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
Venue: NeurIPS 2025 poster ()
Keywords: Large language models
efficiency
context processing
context compression
Primary area: deep_learning

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.

Knowing When to Stop:
Efficient Context Processing via Latent Sufficiency Signals
Roy Xiet*
Junlin Wang
Paul Rosu
Chunyuan Deng
Bolun Sun
Zihao Lin
Bhuwan Dhingra
Duke
Rice
SJHU
lUC Davis
Abstract
Large language models (LLMs) process entire input contexts indiscriminately, which is inefficient when the information required answer query is localized within the context. We present dynamic context cutoff, a novel method enabling LLMs self-terminate processing upon acquiring sufficient task-relevant infor- mation. 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 re- veals 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 fami- lies (LLaMA/QQee/MMstral)) IB-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. Code available at
Introduction
Large language models (LLMs) demonstrate markable capabilities across diverse tasks, yet their indiscriminate processing of entire input contexts creates inefficiencies. LLMs process every token with equal computational priority, regardless of its actual relevance to the task (25). This frute-force approach leads to fundamen- tal inefficiencies: models waste computation on irrelevant context while simultaneously strug- phenomenon,
Given the dozuments, could tell which company has largest cotal liabilities?
DDe DDo TechNovu Corporation reports the highest total libilities $15.8 bullion of December 2023, The CEO Teck Jone Doe, emphavisnd
got it it TochNova!
gling with the "lost-in-the-midee where critical information becomes diluted in lengthy inputs [6] 6). For instance, when an- swering simple factual question, models may process an entire document even after gathering sufficient information in the first few sentences.
Figure 1: Our method enables language models to perform early termination by detecting suffi- ciency signals in key attention heads, reducing the amount of processed content while preserv- ing performance.
The human cognitive system offers an instructive contrast. When solving problems, people dynami- cally assess information sufficiency we stop processing we gather enough evidence, ignoring redundant details (4. On the other hand, L.MSs process entire contexts even after acquiring sufficient
"ruoyu.xic dukc.edu
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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information. This raises a question: "Can we enable LLMs to self context sufficiency and terminate early without compromising accuracy?"
In this work, we present dynamic context cutoff, which enables LLMs to identify when they have acquired sufficient information for a task. Our key insight emerges from analysis of model internals: specific attention heads in transformer layers exhibit strong sensitivity to information sufficiency (...) By monitoring these "context sufficiency heads" with lightweight classifiers, we enable models to make early stopping decisions while improving performance.
Context compression offers a promising avenue for improving inference efficiency in LLMs. How- ever, existing compression methods typically operate by predefining a target compression rate, which introduces the risk of information loss. For instance, the LLMLingua family [8 TO] 18] employs small language model to filter out unimportant tokens, reducing context based on a fixed compression target. These methods impose predefined compression rates, enforcing one-size-fits-all reduc- tion regardless of content complexity. Similarly, retrieval-augmenned generation (RAG) methods predefine fixed number of top-k retrieved documents. Although RAG operates differently by retrieving external documents rather than compressing existing input, we include it for comprehensive comparison as it has appeared in the baselines of previous work on context compression [TO] 18) We refer to both compression- -based approaches and RAG as static methods, as they apply uniform compression ratios (e.g.. 50% compression results in every input being compressed to exactly half its length). In contrast, our method is dynamic and context-adaptic amounts of compression based on their information density, with actual compression determined by each input's 's specific content rather than predetermined target. This approach enables models to process only the minimal context needed, expanding it only when necessary, creating a new paradigm where efficiency emerges naturally from the model', 's own understanding rather than from external compression heuristics, as demonstrated in Figure []
different inputs receive different
We conduct comprehensive experiments to evaluate our approach across six QA datasets (context lengths 0.5K-40K tokens) and three model families (LLaMA, Qwen, Mistral; IB-70B parameters). We find that LLMs inherently encode context sufficiency signals in specific attention heads. No- tably, our method reveals behaviors that align well with the scaling properties of modern LLMs: while smaller models (IB-SB parameters) require explicit sufficiency detection to achieve competi- tive efficiency, larger models (14B+) exhibit emergent self-assessment capabilities through simple prompting. Our method achieves a 3.4% average accuracy improvement with 1.33 token reduction, outperforming state-of-the-art context compression methods. Our in-depth analysis explores the sensitivity to classification thresholds, the efficiency gains from different chunking strategies, and the model-specific nature context sufficiency, providing valuable insights into context sufficiency.
Related Work
Efficient Context Processing in LLMs. Improving inference efficiency LLMs has attracted
significant research attention. Existing approaches fall into two orthogonal categories: (1) methods that approximate transformer computations during inference, including speculative decoding 13]
quantization (32). efficient attention mechanisms [1] [2]. and KV cache optimization [37,9] 29
methods that reduce input context length through compression 8 10 I8 28 [5]. These approaches complementary- any context compression method can benefit from computational approximations. Our work focuses on context reduction rather than computational approximation. Specifically, our method aligns with hard prompt compression approaches, like the LLMLingua family, which compress input at the textual level without requiring model retraining, We include the full suite of LLMLingua variants in experiments. For comprehensive comparison, we also include the RAG baselines used in [10 18), though RAG operates differently by retrieving external documents rather than compressing existing input context. Critically, unlike both L.MMinngua and RAG, our method does not require any predefined compression target and dynamically adjusts context length based on the model's own understanding of the input content.
Latent Knowledge in Model Activations. LLMs encode task-relevant knowledge within inter- mediate activations
(23], often more accurately than their final surface outputs [2] [4]. This latent knowledge has been leveraged for various downstream applications, including knowledge graph con- struction (27], reasoning correctness verification (35), hallucination detection [7]. and long-context understanding (34). We extend this line of work by interpreting model internal activation subspaces

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to detect context sufficiency. Unlike prior work, our approach uniquely identifies when the model has internally synthesized adequate information and leverages this insight to reduce information overhead during context processing. To the best of our knowledge, we are the first to propose dynamic context cutoff method that uses the model's internal signals determine when to stop processing context.
2
Methodology
We propose dynamic context cutoff, method that enables LLMs identify and process only the minimal sufficient context required for a given task. Our approach leverages internal model activations detect when enough information has been gathered, reducing token processing while improving performance, as illustrated in Figure
2.1 Problem Formulation
Let M denote a pre-trained language model. Given an input textual context C, we process C sequentially from left to right by partitioning it into an ordered sequence of chunks (sj } where each chunk 5j comprises contiguous subset of C (e.g.. sentence or a fixed percent- age of the total tokens). These chunks form non-overlapping covering of C, meaning:
Query: What the source the Keeini rive??
Context: The Kdehini Rover is
Outpot: The aource i8
Proccss Noxt Chunk
YES
NO
(chunk n).
Furowrd Pass
Cached activations from pravious chunks
Sufficiency Classification
Chunk Activation
m 1- C,
S. nn 5j = 0 for jj
Figure 2: Our method leverages the model's in- ternal 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.
where || denotes concatenation. We define a sequence of cumulative contexts h where each cumulative context C, consists of all chunks up to and including the i-th chunk:
C; $ 52 ..... St,
1 15 isism
By construction, these cumulative contexts satisfy the nested proper subset relationship: C₁ C C2 C
C Cm = C. Given query q, the goal is to identify the smallest prefix C₁ (where k S m) such
that:
M(g. C+) ~ M(g. C).
Here, C₁ represents the minimal sufficient context required for the model to answer q with comparable performance to using the full context C.
At each step i, sufficiency classifier S iteratively checks whether the current cumulative context C; contains enough information, by comparing its confidence (C;) with threshold 7. Formally,
S(C.) = {
if (C,) ,
otherwise
where S. Rd - 1) is the sufficiency confidence score function, and is a predefined threshold. If S(C,) 1, processing terminates, and C, is selected as the minimal sufficient context Cj. The remaining chunks {s141, s142...., sm} C \\ C are ignored.
We formulate our task left-to-right context processing problem rather than searching or selecting an arbitrary subset of documents. This aligns with how LLMs naturally process text from left to right to maintain semantic coherence and continuity between chunks. Our cumulative, non- overlapping chunking approach is essential for computational efficiency: each new chunk extends context incrementally (chunk), chunk chunki+2+3 etc.), allowing us to reuse the KV cache and avoid redundant computation of previously processed tokens. Overlapping chunks would require recomputing activations for the same tokens multiple times, negating the computational efficiency gains that make this approach practical. More discussion in Appendix B.3
3

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2.2 Probing LLMs for "Context Sufficiency"
We are interested in understanding how context sufficiency represented within the model and how it can be leveraged to improve efficiency. To do so, we probe its intermediate activations. Following prior work on neural network interpretability [14 2 we assess whether certain attention heads encode information predictive of sufficiency. The data for probing consists of input cumulative contexts (C.) each labeled as either sufficient (y 1) or insufficient (y 0).
For each C, the model produces attention head activations {x/), where of RD is the activa- tion of the /-th head in the 1-th layer. We train lightweight binary classifier po (xf)) these activations prediet sufficiency: Po (=()))
0.50
6.78
((0, xf)), where 0 E IR are the parameters of the probe, and a denotes the sigmoid function. The dataset split into training and validation sets (4:1 ratio) per task. We discuss more de- tails about the data used for the probe in $A.3 Each classifier's s validation FI score determines the predictive ability of the corresponding head. This selection process is performed only once for all tasks. Figure 3 shows the F1 scores of probes for all attention heads in LLaMA3.2- B. subset of heads, primarily in middle layers, ex- hibit significantly higher predictive performance. However, the performance of the probes may vary depending model architecture.
Figure Validation Fl scores for linear probes across all attention heads in LLaMA3.2 sorted row-wise by FI. Darker blue represents higher Fl scores. Some heads show significantly higher performance. More visualizations can be found in Figure X
These results suggest that the model's internal representations encode latent information about context sufficiency. We leverage this insight to identify the most informative attention heads for further processing, More details about the head selection process can be found in Appendix B.]
2.3 Dynamic Context Cutoff
Sufficiency Classification. After identifying the top heads from the probing step, we train multiple lightweight base classifiers { S.} these heads to form ensemble. The ensemble is constructed using StratifiedKFold with n = 3 folds, with the best performing models selected based on their mean cross- volsdaaaiio AUC scores to form the final weighted ensemble:
Sensemble (C,)= = 1 -
S,,,,)
More details on this ensemble classifier can be found in Appendix F.1
Inference with Iterative Forward Passes. During inference, the full context is processed incre- mentally as sequence of nested (C:) where each (C,) contains all preceding tokens. These progressively expanding subsets are passed through the model to extract activations at each step. Next, the ensemble classifier Sensemble predicts whether the current context (C.)² is sufficient.
The activations of all processed chunks are cached to avoid redundant computation. Let Auahe denote cached activations for C;, containing the activations of all previous chunks by construction. Activations for the current C, are computed as:
A(C.) = Smuxtel (C, C,-1, )
where fiodiel is the model's forward pass function conditioned on the cached activations Acache The iterative process continues until a sufficient context C₁ is identified, as determined by Sensemble- no C, is deemed sufficient, the entire input context is processed. In either cases, the cached activations will reused for generation. discuss potential KV cache optimization in Appendix [.4 The final output is computed as:
M(C+) = M(C2 CC-11, A

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Alternative Sufficiency Detection. As an alternative to the classifier- approach, larger LLMs can leverage their own reasoning capabiliities through self-promp For each cumulative context C, we append meta-prompt asking the model to evaluate whether it has sufficient information to answer query 4. The prompt can be found Appendix[D.T] The model's binary response determines sufficiency, enabling dynamic cutoff without classifiers. In $3.2 we show that self-prompiin becomes increasingly reliable with larger model sizes (14B+ parameters), suggesting that sufficiency detection emerges as capability with scale.
Experiments
In this section, we describe experimental setup in |3.1 and present comprehensive results demonstrating the effectiveness of our method in $3.
3.1 Experimental Setup
Datasets. We use two types of datasets, single-hop and multi-hop, to assess models' ability to locate the key information across varying context structures and tasks. For single-hop reasoning, where answers are typically found within a single passage requiring minimal context dependency, use SQuAD [19), widely used dataset with questions based Wikipedia passages; Natural Questions [T]. containing questions derived from woaldworld search queries with answers located in a single but longer passage; and a Code Understanding dataset, where we GPT--40 synthetically generate multiple single-functinn code snippets as distractors, and use the original PCSD (26) data to create QA task dataset requiring first locate and then understand the relevant code. For multi-hop reasoning, which requires combining information from multiple parts of the context to arrive at the correct answer, we use HotpotQA (11. a popular dataset with multi-hop questions requiring reasoning across multiple paragraphs from Wikipedia; MUSIQUE (24). a dataset with compositional and nested questions requiring multi-step reasoning across multiple documents; and Multi-hop Key-Value Retrieval (36), widely adopted synthetic dataset for evaluating long-context LLMs that requires exact retrieval of dependent key-value pairs across multiple documents.
Data Processing. To evaluate LLMs' long- long-context capabilities, extend these naturally short datasets to approximately 40K tokens. Following Liu et al. [16] and Zhang et al. [36]. we create long- form versions by combining multiple unique documents within each dataset, conducting experiments on both versions ($3.2). For each data point, we define the ground-truth sufficiency cutoff as the normalized position of the last token in the gold information span the minimal context required for correct answers. Importantly, our evaluation datasets are carefully balanced by design: the gold answer locations follow uniform distribution (mean z 0.50, standard deviation 0.25-0.28) across all datasets, ensuring approximately 50% of chunks are classified as "insufficient" and 50% as "sufficient." " This balanced distribution prevents bias toward early- or late-context answers and provides a fair assessment of context sufficiency detection. Detailed dataset statistics are provided in Appendix[A. [A] We examine the inference time-context length trade-off in our method ($4.2). This definition treats sufficiency as dataset property, implying a universal cutoff point across models. However, in practice, different models may need varying amounts of context to generate accurate responses, where sufficiency could be model-dependent. We investigate this phenomenon in $4.4 Additionally, when ground truth cutoff points are not available, we show that synthetically LLM generated sufficiency labels are effective proxies in Appendix A.4
Models and Baselines. We evaluate four open-source L.M families ranging from 1B to 70B parameters: LLaMA-3.2-IB (3) Mistral-8B [17] Qwen-2.5-14B [30]. and LLaMA-3.3-70B (3] Note that our proposed method is model-agnostic and can be applied to any Transformer based LLM. To ensure fair comparison, we follow previous work IT, [8] and evaluate our method against several well-establishe
baselines. For retrieval- based methods (RAG), we include BM25 [21). which ranks chunks by term frequency, and SBERT (20,, which uses transformer based embeddings for semantic relevance. For compression- based methods, we evaluate LLMLingua (8) which removes low-entropy tokens; LongLL.Miingu 10. which applies hierarchical filtering for long contexts; and I.LMIingua2 [18), which learns task-agnostic compression through knowledge distillation. Additionally, we include a Fine-Tuned Classifier baseline that learns to predict context sufficiency (Appendix F3 and Self-Prompting where the LLM assesses sufficiency through prompting (,.
5

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Evaluation Metries. We evaluate two aspects of our dynamic context cutoff method: sufficiency classification and task performance. For sufficiency classification, we use F1 Score, which balances precision and recall, capturing the trade-off between false positives (overestimating sufficiency) and false negatives (underestimating sufficiency). We also report Recall at 90% Precision (R @909))) which measures the percentage of sufficient contexts correctly identified while maintaining precision of at least 90%. This ensures that the method reliably avoids excessive false positives while achieving high recall. For task performance, we evaluate Accuracy, which measures the percentage of correct model outputs against the ground truth after and before context cutoff. We also evaluate Token Reduction, which quantifies the proportion of tokens processed relative to the full context. Following previous work [33]. we perform model based evaluation for accuracy calculation by using 44 Mini More details about the evaluation metrics and prompts can be found in Appendix C and Appendix D.2. respectively.
Implementation Details. The proposed dynamic context cutoff method involves three hyperpa- rameters: the classification threshold т, the number of attention heads used for training, and the number of classifiers in the ensemble. Among these, T is the key hyperparameter as it directly impacts the trade-off between efficiency and performance, as discussed $ The remaining two hyperparameters are determined empirically via a standard hyperparameter sweep on the validation set; details can be found in Appendix F.I Specifically, we k for attention heads with the highest Fl scores and train 8 lightweight classifiers for each head, selecting the top 4 with the highest AUC scores form the ensemble. More details can be found in Appendix [TI]] For all methods, including the proposed dynamic context cutoff method, we evaluate using percentage based chunking with 10% incremental threshold, meaning each chunk contains 10% more of the full context than the previous one. We explore different chunking strategies in Section
3.2 Results
Sufficiency Classification. Table shows that probing internal attention heads achieves su- perior sufficiency detection (F1 91.1) com- pared to supervised fine-tuning (79.5) and self- prompting (83.1) in 70B models, demonstrating that latent sufficiency signals are more reliable than surface-level outputs. We also observe an in- teresting phenomenon: while IB models struggle with self- prompting (F1 = 52.6). 70B versions achieve much higher performance, suggesting larger models intrinsically develop self-
Table 1: Probing (ours) achieves highest F1
scores compared to supervised fine-tuning (FT) and self self- prommtingn across all models.
Model
FT
Prompt
Ours
LLaMA3.2 3-
52.6
88.3
Mistral-8B
69.7
89.8
79.5
Qwen2.5-14B
78.3
87.2
LLaMA3.3-70B
83.1
91.1
assessment capabilities. However, our probing approach maintains consistent high performance across all model sizes, confirming that internal activation provides the most reliable sufficiency detection regardless of model scale.
Efficiency vs. Performance. Figure 4 shows the efficiency and performance trade-off between different methods. Unlike static methods (RAG and the Lingua family) that require predefined compression rates or top-k document selection, dynamic methods (FT, self-prompting, and our approach) adaptively determine cutoff points based on content understanding. For our proposed method, we sweep through 4 different values. For 1B models, our method matches RAG and LLMLingua2 in token reduction and achieves comparable accuracy. At 8B, it processes about 1.5x fewer tokens with minimal accuracy drop, outperforming all baselines. For 14B+ models, the method not only reduces tokens up to 1.22x x but also improves accuracy. In contrast, RAG degrades sharply with scale. FT underperforms universally, likely due to misaligned sufficiency signals across models. Interestingly. larger models (14B+) exhibit emergent self awarenesssssss via prompting, whereas smaller models (1B-8B) perform poorly in prompting, self-prompting to work effectively. The results suggest that context truncation may also mitigate the teatiin-the-middll problem [16] 6), models focus more the end of the context, which is likely
insruection-folloiin ability is critical for
to contain key information after removal.
"ggpt-o-min minooommii-io 10mi--022-07-18

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Llama3.2-18
Ministral-88
Qwen2.5-14B
Llama3.3-70B
0.13
0.34
0.55
Accurscy
0.11
0.31
0.19
0.09
0.27
0.27
0.43
0.00
1.5
2.0
2.5
0.23
1.2
14
1.6
0.21
1.2
1.4
1.6
1.8
2.0
0.37
1,25
1.50
1.75
2,00
Token Reduction
Taken Reduction (x)
Token Reduction (x)
Token Reduction
BM25
SBERT
Lingna
Longlingua
Linguaz
Ours
FT
Prompt
Pull Contect
Figure 4: Our method achieves superior efficiency-accuracy trade-off's 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.
Table Performance comparison across different models on Single-hop and Multi-hop tasks on the short-form dataset. Our method achieves token reduction of 1.33x, while outperforming static methods with targeted compression rate at 1.25x.
Method
LLMMA32-1B
Ministral-Sf
Qwen2.5.14B
L.aMA3.3-70R
Avg.
Multi
Single
Arg
Multi
Single
Avg
Mulli
Single
Multi
Single
Avg
Multi
Single
Total
10.4
17.2
57.6
75.0
26.6
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
LLMiingu
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
LMlinLM
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
LLMiingua2
35.8
26.1
FT
6.2
13.8
10.0
21.5
34.7
28.1
22.1
35.1
28.7
35.6
52.4
44.0
21.4
34.0
27.7
Self-Prompt
23.8
36.2
30.0
52.0
45.
69.9
59. 1
28.9
35.8
Own
10.3
17.5
13.9
28.8
45.8
37.3
33.3
89.2
46.3
43.8
75.3
59.5
29.0
49.4
39.2
Table 3: Performance comparison across different models Single-hop and Multi-ho hop tasks the long form dataset. Our method achieves a token reduction of 1.27 while outperforming static methods with targeted compression at 1.25x
Method
LLaMA3.2-1B
Ministral-SB
Qwen2.5-14B
LLaMA3.3-70R
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
29.4
30.3
M..iing
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
28.1
LMingLMi
3.3
12.1
26.0
40.1
34.1
27.5
67.9
18.5
28.9
LLMiingua2
2.7
9.6
6.2
17.1
38.3
27.7
28.8
42.9
35.8
28.2
69.2
48.7
19.2
40.0
29.6
FT
2.6
8.4
5.5
14.9
31.5
23,3
21.4
33.2
27.3
21.4
47.1
34.3
15.1
30.0
22.6
Self Prompt
4.2
7.3
5.7
17.4
32.6
25.0
30.5
45.8
37.6
29.0
65.4
47.2
20.3
37.8
29.0
Owr
43.2
Individual Task Performance. Table 2 shows that method maintains consistent performance on both single-hop tasks (49.4% average accuracy) and multi-hop tasks (29%), outperforming the top static baseline, LLMLingua2, by +1.5% in absolute accuracy experience a considerable drop in both settings. For a fair comparison, we report RAG results only for k = 8, which corresponds to compression rate of 0.8 for the Lingua family a token reduction factor of 1.25x. Note that dynamic methods stop naturally and do not target a specific token reduction rate. Our method achieves 1.33x reduction in tokens, while the FT and Prompt methods achieve reductions of 1.54x and 1.42x on average, respectively.
In contrast, RAG methods
Long Context Scenario. We evaluate our method with longer contexts to assess its scalability. For fair comparison, static methods are evaluated at fixed 1.25x token reduction. Table shows that our method consistently outperforms baselines, especially the multi-hop setting. Dynamic methods adaptively determine cutoff points, with FT achieving 1.61x, Self-Prompt 1. 1.41 and our method
7

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1.27x, ensuring minimal performance loss. Notably, RAG performs better in long-conme context settings, particularly for multi-hop reasoning. However, FT remains the weakest method, struggling with generalization. Self-Prompting improves with model size, as larger models better follow instructions for self-assessment, The results confirm that dynamic cutoff outperforms static heuristics. For longer contexts, our method provides an alternative and scalable solution for efficient inference.
Analysis and Discussion
4.1 Classification Threshold
1.0
The balance between the model's prediction con- fidence and the classification threshold is key factor in our proposed method. In Figure we plot the model's prediction confidence av- eraged over different numbers of chunks. We observe that the confidence in sufficiency predic- tions grows steadily as more context is processed, which indicates that useful signals are accumu- lating over the chunks. Consequently, once the model's 's confidence exceeds т, has likely inte- grated enough information. Note that stopping too early can cause information loss when criti- cal elements of the context are excluded.
Contidencs
0.6
0.4
0.2
Chunk
Figure 5: Confidence progression across context chunks. Model's 's prediction confidence increases monotonically with more context.
Although the Fl score is a useful measure for de- tecting context sufficiency, we also report Recall at high Precision to show how well our method identifies truly sufficient contexts while minimiz- ing false positives. In Figure 6 we show results at 90% precision and provide further findings at 95% and 98% precision in the appendix. This metric measures the fraction of actually suffi- cient contexts that are correctly identified when precision is at least 90%. Such metric is critical for our task, as mistaken early cutoff (false pos- itive) can exclude relevant content and degrade the final performance.
locall
Ihrescold
0.9
3.293
gu172
cJ044
0.8
M1742
Score
0.7
W6637
0.6
0.5
F1. MA
Mistroi-Bit
Qwan2 5-148
11-MAY3--00
Figure F1 score and Recall 90% precision for sufficiency detection. Our approach reliably identifies when enough context is present while minimizing false positives. More results can be found Appendix G
4.2 Chunking and Inference Time
Chunking determines how efficiently the model processes and evaluates context sufficiency. Ta- compares different chunking strategies for Qwen-2.5-145 Percentage-base based chunking per- forms consistently well, with 10% chunking of- fering the best trade-off between accuracy and ef- ficiency. While sentence-level chunking achieves the highest classification performance, it is im- practical due to the increased overhead of fre- quent sufficiency checks. Since these checks require processing chunks sequentially, smaller chunks lead to higher latency, as each additional step incurs computational overhead before reach- ing a decision even with caching.
Table 4: Sentence-leved chunking achieves the highest performance but is computationally ex- pensive. 10% chunking offers the best balance between accuracy and efficiency.
Metric
Sent.
1%
5%
10%
20 %
F1-Score R 900P
96.8
87.2
87.0
88.3
88.3
95.4
90.9
78.4
85.9
85.8
Acc.
14.5
13.7
12.8
13.9
13.7
Therefore, 10% chunking is chosen to best balance granularity and efficiency. Figure 7 shows inference time between our method (10% chunking) and full-context processing. For short contexts (IK tokens), directly processing the full context faster; however, beyond 2K tokens, our method provides significant inference time savings when fewer than six chunks (60% of the full context) are processed. This demonstrates that our approach scales efficiently, offering increasing benefits for longer inputs.
8

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7.4
8.3
10.1
12.5
1K tokeas
2K tokeas
4K tokens
8K tokens
Inference Time
7.0
7.8
9:2
11.0
6.6
7.2
8.4
9.5
6.1
6.7
7.5
8.0
5.7
6.1
6.7
6.5
10
10
2
10
2
Number of Chunles
Number of Chunies
Number of Chunks
Number of Chunks
Catodf Generation
PPll Contect Generation
Figure 7: For short contexts (IK 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.
4.3 Wall-Clock Time vs. Accuracy
Beyond token reduction, we compare wall-clock time in Table 5 using the same configuration
Table Comparison of average wall-clock in- ference time (seconds per sample) and average accuracy across various methods.
from Section 4.2 All experiments were run on the same hardware configurations as detailed in Appendix F2. Our method achieves faster in- ference time than full context processing while also improving accuracy from 35.0% to 36.5%. LLMLingua2 is the fastest overall 6.97s with comparable accuracy of 35.8%. Self-Prompting, while achieving the highest accuracy (37.6%), is the slowest among all methods. RAG methods (BM25 and SBERT) and other Lingua variants offer some speedup over full context but gen- erally at the cost of accuracy. The FT method achieves faster inference than full context.
Method
Time (s)
Acc.
Full
9.02
35.0
BM25 SBERT
8.68
34.7
8.93
34.4
LLMLingua
7,35
34.5
LMLinnn
8.47
34.1
LLMLingua2
6.97
35.8
8.01
27.3
Self-Prompt
10.5
37.6
Ours
8.13
36.5
However, it results in significant drop in accuracy. Overall, our method offers balanced trade-off, reducing latency without external heuristics while preserving answer quality.
4.4 Universal vs. Model-Specifii Cutoffs
From human perspective, each task has "gold" location in the context where the final relevant information resides-once once an answer directly obtained, any further context is redundant. In such cases, a universal stopping point may be plausible. However, from model perspective, defining a single optimal cutoff is challenging and ambiguous. For example, in in-context learning (ICL), models observe demonstration examples without clear threshold for sufficiency. Smaller models may require more examples to generalize, while larger models may reach high confidence with fewer. This suggests model-specific cutoff, where each model determines than adhering to a universal standard. This is particularly relevant in real-world applications, where different LLMs and tasks have varying context requirements. We provide preliminary exploration of tasks without explicit answer locations in Appendix E using ICL. representative case.
own stopping threshold rather
4.5 Beyond Factoid QA
Our work focuses on tasks where the information needed to answer query is localized within specific parts of the context. While this represents a substantial portion of real-world applications (e.g.. question answering, information retrieval, fact verification), we acknowledge that not all tasks benefit from early stopping. Tasks requiring holistic understanding of the entire context, such as summarization or passage rewriting, may not be suitable candidates for dynamic cutoff. However, key advantage of our method is its ability to handle both scenarios naturally. Unlike compression methods that reduce context regardless of task requirements, our sufficiency classifier can process the full context when necessary when all information is crucial, the classifier would not trigger early stopping, effectively using the entire input. We also demonstrate Appendix A.4 that synthetically generated sufficiency labels (via GPT-4o) achieve competitive performance (FI: 84.6-87.0 vs 88.3-

---

89.8 for original labels), enabling extension beyond factoid QA. Additionally, for larger models (14B+), our peompptn approach eliminates dependence on labeled data entirely, suggesting potential for broader task coverage.
4.6 Limitations and Future Work
While our sufficiency classifier demonstrates promising generalization through synthetic labels and self- promptro,,,,g, its applicability all task types (c.g.. creative writing, open-ended dialogue) remains an open question. Future work could investigate classifier performance across broader task spectrums and develop adaptive threshold selection mechanisms that automatically adjust r based on model characteristics and task requirements, rather than relying on validation-basee hyperparameter tuning.
Conclusion
We introduce dynamic context cutoff, method that enables LLMs to process only the minimal necessary context by detecting context sufficiency signals using the model's internal representations. This approach reduces token processing by 1.33x on average while improving accuracy by 3.4%, outperforming static methods like RAG and compression-base -based heuristies. We find that larger models develop emergent self-assessment capabilities, allowing them to detect sufficiency through self- prompting. By enabling models terminate processing dynamically, method enhances efficiency and scalability for LLM inference, paving the way for more intelligent context processing.
Acknowledgements
We thank Shuyan Zhou, Sanxing Chen, Raghuveer Thirukovalluru, Saloni Potdar, and Dong Lin for thoughtful initial discussions, and other members of the DukeNLP lab for their valuable feedback. Roy Xie is supported by the Apple Scholars in AI/ML PhD fellowship and NSF Graduate Research Fellowship. This work was also supported by the Learning Engineering Virtual Institute, funded by leading education philanthropists and organizations through Grant G-23-2137070 to the University of Florida and its partner institutions. The opinions expressed are those of the authors and do not represent the views of the universities, institutions, those of the philanthropists and organizations.
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Appendix
Dataset
A.1
Statistics
A.2
Dataset Balance
21
A.3
Sufficiency Label Collection Process
22
A.4
Synthetic Sufficiency Labels
22
Additional Probing Details
23
B.1
Activation Head Selection
23
B.2
Additional Probing Results
23
B.3
Left-to-Righ Right Context Processing
23
Evaluation Metries
24
Prompts
25
D.1
Self-Sufficiency Prompt
25
D.2
Evaluation Prompt
25
D.3
Answer Generation Prompt
25
Model -Seciffce Cutoffs
26
E.1
Case Study: In-Context Learning
26
E.2
Analysis
26
E.3
Implications
26
Implementation Details
26
E.1
Ensemble Classifier
26
F22
Memory Requirements and Computational Requirements.
27
F33
Tine-Tuned Classifier (FT)
28
F.4
Potential Combination with KV Cache Optimization
28
Recall at High Precision
28
20

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A Dataset
A.1 Statisties
We define gold location as the gold information span's end position divided by the total number of tokens in the input, which is uniformly distributed. We provide statistics for the Long datasets as shown below in Table and Short-form dataset in Table Each dataset contains 600 data points, and the rein-validation-tesl split is 80%, 10%, and 10%, respectively.
Table 6: The datasets are grouped based on reasoning type. Code Understanding (Code) is distinct as 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 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.
Statistic
Single-hop
Multi-hop
Code
SQuAD & Natural Questions
KV
HotpotQA & MUSIQUE
Token Count
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
Min 10,107
9,955
10,023
10,430
Gold Location
Mean
0.50
0.50
0.51
0.49
Median
0.50
0.50
0.49
0.49
Std Dev
0.28
0.28
0.25
0.26
Max 0.99
0.99
0.96
0.96
Min
0.01
0.01
0.04
0.02
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.
Statistic
Single-hop
Multi-hop
Code
SQuAD & Natural Questions
KV
HotpotQA & MUSIQUE
Token Count
Mcan
2951.23
1678.98 694.50
2723.65
1676.15
Median
2910.50
2704.00
1735.00
Std Dev
562.14
1269.42
450.02
646.41
Max
4990
4993
4616
2815
Min
1489
600
1572
578
Gold Location
Mean
0.51
0.48
0.56
0,53
Median
0.52
0.49
0.55
0.50
Std Dev
0.23
0.28
0.20
0.28
Max
0.98
0,99
0.91
0.99
Min
0.02
0.02
0.21
0.02
A.2 Dataset Balance
Our information sufficiency evaluation dataset carefully balanced by design. As described in Appendix A the gold location in our context is sampled from uniform distribution, placing the required information approximately the middle of the context. This creates balanced evaluation where approximately 50% of context chunks classified "insufficient" (before the gold location)
21

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and 50% as "sufficient" (after and including the gold location). This balanced distribution ensures that our evaluation not biased towards either early or late stopping decisions, providing a fair assessment of the method's s ability to detect context sufficiency.
A.3 Sufficiency Label Collection Process
To train our sufficiency detection classifiers, we create datapoints by labeling context chunks as either sufficient or insufficient for answering the given question. Here describe methodology for generating these sufficiency labels.
Label Generation Process. We generate sufficiency labels by first splitting the context into non- overlapping chunks according to our chunking strategy (e.g.. 10% of total tokens per chunk). Using the ground truth answer location(s), we identify the answer-containing chunk(s) in the document. We then label all chunks that appear before the answer-containing chunk as insufficient (0), while marking the answer-containing chunk itself and all subsequent chunks sufficient (1). This labeling approach based on the intuition that a question becomes answerable if and only if all necessary information chunks are present in the context. Note that the labeling process varies slightly for different question types:
These typically require information from single passage or section within the document. Depending on the chunking strategy, there is usually only one answer- containing chunk. All chunks before this are labeled as insufficient, while this chunk and all subsequent chunks are labeled as sufficient.
Single-hop Questions:
Multi-hop Questions: These questions require integrating information from multiple parts of the document. There may be multiple answer-containing chunks (e.g., different pieces of information needed from different sections). In these cases, only the last answer-come monter-containing chunk and all subsequent chunks are labeled sufficient, as all required information is only available after that point.
A.4 Synthetic Sufficiency Labels
Most existing QA datasets (including all six datasets used in our paper) are constructed with known an- swer locations, making straightforward to generate sufficiency labels as described above. However, this approach may not be directly applicable to scenarios where answer locations are not explicitly provided. To address this limitation, we investigated whether large language models could generate synthetic sufficiency labels that perform comparably those derived from annan--ndotated ground truth locations. We conducted experiments comparing classifiers trained with two types of labels:
Original Labels: Generated using the ground truth answer locations described above.
Synthetic Labels: Generated using GPT-4o predict answer locations within the docu- ments.
For the synthetic label generation, we prompt GPT-4o to identify the minimal set of context chunks required to answer each question completely. We then use these predictions to label chunks as sufficient or insufficient following the same methodology used for original labels. Table 8 shows the performance comparison between classifiers trained with synthetic versus original labels. The evaluation pipeline for both remained identical, relying the same ground-truth labels for testing.
The results show that while there modest performance gap, classifiers trained with synthetic labels still achieve strong performance that is competitive with those trained on original labels. This indicates that our approach can be effectively extended to scenarios where explicit answer locations not available, by leveraging LLMs generate reasonably accurate sufficiency labels.
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Table 8: Performance comparison between classifiers trained with synthetic (GPT-4o generated) sufficiency labels across different model sizes.
versus original
Task Type
Synthetic
Original
1B
8B
1B
8B
Single-hop Multi-hop
82.1
84.4
85.7
89.3
87.1
89.6
90.9
90.3
Overall F1 P90
84.6
87.0
88.3
89.8
79.3
82.7
85.9
B Additional Probing Details
B.1 Activation Head Selection
For efficient context cutoff, method does activations from all layers of the model, but rather selectively identifies the most informative attention heads in specifie layers through probing. The activation selection process works as follows:
We initially probe all attention heads across all layers of the model identify which ones encode the strongest sufficiency signals.
As shown in Figure 3 for LLaMA3.2- and Figure 8 for Qwen2.5- 14B, we discovered that subset of heads, primarily from middle layers, exhibit significantly higher predictive performance for context sufficiency. This aligns with findings in other interpretability work that middle layers often capture higher-level semantic information.
After identifying these predictive heads, we select only the top-k heads with the highest F1 scores the validation set (I: 5 in our implementation) to train our ensemble classifier.
As demonstrated in Table 9 we found that using just the top 5 attention heads yields the best performance, with minimal gains or even decreased performance when more heads are included. This confirms our hypothesis that context sufficiency signals are concentrated in specific architectural components rather than distributed throughout the entire model.
The specific layers used can vary across model architectures we don't 't restrict our approach to predetermined layers, but rather let the probing results guide which heads (and consequently which layers) provide the most reliable sufficiency signals. This approach ensures our classifier focuses only on the most informative components of the model's s internal representations while keeping computational overhead minimal.
B.2 Additional Probing Results
Figure g iilustrates the probing results for the Qwen2.5 14B model, revealing that, similar to LLaMA models, the highest Fi scores concentrated in the middle layers. However, the distribution of these high-performing heads differs between the two model families. While both models exhibit darker regions indicating stronger sufficiency signals in their intermediate layers, LLaMA3.2- shows a more dispersed pattern high F1 scores across various heads within these layers. This suggests that although both LLaMA and Qwen models tend encode context sufficiency signals primarily in their middle layers, the specific attention heads responsible and their activation patterns vary between architectures.
B.3 Left-to-Right Context Processing
Our choice of left-to-right processing is motivated by two main factors. First, Transformer models are typically trained on left-to-right sequences, making this order naturally compatible with their internal representations. This avoids the need for significant architectural changes or retraining. Second, enables efficient use of the key-value (KV) cache while preserving semantic consistency. As described in 22.3 lcft-to-right processing allows to reuse cached activations from previous
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0.02
0.00
Laysr
0.74
-0.72
Figure 8: Probing results for the Qwen2.5 14B model. The heatmap shows the average Fl score for each head across all layers, which is different from LLaMA models.
chunks, maintaining contextual coherence across the sequence. As shown in Figure 5 the model's s confidence in sufficiency predictions increases steadily as more context is processed, suggesting that meaningful information accumulates effectively under left-to-right processing.
Although alternative processing orders (e.g., reversed or random) methods (e-g.. RAG, which selects arbitrary subsets of context) are possible, our approach preserves semantic continuity between chunks. leave the investigation of these alternatives for future work.
Note that when processing a chunk, we retain the KV cache from all preceding chunks. This means there is no computational difference between processing the context blunk-by-chunk from left to right and processing the entire context in a single pass. We do not alter the computation over the context; we simply segment into chunks determine when to stop. Each token receives exactly the same context as it would without chunking, due to reuse of the KV cache.
C Evaluation Metrics
Our evaluation framework employs categories of metrics to comprehensively assess different aspects of our method:
Information Sufficiency Classification metries:
Fl Score: Measures the overall balance between precision and recall in detecting sufficient context. This metric is particularly important as penalizes both false positives (stopping too early) and false negatives (processing unnecessary context). A high score indicates that our method can reliably identify when enough information has been processed while avoiding premature cutoffs.
Recall at 90% Precision (R @ @R990P): Ensures high confidence in sufficiency predictions while maintaining good coverage. This metric crucial for our task as it measures how many truly sufficient contexts we can identify while keeping false positives (incorrect early cutoffs) below 10%. This conservative approach helps prevent information loss while still achieving efficiency gains.
QA Task Performance metrics:
Accuracy: Measures answer correctness before and after context cutoff. This metric is calculated as the percentage of questions answered correctly by comparing model outputs with ground truth answers. We use GPT-4o Mini an automated judge to evaluate answer correctness, following established practices in QA evaluation [33] This approach is more reliable than exact string matching, especially for long-form answers where semantic equivalence is more important than lexical matching.
Token Reduction: Quantifies the proportion of tokens processed relative to full context. This metric directly measures computational efficiency gains, calculated as the ratio between the number of tokens processed with our method versus processing the full context. higher token reduction indicates greater computational savings while improving performance.
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D Prompts
D.1 Self-Sufficiency Prompt
Self-Sufiicienc Prompt
Given the following context and question, determine if the context contains enough information needed to answer the question.
[QUESTION]: (question)
[CONTEXT]: (context)
Your response should strictly ONLY consist of '[[YES]]' if context is enough, "[[NO]]' if context is not enough. Omit any other output.
Your response:
D.2
Evaluation Prompt
Evaluation Prompt
You are an expert model evaluator specializing in natural language understanding. Your task determine if model's answer is correct by comparing it with the provided gold answers, accounting for valid paraphrasing and alternate expressions of the same answers.
[QUESTION] [question) [/QUESTION]
[GOLD_ANSWERG |correct_anse answers)
[MODEL_ANSWER] (model_answer) /MOODEL_ANSWER
Evaluation criteria: Answer must convey the same core meaning as gold answers Partial matches should marked incorrect Additional correct information beyond gold answers is acceptable Empty off-topic responses are incorrect
Your response should strictly ONLY consist of "[[YES]]' if model answers question correctly, or '[[NO]]' if model answers question incorrectly. Omit any other output.
Your response:
D.3 Answer Generation Prompt
Answer Generation Prompt
Please provide response to the query based only on the given context:
[QUESTION]: |question]
[CONTEXT]: [context]
Your response:
25

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E Model-Specific Cutoffs
While our work focuses factoid queries where evidence is localized (data with ground truth sufficient information label), our approach can be extended to rationale queries. We believe that the model's internal representations still encode when it has gathered sufficient information to form a coherent response, even if that information is distributed across the document. Sufficiency is ultimately property of the model's understanding, not just the dataset structure. In this section, we discuss some preliminary findings and ideas under an in-context-learning setting, which does not have explicit answers located in the context. Furthermore, our experiments show synthetically generated sufficiency labels (via GPT--4o) are effective proxies when explicit answer locations are unavailable Appendix A.4
The modest performance gap indicates potential for rationale queries.
E.1
Case Study: In- n--Context Learning
To explore the challenges of defining model-specific cutoffs, we utilized the TREC dataset [ for In-Context Learning (ICL) task. TREC comprises series of questions categorized into six distinct types, Abbreviation, Entity, Description and abstract concept, Human being, Location, and Numeric value; these six types are labled from zero to five respectively. Each question type serves category label, and the dataset is structured to provide multiple examples per category without revealing these labels to the models. This setup requires the model to generalize from demonstrated examples to accurately classify unseen queries. For our experiments, we employed two models of differing scales: 8-billion-partee parameter (8B) Mistral model and 1 1-billion-paramette (14B) Qwen2.5 model. These models were selected to illustrate the variance in context processing capabilities across different model sizes, providing insights into how each handles the accumulation of context in ICL setting.
E.2 Analysis
Figure 9 presents the probability of outputting labels as the models process sequential examples from the TREC ICL dataset. The 8B model exhibits gradual increase in confidence. requiring nearly all available examples to achieve its highest accuracy. In contrast, the 14B model reaches peak confidence after processing only a subset of the examples, demonstrating more rapid understanding of the underlying category structure. This discrepancy highlights that larger models can infer task requirements more efficiently, suggesting that a universal cutoff applicable to all model would be suboptimal. The figure also reveals instances where the 8B model remains uncertain despite processing additional examples, whereas the 14B model consistently converges on the correct label with fewer demonstrations. These observations underscore the necessity for model-specific thresholds that account for each model's unique capacity to assimilate and generalize from context.
E.3 Implications
The variability in cutoff points between the 8B and 14B models in the ICL setting indicates that one-size-fits-all approach to context cutoff is inadequate for more nuanced tasks. Methods, such as halting after fixed number of examples relying solely confidence thresholds, may lead to inconsistent performance across different model architectures. For instance, early examples in the ICL dataset can sometimes mislead smaller models, causing them to miselassify subsequent queries. Addressing this requires developing adaptive cutoff mechanisms that dynamically adjust based on the model's internal state and the specific characteristics of the task. Future research should focus on designing algorithms that can learn these individualized thresholds, potentially leveraging additional signals from the model's activations or exploring hybrid approaches that combine universal and model-specific criteria. Furthermore, applying such techniques to datasets where the gold information not easily identifiable will be crucial for validating the robustness and generalizability of -nodel-ppecffic cutoff strategies.
F
Implementation Details
F11 Ensemble Classifier
For the ensemble classifier, the folds are constructed from the training split during cross-validation. The validation split is held out for the evaluation after the classifier is built. Tables 9 the perfor-
26

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Corecc Label
Label
08
Label
Mistral-Bn
Qwien 148
0.6
100
Cortoxt Percentage D91
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.
mance comparison in different number of attention heads and different classifiers used in ensemble. For attention heads, we found that using only the top 5 selected heads yield best performance, and use the top 4 out of 7 classifiers is the best configuration.
Table 9: Performance comparison across head selections and number of classifiers for our method.
Head Numbers
Classifier Numbers
Metries
5
10
20
2
4
6
F1-Score
88.3
87.3
87.9
87.4
88.3
87.3
R @900P
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
F.2 Memory Requirements and Computational Requirements
Our ensemble classifier consists of small tree-based and linear models with extremely minimal memory footprints, typically in the range of few megabytes per model. The full ensemble model consists of 8 linear/tree-base classifiers, from which we select the top 4 with the highest validation F1 scores final ensemble. The total memory requirement for complete ensemble is less than 15MB, which is negligible compared to the multi-gigabyte memory requirements of the LLMs themselves (often 6-140GB depending on model size).
During our experiments, we ran these classifiers on GPUs alongside the LLMs for convenience and faster iteration. We were able to all experiments (including with 70B models) just 2-4 A5000/A6000 GPUs (as detailed in Table [0), as the classifier's memory requirements are negligible in the overall GPU memory budget. For deployment scenarios where GPU memory efficiency is particularly important, offloading the classifier to CPU while keeping only the L.M on GPU is a viable option. This approach incurs minimal latency overhead since the classifier's computation is lightweight compared to the LLM''s forward pass. We leave the detailed analysis the memory and latency trade-off for future work.
Table 10: GPU configurations used for different models in our experiments.
Model
GPUs Used
I.LMMA 3.2-1B
x Nvidia A5000
Mistral 8B
4 x Nvidia A5000
Qwen 2.B-14B
4 Nvidia A5000
LLaMA 3.3-70B
Nvidia A6000
27

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F.3 Fine-Tuunes Classifier (FT)
We tine-tune meta- Laaa-llaaaaaaaa..22211
predict the context cutoff point in long-contex: inputs, formulating this as binary classification task. The model is trained on the Short-form dataset specified in Appendix A We optimize using the AdamW optimizer with learning rate 8.0e-05 and batch size of 32, employing cosine learning rate schedule with linear warmup. The fine-tuned model achieves development set accuracy of 0.8346, demonstrating strong predictive capability. aalamm/Lllama33.... - due to its efficiency in capturing long-range dependencies while
We chose maintaining manageable computational costs. Additionally, framing the task binary classification simplifies optimization and enables robust generalization across diverse long-context scenarios. We include meta-llama/Llama-322 results and the performance of training on long dataset Table I1 for reference. All models are fine-tuned for one epoch.
Table 11: Performance of fine-tuned classifiers tuned on different datasets.
Base Model
Trained & Evaluated on
Test Accuracy
Llama3.2-10
Short Dataset
0.8346
Llama3.2- 3.2na
Long Dataset
0.7515
Llama3.2 3.b3b
Short Dataset
0.8413
Llama3.2 3.b33
Long Dataset
0.7456
F.4 Potential Combination with KV Cache Optimization
Recent work has explored K.V cache optimization techniques to improve inference efficiency. As also discussed in S while KV cache optimization focuses on reducing or evicting less important KV cache entries to reduce memory usage for decoding speedup, our method reduces initial text processing the input level (like ..nuua This means these approaches are complementary and can be potentially combined our method reduces input size, and KV cache optimization could further improve decoding speed. While combining both methods could lead to additional efficiency gains, it is beyond the scope of this work. We consider this interesting direction for future research.
G Recall at High Precision
0.9
0.914
0LERO
Threshold
0.9
1.0
0.520
0.957
3,005
0.8
30008
0.7
5,734
1UMM
0.7
0.6
Racall
0,543
Threshold
0.5
BV429
0.4
0.4
0.3
0.274
11.MA39--1
Mixtrsl-08
Dwwnn
11.MAA33-00
Dwnn2. 5-148
HLMARR3-0R
Figure 10: Fl score and Recall at 95% precision (R @95P) for sufficiency detection.
Figure 11: Fl score and Recall at 98% precision (R @98P) for sufficiency detection.
28


Reviews:
[{"review_id": "P97XDlPT5Y", "reviewer": "Reviewer_r53E", "summary": "This paper introduces dynamic context cutoff to enable LLMs to self-identify when they have acquired sufficient information for a task, like the human cognitive system. Through analyzing the model internals, the author found that specific attention heads present strong sensitivity to the information sufficiency. By monitoring these attention heads with classifiers, this paper enables LLMs to stop early with sufficient information to save computations. Experiments with static methods, including compression-based approaches and RAG, demonstrate its superiority in both accuracy and token reduction.", "questions": "**I have following questions:**\n\n1. Why not chunk input tokens with overlaps?\n2. In the introduction section, the author raises one question mentioning ‘teaching’ LLMs which does not appear to align with the paper’s actual methodology. The use of this term may cause confusion.", "limitations": "Limitations are pointed out in the paper by the authors.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n\n1. This paper introduces a new paradigm for model context reduction, where the capability arises naturally from the model’s own understanding rather than from external compression heuristics, which is both interesting and valuable.  \n2. The experiments are sound and comprehensive.  \n3. It is interesting to observe emergent self-awareness of sufficiency through prompting in models with 14B+ parameters.\n\n**Weakness:**\n\n1. As highlighted in the abstract, LLMs are inefficient when the information needed to answer a query is localized within a specific part of the context. Left-to-right cumulative processing remains inadequate in such cases.\n2. I have a concern regarding the dataset used to probe LLMs' understanding of context sufficiency in Section 2.2. For instance, if the answer-containing chunk consistently appears near the beginning (left) of the input, it may lead to artificially high classification accuracy without truly demonstrating that specific attention heads are effective indicators of context sufficiency. It would be helpful to report the distribution of the answer-containing chunk index across the dataset. Additionally, more details on the total number of tokens per example should be provided. Another related question is: does reducing the chunk size negatively impact classification performance?\n3. More analysis about information loss in this methodology should be provided.", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer r53E,\n\n\nThank you for your insightful reviews and comments! We appreciate your recognition of the novel paradigm and comprehensive evaluation of our work. We would like to address your concerns and questions as follows:\n\n---\n\n\n> **Dataset Bias and Data Distribution**\n\nYou raise an important concern about potential bias for data distribution. Due to page limits, we had to move the dataset details to Appendix. However, we carefully designed our evaluation to avoid this bias. As detailed in Appendix A.1 (Table 6, 7), the gold location (answer position) follows a *uniform distribution* across all datasets:\n- Mean gold location: 0.49-0.51 (middle of context) \n- Standard deviation: 0.25-0.28 (wide spread)\n- Range: 0.01-0.99 (covers entire context span)\n\nThis uniform distribution ensures approximately *50% of chunks are insufficient* (before answer) and *50% are sufficient* (after answer), creating a balanced evaluation that prevents the bias you mentioned. We also discuss dataset balance in Appendix A.2 and the collection process in Appendix A.3 in detail.\n\nRegarding token counts: Our contexts range from 0.5K-40K tokens (mean ~23K for long datasets, ~2K for short), with detailed statistics also in Tables 6-7 of Appendix A.1.\n\n---\n\n\n> **Chunk Size Impact on Classification**\n\nThank you for this important question! To clarify, our chunks are cumulative and maintained through KV cache reuse. When we process a new chunk, it contains *all previous chunks plus the new segment*, not just the isolated segment alone. This is a crucial distinction that ensures we preserve all contextual information throughout the processing.\n\nTable 4 in Section 4.2 demonstrates that smaller chunks actually improve classification performance, with sentence-level chunking achieving F1=96.8 compared to 88.3 for 10% chunking. This is because smaller chunks allow for more frequent sufficiency checks, enabling earlier detection of relevant information. However, smaller chunks increase computational overhead due to more frequent forward passes. Therefore, we chose 10% chunking as the optimal balance between accuracy and efficiency.\n\n---\n\n\n\n> **Left-to-Right Processing**\n\nThank you for this excellent question. While left-to-right processing may limit efficiency in some cases, our method offers a couple other advantages. Due to page limits, we had to move the discussion on the processing order to Appendix. However, as discussed in Appendix B.3, our approach offers several advantages compared to other processing orders: it maintains semantic coherence by preserving natural left-to-right text flow, enables KV cache reuse for computational efficiency, and most importantly, this processing order aligns with how current transformer models are trained and operate in practice.\n\nWhile alternative processing orders could be explored, our current approach still achieves significant improvements with a 3.4% accuracy gain and 1.33X token reduction compared to static methods. We believe investigating optimal processing orders remains an interesting direction for future work, but our current results demonstrate the effectiveness of the left-to-right approach.\n\n---\n\n\n> **Information Loss Analysis**\n\n\nThank you for this excellent suggestion! To comprehensively understand the tradeoffs between information loss and performance in our methodology, we would like to highlight the analysis in Sections 4.1 (Classification Threshold) and 4.2 (Chunking Strategy). However, the most compelling evidence that our method preserves rather than loses critical information is the consistent performance improvement. Specifically, we see a 3.4% average accuracy improvement (Tables 2-3), indicating our method helps models focus on relevant content rather than discarding important information.\n\nTo address concerns about false positives that could potentially lead to information loss, we report Recall at Precision 90-98% (Figures 4, 10, 11). Our threshold mechanism allows trading off precision for efficiency, where higher precision settings ensure no information loss at the cost of lower efficiency. Additionally, Figure 6 shows that models accumulate useful signals progressively, suggesting our approach captures relevant information effectively rather than making premature cutoff decisions.\n\n\n---\n\n\n> **Why not chunk input tokens with overlaps?** \n\nThank you for this important question! We would like to clarify that each chunk extends the context incrementally (chunk₁, chunk₁+₂, chunk₁+₂+₃, etc.), which allows us to reuse the KV cache and avoid redundant computation of previously processed tokens. This cumulative approach maintains semantic continuity by preserving natural text flow without creating artificial boundaries that could disrupt understanding. On the other hand, overlapping chunks would require recomputing activations for the same tokens multiple times, which would negate our computational efficiency gains while disrupting the cumulative context semantics. The non-overlapping design ensures that each new chunk only requires computing activations for the truly new tokens, maximizing both computational efficiency and contextual coherence.\n\n\n---\n\n\n> **”Teaching” Terminology Confusion**\n\nThis is an excellent point! Our method doesn't “teach” LLMs new capabilities but rather leverages existing internal representations. We will use “enabling” or “allowing” to avoid confusion.\n\n---\n\nWe appreciate your suggestions and will incorporate them to enhance our work further. Thank you once again for your valuable feedback!\n\n\nBest regards,\n\nThe Authors"}, {"review_id": "PizGQP25XW", "reviewer": "Reviewer_6R5V", "summary": "The paper aims to make LLM inference more efficient, on specialized tasks, by teasing out sufficiency signals from the model internals itself. This is mostly relevant for tasks which “find” information in a longer context document, where the model does not have to process the whole text to “find the answer”. Previous work like LLMLingua and RAG methods, always pre-decides a static compression/top-k factor whereas this method always relies on the internal model state to reflect a more dynamic per-context decision of what is sufficient. \n\nMaking use of latent knowledge in model activations, is a useful tool that is heavily used in many areas today. This paper attempts to apply this tool for teasing out sufficiency signals to make task-specific inference more efficient and accurate. \n\nThe basic idea of the technique is to train a classifier against an appropriately crafted dataset with sufficiency labels, from the internal model state to detect sufficiency signals on the input for the task. This introduces techniques such as chunking where a decision has to be made on how to balance additional inference cost vs accuracy and compression. \n\nThe following studies were also done.\n- Using self-prompting to detect sufficiency on models of varying sizes.\n- Studies on effectiveness signals from attention heads through different layers, on sufficiency signals.\n- Comparison of this method against RAG and Lingua2\n- Comparison of classifier based methods against supervised fine-tuning and self-prompting methods.\n- Efficiency vs Performance trade-off studies  on all methods across multiple models.\n- Performance comparisons across multiple models, on different hop characteristics, on short form and long form data.\n- Comprehensive classification threshold analysis\n- Studies of performance vs cost across different chunking strategies.\n- Latency vs accuracy studies", "questions": "- Has there been any analysis done on, if the tasks for the prompts need early stopping. For some prompts, it may not need early stopping. Any studies done on detecting whether such early stopping is needed at all? For example, if we extrapolate this to tasks where questions come at the end of the contextual information, can we apply this somehow?\n- The authors call the methods of LLMLingua and RAG approach as \"static\" since they pre-decide some hyper parameters such as compression factor and top-k documents. But in the approach in the paper also, you fix some parameters such as type of chunking and threshold value for the classifiers, which also feel quite similar. How would you clarify this as more dynamic than other static parameters? Can't this be considered as static choices too, especially comparing to the LLMLingua approach? How would you clarify this?\n- Since the underlying model already knows the sufficiency signals, especially as the models get bigger, has there been any studies done to let the model itself predict sufficiency using a special token after every chunk? And hence can we get rid of the separate classifier to do the task? Has such a line of thought been explored?\n- Is there a chart about the distribution of sufficiency chunk index across multiple datasets? If there is some bias in the distribution of sufficiency signals in the data? Any analysis done on this?", "limitations": "Yes. The authors have discussed about the limitations in universal cut-off in sufficiency signals. Further in-depth analysis on it are left to future work.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Overall the paper feels quite strong in all areas. \n\n- The paper is very clear about the problem, methodology, background, experiment, results and interpretation. \n\n- **Strong Arguments**: The paper very clearly presents the arguments to make the case here. It talks about the background of other approaches like LLM Lingua and RAG. It classifies them as static approaches. The authors clearly explain the novelty in the approach in the paper, as making use of model internal state as the differentiating factor. The F1 score of classifiers as a successful metric in distinguishing sufficiency signals are presented as proof that the model internals are sufficient for the purpose. The problem formulation has been well presented in a technically rigorous way and explains the notion of chunks well. Also defines the notion of sufficiency very clearly. A good amount of studies have been done to present a comprehensive argument towards the effectiveness of the method. \n- **Clear Presentation**: The approach, data, evaluation benchmarks, comparison data etc have been very clearly mentioned in the paper. Each concept is well articulated in its own section, with the right level of detail. The choice of models to run the experiments on are also appropriate. The baseline comparisons of Fine-Tuned Classifier and Self-Prompting are appropriate baselines to compare against this method. \n- **Strong Results**: The results attained by the authors seem to show that with this technique they improve on previous methods on both accuracy and token reduction dimensions, which is decently good. The gap against the baselines seems significant enough to consider this method to be quite effective at token reduction without sacrificing accuracy. \n- **Variety of Relevant Studies**: A lot of trade-offs and peripheral impact studies have been explored in the paper which make the results look pretty robust. The list of results along with a combination of dimensions of multiple models, methods, Muti/single hop data types along with comparison to the current methodology gives a good understanding of improvement over the whole landscape of such experiments. In most cases, the method performs the best or close to the best with added advantages. This is presented for short form and long form datasets. \n- **Lots of low level details in Code and Appendix**: The presence of a lot of additional prompts, figures and methodologies make it very clear what’s been done. Also appreciate sharing the code as supplementary material. \n- The confidence progression chart gives a pretty good confidence that this is indeed doing the right thing. The confidence improves as you add more relevant chunks, which is a good indication. \n\nOverall I feel this builds a good end to end story on the effectiveness of this technique on token reduction without sacrificing accuracy. There are no gaps which are unanswered. \n\nI don’t see any major weakness in the setup, methodology, presentation or arguments. \n\nI would score the paper as 4 on Quality, Clarity and Significance and 3 on Originality.", "was_revised": true, "final_justification": "I think this is a strong paper. I maintain my original ratings. The authors have satisfactorily addressed most of the questions raised and have provided clear additional details.", "rebuttal": "Dear Reviewer 6R5V,\n\nWe sincerely thank you for your thorough and constructive review. We are grateful for your positive assessment of our paper's strengths, including the clear problem presentation, strong arguments, comprehensive experiments, and robust results. We especially appreciate your recognition that our work builds “a good end to end story on the effectiveness of this technique” and that you found our approach technically rigorous with no major weaknesses.\n\nWe would like to address your questions as follows:\n\n---\n\n\n> **Has there been any analysis done on whether tasks actually need early stopping? What about tasks where questions come at the end of the contextual information?**\n\nThank you for this insightful question. We acknowledge that there are tasks that may not benefit from early stopping (eg: passage rewriting, summarization). However, this study focuses on tasks that would benefit from early stopping, which represents a substantial portion of real-world applications (eg: question answering, information retrieval, or fact verification).\n\nA key advantage of our method is the potential to handle both scenarios naturally. Unlike other compression methods that compress regardless of the task, our approach can process the full context when necessary. When all information is crucial for a task, our sufficiency classifier would not trigger early stopping, effectively using the entire input.\n\nRegarding tasks where questions appear at the end of the contextual information: our method is designed to detect when sufficient information has been processed regardless of where the key information appears. In principle, this can be enabled by mixing in training data where we know the full context is needed and training the classifier accordingly. Analyzing whether tasks need early stopping and detecting them explicitly would be a valuable future research direction that could further enhance the applicability of our approach - thank you for pointing out this interesting direction!\n\n---\n\n> **How is this more “dynamic” than other methods when you still fix parameters like chunking type and threshold value?**\n\nThank you for this excellent question! We acknowledge this similarity and appreciate the opportunity to clarify our terminology. The key distinction lies in the compression behavior, not the parameter setting approach. In static methods like LLMLingua, a fixed compression ratio is applied uniformly, where a compression ratio of 50% results every input gets compressed to exactly half its original length regardless of content. In contrast, our method uses static hyperparameters to enable content-adaptive compression decisions. Some inputs may receive minimal compression (eg: 5%) while others may be heavily compressed (eg: 95%), with the actual compression amount determined by each input's specific information density and complexity.\n\nThe “dynamicity” we refer to is that each input receives different treatment based on its content, whereas other methods compress every input to the same predetermined level. The compression ratios we report are averages across all examples, not fixed targets applied uniformly. We acknowledge that “dynamic” may be misleading terminology and would be happy to use “content-adaptive” or “input-specific” to better describe this distinction.\n\n---\n\n> **Since larger models already know sufficiency signals, has there been study to let the model predict sufficiency using special tokens instead of separate classifiers?**\n\nThank you for this excellent observation! The self-prompting (L136-142) method shows the scaling phenomenon as you mentioned. However, even for large models, the probing approach consistently outperforms self-prompting (91.1 vs. 83.1 F1 for 70B model in Table 1). This suggests that while models develop some intrinsic sufficiency awareness, their internal representations contain richer signals than what they can explicitly verbalize.\n\nThe special token approach you suggest is intriguing and represents a natural extension. Our probing results (Figure 2) demonstrate that sufficiency signals are most concentrated in specific attention heads of middle layers, which could guide the design of such special tokens. We view this as promising future work that could eliminate the lightweight classifier overhead entirely.\n\n---\n\n> **Is there analysis of the distribution of sufficiency chunk index across datasets? Any bias in the distribution of sufficiency signals?**\n\nThank you for this important question! Our datasets are carefully balanced by design (Appendix A.1, Table 7-8), where the gold locations follow uniform distributions across all datasets (mean ≈ 0.50, with standard deviations of 0.25-0.28). This balanced design ensures that ~50% of chunks are classified as “insufficient” and ~50% as “sufficient”. The uniform distribution was intentionally created to provide fair evaluation across different information positioning scenarios.\n\nOur confidence progression analysis (Figure 4) shows that the model's sufficiency confidence increases monotonically as more relevant information is processed, regardless of where that information appears in the context. This validates that our approach can handle diverse information distributions rather than simply learning positional biases.\n\nWe also tested our method on naturally occurring datasets where information placement wasn't controlled (see Appendix A.3 on synthetic labels), demonstrating robustness beyond balanced evaluation setup.\n\n---\n\nWe appreciate your thoughtful engagement with our work. Your questions and feedback have allowed us to clarify key aspects of our approach and have highlighted important directions for future research. Thank you once again for your valuable feedback!\n\n\nBest regards,\n\nThe Authors"}, {"review_id": "OWeftz1qn9", "reviewer": "Reviewer_xgYF", "summary": "This paper introduces a novel approach called dynamic context cutoff for large language model (LLMs), allowing the LLMs to self-assess when they have processed sufficient information from a context and terminate further processing. This is achieved by using lightweight classifiers to detect “sufficiency signals” inherent in certain attention heads. Larger models can also perform this assessment using self-prompting. Experiments demonstrate that this approach can achieve better accuracy and balanced token consumption across various QA tasks, models, and token budget, outperforming current statistic compression and retrieve-based schemes.", "questions": "- As the classification threshold is important for the performance, is there a way to automatically set this value for different models, rather than tuning it manually on the validation set?\n- Any solution when facing the early cutoff?\n- For open-ended, subjective, or tasks lacking clear ground truth positions, how can sufficiency signals be automated? Can synthetic labels be extended to more complex scenarios?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n- The dynamic cutoff method leverages internal model understanding for context truncation, moving away from fixed compression target. \n- By identifying sufficiency signals within attention heads, this paper adds evidence to the interpretability of Transformer architecture.\n- Achieves significant token savings and accuracy enhancements\n\nWeaknesses:\n- Potential early cutoff when the necessary information for answering spans across different parts of the content (like the beginning and end parts). \n- This approach depends on the availability of gold answer spans or synthetic sufficiency label generated by another LLM, which could limit the applicability to open-ended, subjective or real-world tasks.", "was_revised": true, "final_justification": "I have read the reviews, participated in the discussions, and provided final feedback.", "rebuttal": "Dear Reviewer xgYF,\n\n\nThank you for your thoughtful and constructive feedback. We appreciate your recognition of our method's strengths, including eliminating the need for pre-defined compression targets, insights into Transformer interpretability, and significant performance improvements. We would like to address your concerns and questions below:\n\n---\n\n> **Early cutoff when information spans across different parts of content & Solutions for early cutoff**\n\nYou raise an important concern about early cutoff. We would like to highlight that our method is carefully designed to handle this scenario. Our dataset construction ensures gold information spans *uniformly* across contexts (Appendix A.2), specifically testing scenarios where information is distributed across different parts. Our multi-hop evaluation on HotpotQA, MUSIQUE, and Multi-hop Key-Value Retrieval requires integrating information from *multiple locations throughout the context*. Table 2 shows our method achieves 29% accuracy on multi-hop tasks, outperforming LLMLingua2 (27.8%) with 1.33× token reduction, demonstrating effectiveness when information spans across distant locations.\n\nAdditionally, we address early cutoff through multiple robust mechanisms: (1) High-precision operation: Our method operates at 90% precision with Recall@90P showing 79.3-90.1% coverage, minimizing false positives that cause premature stopping; (2) Ensemble robustness: Using top 4 of 8 trained classifiers provides robustness against individual classifier errors; (3) Adaptive thresholds: Lower thresholds increase recall at efficiency cost, allowing flexible conservativeness-efficiency trade-offs; (4) Fallback guarantee: If no chunk reaches sufficient confidence, we process the entire context, ensuring no information loss in edge cases.\n\n---\n\n> **Dependence on gold answer spans limiting applicability to open-ended tasks & Extension of synthetic labels to more complex scenarios**\n\nThank you for the excellent question! Applying our method to scenarios without explicit answer locations is indeed a relevant research direction. We attempted to provide several solutions. In Appendix A.4, we conduct experiments on synthetic labels and find that GPT-4o-generated synthetic labels achieve competitive performance (F1: 84.6-87.0 vs 88.3-89.8 for original labels), enabling extension beyond factoid QA. For larger models (14B+), our self-prompting approach (L180-185 in §2.3) eliminates dependence on labeled data entirely, with Table 1 showing F1 scores of 78.3-83.1. The emergence of self-assessment capabilities also suggests the potential of our method to handle more complex scenarios with a training-free alternative. \n\nFor subjective tasks, we believe that the model's internal representations still encode when it has gathered sufficient information to form a coherent response, even if that information is distributed across the document or the answer is not explicitly located in the context. The key point is that *sufficiency is ultimately a property of the model's understanding, not just the dataset structure*. We left this question as a direction for further work but discussed some preliminary findings and ideas under an in-context-learning setting (Appendix G), which does not have explicit answers located in the context.\n\n---\n\n> **Automatic threshold selection for different models**\n\nThis excellent suggestion addresses a key practical deployment challenge. Currently, threshold τ is selected on validation sets per model following standard hyperparameter tuning practices. However, the monotonic confidence progression (Figure 6) suggests potential for automatic threshold adaptation based on confidence curves, which is a valuable direction we acknowledge for future work. Thank you for highlighting this practical insight that could enhance deployment scalability of our method!\n\n---\n\nThank you once again for your thoughtful engagement with our work. We appreciate your constructive feedback and look forward to incorporating these insights to further enhance our work. \n\nBest regards,\n\nThe Authors"}, {"review_id": "fRogiOXaA0", "reviewer": "Reviewer_2h3k", "summary": "This paper introduces dynamic context cutoff, a novel human-inspired method for large language models (LLMs) that allows them to self-terminate processing once sufficient task-relevant information is acquired. However, the authors claim that *this approach challenges traditional reliance on external compression heuristics, instead leveraging the model's internal understanding to dictate processing needs*, being worth considering, as the method proposed by the authors is a classifier-based approach.", "questions": "Could you explain whether the selection of these hyperparameters is sensitive? Could you also clarify if obtaining the classifiers is straightforward? Additionally, can the same classifier be used for judgment across different model families?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**strengths**\n\n- The writing of this paper is very clear, and the introduction is also quite detailed. \n- The experiments in this paper are abundant, especially the paper has conducted validations on many datasets.\n\n**weaknesses**\n\n- The method proposed in this paper is classifier-based and requires a series of classifiers. the technical innovation of this approach is not particularly high.  In scenarios where the context length is dynamically determined, a substantial number of researchers have already conducted studies and published relevant reports on this topic. Some papers published before the submission deadline are listed below. In contrast, using a classifier for context processing is more straightforward and less flexible.\n\n- since there are three hyperparameters in this paper, the persuasiveness of the experimental results' improvement becomes weaker, and determining how to set these hyperparameters will be an issue.\n\n[1] Wu, Wei, et al. \"Tokenselect: Efficient long-context inference and length extrapolation for llms via dynamic token-level kv cache selection.\" arXiv preprint arXiv:2411.02886 (2024).\n\n[2] Li, Yucheng, et al. \"Compressing context to enhance inference efficiency of large language models.\" arXiv preprint arXiv:2310.06201 (2023).", "was_revised": true, "final_justification": "Most of the concerns have been eliminated.", "rebuttal": "Dear Reviewer 2h3k,  \n\nWe sincerely thank you for your constructive feedback and recognition of our paper's strengths, including clear writing and comprehensive experiments and evaluations. We appreciate you taking the time to review our paper and would like to address your concerns as follows:\n\n---\n\n> **Technical Innovation and Core Contributions**\n\nThank you for this important question! We would like to clarify that there are two orthogonal approaches to efficiency: (1) methods that approximate transformer computations during inference (like TokenSelect, which performs sparse attention by dropping low-attention tokens), and (2) methods that reduce input context length. These approaches are complementary, as any context compression method can benefit from computational approximations like KV optimization.\n\nOur work falls in the *second* category, focusing on context reduction rather than computational approximation. Within this space, we make two novel contributions that distinguish our approach from existing context compression methods:\n\n*First*, we are the first to leverage internal LLM representations to decide when to stop processing, discovering that certain attention heads naturally encode “sufficiency signals” that indicate when enough context has been seen. This is a previously unexplored aspect of transformer interpretability.\n\n*Second*, rather than treating context compression as a separate preprocessing module (as in prior work [1]), we make it an inherent part of how the model encodes text during inference. This integration enables dynamic, model-driven decisions rather than fixed compression rules.\n\nWe compare against all relevant baselines within the context compression category. Regarding the Li et al. (2023) reference, we note this was the proof-of-concept version later developed into LLMLingua, and we do compare against the improved LLMLingua methods in our experiments. \n\nDue to page limits, our discussion of the complementary nature of these two categories was moved to Appendix H.4 (Potential Combination with KV Cache Optimization). In the revision, we will expand the discussion in the main text and include both mentioned relevant work in the related work section to better position our contributions. Thank you again for raising this excellent question!\n\n---\n\n> **Hyperparameter Sensitivity and Selection**\n\nThank you for this insightful question! Among our three hyperparameters, only the classification threshold τ impacts the efficiency-accuracy trade-off, while the other two (number of heads and ensemble size) are determined through standard hyperparameter search. Importantly, *we use a single global threshold across all datasets (Tables 2 and 3)*, as our method does not require dataset-specific tuning. As shown in Figure 1, different threshold values create predictable efficiency-accuracy trade-offs, but the key finding is that a single global setting works effectively across all experimental conditions. This addresses the core concern about sensitivity - we don't need separate thresholds for each dataset type.\n\nPractically, the threshold τ can be set once based on desired efficiency-accuracy preferences and deployed universally. We also provide detailed sensitivity analysis on number of heads and ensemble size in Appendix B.1 and H.1, showing stable performance across reasonable ranges. Additionally, for larger models, our self-prompting approach eliminates dependence on trained classifiers entirely, which highlights the potential extension of our method with a training-free alternative which does not require hyperparameter tuning.\n\n---\n\n> **Classifier Complexity and Obtainment**\n\nYou raise an important question regarding the ease of obtaining classifiers and their applicability across model families. We clarify that training classifiers is straightforward, requiring only attention head activations and binary sufficiency labels. The training process follows a simple procedure: probe all attention heads, select top-performing heads, and train the classifiers. This one-time setup enables deployment across *all tasks* for that model. The classifiers are lightweight, using linear or tree-based architectures with minimal memory footprint (Appendix H.2). Binary sufficiency labels can be obtained from existing datasets when available (Appendix A.3), or generated using language model judges through prompting (Appendix A.4). \n\nIn terms of cross-model applicability, classifiers cannot be directly transferred across model families due to architectural differences in attention mechanisms and internal representations. This model-specific requirement aligns with previous interpretability work [2] and reflects a core research contribution: demonstrating that internal model representations encode sufficiency signals. While extracting these signals requires model-specific classifiers, our probing methodology itself is universal. Once trained, classifiers show consistent performance across tasks within the same model. This model-specific nature also reflects realistic differences in how various architectures process context, as different models exhibit different context utilization patterns (L220-222).\n\n---\n\n[1] Li, Yucheng, et al. \"Compressing context to enhance inference efficiency of large language models.\" arXiv preprint arXiv:2310.06201 (2023).\n\n[2] Inference-Time Intervention: Eliciting Truthful Answers from a Language Model\n\n---\n\nOnce again, thank you for the thoughtful review and the opportunity to clarify these important aspects of our work. Your questions have helped us identify areas where we can strengthen our presentation and better highlight the novel contributions of our approach. Thank you for your valuable feedback and consideration of our work!\n\nBest regards,\n\nAuthors"}]

Author rebuttal:

Meta-review: This paper introduces dynamic context cutoff for LLMs to self-identify sufficient context in processing longer context document, where usually the information required is localized. The authors analyze model internals and found that specific attention heads inherently encode such sufficiency signals accessible through lightweight probing classifier. Furthermore, larger models also exhibit this intrinsic self-assessment through prompting. The author showcase that this intrinsic self-assessment capability enables LLMs to stop early with sufficient information, and in turn, demonstrates token reduction without sacrificing accuracy—or even improving it—across various QA tasks and models, outperforming current compression-based and retrieval-based methods.

__Strengths__
- The introduction of new paradigm for intrinsic model context reduction is both interesting and valuable
- The writing of this paper is very clear
- The experiments are abundant, sound, and comprehensive
- Observation of emergent self-awareness of sufficiency through prompting in larger models is interesting

__Weakness__
- The rebuttals addressed well on the concern of technical innovation, probing dataset, information loss, cutoff failures, hyperparameter sensitivity, and generalization towards broader real-world tasks.

The empirical gains, breadth of validation, and mechanistic insights outweigh concerns about generality and the simplicity of the probing classifier. The rebuttals well addressed the concern of technical innovation, probing dataset, information loss, cutoff failures, hyperparameter sensitivity, and generalization towards broader real-world tasks. In conclusion, I believe the paper presents an interesting and meaningful contribution to the community.
Decision: Accept (poster)