When Can Attention Heads Be Statically Defined?

Weixian Waylon Li; Yintao Tai; Marcio Fonseca; Shay B. Cohen

cs.LGSep 28, 2026

When Can Attention Heads Be Statically Defined?

Authors: Weixian Waylon Li, Yintao Tai, Marcio Fonseca, Shay B. Cohen

Organizations: University of Edinburgh, United Kingdom · Chamber of Deputies, Brazil

Abstract

Some attention heads learn similar patterns across inputs. Reusing these patterns could reduce training cost by avoiding repeated query-key score computation and softmax. Through controlled pretraining comparisons, we identify Selective Attention Freezing (SAF), which selects heads with low attention-pattern variance and replaces their attention weights with fitted post-softmax means halfway through training. We represent these fixed patterns with absolute-position and relative-distance preferences, reducing storage from quadratic to linear in sequence length. A fused kernel reconstructs the patterns and executes ordinary-attention and replaced heads together. At matched training-token budgets, replacing 25% of attention heads gives 1.056x faster post-replacement optimiser updates at 124M parameters and 4K context, with a 0.77% perplexity increase. At 1B and 8K context, post-replacement updates are 1.068x faster on four GPUs including communication, with a 0.51% perplexity increase. The resulting models also accelerate long-input finetuning and causal prefill. After associative-recall adaptation, the 124M model with 25% replacement generalises to more key-value pairs at a fixed length better than ordinary attention and two pruning controls.

Figures & tables

Figure 1: Overview of fixed-pattern replacement of ordinary attention.

Figure 2: Compact representation and execution of a fixed attention pattern.

Figure 3: Perplexity increases for five fixed patterns and four replacement rates.

Operation + selection	25%	50%	25%	50%	25%	50%
	Matched tokens: 2.4576B				Matched time
	$\Delta\mathrm{PPL}$ (%)		Time (min)		$\Delta\mathrm{PPL}$ (%)
Mean + variance	$0.663\pm 0.018$	$2.495\pm 0.024$	184.7	178.7	$0.456\pm 0.108$	$1.911\pm 0.112$
Pruning + variance	$0.806\pm 0.018$	$3.033\pm 0.056$	178.9	168.3	$0.113\pm 0.169$	$0.978\pm 0.108$
Mean + random heads	$1.282\pm 0.056$	$3.630\pm 0.093$	183.4	178.8	$1.243\pm 0.112$	$2.778\pm 0.125$

Table 1: Perplexity increase (%, lower is better) at matched tokens or time: mean

\pm

SE over three seeds. Times include the initial training with ordinary attention and intervention.

(a) Pretraining on FineWeb-Edu		Perplexity $\downarrow$			Update time		Memory
Context ( $B$ )	Replaced heads	Ordinary attn.	Replaced	$\Delta$ PPL (%)	Ord./repl. (ms)	Speedup	Peak change
4K (8)	25%	23.862	24.045	$+0.768\pm 0.067\%$	2249.49/2129.42	1.056 $\times$	$-1.96\%$
4K (8)	50%	23.862	24.457	$+2.492\pm 0.083\%$	2229.80/1991.85	1.119 $\times$	$-3.04\%$
8K (4)	25%	24.179	24.318	$+0.578\pm 0.011\%$	2710.24/2636.22	1.030 $\times$	$-1.63\%$
8K (4)	50%	24.179	24.676	$+2.058\pm 0.066\%$	2782.97/2524.39	1.101 $\times$	$-2.55\%$
16K (2)	25%	24.428	24.538	$+0.450\pm 0.012\%$	3741.42/3649.78	1.025 $\times$	$-0.82\%$

Table 2: Quality and training costs for ordinary-attention and SAF 124M checkpoints. Ord./repl. (ms) reports optimiser-update times for the ordinary-attention and replaced models.

Figure 4: SAF speedup over FlashAttention: (a) pretraining updates; (b) finetuning updates; (c) causal prefill by input length at

B=64

; (d) prefill by batch size at

T=4096

Figure 5: MQAR accuracy for later-intervention, matched-time 124M checkpoints. Models train on eight pairs at 512 tokens. (The ordinary-attention and gate-Taylor pruning curves nearly overlap.)

Figure 6: Qwen3-4B zero-shot accuracy changes (pp) relative to the ordinary-attention model.

		Finetuning accuracy (%) $\uparrow$
Model	PPL $\downarrow$	SST-2	BoolQ	QuALITY
Ordinary attention	12.446	$91.44\pm 0.25$	$75.98\pm 0.24$	$27.44\pm 1.25$
SAF 25%	12.509	$91.63\pm 0.35$	$75.27\pm 1.45$	$27.60\pm 0.21$
Pruning 25% (gate-Taylor)	12.529	$91.70\pm 0.34$	$75.46\pm 1.19$	$27.66\pm 0.71$
SAF 50%	12.698	$90.98\pm 0.43$	$75.68\pm 0.31$	$25.12\pm 0.75$

Table 3: 1B quality at 8K context and 19.667B training tokens. PPL uses one pretraining seed; task accuracies (%) show mean

\pm

SE over three finetuning seeds.

Appendix figures & tables31 assets

Supplementary material from the paper’s appendix.

Appendix

Distance from mean	10%	25%	50%	75%	Stratified
Squared error	0.900	1.000	1.000	0.900	0.950
$D_{\mathrm{KL}}(\overline{\mathbf{A}}\\|\mathbf{P})$	0.600	0.600	0.600	0.700	0.625
Squared Hellinger	0.800	0.900	0.900	1.000	0.900
Total variation	0.800	0.900	0.900	1.000	0.900

Appendix

Table 4: Fixed-pattern geometry: within-rate correlation between distance from the post-softmax mean and final perplexity increase.

Risk score	Spearman $\uparrow$	25% mean cost $\downarrow$	50% mean cost $\downarrow$
Attention variance, $s^{\mathrm{var}}$	0.723	0.058%	0.115%
Residual cosine, $s^{\mathrm{cos}}$	0.810	0.033%	0.121%
Relative attention-output change, $s^{\mathrm{rel}}$	0.703	0.052%	0.150%
Projected-output magnitude, $s^{\mathrm{mag}}$	0.654	0.166%	0.129%
Projected-output redundancy, $s^{\mathrm{red}}$	$-0.013$	0.320%	0.367%

Appendix

Table 5: Immediate one-head diagnostics at the 4K RoPE midpoint. Spearman correlates scores with NLL changes; mean costs are relative perplexity increases.

Context	Replaced	Variance	Forward KL	KL–variance
	heads	$\Delta$ PPL	$\Delta$ PPL	(pp)
4K	25%	0.665%	1.068%	+0.402
4K	50%	2.649%	3.554%	+0.905
8K	25%	0.557%	0.836%	+0.279
8K	50%	2.191%	2.988%	+0.797
16K	25%	0.474%	0.876%	+0.402

Appendix

Table 6: Final perplexity increase after joint head selection, midpoint replacement, and 2,500 continuation updates.

Figure 7: Selected-head diagnostics: (a) layer distribution; (b) quarter-to-midpoint retention; (c) midpoint attention profiles; (d) entropy and variance in the original one-head probe. Bars in (a,c) show mean

\pm

SE over three seeds.

Figure 8: Paired train-loss gap after replacing 25% of heads at training fraction

\tau

Method choice	Alternatives evaluated
Fixed pattern	Post-softmax mean; sharp mean; Gaussian sample; Dirichlet sample; structured random
Head selection	Attention variance; row-wise forward KL; Q/K gradient norm; rank combinations; projected-output magnitude and redundancy; attention-residual influence
Amount and timing	Rates: 10%, 25%, 50%, 75%; times: 25%, 40%, 50%, 60%, 75%, 100%; one-time, gradual, validation-budget, and train-loss-triggered schedules
Representation and execution	Exact dense or absolute-plus-relative pattern; reference or fused mixed-head execution

Appendix

Table 7: Method choices evaluated in controlled 124M pretraining.

$\tau$	$M-t$	$\Delta\mathrm{PPL}$ (%)
25%	3750	0.724
40%	3000	0.679
50%	2500	0.662
60%	2000	0.711
75%	1250	0.936
100%	0	8.168

Appendix

Table 8: Timing at 25% replacement with the post-softmax mean.

\tau

: fraction of training completed;

M-t

: remaining updates;

\Delta\mathrm{PPL}

: final perplexity increase.

Comparison	Setting	Rate	$\Delta\mathrm{PPL}$ (%)
Preset schedule	one replacement	25.00%	0.662
	gradual, five groups	25.00%	0.689
	one replacement	50.00%	2.649
	gradual, five groups	50.00%	2.832
Adaptive schedule	validation budget 1%	6.25%	0.093
	validation budget 2%	12.50%	0.348

Appendix

Table 9: Single-seed comparison of preset and adaptive replacement schedules.

Rate	One-time $\Delta\mathrm{PPL}$ (%)	Train-loss $\Delta\mathrm{PPL}$ (%)	Difference (pp)	Time ratio (triggered/one-time)
25%	$0.768\pm 0.067$	$0.858\pm 0.180$	$+0.091\pm 0.123$	1.027 $\times$
50%	$2.492\pm 0.083$	$2.495\pm 0.189$	$+0.003\pm 0.267$	1.066 $\times$

Appendix

Table 10: Three-seed comparison of one-time and train-loss-triggered schedules at matched final rates.

Replaced heads	Update speedup	Peak allocated	Saving
0%	1.000 $\times$	32.21 GiB	0.00 GiB
10%	1.016 $\times$	31.85 GiB	0.36 GiB
25%	1.056 $\times$	31.58 GiB	0.63 GiB
50%	1.119 $\times$	31.24 GiB	0.98 GiB
75%	1.215 $\times$	30.84 GiB	1.38 GiB

Appendix

Table 11: Post-replacement training speed and memory at

T=4096

and batch size eight.

Figure 9: Fixed-entry CDFs at

i=1024

j=1023

: logits (top) and attention probabilities (bottom, logarithmic horizontal axis).

Figure 10: UMAP of empirical binned attention profiles and fitted Dirichlet samples at concentration scale 4.

				Paired differences
Setting (rate)	PPL $\downarrow$	Training (min)	Intervention (s)	$\Delta\mathrm{PPL}$ (pp)	Time (s)
Ordinary attention	$23.896\pm 0.038$	$188.7\pm 2.7$	—	—	—
Mean + variance (25%)	$24.054\pm 0.041$	$184.7\pm 1.6$	$54.9\pm 6.0$	—	—
Mean + variance (50%)	$24.492\pm 0.035$	$178.7\pm 2.0$	$46.3\pm 1.2$	—	—
Pruning + variance (25%)	$24.088\pm 0.042$	$178.9\pm 1.6$	$46.3\pm 3.1$	$+0.1428\pm 0.0040$	$-343.9\pm 27.8$
Pruning + variance (50%)	$24.621\pm 0.049$	$168.3\pm 1.3$	$50.8\pm 5.8$	$+0.5378\pm 0.0622$	$-620.9\pm 65.3$

Appendix

Table 12: Matched-token controls: mean

\pm

SE over three pretraining seeds. Paired columns subtract midpoint mean + variance at the same rate.

Model	Rate	Capture and mean	Fit $\alpha,\rho$	Total intervention
124M, 4K	25%	$51.56\pm 5.96$	$3.20\pm 0.16$	$54.86\pm 6.04$
124M, 4K	50%	$40.89\pm 1.11$	$5.24\pm 0.09$	$46.30\pm 1.22$
1B, 8K	25%	1422.37	39.56	1464.41
1B, 8K	50%	1313.35	76.28	1392.64

Appendix

Table 13: One-time calibration and fitting costs in seconds. The 124M controls report mean

\pm

SE over three seeds; the 1B runs use one seed. Fitting is included in the total.

(a) Matched-token pretraining: 124M, 4K, 2.4576B tokens
Pattern	PPL $\downarrow$	Update (s) $\downarrow$	Continuation (min) $\downarrow$	Peak (GiB) $\downarrow$
Frozen mean	$24.055\pm 0.040$	$2.159\pm 0.012$	$90.96\pm 0.56$	$31.668\pm 0.002$
Trainable mean	$24.052\pm 0.042$	$2.416\pm 0.013$	$102.27\pm 0.82$	$31.801\pm 0.002$

Appendix

Table 14: Pattern controls at 25% replacement: mean

\pm

SE over three pretraining seeds.

Operation + selection	Rate	PPL $\downarrow$	Updates	Time (min)
Ordinary attention	0%	$23.882\pm 0.023$	$5008\pm 13$	$187.65\pm 0.25$
Mean + variance	25%	$23.991\pm 0.006$	$5109\pm 11$	$187.64\pm 0.25$
Mean + variance	50%	$24.338\pm 0.020$	$5221\pm 18$	$187.64\pm 0.25$
Pruning + variance	25%	$23.909\pm 0.024$	$5267\pm 30$	$187.64\pm 0.25$
Pruning + variance	50%	$24.115\pm 0.003$	$5685\pm 36$	$187.65\pm 0.25$
Mean + random heads	25%	$24.178\pm 0.024$	$5097\pm 28$	$187.64\pm 0.25$

Appendix

Table 15: Matched-time controls: mean

\pm

SE over three pretraining seeds. Updates and times include the initial training with ordinary attention.

	Pretraining PPL $\downarrow$		Finetuning accuracy (%) $\uparrow$
Operation + selection	Matched tokens	Matched time	SST-2	BoolQ
Ordinary attention	$22.563\pm 0.010$	$22.561\pm 0.011$	$87.00\pm 0.33$	$71.78\pm 0.46$
SAF + variance	$22.923\pm 0.013$	$22.895\pm 0.012$	$87.73\pm 0.46$	$71.27\pm 0.67$
Pruning + variance	$23.019\pm 0.028$	$22.953\pm 0.026$	$87.81\pm 0.34$	$71.75\pm 0.29$
Pruning + gate-Taylor	$22.865\pm 0.019$	$22.798\pm 0.021$	$86.96\pm 0.49$	$71.33\pm 0.16$

Appendix

Table 16: Later 25% intervention: mean

\pm

SE across three pretraining seeds. Finetuning uses the matched-time checkpoints and one fixed seed.

Midpoint, matched time: three pretraining seeds, one finetuning seed
		Accuracy (%) $\uparrow$
Operation + selection	Rate	SST-2	BoolQ
Ordinary attention	0%	$86.24\pm 0.93$	$71.86\pm 0.40$
Mean + variance	25%	$86.05\pm 0.54$	$70.52\pm 0.16$
Mean + variance	50%	$86.12\pm 0.54$	$70.66\pm 0.26$
Pruning + variance	25%	$86.85\pm 0.56$	$70.97\pm 0.66$

Appendix

Table 17: Supplementary finetuning comparisons. Each group specifies which seed varies; errors are standard errors.

Adaptation: three pretraining seeds, task seed 1337
				Pruning 25%
Updates	Tokens / pairs	Ordinary attn.	SAF 25%	Variance	Gate-Taylor
1,500	512 / 8	$99.58\pm 0.05$	$99.73\pm 0.04$	$99.72\pm 0.03$	$99.62\pm 0.06$
	512 / 16	$81.32\pm 3.21$	$91.08\pm 1.58$	$85.43\pm 3.07$	$80.79\pm 4.50$
	512 / 24	$60.56\pm 4.22$	$79.76\pm 2.72$	$66.25\pm 3.22$	$60.59\pm 6.42$
	512 / 32	$47.54\pm 3.87$	$70.83\pm 4.86$	$52.32\pm 2.41$	$47.95\pm 6.06$

Appendix

Table 18: MQAR query accuracy (%). Adaptation varies one seed axis at a time (mean

\pm

SE); immediate replacement and recovery use one task-trained ordinary-attention parent.

Context	$B$	Rate	Ordinary attn.	Mean	Pruning	Mean	Pruning
			Update time (ms) $\downarrow$			Peak change (%)
4K	20	25%	2172.80	2057.35	1923.26	$-2.15$	$-2.19$
4K	20	50%	2173.12	1929.10	1678.24	$-3.34$	$-4.22$
8K	10	25%	2651.80	2573.68	2307.18	$-2.24$	$-2.88$
8K	10	50%	2652.83	2408.64	1933.85	$-3.27$	$-4.95$
16K	5	25%	3714.63	3621.11	3083.03	$-1.76$	$-5.08$

Appendix

Table 19: Native-context training-update costs for matched head sets. Memory changes are relative to ordinary attention at the same context and batch size.

Operation + selection	Ordinary attn. (ms)	Method (ms)	Speedup ( $\times$ )	Peak change (%)
Matched-token pretraining
Mean + variance	154.59	142.52	$1.0847\pm 0.0024$	$+3.75$
Pruning + variance	155.26	133.43	$1.1636\pm 0.0012$	$-4.75$
Pruning + gate-Taylor	154.22	132.63	$1.1628\pm 0.0009$	$-5.66$
Pruning from initialisation	154.27	132.64	$1.1631\pm 0.0005$	$-4.75$
Matched-time pretraining

Appendix

Table 20: Causal prefill at

B=32

T=4096

for the single-seed 25% controls. Speedup SE is over four timing pairs.

Task / checkpoint	$B$	Accuracy (%)	$\Delta$ accuracy (pp)	Time/update	Speedup	Peak memory change
SST-2, ordinary attn.	256	$86.39\pm 0.27$	—	$50.11\pm 0.61$ ms	1.000 $\times$	0.00%
SST-2, fixed 25%	256	$86.54\pm 0.36$	$+0.15\pm 0.53$	$58.22\pm 9.58$ ms	$0.901\pm 0.122\times$	$-3.65\%$
SST-2, fixed 50%	256	$86.77\pm 0.74$	$+0.38\pm 1.00$	$56.70\pm 9.43$ ms	$0.925\pm 0.123\times$	$-5.95\%$
BoolQ, ordinary attn.	64	$71.40\pm 0.22$	—	$67.36\pm 0.51$ ms	1.000 $\times$	0.00%
BoolQ, fixed 25%	64	$70.62\pm 0.55$	$-0.77\pm 0.56$	$70.91\pm 7.25$ ms	$0.967\pm 0.083\times$	$-4.63\%$
BoolQ, fixed 50%	64	$70.84\pm 0.49$	$-0.56\pm 0.70$	$69.35\pm 7.61$ ms	$0.992\pm 0.093\times$	$-7.19\%$

Appendix

Table 21: Finetuning quality, update time, and peak-memory change for inherited fixed-attention checkpoints.

Task	Schedule	$\Delta$ accuracy (pp)	Time/update	Controller cost
SST-2	One-time	$-0.19\pm 0.40$	33.27 ms	0.70 s
	Train-loss	$-0.65\pm 0.77$	36.15 ms	45.12 s
	Post-training	$-1.03\pm 0.24$	30.75 ms	0.71 s
BoolQ	One-time	$-1.27\pm 1.29$	40.85 ms	6.18 s
	Train-loss	$-0.38\pm 0.15$	66.34 ms	70.15 s
	Post-training	$-1.03\pm 0.33$	31.44 ms	6.17 s

Appendix

Table 22: Replacing 25% of heads under three RoPE finetuning schedules.

GPUs / $B$	Rate	Ordinary attn. (ms)	SAF (ms)	Speedup ( $\times$ )	Peak change (%)
Four GPUs, including communication: 524,288 tokens per update
4 / 2	25%	4258.67	3986.67	$1.0682\pm 0.0005$	—
4 / 2	50%	4263.01	3653.78	$1.1667\pm 0.0003$	—
Single GPU, no communication: 131,072 tokens per update
1 / 1	25%	4224.85	3958.26	1.067	$-0.57$
1 / 1	50%	4226.87	3632.25	1.163	$-2.31$

Appendix

Table 23: 1B post-replacement update cost at 8K. Four-GPU speedup errors are SE over four timing pairs; single-GPU results use medians.

Task	Model	1337	1338	1339	Change (pp)	Update (ms)
		Accuracy by seed $\uparrow$
SST-2	Ordinary attention	91.63	90.94	91.74	—	$173.58\pm 0.74$
	SAF 25%	90.94	91.86	92.09	$+0.19\pm 0.47$	$166.78\pm 2.75$
	Pruning 25%	91.06	91.86	92.20	$+0.27\pm 0.44$	$156.60\pm 0.97$
	SAF 50%	91.28	91.51	90.14	$-0.46\pm 0.63$	$158.36\pm 3.67$
BoolQ	Ordinary attention	75.99	76.39	75.57	—	$222.70\pm 0.32$

Appendix

Table 24: 1B finetuning by seed: accuracy (%), paired change from ordinary attention (pp), and observed mean update time. Errors are SE across finetuning seeds.

		Attention variance			Forward KL
Task	Ordinary attn.	10%	20%	30%	10%	20%	30%
HellaSwag	52.28	52.02	51.21	48.92	49.10	45.05	43.81
PIQA	74.86	75.19	74.48	73.56	74.70	72.58	70.40
ARC-Easy	80.43	79.63	77.61	75.72	72.90	68.06	64.10
SST-2	88.65	88.65	87.96	85.44	87.61	63.07	68.81
BoolQ	84.46	83.33	80.49	73.12	80.03	71.38	71.38

Appendix

Table 25: Qwen3-4B zero-shot accuracy (%) with compact FineWeb-Edu mean patterns. The last row gives the mean change from the ordinary-attention model in percentage points.

$B$	$T$	10%	20%	30%	50%	75%
4	2048	1.011 (1.16)	1.028 (1.18)	1.068 (1.21)	1.135 (1.29)	1.225 (1.38)
4	4096	1.027 (1.16)	1.057 (1.19)	1.097 (1.23)	1.156 (1.28)	1.246 (1.38)
4	8192	1.042 (1.15)	1.072 (1.19)	1.115 (1.23)	1.184 (1.30)	1.250 (1.37)
8	2048	1.015 (1.15)	1.042 (1.17)	1.079 (1.21)	1.148 (1.28)	1.231 (1.37)
8	4096	1.029 (1.15)	1.061 (1.19)	1.080 (1.18)	1.156 (1.27)	1.252 (1.37)
8	8192	1.040 (1.15)	1.071 (1.18)	1.110 (1.22)	1.178 (1.30)	1.272 (1.40)

Appendix

Table 26: Qwen3-4B prefill speedup (

\times

): end-to-end, with attention-operation speedup in parentheses.

Position	Ordinary attn. PPL	Dense PPL	Abs.+rel. PPL	Storage reduction
Absolute	26.0252	26.1184	26.1150	59.9 $\times$
RoPE	23.9571	24.1180	24.1165	60.6 $\times$

Appendix

Table 27: Representation quality at 25% replacement (one seed), prior storage, and kernel checks.

	FP32		BF16 autocast
Tensor	Max. absolute	Relative $L_{2}$	Max. absolute	Relative $L_{2}$
Output	$1.19\times 10^{-7}$	$1.86\times 10^{-7}$	$3.91\times 10^{-3}$	$2.80\times 10^{-3}$
Input gradient $dX$	$3.58\times 10^{-7}$	$2.51\times 10^{-7}$	$7.81\times 10^{-3}$	$3.68\times 10^{-3}$
Value gradient $dV$	$3.58\times 10^{-7}$	$1.78\times 10^{-7}$	$7.81\times 10^{-3}$	$1.46\times 10^{-3}$
Query projection	$8.34\times 10^{-7}$	$2.82\times 10^{-7}$	$1.56\times 10^{-2}$	$4.53\times 10^{-3}$
Key projection	$1.19\times 10^{-6}$	$3.01\times 10^{-7}$	$1.56\times 10^{-2}$	$4.43\times 10^{-3}$

Appendix

Table 28: Numerical agreement with the eager reference for a layer with ordinary-attention and fixed-mean heads. Projection rows report weight gradients.

Input length	Batch size $B$	Replaced heads	Ordinary attn. (ms)	Replaced (ms)	Speedup
4K	64	25%	306.70	278.33	1.100 $\times$
4K	64	50%	303.94	247.47	1.229 $\times$
8K	64	25%	776.17	705.03	1.100 $\times$
8K	64	50%	760.20	622.94	1.221 $\times$
16K	64	25%	2075.04	1900.13	1.092 $\times$
16K	64	50%	2045.38	1703.87	1.200 $\times$

Appendix

Table 29: Selected causal-prefill latencies for the ordinary-attention and replaced 16K RoPE checkpoints at

B=64

, without CUDA Graph replay. Input length varies while the checkpoints remain fixed.

Asset	Use	Upstream licence or terms
Datasets
FineWeb-Edu	Pretraining; calibration and evaluation	ODC-By 1.0 ; Common Crawl terms
SST-2 (GLUE)	Finetuning and zero-shot evaluation	Not specified in the original dataset card
BoolQ (SuperGLUE)	Finetuning and zero-shot evaluation	CC BY-SA 3.0
QuALITY	Long-input finetuning	CC BY 4.0 ; article-level licences
HellaSwag	Zero-shot evaluation	MIT

Appendix

Table 30: External datasets and pretrained models.

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Prefill Efficient Long-Context Inference Kimi Delta Attention Visual Token Pruning Redundancy

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Prefilling computational costs pose a significant bottleneck for Large Language Models (LLMs) and Large Multimodal Models (LMMs) in long-context settings. While token pruning reduces sequence length, prior methods rely on heuristics that break compatibility with hardware-efficient kernels like FlashAttention. In this work, we observe that tokens evolve toward \textit{semantic fixing points}, making further processing redundant. To this end, we introduce Delta Attention Selective Halting (DASH), a training-free policy that monitors the layer-wise update dynamics of the self-attention mechanism to selectively halt stabilized tokens. Extensive evaluation confirms that DASH generalizes across language and vision benchmarks, delivering significant prefill speedups while preserving model accuracy and hardware efficiency. Code will be released at https://github.com/verach3n/DASH.git.

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