Token-Based Endpoint Prediction

• Token-based endpoint prediction is a mechanism that uses token-level signals, such as emitted tokens and probabilities, to mark decision boundaries in various applications.
• It integrates acoustic and linguistic cues in systems like multi-talker ASR to improve detection accuracy and reduce delays compared to traditional VAD approaches.
• This approach enables advanced applications including future token prediction, dynamic routing, and efficient resource management in AI inference platforms.

A token-based endpoint predictor is a predictive mechanism that infers an endpoint event from token-level information rather than from coarse heuristics such as acoustic silence, request counts, or aggregate utilization. In the recent literature, the term “endpoint” is used in several technically distinct ways: the timestamp at which a speaker finishes speaking in streaming ASR, the future extent or utility of a generated continuation in LLMs and diffusion models, and the deployment endpoint to which an inference request should be routed or for which cost and latency should be estimated. Taken together, these works suggest a common methodological shift: endpoint decisions are increasingly driven by emitted tokens, token probabilities, token embeddings, token throughput, or token-derived structure vectors rather than by hand-tuned global proxies (Lu et al., 2022, Liu et al., 6 Jun 2025, Choi et al., 14 May 2025, Gao et al., 1 May 2026).

1. Scope and formal usage

Taken together, the literature suggests that token-based endpoint prediction has at least three major meanings. In speech systems, the endpoint is an utterance boundary. In generative modeling, it is a future continuation block, a decoded clean sample, or a decision boundary between alternative decoding modes. In serving systems, it is the concrete inference endpoint or the completion boundary implied by remaining-token estimates (Lu et al., 2022, Walker, 2024, Gao et al., 1 May 2026).

Domain	Endpoint target	Representative token signal
Streaming multi-talker ASR	Speaker end timestamp	Emitted $\langle \text{eos} \rangle$ token
Generative modeling and reasoning	Future token block, clean endpoint $x_0$, or CoT trigger	Token probabilities, semantic state vectors, mask-token logits
LLM serving and routing	Inference endpoint choice or completion length	Response-length estimates, text-structure vectors, token velocity

This breadth matters because it separates token-based endpoint predictors from older endpointing conventions. In the multi-talker ASR setting, the alternative is VAD-like segmentation that relies exclusively on acoustic cues and is unsuitable for overlapped speech. In serving, the alternative is static token counts, rolling latency averages, GPU utilization, or request counts, all of which are treated in the cited systems papers as lagging or incomplete signals (Lu et al., 2022, Banerjee, 6 Jul 2026, Lai et al., 3 Dec 2025).

2. Streaming speech endpoint detection

In "Endpoint Detection for Streaming End-to-End Multi-talker ASR" (Lu et al., 2022), endpoint detection is implemented by adding an end-of-sentence token, $\langle \text{eos} \rangle$, to the output vocabulary of the Streaming Unmixing and Recognition Transducer (SURT). The model’s unmixing module produces channel-specific hidden representations $H_1$ and $H_2$, and the recognizer—either RNN-T or Transformer Transducer—emits token sequences that now include $\langle \text{eos} \rangle$. During inference, the timestamp at which the model emits $\langle \text{eos} \rangle$ is taken as the endpoint timestamp for that speaker, denoted $\hat{t}_{\langle \text{eos} \rangle}$ (Lu et al., 2022).

The paper’s main contrast is with VAD-based endpoint detection. VAD relies exclusively on acoustic cues for speech-silence segmentation, is unsuitable for overlapped or multi-talker scenarios, and ignores language-model information. The token-based approach integrates acoustic and language information through the end-to-end model, handles overlapping speech through channel-specific $\langle \text{eos} \rangle$ predictions, and does so with negligible computational overhead and no standalone VAD module (Lu et al., 2022).

The quantitative trade-off is explicit. On the 2-speaker LibrispeechMix dataset, adding $\langle \text{eos} \rangle$ changes WER from $x_0$0 to $x_0$1 for LSTM-SURT and from $x_0$2 to $x_0$3 for Transformer-SURT. Adding the latency penalty raises WER to $x_0$4 and $x_0$5, respectively. Endpoint latency is summarized by $x_0$6, where positive $x_0$7 indicates delayed detection and negative $x_0$8 indicates premature detection. Without latency penalty, $x_0$9 is often positive and widely distributed; with latency penalty, it is sharply centered around $\langle \text{eos} \rangle$0 and yields many more detections within $\langle \text{eos} \rangle$1 frames of ground truth. At a frame rate of $\langle \text{eos} \rangle$2 Hz, the recall for $\langle \text{eos} \rangle$3 improves from $\langle \text{eos} \rangle$4 to $\langle \text{eos} \rangle$5 on channel 1 and from $\langle \text{eos} \rangle$6 to $\langle \text{eos} \rangle$7 on channel 2 (Lu et al., 2022).

This line of work established a specific and influential meaning of token-based endpoint prediction: the endpoint is not inferred from silence alone, but from the model’s own symbolic termination token.

3. Future-token and multi-token endpoint prediction

A second research strand uses token-level states to predict not merely a boundary token, but a future continuation block. In "Future Token Prediction -- Causal Language Modelling with Per-Token Semantic State Vector for Multi-Token Prediction" (Walker, 2024), each token position is trained to predict the next $\langle \text{eos} \rangle$8 future tokens. The architecture uses a causal transformer encoder, a learnable linear projection from each top-layer embedding to a pseudo-sequence, and a small transformer decoder that cross-attends to that pseudo-sequence. The loss for the $\langle \text{eos} \rangle$9-th future token is discounted by $H_1$0 with $H_1$1 (Walker, 2024).

The stated consequence is that the top-layer embedding becomes a per-token semantic state vector rather than a purely immediate-token representation. The paper reports smoother adjacent-token cosine similarity, better topic coherence, better text classification features, and better performance on a toy coding problem. On semantic adherence, it reports BERTScore $H_1$2 for FTP versus $H_1$3 for GPT, and sentence-embedding similarity to the prompt over long generations of $H_1$4 for FTP versus $H_1$5 for GPT (Walker, 2024). A plausible implication is that endpoint prediction here is a property of the representation itself: each token state is trained to summarize a multi-token future.

A training-free variant appears in "Efficient Training-Free Multi-Token Prediction via Embedding-Space Probing" (Goel et al., 18 Mar 2026). There, mask tokens are synthesized in embedding space and appended to the prompt. The model’s logits at mask positions define a speculative token tree built from Top-$H_1$6 candidates; a lightweight pruning strategy removes adjacent repeated tokens and retains high-probability continuations. Candidate predictions are then verified in parallel, yielding lossless decoding. The paper reports acceptance-length gains of approximately $H_1$7 on LLaMA3 and $H_1$8--$H_1$9 on Qwen3, with throughput gains of up to $H_2$0--$H_2$1 (Goel et al., 18 Mar 2026).

A mathematically stronger notion appears in "x-Prediction Is All You Need: Training-Free Accelerated Generation via Endpoint Decodability" (Peng et al., 7 Jul 2026). For affine probability paths,

$H_2$2

the clean endpoint is decodable whenever $H_2$3, with decoder

$H_2$4

The paper shows that plugging the optimal velocity predictor into this decoder yields the minimum-MSE estimator $H_2$5, and proposes Truncated Jump Sampling, which exits early at time $H_2$6 and returns the decoded endpoint. Across SDXL, SD3.5M, Z-Image-Turbo, and three class-conditional benchmarks, the reported reduction in neural function evaluations is $H_2$7--$H_2$8 with near-matched quality (Peng et al., 7 Jul 2026). The paper further argues that endpoint prediction can work without trajectory straightening.

4. Token-probability signatures, dynamic triggering, and auditing

A third meaning of token-based endpoint prediction is decision prediction from early decoding statistics. In "Token Signature: Predicting Chain-of-Thought Gains with Token Decoding Feature in LLMs" (Liu et al., 6 Jun 2025), the first $H_2$9 greedy-decoded token probabilities are collected as

$\langle \text{eos} \rangle$0

and correlated with token position using Spearman correlation. The paper defines Instance SC and Aggregated SC, and uses the sign of the indicator as a benchmark-level decision rule: if the indicator is $\langle \text{eos} \rangle$1, CoT is predicted to be beneficial; if the indicator is $\langle \text{eos} \rangle$2, CoT is predicted to bring no benefit or may be detrimental. A logistic regression model over Instance SC yields Dynamic CoT, which chooses between CoT reasoning and direct answer on a per-instance basis (Liu et al., 6 Jun 2025).

The reported benchmark-level prediction accuracy is up to $\langle \text{eos} \rangle$3 for Aggregated SC and $\langle \text{eos} \rangle$4 for Instance SC. Dynamic CoT reduces average token consumption by $\langle \text{eos} \rangle$5 in open-source models and by $\langle \text{eos} \rangle$6 when transferred to closed-source models, compared to always generating CoT reasoning (Liu et al., 6 Jun 2025). In this literature, the “endpoint” is a decision endpoint in the inference policy: whether the system should traverse the longer reasoning trajectory at all.

An accountability-oriented variant appears in "Predictive Auditing of Hidden Tokens in LLM APIs via Reasoning Length Estimation" (Wang et al., 29 Jul 2025). PALACE estimates hidden reasoning token counts from prompt-answer pairs without access to internal traces. Its architecture combines a GRPO-augmented adaptation module with a lightweight domain router, and it is evaluated on math, coding, medical, and general reasoning data. On Qwen2.5-3B, the reported Pass@1 values are $\langle \text{eos} \rangle$7 on general, $\langle \text{eos} \rangle$8 on math, $\langle \text{eos} \rangle$9 on coding, and $\langle \text{eos} \rangle$0 on medical; these results exceed the corresponding LoRA, CoIn, and MLP baselines listed in the paper (Wang et al., 29 Jul 2025). Here the predicted endpoint is not a semantic boundary but a hidden token budget.

5. Serving, routing, and deployment endpoints

In systems research, “endpoint” often denotes the concrete serving target rather than the end of a sequence. "Token Arena: A Continuous Benchmark Unifying Energy and Cognition in AI Inference" (Gao et al., 1 May 2026) formalizes an endpoint as

$\langle \text{eos} \rangle$1

TokenArena measures five core axes—output speed, time to first token, workload-blended price, effective context, and quality on the live endpoint—and combines them with modeled energy into joules per correct answer, dollars per correct answer, and endpoint fidelity. Across $\langle \text{eos} \rangle$2 endpoints serving $\langle \text{eos} \rangle$3 model families, it reports that the same model on different endpoints differs in mean accuracy by up to $\langle \text{eos} \rangle$4 points on math and code, in fingerprint similarity to first party by up to $\langle \text{eos} \rangle$5 points, in tail latency by an order of magnitude, and in modeled joules per correct answer by a factor of $\langle \text{eos} \rangle$6 (Gao et al., 1 May 2026).

Scheduling systems convert token-based prediction directly into endpoint assignment or queue priority. "ELIS: Efficient LLM Iterative Scheduling System with Response Length Predictor" (Choi et al., 14 May 2025) uses a frozen BGE encoder, the CLS embedding, and eight fully connected layers to predict the remaining number of output tokens. The predictor is updated iteratively using prompt plus partial output, and on the vLLM dataset it reports MAE $\langle \text{eos} \rangle$7, RMSE $\langle \text{eos} \rangle$8, and $\langle \text{eos} \rangle$9. The resulting ISRTF scheduler reduces average job completion time by up to $\hat{t}_{\langle \text{eos} \rangle}$0 (Choi et al., 14 May 2025).

"When Words Predict Workload" (Banerjee, 6 Jul 2026) makes the routing interpretation explicit. A CPU-side Linguistic Resource Forecasting gateway extracts a $\hat{t}_{\langle \text{eos} \rangle}$1-dimensional text-structure vector, applies an XGBoost predictor to estimate escalation probability $\hat{t}_{\langle \text{eos} \rangle}$2, and compares that probability to a dynamic routing threshold $\hat{t}_{\langle \text{eos} \rangle}$3 computed from live latency telemetry. In a $\hat{t}_{\langle \text{eos} \rangle}$4-request live trial, the reported operational misroute fraction $\hat{t}_{\langle \text{eos} \rangle}$5 falls to $\hat{t}_{\langle \text{eos} \rangle}$6--$\hat{t}_{\langle \text{eos} \rangle}$7, versus $\hat{t}_{\langle \text{eos} \rangle}$8 for the token-count baseline; AUROC is $\hat{t}_{\langle \text{eos} \rangle}$9; peak edge VRAM remains at $\langle \text{eos} \rangle$0 under an $\langle \text{eos} \rangle$1 ceiling across a $\langle \text{eos} \rangle$2 variation in WAN delay; and the dynamic controller yields an $\langle \text{eos} \rangle$3 relative reduction in misroutes compared to an equivalent static threshold (Banerjee, 6 Jul 2026).

Closely related work treats tokens as the native workload unit for autoscaling and admission. "TokenScale: Timely and Accurate Autoscaling for Disaggregated LLM Serving with Token Velocity" (Lai et al., 3 Dec 2025) introduces Token Velocity as a leading indicator for prefill, network, and decode stages, improving SLO attainment from $\langle \text{eos} \rangle$4--$\langle \text{eos} \rangle$5 to $\langle \text{eos} \rangle$6--$\langle \text{eos} \rangle$7 and reducing costs by $\langle \text{eos} \rangle$8--$\langle \text{eos} \rangle$9. "Token Management in Multi-Tenant AI Inference Platforms" (Cunningham, 27 Feb 2026) introduces token pools, with entitlements in token throughput, KV cache, and concurrency, and reports bounded P99 latency for guaranteed workloads during overload, in contrast to unbounded latency degradation without admission control. These systems are not endpoint predictors in the narrow ASR sense, but they extend the same token-native logic to endpoint selection and provisioning (Lai et al., 3 Dec 2025, Cunningham, 27 Feb 2026).

6. Limitations, misconceptions, and adjacent predictor families

A recurring misconception is that next-token prediction alone is sufficient for endpoint prediction. "The pitfalls of next-token prediction" (Bachmann et al., 2024) distinguishes autoregressive inference from teacher-forced training and argues that, in some planning tasks, teacher-forcing can fail to learn an accurate next-token predictor in the first place. On the paper’s minimal path-star graph task, both Transformer and Mamba models fail under teacher-forced training despite the task being straightforward to learn; the paper presents preliminary evidence that predicting multiple tokens in advance can resolve this failure (Bachmann et al., 2024). This is directly relevant to endpoint prediction because many endpoint decisions depend on global plan structure rather than on local next-token statistics alone.

A second misconception is that simple surrogates such as VAD silence or raw token count are adequate. The SURT endpointing paper treats VAD as unsuitable for overlapped speech because it ignores language information (Lu et al., 2022), while the LRF gateway paper shows that a token-count baseline yields an operational misroute fraction near $\langle \text{eos} \rangle$0 in a workload-routing setting (Banerjee, 6 Jul 2026). The systems literature therefore repeatedly replaces one-dimensional counts with richer token-derived structure.

A third limitation is scalability of supervision. "Beyond Next-Token Prediction: An RLVR Proof of Concept for Tool-Use Agents on Atlassian Workflows" (Vissa et al., 1 Jul 2026) addresses API-endpoint correctness rather than sequence-boundary detection. In five synthetic Jira REST v3 and Confluence v2 environments, GRPO training with verifiable rewards raises average reward from a $\langle \text{eos} \rangle$1B-baseline range of $\langle \text{eos} \rangle$2--$\langle \text{eos} \rangle$3 to $\langle \text{eos} \rangle$4--$\langle \text{eos} \rangle$5 on the four scenarios whose rewards are non-degenerate, with the largest single gain on Confluence page creation from $\langle \text{eos} \rangle$6 to $\langle \text{eos} \rangle$7. The paper simultaneously emphasizes two limitations: hand-crafting verifiable rewards does not scale beyond the handful of endpoints reported, and one scenario has a saturating reward shape that the prompted $\langle \text{eos} \rangle$8B already maxes out (Vissa et al., 1 Jul 2026). This suggests that endpoint prediction for tool use can be highly effective when endpoint correctness is mechanically checkable, but difficult to generalize across large API surfaces.

Adjacent token-granular predictor families reinforce the broader trend. "TokenButler: Token Importance is Predictable" (Akhauri et al., 10 Mar 2025) introduces a query-aware token-importance predictor with less than $\langle \text{eos} \rangle$9 parameter overhead and reports over $x_0$00 improvement relative to state-of-the-art token-importance estimation. "Token Caching for Diffusion Transformer Acceleration" (Lou et al., 2024) introduces a Cache Predictor for selective pruning and reports speedups up to $x_0$01--$x_0$02. "TAP: A Token-Adaptive Predictor Framework for Training-Free Diffusion Acceleration" (Zhu et al., 4 Mar 2026) performs per-token predictor selection and reports speedups such as $x_0$03 on FLUX.1-dev. These are not endpoint predictors in the narrow sense, but they show the same architectural move away from global heuristics and toward token-level prediction (Akhauri et al., 10 Mar 2025, Lou et al., 2024, Zhu et al., 4 Mar 2026).

The cumulative record suggests that token-based endpoint prediction is best understood not as a single algorithm, but as a family of token-native inference strategies. Its unifying principle is that endpoint decisions become more accurate when the predictor is allowed to use token-level evidence commensurate with the structure of the task—symbolic termination in speech, multi-token future structure in generation, or workload-aware routing and accounting in serving systems.