Title: Knowing When to Stop: Efficient Context Processing via Latent Sufficiency Signals
Venue: NeurIPS 2025
Accepted: yes
Keywords: Large language models

efficiency

context processing

context compression
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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).\n\nQUESTIONS:\nCould 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-11"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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\n\nSTRENGTHS AND WEAKNESSES:\nOverall 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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nYes. 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n**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.\n\nLIMITATIONS:\nLimitations are pointed out in the paper by the authors.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}