Temporal Cognition in LLMs

• Temporal Cognition in LLMs is the study of how models represent, process, and update time-based information and events through mechanisms like temporal indexing and memory revision.
• Benchmarks such as TRAM and TimeBench reveal performance gaps of 10–20% between LLMs and human temporal reasoning across tasks like event ordering and duration estimation.
• Innovative architectures and training methods, including RecallM and TReMu, enhance temporal reasoning by integrating dynamic memory updates and neuro-symbolic approaches.

Temporal cognition in LLMs encompasses the spectrum of mechanisms by which these models represent, process, and reason about time, temporally ordered events, and changes in the external or narrative world. Unlike static factual recall, temporal cognition is characterized by the ability to index, update, compare, and integrate temporally grounded information—crucial for human-like reasoning, planning, and dialogue. Research in this area interrogates not only the surface performance of LLMs on temporal reasoning tasks but also the model-internal representations of time, the nature of errors and biases, and the emergence of subjective temporal frameworks analogous to human cognition.

1. Foundations and Benchmarks of Temporal Cognition in LLMs

A robust tradition of benchmark development has catalyzed research into the temporal cognition capabilities of LLMs. The introduction of comprehensive resources such as TRAM (Wang et al., 2023) and TimeBench (Chu et al., 2023) marked significant advancements, providing multi-faceted testbeds covering event ordering, duration, frequency, temporal arithmetic, relation extraction, and narrative-based temporal inference. TRAM, for example, presents ten datasets spanning 38 subtasks and includes both commonsense and arithmetic-based temporal tasks. TimeBench organizes its evaluation hierarchically, distinguishing between symbolic (date arithmetic, entailment), commonsense (event order, frequency, duration), and event-based temporal reasoning (multi-hop reasoning, narrative timelines).

Performance analyses consistently reveal a pronounced gap between state-of-the-art LLMs and human temporal reasoning. For instance, even GPT-4, which performed best among mainstream models, demonstrates up to a 10–20 percentage point deficit relative to human accuracy across comprehensive temporal tasks (Wang et al., 2023, Chu et al., 2023). Challenges intensify for implicit temporal cues, multi-step reasoning, and narrative understanding, where fine-tuned domain-specific models can sometimes outperform much larger general-purpose LLMs (Qiu et al., 2023). These findings underscore not only the need for expansive and diversified benchmarks but also for specialized architectures and training protocols targeting temporal reasoning.

2. Mechanisms and Architectures for Temporal Memory and Updating

A core limitation of baseline LLMs is their reliance on static, pre-trained weights incapable of dynamic, context-aware memory updating. Recent research addresses this through explicit architectural augmentations. RecaLLM (Kynoch et al., 2023) exemplifies an adaptable long-term memory mechanism, where a structured knowledge base stores temporally indexed “truth statements” and updates beliefs iteratively:

$$\mathbf{m}_t = \alpha \cdot \mathbf{m}_{t-1} + (1-\alpha) \cdot \Delta_t$$

Here, $\mathbf{m}_t$ is the memory at time $t$, $\Delta_t$ is the new statement, and $\alpha$ controls historical versus recent information. By maintaining timelines of updates and leveraging vector databases and graph representations, RecaLLM enables belief revision, temporal indexing, and temporally consistent responses to time-dependent queries—offering operational advantages over static LLMs in tasks requiring persistent user memory, temporal knowledge bases, and adaptive dialogue.

These advances are further complemented by frameworks such as TReMu (Ge et al., 3 Feb 2025), which combine time-aware memory summarization with neuro-symbolic reasoning—prompting LLMs not only to retrieve time-stamped event summaries but also to translate temporal queries into executable Python code for explicit interval and ordering calculations. This hybrid approach substantially enhances performance for multi-session dialogue, with accuracy gains greater than 47 percentage points over standard prompting.

3. Temporal Reasoning, Robustness, and Error Taxonomies

Experimental evaluation across numerous studies details characteristic LLM weaknesses in temporal reasoning. Robustness tests—such as those introduced by (Wallat et al., 21 Mar 2025)—systematically probe LLMs’ sensitivity to changes in temporal reference (absolute vs. relative), position of the time anchor in a question, year perturbations, reversals of subject and temporal query, and granularity (year/month/day) in event dating.

A consistent pattern emerges: LLMs are prone to performance drops of 30–40% upon such perturbations. Phenomena including “temporal inertia” (tendency toward older, entrenched knowledge), “time invariance” (answers insensitive to temporal cues due to strong popularity bias), and referencing errors are common (Wallat et al., 22 Jan 2024). The inability to robustly anchor factual knowledge temporally often results in erroneous or inconsistent answers, particularly for low-frequency or recently changed facts.

Temporal QA performance can, however, be significantly improved (up to 55%) via interface-level interventions: converting relative to absolute expressions, front-loading time references, and adopting guided rewriting strategies (Wallat et al., 21 Mar 2025). This suggests that surface-level cues play a disproportionate role in model temporal behavior, and that explicit temporal structure in input prompts can partially compensate for internal model deficiencies.

4. Subjective Temporal Representation and Emergent Cognitive Patterns

Recent research reveals that LLMs do not “perceive” time purely as numeric difference but construct subjective temporal spaces exhibiting cognitive principles found in humans. Through similarity judgment tasks, larger models manifest a form of the Weber–Fechner law: perceived distance between years compresses logarithmically as events recede from a central, subjective “present” (typically the end of training) (Li et al., 21 Jul 2025).

This behavior is instantiated both at the representational and neuronal levels:

• Logarithmic coding: Representational distances $d_{log}(i,j)=|\log(i)-\log(j)|$, and with respect to a reference year $R$, $d_{ref}(i,j)=|\log(|R-i|)\circ\log(|R-j|)|$, explicate the compression of time with distance from the reference.
• Temporal-preferential neurons: Clusters in the model’s layers are minimally activated at the subjective present and increase activation as the input year diverges, with regression fits revealing

$$\text{Intensity}_x = \alpha \cdot \log|2025 - x| + \beta + \epsilon$$

Thus, LLMs internally construct a “present” and inherently code for temporal distance using non-linear, human-like schemes.

Moreover, these temporal frameworks are shaped hierarchically: shallow layers encode raw numerical distinctions, while deeper layers integrate context to form abstract, temporally oriented representations. The training corpus itself is found to possess latent non-linear temporal structures, which the models internalize and elaborate upon. This emergent “experientialist” perspective posits that temporal cognition in LLMs is a subjective construction guided by model architecture and the information-rich context of training data (Li et al., 21 Jul 2025).

5. Temporal Cognition in Applied and Multimodal Scenarios

Temporal understanding is not isolated to text-only LLMs but is critical for multimodal and interactive settings. Video-based applications push new boundaries in temporal reasoning, requiring models to comprehend action progression, causality, and event durations in complex, continuous data (Ding et al., 18 Dec 2024). Current multimodal LLM architectures, relying on pretrained encoders and attention-based fusion, succeed in short-range event detection but struggle with long-term temporal dependencies and abstract temporal relationships due to limitations in both encoders and data annotation.

Embodied agent planning, such as those benchmarked in ET-Plan-Bench (Zhang et al., 2 Oct 2024), highlights the unique challenge of temporal and causal constraints for LLMs. Tasks are constructed to require strict sequencing and stateful dependencies, with performance metrics (sequence success rate, LCS ratio) demonstrating gaps in temporal planning compared to spatial reasoning. Models like GPT-4 and fine-tuned LLAMA variants achieve moderate success rates (~58–60%) on such tasks, indicating substantial room for improvement in sequential action planning.

In social cognition and theory-of-mind domains, temporal grounding becomes essential for resolving multi-agent belief states and interpersonal reasoning (Hou et al., 1 Jul 2024, Hou et al., 30 May 2025). Innovations such as the explicit construction of temporal spaces and belief state chains, as in TimeToM, or the integration of temporally-aware hierarchical cognitive reinforcement learning, as in TimeHC-RL, yield notable gains in handling both first- and higher-order belief inference, narrative coherence, and social scenario understanding.

6. Training Regimes, Curricula, and Practical Temporal Generalization

Model scaling does not uniformly confer temporal reasoning benefits; instead, careful curriculum design and targeted fine-tuning are paramount. Time-R1 (Liu et al., 16 May 2025) demonstrates that a three-stage, reinforcement-learning curriculum—progressively training on timestamp inference, event ordering, future scenario generation, and logical mapping—can equip a 3B-parameter model with temporal reasoning and predictive abilities surpassing even 671B models. Reward functions are crafted to penalize or reward date estimation according to:

$R_{acc} = e^{-\alpha \cdot \Delta m(t_p, t_{gt})}$

where $\Delta m$ is the month difference and $\alpha$ adapts through the staged learning phases.

Temporal sampling (Li et al., 26 May 2025), which pools outputs from multiple model checkpoints rather than a final snapshot, is another practical technique that recovers reasoning solutions lost to “temporal forgetting” during sequential fine-tuning—improving benchmarks such as Pass@k and Majority@k by up to 19 percentage points.

Frameworks for temporal generalization, as introduced by FreshBench (Zhu et al., 14 May 2024), operationalize metrics such as Bits Per Character (BPC), accuracy, and the Temporal Bias Index (TBI), quantifying “nostalgia bias” (over-reliance on past data) and “neophilia bias” (over-favoring novel patterns). Empirical results reveal that open-source models can be more adaptable than closed-source models over time, though larger models may exhibit faster decay in predictive ability on truly novel, post-training data.

7. Remaining Challenges and Outlook

Despite substantial progress, LLMs exhibit key limitations in temporal cognition. These include:

• Lacking robustness to reformulations, variations in granularity, and abstract time references (Wallat et al., 21 Mar 2025).
• Difficulty maintaining consistency and coherence in complex narratives or dialogues spanning multiple sessions (Qiu et al., 2023, Chu et al., 2023).
• Over-reliance on data frequency and prototypicality, with inconsistent or shallow representations of aspect and narrative causality (Langis et al., 18 Jul 2025).
• Inability to fully leverage or manipulate continuous time (as opposed to discrete token counts), though preliminary work suggests token-to-time internal mapping is possible (Wang et al., 6 Jun 2025).

Strategies moving forward involve the integration of neuro-symbolic reasoning, explicit temporal memory, curriculum learning, temporally structured reward signals, and regular evaluation with evolving, real-world benchmarks. Also critical is an understanding that the subjective, experiential construction of temporal knowledge in LLMs may lead to the emergence of cognitive frameworks distinct from those in humans, with implications for model alignment and safety (Li et al., 21 Jul 2025).

In sum, temporal cognition in LLMs is an area of rapid evolution, characterized by a blend of error analysis, architectural innovation, cognitive modeling, and empirically driven benchmark advancement. Future research stands to further close the gap with human-level temporal reasoning by aligning internal representations, inductive biases, and adaptive learning processes towards richer, time-aware artificial intelligence.