Endpoint Anticipation in Predictive Systems

• Endpoint Anticipation is a predictive formulation that defines future event timing using remaining-time targets and fixed-horizon labels, shifting from reactive to proactive systems.
• It employs regression and classification methods to forecast endpoints in applications ranging from speech turn detection to autonomous driving and surgical instrument usage.
• Evaluation metrics like ACC within tolerance windows and online latency highlight its practical benefits and trade-offs in real-time performance and safety.

Endpoint anticipation denotes a family of predictive formulations in which a system forecasts a salient terminal, onset-adjacent, or target endpoint event before that event is fully realized. Across recent arXiv literature, the term appears in several closely related senses: proactive forecasting of end-of-turn signals in spoken dialogue, duration-aware prediction of time to next speech onset in streaming endpoint detection, remaining-time prediction for sparse surgical instrument usage, time-to-contact estimation in egocentric interaction anticipation, anticipatory control before vehicle cut-in sensing, and endpoint-aware anchoring of future trajectory context in streaming prediction (Udupa et al., 11 Jun 2026). In each case, the common shift is from reactive state labeling toward explicit modeling of future event timing, endpoint proximity, or endpoint-conditioned context.

1. Conceptual definition and scope

In spoken dialogue, endpoint anticipation is introduced as a replacement for reactive turn-completion detection: instead of deciding only whether a speaker has already finished, the system predicts whether an end-of-turn will occur within a specified future horizon, so that downstream ASR, LLM, and TTS computation can begin before the user has stopped speaking (Udupa et al., 11 Jun 2026). In streaming speech endpoint detection, the same general move appears as prediction of the time-to-next-speech-onset rather than a binary endpoint label, with the argument that the central difficulty is the ambiguity of silence, since hesitations and disfluencies make pauses resemble endpoints even when the speaker is not done (Tsoi et al., 16 Jun 2026).

Outside speech, analogous formulations recur. In laparoscopic video, the task is redefined from “what action happens next?” to “when will a specific sparse instrument usage occur within a future time horizon?”, using remaining-time regression rather than dense action segmentation (Rivoir et al., 2020). In egocentric vision, short-term object interaction anticipation predicts the next active object, the future verb, and the time to contact before the interaction begins (Ragusa et al., 2023). In autonomous driving, anticipatory sensing for Adaptive Cruise Control advances the follower’s response relative to sensing delay in cut-in scenarios (Zhang et al., 2024), while streaming trajectory prediction uses previously forecast trajectory endpoints as anchors for extracting target-centric scene context at the next timestep (Prutsch et al., 2 Mar 2026).

A plausible synthesis is that endpoint anticipation is not a single task but a design principle: future event structure is made explicit either as a time-to-event target, a fixed-horizon anticipation label, or an endpoint-conditioned representation. This distinguishes it from conventional forecasting pipelines that either classify the present, detect an endpoint only after it occurs, or predict future states without representing endpoint structure directly.

2. Task formulations and mathematical representations

A recurring formal pattern is conversion of future events into continuous or horizon-capped time targets. In sparse surgical anticipation, for frame $x$, instrument $\tau$, and horizon $h$, the remaining-time target is

$r_h(x,\tau) = \min\{t_x(\tau), h\},$

where $t_x(\tau)$ is the true remaining time until the instrument appears and truncation at $h$ represents “no anticipation within horizon” (Rivoir et al., 2020). The same model adds a three-class auxiliary objective $$c_h(x,\tau)\in\{\text{anticipating},\text{present},\text{background}\}$$, separating future occurrence within horizon from current visibility and background (Rivoir et al., 2020).

In streaming endpoint detection, Next-Turn defines the target as the time to the next speech onset. Frames in speech map to $0$, frames in a mid-utterance pause map to the remaining time until the next onset, and post-utterance silence maps to a capped value $\tau_{\max}$ because the next turn is unknown within a single utterance (Tsoi et al., 16 Jun 2026). This converts endpoint detection into a duration-aware regression or discretized classification problem rather than an instantaneous binary decision (Tsoi et al., 16 Jun 2026).

In low-latency dialogue, Endpoint Anticipation defines a family of anticipation horizons

$\mathcal{H}=\{320,640,\dots,2560\}\text{ ms},$

and for each horizon $\tau$0 predicts whether the current frame lies within the final $\tau$1 milliseconds before the true end-of-turn (Udupa et al., 11 Jun 2026). The first positive trigger becomes the activation point for speculative downstream execution (Udupa et al., 11 Jun 2026).

Other domains use endpoint structure differently. In short-term object interaction anticipation, the endpoint is the imminent start of interaction, and the model predicts a future object box, noun, verb, and real-valued time-to-contact (Ragusa et al., 2023). In ACC under cut-in, anticipation is represented as a look-ahead offset $\tau$2 that pulls the follower’s response earlier in time relative to sensing delay $\tau$3; when anticipation equals delay, the following vehicle can respond to the cut-in immediately, and when anticipation exceeds delay, response can begin before cut-in completion (Zhang et al., 2024). In streaming trajectory prediction, the endpoint is not the label target itself but the endpoint of the previously forecast trajectory, which becomes an anchor for selecting future-relevant scene tokens at the next streaming step (Prutsch et al., 2 Mar 2026).

3. Supervision, architectures, and inference patterns

One major attraction of endpoint anticipation is that supervision can often be derived from timestamps or sparse labels rather than dense semantic annotation. Next-Turn derives time-to-next-speech-onset directly from speech timestamps obtained through utterance segmentation, forced alignment, and silence thresholds, requiring no additional annotation of semantic endpoints (Tsoi et al., 16 Jun 2026). The surgical remaining-time framework requires only sparse instrument annotations during training and uses image data alone at inference time, avoiding dense temporal segmentations, phase annotations, or action boundary metadata (Rivoir et al., 2020).

Architecturally, several distinct patterns recur. Next-Turn uses a Whisper encoder fine-tuned with LoRA and attaches either a binary endpoint head, a duration head, or both jointly; the joint formulation uses a shared encoder with a combined loss, and duration-derived scores can be fused with binary scores at inference (Tsoi et al., 16 Jun 2026). Endpoint Anticipation for spoken dialogue uses a dual-stream audio model with separate streaming Transformers for user and system audio, concatenated into a shared conversational representation, and studies both single-target learning with one model per horizon and multi-target learning with a shared backbone and horizon-specific heads (Udupa et al., 11 Jun 2026).

In vision, StillFast uses a two-stream architecture that processes a high-resolution still frame with a 2D CNN and a lower-resolution observation clip with a 3D CNN, fuses them through a Combined Feature Pyramid Layer, and predicts noun, verb, bounding box, time-to-contact, and a confidence score in a unified Faster R-CNN-style head (Ragusa et al., 2023). In trajectory forecasting, SEAM combines temporal context propagation, agent-centric scene encoding, endpoint-aware target-centric encoding, and dual-context decoding, with the previous forecast endpoints defining $\tau$4 candidate target regions around which current scene tokens are gathered (Prutsch et al., 2 Mar 2026).

A notable methodological pattern is that endpoint anticipation often replaces sparse, ambiguous binary supervision with denser temporal signals. This is explicit in Next-Turn, where the duration objective is described as complementing standard binary endpoint detection because it supplies a denser temporal learning signal (Tsoi et al., 16 Jun 2026). The same logic appears in surgery, where remaining-time regression avoids the assumption that an action must occur imminently and allows the model to stay inactive most of the time until evidence for a sparse future event becomes visible (Rivoir et al., 2020).

4. Evaluation criteria and operational trade-offs

Because endpoint anticipation is usually deployed in online systems, evaluation extends beyond standard accuracy. In streaming speech endpoint detection, the main metric is endpoint accuracy within a tolerance window, especially $\tau$5, which measures whether the first trigger occurs within 320 ms after the true endpoint; early interruption (EI) measures how often the model fires before the true endpoint (Tsoi et al., 16 Jun 2026). This pairing captures the trade-off between promptness and safety (Tsoi et al., 16 Jun 2026).

In low-latency dialogue, evaluation is explicitly system-level. The paper introduces Median Realized Anticipation (MRA), Premature Anticipation Rate (PAR), Expected Redundant Computation (ERC), and Horizon Entry Accuracy (HEA), thereby quantifying both realized latency reduction and speculative compute waste (Udupa et al., 11 Jun 2026). At $\tau$6 ms and ERC near $\tau$7, EPA-M achieves MRA $\tau$8 ms and HEA $\tau$9, while the adapted VAP baseline reaches MRA $h$0 ms and HEA $h$1 (Udupa et al., 11 Jun 2026). At $h$2 ms and ERC near $h$3, EPA-M attains MRA $h$4 ms and HEA $h$5, compared with VAP at MRA $h$6 ms and HEA $h$7 (Udupa et al., 11 Jun 2026).

The same concern with actionability appears in other fields. In surgery, the paper reports weighted and precision-oriented mean absolute error and shows that filtering predictions by low epistemic uncertainty improves the precision-oriented error metric (Rivoir et al., 2020). In egocentric interaction anticipation, evaluation uses Top-$h$8 mean Average Precision, with separate reporting for noun, noun+verb, noun+TTC, and overall noun+verb+TTC anticipation (Ragusa et al., 2023). In ACC cut-in analysis, stochastic safety is quantified through collision probability and expectation of inverse time-to-collision-like quantities under empirically calibrated parameter distributions (Zhang et al., 2024). In streaming trajectory prediction, endpoint-aware modeling is assessed both by displacement metrics and by online latency, because the method is designed for real-time deployment rather than only snapshot accuracy (Prutsch et al., 2 Mar 2026).

A consistent implication is that endpoint anticipation is valuable only insofar as it improves the latency-risk or latency-redundancy frontier. This is explicit in dialogue and speech, but it is equally visible in driving and robotics, where acting too early or on the wrong endpoint forecast can degrade safety or waste computation.

5. Domain-specific realizations

The most direct present-day instantiations are in speech and dialogue. Next-Turn reports that the strongest single-task duration-regression model improves $h$9 from $r_h(x,\tau) = \min\{t_x(\tau), h\},$0 to $r_h(x,\tau) = \min\{t_x(\tau), h\},$1 and reduces EI from $r_h(x,\tau) = \min\{t_x(\tau), h\},$2 to $r_h(x,\tau) = \min\{t_x(\tau), h\},$3, while the best joint binary plus duration classification system achieves $r_h(x,\tau) = \min\{t_x(\tau), h\},$4 and $r_h(x,\tau) = \min\{t_x(\tau), h\},$5, reported as a $r_h(x,\tau) = \min\{t_x(\tau), h\},$6 absolute improvement in endpoint accuracy within 320 ms over the strongest baseline in its broader comparison (Tsoi et al., 16 Jun 2026). Endpoint Anticipation for spoken dialogue forecasts end-of-turn signals up to $r_h(x,\tau) = \min\{t_x(\tau), h\},$7 seconds in advance and, when integrated with Unmute, reduces average latency from $r_h(x,\tau) = \min\{t_x(\tau), h\},$8 ms to $r_h(x,\tau) = \min\{t_x(\tau), h\},$9 ms, a $t_x(\tau)$0 ms reduction, with ERC $t_x(\tau)$1 (Udupa et al., 11 Jun 2026).

In surgery, endpoint anticipation is formulated as sparse-event timing. On Cholec80, the Bayesian CNN-LSTM model trained only on sparse annotations is competitive with an offline histogram baseline that knows procedure duration and outperforms a mean-duration baseline substantially; at $t_x(\tau)$2 minutes, the reported values are wMAE/pMAE $t_x(\tau)$3 for the Bayesian model, $t_x(\tau)$4 for the non-Bayesian variant, $t_x(\tau)$5 for the phase-supervised variant, $t_x(\tau)$6 for OracleHist, and $t_x(\tau)$7 for MeanHist (Rivoir et al., 2020). The same paper emphasizes that uncertainty is operationally useful: when the clipper appears, the model becomes more confident about future scissors usage, illustrating anticipation of trigger events (Rivoir et al., 2020).

In egocentric video, StillFast jointly predicts next-active object localization, future verb, and time-to-contact. On EGO4D v2 test, it reports Top-5 mAP values of $t_x(\tau)$8 for noun, $t_x(\tau)$9 for noun+verb, $h$0 for noun+TTC, and $h$1 overall, and the method was ranked first on the public leaderboard of the EGO4D short-term object interaction anticipation challenge 2022 (Ragusa et al., 2023). This establishes endpoint anticipation in a perception setting where the endpoint is the imminent start of physical interaction rather than turn completion or sparse instrument appearance (Ragusa et al., 2023).

In autonomous driving, endpoint anticipation appears both in control and in perception-planning interfaces. For commercial ACC under cut-in, anticipation mitigates risk introduced by sensing delays: with a $h$2 s sensing delay and a $h$3 s anticipation example, the reported minimum gap changes from a baseline $h$4 m to $h$5 m under delay and $h$6 m with anticipation (Zhang et al., 2024). In highly adverse scenarios, $h$7 s anticipation reduces collision risk by $h$8, and a $h$9 s anticipation period effectively ensures safety in aggressive cut-in conditions even in the presence of sensing delays (Zhang et al., 2024). In streaming trajectory prediction, endpoint-aware modeling yields state-of-the-art streaming results on Argoverse 2 while remaining lightweight; on the single-agent benchmark, SEAM reports brier-minFDE$$c_h(x,\tau)\in\{\text{anticipating},\text{present},\text{background}\}$$0, minADE$$c_h(x,\tau)\in\{\text{anticipating},\text{present},\text{background}\}$$1, minFDE$$c_h(x,\tau)\in\{\text{anticipating},\text{present},\text{background}\}$$2, and MR$$c_h(x,\tau)\in\{\text{anticipating},\text{present},\text{background}\}$$3, with online latency $$c_h(x,\tau)\in\{\text{anticipating},\text{present},\text{background}\}$$4 ms at batch size $$c_h(x,\tau)\in\{\text{anticipating},\text{present},\text{background}\}$$5 on a V100 GPU (Prutsch et al., 2 Mar 2026).

6. Limitations, ambiguities, and research directions

Several papers identify ambiguity in the endpoint itself as the central obstacle. In streaming speech, silence is not equivalent to completion because speakers pause mid-thought, and semantic endpoint detection is hindered by ambiguous supervision and strict streaming constraints (Tsoi et al., 16 Jun 2026). In low-latency dialogue, open-domain interaction remains harder than task-oriented dialogue: Endpoint Anticipation performs better on SpokenWOZ than on Switchboard, and the paper highlights unresolved edge cases such as mid-turn backtracking and late-arriving critical information (Udupa et al., 11 Jun 2026). In surgery, uncertainty is instrument-dependent: for predictable instruments such as scissors, clipper, and specimen bag, error–uncertainty correlation is stronger, whereas uncertainty is less reliable for more dynamic instruments such as irrigator and bipolar (Rivoir et al., 2020).

A second limitation is that anticipation can trade latency for redundancy or safety margin for false triggers. ERC in dialogue exists precisely because speculative LLM and TTS work may be discarded (Udupa et al., 11 Jun 2026). In streaming speech, gains increase with pause count, but early interruption remains a central failure mode (Tsoi et al., 16 Jun 2026). In ACC, anticipation offsets delay but does not remove all sources of risk, since the follower may still be influenced by the original leader’s state prior to cut-in (Zhang et al., 2024). In trajectory forecasting, endpoint-aware anchoring depends on previous predictions, although robustness experiments indicate only modest degradation under noisy endpoint perturbations (Prutsch et al., 2 Mar 2026).

A broader implication is that endpoint anticipation works best when future event timing can be operationalized as a measurable target and when the system can exploit that forecast immediately. The cited literature suggests three especially durable formulations: remaining-time regression, fixed-horizon anticipation classification, and endpoint-conditioned context extraction. Together they indicate a general move from retrospective endpointing toward proactive temporal reasoning, with success measured not only by predictive fidelity but by whether the forecast usefully changes downstream computation, control, or interaction behavior (Udupa et al., 11 Jun 2026).