Timely-Eval: Temporal Evaluation Framework

• Timely-Eval is a unified framework that integrates time-aware evaluation protocols, benchmarks, and modeling solutions to measure system performance under time constraints.
• It establishes formal mathematical foundations to parameterize evaluation based on wall-clock budgets, balancing inference latency and task accuracy.
• Timely-Eval addresses temporal drift and robustness with dynamic metrics and adaptive feedback across various domains, ensuring practical and future-proof evaluation.

Timely-Eval is a comprehensive paradigm that unifies evaluation protocols, benchmarks, and modeling solutions for assessing time-aware and temporally robust system behavior. Its central goal is to measure, analyze, and optimize how models, evaluators, and learning algorithms perform under explicit temporal constraints, adapt to data or environment shifts across time, and maintain reliability as input distributions and tasks evolve. The Timely-Eval framework encapsulates a diversity of instantiations, from LLM agent test-time scaling under wall-clock budgets to evaluation of temporal drift in sentiment analysis, information retrieval, and programmatic assessment.

1. Formal Foundations of Timely-Eval

Timely-Eval systems are characterized by the explicit parameterization of evaluation or operation with respect to time. In agentic LLM settings, the core mathematical definition formalizes the evaluation instance as governed by a total time budget $T$; at each of $N$ reasoning steps, the cumulative wall-clock time cost $$t_{\mathrm{all}} = \sum_{i=1}^N (t_{\mathrm{gen}}^{(i)} + t_{\mathrm{tool}}^{(i)})$$ must not exceed $T$. This time-accumulation includes both model inference ($t_{\mathrm{gen}}$) and exogenous tool latency ($t_{\mathrm{tool}}$). Success is measured via an instance-level functional

$$S_{\mathrm{inst}} = \begin{cases} 0 & t_{\mathrm{all}} > T \ r_{\mathrm{f}} & t_{\mathrm{all}} \leq T,~r=0 \ r_{\mathrm{f}} + r + \lambda U(t_{\mathrm{all}}) & t_{\mathrm{all}} \leq T,~r>0 \end{cases}$$

where $r_{\mathrm{f}}$ is a formatting reward, $r$ is task accuracy, $\lambda$ weights a sinusoidal time-utilization function $U(t) = \sin(\frac{\pi}{2} \min(\frac{t}{T}, 1))$. The aggregate benchmark score is the mean over all evaluated instances: $$S = \frac{1}{N}\sum_{i=1}^N S_{\mathrm{inst}}^{(i)}.$$ This structure generalizes across tasks where the “Timely” aspect can refer to real-time interaction, response timing, or temporal persistence in data or evaluation (Ma et al., 23 Jan 2026).

2. Timely-Eval in Time-Budgeted Reasoning and Agentics

A central test case for Timely-Eval is the evaluation of LLM-based agents operating under hard wall-clock constraints, particularly in scenarios where tool use induces nontrivial latency. The Timely-Eval benchmark comprises three axes:

• High-frequency tool use: Interactive text games (Jericho suite), requiring rapid sequence of decisions and adaptation to injected tool call latency (e.g., 0, 2, 10, 50 s/turn). Performance is a function of the interplay between agent strategy, reasoning depth, and available time per action.
• Low-frequency tool use: Machine learning code-generation tasks with a single, long-running execution budget. The agent must optimize for both correctness and elapsed runtime, reflecting realistic computational constraints.
• Time-constrained pure reasoning: Strictly internal model deliberation (e.g., math problem solving) under explicit generation-length-to-time scaling, with budgets set as fractions or multiples of baseline unconstrained completion times (Ma et al., 23 Jan 2026).

A key result is the demonstration of “scaling winner flips”: under low latency, smaller models can outperform larger ones by enabling more actions within the budget; under high latency, larger models dominate via superior per-step quality. Timely-Eval makes clear that optimal architecture, strategy, and planning are regime-dependent.

3. Temporal Robustness and Drift Analysis

Timely-Eval subsumes methodologies for evaluating how systems handle time-varying data distributions, concept drift, and label/relevance volatility. In information retrieval, the LongEval framework introduces multi-snapshot evaluation, measuring nDCG@k independently for each time lag, with robustness captured by the Relative nDCG Drop (RnD): $$\mathrm{RnD}(t_1 \rightarrow t_2) = \mathrm{nDCG}@k(t_1) - \mathrm{nDCG}@k(t_2)$$ Temporal generalization is assessed by comparing early and late test splits; high-performing systems on static data may exhibit large performance drops (high RnD) over time, while some less performant baselines (e.g., pure BM25) show greater temporal stability (Cancellieri et al., 11 Mar 2025). In sentiment analysis, “Relative Performance Drop” (RPD), defined as $\mathrm{RPD}(A,B) = (f_A - f_B) / f_A$ for macro-F1 scores $f_A, f_B$, quantifies dyadic performance decay across temporal splits (Ninalga, 2023).

4. Timely-Eval for Evaluation Pipelines and Feedback

Practical applications of Timely-Eval span education, dialog systems, and machine learning evaluation:

• Automated code assessment: CodEval integrates with LMS platforms to provide feedback “within a few minutes of submission” by orchestrating periodic polling, containerized test execution, and structured pass/fail summaries. The average feedback latency ($T_e$) is tightly bounded ($<6$ minutes), with observed improvements in student code quality and assignment completion rates (Agrawal et al., 2022).
• LLM Evaluator Self-Improvement: Learning While Evaluating (LWE) meta-evaluators continually refine their meta-prompts during deployment by harvesting self-generated feedback and updating evaluation rubrics particularly on difficult or ambiguous cases. Selective LWE methodologies maintain nearly optimal accuracy and consistency at a fraction of the inference cost, demonstrating cost-effective, adaptive Timely-Eval in LLM-judge settings (Jwa et al., 7 Dec 2025).
• Dialogue agent timeliness: The “TimelyChat” benchmark and TIMER agent task models the joint probability of responding with both an appropriate delay ($T_t$) and message ($r_t$), with specialized metrics (timing F1, RMSLE, “time-specificity”) validating the system’s awareness and exploitation of temporal context (Jang et al., 17 Jun 2025).

5. Methodologies for Temporal Generalization and Drift Mitigation

Timely-Eval frameworks emphasize explicit temporal feature representations and adaptive training pipelines. In text-based tasks, input prefixing with date/timestamp (“year: <YYYY> text: ...”) conditions model inference on time information, mitigating concept drift. Date perturbation and self-labeling augmentations train temporally smoother decision boundaries, as shown by the lowest short-term RPD on the LongEval-Classification benchmark (Ninalga, 2023). In retrieval, timestamp features and time-aware query expansions are advocated to modulate temporal drift and improve robustness (Cancellieri et al., 11 Mar 2025).

In NLG and MT evaluation, time-efficiency is measured in wall-clock inference speed, with practical gains achieved by swapping heavy encoders for lightweight distillations, using linear/quadratic approximations (e.g. WCD, RWMD) where quality loss is minimal, and training with adapters for throughput and energy efficiency. These practices enable NLG Timely-Eval protocols that can scale to real-world data and usage constraints (Larionov et al., 2022).

6. Statistical and Sequential Evaluation under Time Constraints

Timely-Eval encompasses sequential, anytime-valid hypothesis testing and dynamic monitoring. In forecasting, e-value-based sequential testing allows real-time assessment of calibration, providing strict type I error control under optional stopping. The methodology involves tracking cumulative products of e-values (supermartingales), applying mixture or kernel-based alternatives, and enabling immediate detection of temporal miscalibration missed by fixed-horizon tests. The approach is operationalized through concise pseudocode for online, time-aware evaluation (Arnold et al., 2021).

7. Limitations, Open Directions, and Recommendations

Observed limitations include (a) static models failing to modulate reasoning/resource allocation in response to time budgets, (b) incomplete self-diagnosis heuristics in evaluator learning (e.g., unable to detect “consistent but wrong” judgments absent labels), (c) challenges in modeling continuous rather than discrete temporal variables, and (d) meta-prompt management overheads in sequential evaluators. Future research is directed at richer temporal representation (e.g., continuous intervals, explicit event reasoning), expansion to human-in-the-loop or multi-day asynchronous evaluation regimes, and integrating uncertainty quantification for time-aware planning. Timely-Eval methodology recurrently demonstrates that temporal awareness, explicit time features, and adaptive strategies are essential for robust, future-proof deployment of ML systems across domains (Ma et al., 23 Jan 2026, Cancellieri et al., 11 Mar 2025, Ninalga, 2023).