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Next-ToBE: Probabilistic Next Token-Bag Exploitation for Activating Anticipatory Capacity in LLMs

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
Large language modelsanticipatory capacity
Abstract:

Auto-regressive large language models (LLMs) have achieved remarkable success recently. Though trained to predict only one token at a time, LLMs intriguingly exhibit longer-term foresight and a degree of anticipatory capacity. Yet, how to profile, enhance and leverage this capacity to improve reasoning performance remains an open question. In this paper, we propose Next Token-Bag Exploitation (Next-ToBE), a simple yet effective method to tackle the challenges. Next-ToBE quantifies LLM’s anticipatory capacity by measuring how well tokens in the future window are pre-captured within the model’s current prediction. Empirically, this capacity strongly correlates with the model’s generative quality, but it is often suppressed by the rigid one-hot objective in next-token prediction. To address this, Next-ToBE replaces the one-hot target vector in the next-token prediction paradigm with a soft target distribution spanning additional future tokens beyond the current step. In this formulation, the immediate next token retains the highest importance, while more distant "look-ahead tokens" are also included to enrich supervision, with their importance dynamically determined by temporal and semantic relevance patterns. Furthermore, the fitting process emphasizes the model’s intrinsic anticipatory tendencies, thus preserving the confidence and fidelity of the original pre-trained model while also improving training stability. Overall, Next-ToBE effectively activates the anticipatory capacity of LLMs, yielding up to a 3.9% absolute accuracy gain over MTP baselines on complex reasoning benchmarks (math, code, and commonsense reasoning), while reducing peak memory consumption by as much as 68%. This highlights its value as a scalable and lightweight strategy to make LLM see further and reason more effectively.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes Next-ToBE, a method that replaces one-hot next-token targets with soft distributions spanning future tokens to enhance anticipatory capacity in autoregressive LLMs. According to the taxonomy, this work resides in the 'Future Token-Bag Prediction with Soft Targets' leaf under 'Anticipatory Capacity Enhancement in Autoregressive Models'. Notably, this leaf contains only the original paper itself—no sibling papers are present—indicating this is a relatively sparse and potentially underexplored research direction within the broader field of soft target training for language models.

The taxonomy reveals that neighboring branches focus on teacher-student distillation (e.g., transferring knowledge from external models) and synthetic data generation with soft annotations (e.g., creating pseudo-labeled training sets). These approaches differ fundamentally from Next-ToBE's strategy: rather than relying on external knowledge sources or synthetic data, the paper modifies the autoregressive objective itself to incorporate future token information directly from the model's own forward pass. This positions the work at a distinct methodological boundary, diverging from distillation-based smoothing techniques while sharing the broader goal of enriching training signals beyond hard one-hot targets.

Among 29 candidates examined across three contributions, no refutable prior work was identified. Contribution A (Next-ToBE method) examined 10 candidates with 0 refutations; Contribution B (dynamic weighting scheme) examined 10 candidates with 0 refutations; Contribution C (empirical validation) examined 9 candidates with 0 refutations. This suggests that within the limited search scope—focused on top-K semantic matches and citation expansion—the specific combination of future token-bag prediction with temporally-weighted soft targets appears novel. However, the absence of sibling papers in the taxonomy leaf and the modest search scale (29 candidates) mean this assessment reflects a snapshot rather than exhaustive coverage of the literature.

Given the sparse taxonomy leaf and zero refutations across all contributions within the examined scope, the work appears to occupy a relatively unexplored niche. The limited search scale (29 candidates) and lack of sibling papers suggest either genuine novelty in this specific formulation or insufficient prior work indexed in the search process. A broader literature review—particularly examining multi-token prediction, speculative decoding, or auxiliary prediction objectives—would strengthen confidence in the novelty assessment.

Taxonomy

Core-task Taxonomy Papers
8
3
Claimed Contributions
29
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Enhancing anticipatory capacity in autoregressive language models through soft target distributions. The field explores how to move beyond hard next-token prediction by incorporating richer training signals that capture uncertainty, future context, or distributional knowledge. The taxonomy organizes work into four main branches: Soft Label Generation and Knowledge Distillation focuses on transferring knowledge from teacher models or creating smoothed targets; Synthetic Data Generation with Soft Annotations examines how to produce training data with probabilistic labels; Anticipatory Capacity Enhancement in Autoregressive Models directly addresses methods that enable models to look ahead or predict multiple future tokens; and Representation Learning with Language Model Guidance investigates how soft distributions can shape learned representations. Representative works like Generate Annotate Learn[3] and Generative Self-Training[4] illustrate how synthetic data with soft annotations can bootstrap model performance, while Distilling BERT ASR[5] and DistilCypherGPT[6] demonstrate knowledge distillation strategies that compress or transfer distributional information. A particularly active line of work centers on using soft targets to mitigate overconfidence and hallucination, as seen in Smoothing Hallucinations[1], which applies label smoothing techniques to reduce spurious certainty. Another contrasting direction involves disentangling different types of uncertainty or belief, exemplified by Disentangled Belief[2], which separates epistemic from aleatoric components. The original paper, Next-ToBE[0], sits within the Anticipatory Capacity Enhancement branch and specifically targets future token-bag prediction with soft targets—an approach that enables the model to anticipate sets of plausible upcoming tokens rather than committing to a single next token. This emphasis on multi-token lookahead distinguishes Next-ToBE[0] from works like Smoothing Hallucinations[1], which primarily smooth existing next-token distributions, and aligns it more closely with methods that explicitly model future context or token sets. The trade-off between computational overhead and improved anticipatory reasoning remains an open question across these directions.

Claimed Contributions

Next-ToBE method for activating anticipatory capacity in LLMs

The authors propose Next-ToBE, a training method that replaces the conventional one-hot objective in next-token prediction with a soft target distribution spanning multiple future tokens. This approach aims to quantify and enhance the anticipatory capacity of LLMs without requiring architectural modifications or additional parameters.

10 retrieved papers
Dynamic weighting scheme based on semantic-temporal relevance

The authors develop a weighting mechanism that combines the model's intrinsic anticipatory preferences with temporal and semantic relevance patterns using a random-walk-based ranking scheme. This determines how much importance to assign to each future token in the training objective.

10 retrieved papers
Empirical validation showing performance gains on reasoning benchmarks

The authors demonstrate through experiments that Next-ToBE achieves up to 3.9% absolute accuracy improvements over multi-token prediction baselines on mathematical reasoning, code generation, and commonsense reasoning tasks, while reducing memory consumption by up to 68%.

9 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Next-ToBE method for activating anticipatory capacity in LLMs

The authors propose Next-ToBE, a training method that replaces the conventional one-hot objective in next-token prediction with a soft target distribution spanning multiple future tokens. This approach aims to quantify and enhance the anticipatory capacity of LLMs without requiring architectural modifications or additional parameters.

Contribution

Dynamic weighting scheme based on semantic-temporal relevance

The authors develop a weighting mechanism that combines the model's intrinsic anticipatory preferences with temporal and semantic relevance patterns using a random-walk-based ranking scheme. This determines how much importance to assign to each future token in the training objective.

Contribution

Empirical validation showing performance gains on reasoning benchmarks

The authors demonstrate through experiments that Next-ToBE achieves up to 3.9% absolute accuracy improvements over multi-token prediction baselines on mathematical reasoning, code generation, and commonsense reasoning tasks, while reducing memory consumption by up to 68%.

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