Knowing When to Stop: Efficient Context Processing via Latent Sufficiency Signals

Roy Xie; Junlin Wang; Paul Rosu; Chunyuan Deng; Bolun Sun; Zihao Lin; Bhuwan Dhingra

cs.CLFeb 3, 2025

Knowing When to Stop: Efficient Context Processing via Latent Sufficiency Signals

Authors: Roy Xie, Junlin Wang, Paul Rosu, Chunyuan Deng, Bolun Sun, Zihao Lin, Bhuwan Dhingra

Organizations: Duke · Rice · JHU · UC Davis

Abstract

Large language models (LLMs) process entire input contexts indiscriminately, which is inefficient when the information required to answer a query is localized within the context. We present dynamic context cutoff, a novel method enabling LLMs to self-terminate processing upon acquiring sufficient task-relevant information. Through analysis of model internals, we discover that specific attention heads inherently encode "sufficiency signals" -- detectable through lightweight classifiers -- that predict when critical information has been processed. This reveals a new efficiency paradigm: models' internal understanding naturally dictates processing needs rather than external compression heuristics. Comprehensive experiments across six QA datasets (up to 40K tokens) with three model families (LLaMA/Qwen/Mistral, 1B-70B) demonstrate 3.4% accuracy improvement while achieving 1.33x token reduction on average. Furthermore, our method demonstrates superior performance compared to other context efficiency methods at equivalent token reduction rates. Additionally, we observe an emergent scaling phenomenon: while smaller models require probing for sufficiency detection, larger models exhibit intrinsic self-assessment capabilities through prompting.

Figures & tables

Figure 2: Our method leverages the model’s internal representations to identify when sufficient information has been processed. A lightweight classifier is trained on selected attention heads to detect context sufficiency, leading to token savings while improving task performance.

Figure 4: Our method achieves superior efficiency-accuracy trade-offs compared to baselines. RAG degrades with scale, while Lingua2 remains competitive but lags on multihop tasks. Larger models (14B+) exhibit emergent self-awareness on context sufficiency through prompting.

Method	LLaMA3.2-1B			Ministral-8B			Qwen2.5-14B			LLaMA3.3-70B			Avg.
	Multi	Single	Avg	Multi	Single	Avg	Multi	Single	Avg	Multi	Single	Avg	Multi	Single	Total
Full Context	10.4	17.9	14.2	29.6	44.8	37.2	30.4	57.6	44.0	37.1	75.0	56.1	26.6	48.7	37.9
BM25	11.2	16.2	13.7	20.8	27.5	35.6	25.8	40.8	36.5	21.7	37.1	41.7	19.9	30.4	31.9
SBERT	10.2	17.8	14.0	19.6	37.5	35.2	26.3	51.3	42.3	22.1	41.7	40.8	19.6	37.1	33.1
LLMlingua	6.3	18.3	12.3	22.1	41.7	31.9	24.2	52.5	38.3	35.8	74.1	55.0	22.1	46.7	34.4
LongLLMlingua	6.7	20.0	13.3	22.1	41.7	31.9	26.3	55.8	41.1	35.4	71.7	53.6	22.6	47.3	35.0

Table 2: Performance comparison across different models on Single-hop and Multi-hop tasks on the short-form dataset. Our method achieves a token reduction of

1.33\times

, while outperforming static methods with a targeted compression rate at

1.25\times

Method	LLaMA3.2-1B			Ministral-8B			Qwen2.5-14B			LLaMA3.3-70B			Avg.
	Multi	Single	Avg	Multi	Single	Avg	Multi	Single	Avg	Multi	Single	Avg	Multi	Single	Total
Full Context	5.0	10.4	7.7	18.3	38.8	28.5	29.9	40.0	35.0	29.3	70.8	50.0	20.6	40.0	30.3
BM25	5.7	12.2	8.9	20.9	38.7	29.8	30.2	39.2	34.7	28.7	68.7	48.7	21.3	40.0	30.5
SBERT	5.6	12.5	9.1	20.2	37.7	29.4	30.0	38.9	34.4	27.7	68.8	48.3	21.1	39.5	30.3
LLMlingua	3.8	12.1	7.9	17.1	35.8	26.5	27.1	41.8	34.5	23.3	65.4	44.4	17.8	38.8	28.3
LongLLMlingua	3.3	12.1	7.7	15.0	37.1	26.0	28.0	40.1	34.1	27.5	67.9	47.7	18.5	39.3	28.9

Table 3: Performance comparison across different models on Single-hop and Multi-hop tasks on the long form dataset. Our method achieves a token reduction of

1.27\times

, while outperforming static methods with a targeted compression rate at

1.25\times

Figure 7: For short contexts (1K tokens), full-context processing is faster. However, beyond 2K tokens, our method becomes more efficient, achieving faster inference when fewer than six chunks (60% of the full context) are processed.

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

Token Count
Statistic	Single-hop		Multi-hop
	Code	SQuAD & Natural Questions	KV	HotpotQA & MUSIQUE
Mean	22,758.96	22,653.68	22,494.19	24,970.78
Median	22,739.50	22,584.00	22,619.00	25,168.50
Std Dev	7,299.95	6,758.97	7,290.87	7,159.93
Max	35,465	36,888	35,426	40,622

Appendix

Table 6: The datasets are grouped based on reasoning type. Code Understanding (Code) is distinct as it involves synthetic code understanding, whereas SQuAD and Natural Questions focus on retrieving answers from a single passage. Multi-hop Key-Value Retrieval (KV) is separate as a synthetic multi-hop key-value retrieval task, while HotpotQA and MUSIQUE involve natural multi-hop reasoning across multiple passages. Statistics include Token Count and Gold Location.

Token Count
Statistic	Single-hop		Multi-hop
	Code	SQuAD & Natural Questions	KV	HotpotQA & MUSIQUE
Mean	2951.23	1678.98	2723.65	1676.15
Median	2910.50	694.50	2704.00	1735.00
Std Dev	562.14	1269.42	450.02	646.41
Max	4990	4993	4616	2815

Appendix

Table 7: Categorized by reasoning type, with single-hop tasks (Code Understanding, SQuAD, and Natural Questions) involving direct retrieval from a passage, and multi-hop tasks (Multi-hop Key-Value Retrieval, HotpotQA, and MUSIQUE) requiring inference across multiple segments. Statistics include both token count and gold location.

Task Type	Synthetic		Original
	1B	8B	1B	8B
Single-hop	82.1	84.4	85.7	89.3
Multi-hop	87.1	89.6	90.9	90.3
Overall F1	84.6	87.0	88.3	89.8
P90	79.3	82.7	85.9	90.1

Appendix

Table 8: Performance comparison between classifiers trained with synthetic (GPT-4o generated) versus original (human-annotated) sufficiency labels across different model sizes.

Figure 8: Probing results for the Qwen2.5 14B model. The heatmap shows the average F1 score for each head across all layers, which is different from LLaMA models.

Figure 9: Confidence progression in TREC ICL task: The 8B model requires nearly all examples to achieve its highest confidence, whereas the 14B model attains peak confidence after processing fewer examples. This illustrates the need for model-specific cutoff thresholds.

	Head Numbers			Classifier Numbers
Metrics	5	10	20	2	4	6
F1-Score	88.3	87.3	87.9	87.4	88.3	87.3
R@90P	85.9	78.0	78.0	77.6	85.9	78.0
Acc.	13.9	13.0	12.8	12.7	13.9	12.9

Appendix

Table 9: Performance comparison across head selections and number of classifiers for our method.

Model	GPUs Used
LLaMA 3.2-1B	2 × Nvidia A5000
Mistral 8B	4 × Nvidia A5000
Qwen 2.5-14B	4 × Nvidia A5000
LLaMA 3.3-70B	4 × Nvidia A6000

Appendix

Table 10: GPU configurations used for different models in our experiments.

Base Model	Trained & Evaluated on	Test Accuracy
Llama3.2-1b	Short Dataset	0.8346
Llama3.2-1b	Long Dataset	0.7515
Llama3.2-3b	Short Dataset	0.8413
Llama3.2-3b	Long Dataset	0.7456

Appendix

Table 11: Performance of fine-tuned classifiers tuned on different datasets.

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Large language models (LLMs) increasingly rely on long-context processing, but expanding context windows introduces substantial computational and financial costs. Existing context reduction approaches, including retrieval and memory compression methods, are typically evaluated using performance and efficiency metrics independently, limiting systematic comparison and deployment-aware decision-making. This paper introduces The Efficiency Frontier, a unified framework for cost--performance optimization in LLM context management. The framework models context strategy selection as a deployment-aware optimization problem that jointly accounts for task performance, token cost, and preprocessing reuse through amortized cost modeling. Unlike existing evaluations that compare methods in isolation, the proposed framework enables decision-oriented analysis. It identifies when different context management strategies become preferable under varying operational conditions. Experiments on HotpotQA reveal distinct operational regimes and transition boundaries between retrieval-based and preprocessing-based strategies. Results show that deployment-aware optimization reduces effective token usage by approximately 25% at comparable performance, enabling more cost-efficient deployment of large language model systems, while amortized memory compression achieves over 50% lower token cost relative to full-context prompting in higher-performance settings. Overall, the proposed framework provides a principled and practical foundation for evaluating and deploying scalable, efficient, and sustainable LLM systems across enterprise, scientific, and public-sector applications.

Abstract

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