 percent, and reduces active KV memory by 25 percent at 128K context length. A sympathetic reader would care because long prompts no longer require storing or attending to upper-layer KV states for every prompt token, making extended context work more practical on constrained hardware without any retraining. Layer-wise analysis indicates the chosen cutoff keeps the primary prompt-selection and representation-stabilization functions intact.

Core claim

By removing prefill tokens from upper-layer visibility during decode and relying on a minimal BoS anchor plus lower-layer prompt KV states, the model maintains broad benchmark performance while lowering the cost of long-context inference; controlled tests show that limiting prefill to 75 percent of layers yields essentially the same 51.2 score as the full-depth baseline.

What carries the argument

The layer-asymmetric KV-visibility policy that keeps prompt-token KV states visible only in lower layers during prefill while enforcing full-depth visibility for all tokens in the decode phase.

Load-bearing premise

A single beginning-of-sequence anchor plus lower-layer prompt KV states are enough to carry out the prompt-selection and representation-stabilization work that upper layers normally perform during decoding.

What would settle it

A clear drop in OLMES-style benchmark scores below 50 when the same 75-percent layer cutoff is applied to a different model family or to contexts substantially longer than 128K.

Figures

Figures reproduced from arXiv: 2605.06105 by Hyeseo Jeon, Hyunjune Ji, Jay-Yoon Lee, Jungsuk Oh, Kyongmin Kong.

**Figure 1.** Figure 1: Overview and attention behavior of SPEED. Left (a): Decode-time attention-mass heatmaps view at source ↗

**Figure 2.** Figure 2: Long-context efficiency on Llama-3.1-8B with a fixed 128-token continuation. We compare view at source ↗

**Figure 3.** Figure 3: Category-level layer diagnostics on Full-IT using TULU-3-DEV request prompts. Left: view at source ↗

**Figure 4.** Figure 4: Exact match by prompt length on TriviaQA and S-NIAH. SPEED+BoS with moderate or view at source ↗

read the original abstract

Long-context inference in decoder-only language models is costly because long prompts are processed during Prefill, cached at every layer, and repeatedly attended to during autoregressive Decode. We introduce \emph{Shallow Prefill, dEEp Decode} (SPEED), a phase-asymmetric KV-visibility policy that materializes non-anchor prompt-token KV states only in lower layers while keeping Decode-phase tokens full-depth. Unlike previous approaches that make upper-layer prompt KV states cheaper to store or construct, SPEED removes prefill tokens from the upper-layer Decode visibility set altogether. With a minimal BoS anchor, this simple change preserves broad benchmark quality while reducing long-context cost. In a controlled Llama-3.1-8B instruction-tuning study, SPEED using only 75\% of layers for prefill tokens reaches 51.2 average score on OLMES-style benchmarks, compared with 51.4 for the full-depth baseline, while improving TTFT by 33\%, TPOT by 22\%, and reducing active KV memory by 25.0\% at 128K context. Layer-wise diagnostics suggest that this cutoff retains the main prompt-selection and representation-stabilization regions of the full-depth model. These results show that long-context prompt tokens need not always persist as full-depth KV-cache objects when Decode-phase tokens remain full-depth.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SPEED shows you can drop prompt KV from upper layers in decode with almost no quality drop on Llama-3.1-8B, but the 0.2-point benchmark gap lacks the stats needed to call it solid.

read the letter

The paper's core move is to make KV visibility layer-asymmetric by phase: non-anchor prompt tokens only get materialized and attended in the lower layers during prefill, while decode tokens stay full-depth everywhere. A single BoS token anchors the upper layers. On a controlled Llama-3.1-8B instruction-tuned run this yields 33% faster TTFT, 22% faster TPOT, and 25% less active KV memory at 128K context, with average OLMES-style scores of 51.2 versus 51.4 for the full-depth baseline. That is the practical result worth noting first.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes SPEED (Shallow Prefill, dEEp Decode), a phase-asymmetric KV-visibility policy for decoder-only LLMs. Prompt tokens materialize KV states only in lower layers during prefill (using a minimal BoS anchor), while decode-phase tokens remain full-depth. The central claim, supported by a controlled Llama-3.1-8B instruction-tuning study, is that a 75% layer cutoff for prefill preserves broad benchmark quality (51.2 average OLMES-style score vs. 51.4 for full-depth baseline) while delivering 33% TTFT improvement, 22% TPOT improvement, and 25% active KV memory reduction at 128K context; layer-wise diagnostics are said to indicate retention of prompt-selection and representation-stabilization functions.

Significance. If the near-parity result holds under rigorous statistical scrutiny, SPEED would provide a simple, training-free mechanism to reduce KV-cache footprint and inference latency for long-context workloads by entirely removing upper-layer prompt KV from the decode visibility set. The controlled empirical comparison against a full-depth baseline supplies concrete, reproducible efficiency numbers that could inform practical serving optimizations, distinguishing this from prior KV-compression or eviction techniques.

major comments (2)

[Abstract / Llama-3.1-8B study results] The reported 51.2 vs. 51.4 average scores on OLMES-style benchmarks (abstract) are presented as evidence that quality is preserved, yet no per-task scores, standard deviations, run-to-run variance, or hypothesis tests are supplied. Without these, the 0.2-point gap cannot be distinguished from noise, directly undermining the central claim that lower-layer prompt KV plus BoS anchor suffices to preserve prompt-selection and stabilization functions.
[Layer-wise diagnostics] Layer-wise diagnostics are described only as 'suggestive' with no quantitative thresholds, retention metrics, or ablation experiments that isolate the contribution of the omitted upper-layer KV states. This leaves the key modeling assumption—that minimal BoS plus lower-layer states are sufficient—without causal support.

minor comments (2)

[Abstract] The phrase 'OLMES-style benchmarks' is used without definition, citation to the OLMES framework, or enumeration of the constituent tasks.
[Abstract] The term 'BoS anchor' appears without prior expansion (Beginning-of-Sequence) or description of its implementation details.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need for greater statistical rigor and stronger causal evidence. We address each major comment below and indicate the revisions we will make to strengthen the manuscript.

read point-by-point responses

Referee: [Abstract / Llama-3.1-8B study results] The reported 51.2 vs. 51.4 average scores on OLMES-style benchmarks (abstract) are presented as evidence that quality is preserved, yet no per-task scores, standard deviations, run-to-run variance, or hypothesis tests are supplied. Without these, the 0.2-point gap cannot be distinguished from noise, directly undermining the central claim that lower-layer prompt KV plus BoS anchor suffices to preserve prompt-selection and stabilization functions.

Authors: We agree that aggregate scores alone are insufficient to rigorously demonstrate that the 0.2-point difference is within expected variance. In the revised manuscript we will add a table reporting per-task OLMES-style scores for both SPEED (75% cutoff) and the full-depth baseline. We will also include standard deviations computed over three independent instruction-tuning runs and a paired statistical test (Wilcoxon signed-rank) on the per-task differences to assess whether the observed gap is statistically significant. These additions will directly support the claim that lower-layer prompt KV plus the BoS anchor is sufficient for quality preservation. revision: yes
Referee: [Layer-wise diagnostics] Layer-wise diagnostics are described only as 'suggestive' with no quantitative thresholds, retention metrics, or ablation experiments that isolate the contribution of the omitted upper-layer KV states. This leaves the key modeling assumption—that minimal BoS plus lower-layer states are sufficient—without causal support.

Authors: The primary evidence for sufficiency remains the controlled benchmark comparison; the layer-wise diagnostics were provided only as explanatory context. We acknowledge that the current description lacks quantitative grounding. In the revision we will augment the diagnostics section with concrete retention metrics (e.g., mean attention mass on prompt tokens in layers above the cutoff) and explicit thresholds used to select the 75% cutoff. We will also add a small ablation varying the cutoff layer while holding all other factors fixed, thereby isolating the incremental effect of omitting upper-layer prompt KV. revision: partial

Circularity Check

0 steps flagged

No significant circularity; empirical method and results are self-contained.

full rationale

The paper introduces SPEED as a practical KV-visibility policy and supports its claims exclusively through controlled empirical comparisons on Llama-3.1-8B (51.2 vs 51.4 average OLMES scores, plus measured TTFT/TPOT/memory gains at 128K). No derivation chain, equations, or first-principles predictions exist that could reduce to fitted inputs or self-definitions. Layer-wise diagnostics are presented as suggestive observations, not as load-bearing mathematical steps. No self-citations, uniqueness theorems, or ansatzes are invoked to justify the core policy. The reported results therefore stand as independent experimental evidence rather than tautological restatements of their own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the empirical observation that upper-layer prompt KV can be dropped without harming downstream performance, plus the assumption that a single BoS token suffices as anchor.

axioms (1)

domain assumption Lower layers of the model contain the primary prompt-selection and representation-stabilization mechanisms needed for long-context tasks.
Invoked to justify the 75% cutoff; supported only by the paper's layer-wise diagnostics mentioned in the abstract.

pith-pipeline@v0.9.0 · 5556 in / 1262 out tokens · 69977 ms · 2026-05-08T10:29:02.849934+00:00 · methodology

discussion (0)

Reference graph

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, yM } denote assistant target tokens

A Additional Method Details Training-time visibility.For an instruction-tuning example, let P denote prefill tokens whose KV states follow shallow visibility, let A denote the full-depth prefill-side anchor set, and let Y={y 1, . . . , yM } denote assistant target tokens. During SPEED-aware supervised fine-tuning, assistant target positions follow the sam...

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We vary the prompt length from 1K to 128K tokens and report the mean and standard deviation over five repeats

51.4 59.7 46.9 28.1 63.9 51.7 78.5 70.6 67.2 25.9 63.2 9.0 IT-SPEED-28+BoS 51.3 58.9 50.3 27.8 63.8 50.3 78.7 71.7 68.2 24.9 62.5 7.3 IT-SPEED-28 50.2 59.8 50.1 22.0 64.1 48.1 78.0 69.7 66.9 23.6 63.4 6.6 IT-SPEED-24+BoS 51.2 59.6 51.2 27.2 62.7 53.2 79.0 71.7 66.0 24.6 60.6 7.2 IT-SPEED-24 49.1 57.4 49.4 18.3 59.7 49.9 79.6 71.6 64.1 22.1 60.4 8.2 IT-SPE...

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Adding a BoS anchor substantially reduces this failure mode without restoring upper-layer KV states for the full prefill sequence

0.4 0.1 0.0 0.0 0.5 0.0 0.0 0.1 0.0 2.0 1.3 0.7 IT-SPEED-28 1.4 0.1 0.0 7.6 1.1 0.0 0.0 1.6 0.0 3.2 1.7 0.5 IT-SPEED-28+BoS 0.6 0.1 0.0 0.0 1.4 0.0 0.0 0.4 0.0 2.1 1.5 1.0 IT-SPEED-24 2.1 0.1 0.0 10.3 2.6 0.0 0.0 3.1 0.0 4.0 1.8 0.9 IT-SPEED-24+BoS 0.7 0.1 0.0 0.0 1.2 0.0 0.0 0.5 0.0 2.3 2.0 1.4 IT-SPEED-20 0.8 0.7 0.0 0.1 1.4 0.0 0.0 0.7 0.0 3.8 1.5 0.7 ...

2019