Thinking Ahead: Prospection-Guided Retrieval of Memory with Language Models

Harshita ChopraKrishna ChintalapudiSuman NathRyen W. WhiteChirag Shah

article2026arXiv2 citations

Proposes Prospection-Guided Retrieval, a framework that expands user queries into simulated future actions to retrieve semantically distant interaction memories, nearly tripling recall compared to standard retrieval-augmented generation on long-horizon dialogue tasks.

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Conversational artificial intelligence assistants often struggle with long-term personalization when required to retrieve relevant user history across extended interactions. Current retrieval systems rely primarily on retrospective search, querying memory databases using direct semantic or keyword similarity to a user's prompt. In practice, critical user context—such as past constraints, personal preferences, or schedule conflicts—often shares little direct textual similarity with a new request, leading conventional systems to overlook essential background information.

The article evaluates whether simulating future trajectories can improve memory recall and response personalization for AI assistants. It introduces Prospection-Guided Retrieval, an approach inspired by human cognitive prospection that decouples the memory retrieval process from how data is physically stored. Rather than executing a single retrospective query, the system simulates plausible future actions or subgoals, uses those imagined steps as search probes to surface relevant memories, and iteratively refines its simulations based on newly retrieved user context.

The evaluation compared this prospection approach against state-of-the-art memory baselines across 1,625 queries and 185 user profiles across three benchmarks, including a new multi-session dataset named MemoryQuest. The MemoryQuest benchmark specifically challenges retrieval systems by requiring assistants to identify three to five dated, semantically distant memories across long interaction histories. Assessments combined automated evaluations with large language models alongside blinded human evaluations to judge context recall and answer quality.

The findings show that Prospection-Guided Retrieval substantially outperforms standard similarity and graph-based retrieval methods. On the MemoryQuest benchmark, the prospection framework achieved a memory recall score of approximately 0.72, nearly tripling the recall of the strongest baseline at roughly 0.26 and outperforming graph-based retrieval at 0.16. For recovering the full set of required memories for a query, the prospection method achieved an exact recall score of approximately 0.33 compared to under 0.03 for all baselines. Across response quality evaluations, judges preferred answers generated with prospection over baseline outputs in roughly 89% to 98% of automated comparisons, a trend supported by human judges who favored prospection in 60% to 95% of cases.

These results indicate that adopting an active, simulation-driven retrieval policy resolves the structural bottleneck of passive memory lookup in personal assistants. By anticipating informational needs before generating answers, conversational agents can deliver safer, more proactive, and contextually grounded guidance. The primary operational trade-off is computational cost: executing iterative prospection requires an average of four to five model calls per query compared to one or two for standard methods, which may increase latency and computing expenses in high-throughput environments.

Organizations developing personalized conversational agents should consider piloting iterative prospection layers on top of their existing memory databases rather than re-architecting their underlying storage systems. Future implementation efforts should focus on training lightweight, specialized retrieval models directly on generated prospection traces to reduce computational overhead for real-time applications. While the results demonstrate clear performance gains across multiple benchmarks, organizations should note that performance depends on the quality of the underlying reasoning model, and further piloting is advised before deploying prospection in mission-critical or low-latency production workflows.

Microsoft University of Washington dialogue graphrag keyword queries prospection-guided retrieval language models memory retrieval

arXiv: 2605.14177 harshita-chopra/PGR-mem

Paper: Tree of Thoughts: Deliberate Problem Solving with Large Language Models, Shunyu Yao et al. (2023). Introduces the Tree-of-Thought (ToT) deliberate search formulation that Prospection-Guided Retrieval explicitly adapts to generate prospective probe paths for memory retrieval.
Paper: Active Retrieval Augmented Generation, Zhengbao Jiang et al. (2023). Pioneers forward-looking active retrieval using anticipated future generation drafts as search queries, establishing the foundational principle underlying prospection-driven recall.
Paper: Evaluating Very Long-Term Conversational Memory of LLM Agents, Adyasha Maharana et al. (2024). Establishes benchmarks and characterizations of LLM failures in very long-term conversational memory that motivate PGR's focus on personal history retrieval.
Paper: MemGPT: Towards LLMs as Operating Systems, Charles Packer et al. (2023). Provides the foundational architecture for managing hierarchical long-term personal agent memory that standard RAG systems struggle to traverse.
Paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, Patrick Lewis et al. (2020). Defines the standard retrospective retrieval-augmented generation paradigm that PGR extends through forward simulation.
Paper: Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection, Akari Asai et al. (2024). Demonstrates self-reflective retrieval decision-making, providing context for how models dynamically evaluate and probe external memories.
Paper: Language Agent Tree Search Unifies Reasoning, Acting, and Planning in Language Models, Andy Zhou et al. (2024). Unifies tree search planning with environment interaction in language agents, complementing PGR's tree-structured prospection mechanism.
Paper: Personalizing Dialogue Agents: I have a dog, do you have pets too?, Saizheng Zhang et al. (2018). Introduces dialogue agent personalization based on user profiles and persona histories, defining the problem setting addressed by PGR.

Paper: Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents, Shuo Ji et al. (2026). Extends non-retrospective memory access by formulating long-term agent recall as an active multi-step graph reconstruction process.
Paper: MemGym: a Long-Horizon Memory Environment for LLM Agents, Wujiang Xu et al. (2026). Provides an interactive evaluation environment to benchmark dynamic long-horizon memory management across multi-turn agent tasks.
Paper: Human-Inspired Memory Architecture for LLM Agents, Doga Kerestecioglu et al. (2026). Explores complementary biologically inspired memory mechanisms including consolidation, maturation, and reconsolidation over extended agent interactions.
Paper: Selective Forgetting: A Graph-Based Memory Framework for Long-Term LLM Agents, Theo Rusu et al. (2026). Investigates structured graph representations and selective forgetting to manage long-term agent memory growth and noise.
Paper: $δ$-mem: Efficient Online Memory for Large Language Models, Jingdi Lei et al. (2026). Proposes an efficient online state adaptation mechanism to retain historical context without full retriever overhead.
Paper: Learning Personalized Agents from Human Feedback, Kaiqu Liang et al. (2026). Applies explicit per-user memory tracking to continuous preference learning and personalized agent behavior adaptation.

Cover for Thinking Ahead: Prospection-Guided Retrieval of Memory with Language Models

Abstract

Long-horizon personalization requires dialogue assistants to retrieve user-specific facts from extended interaction histories. In practice, many relevant facts often have low semanticsimilarity to the query under dense retrieval. Standard Retrieval-Augmented Generation (RAG) and GraphRAG systems are still largely retrospective: they rely on embedding similarity to the query or on fixed graph traversals, so they often miss facts that matter for the user's needs but lie far from the query in embedding space. Inspired by prospection, the human ability to use imagined futures as cues for recall, we introduce Prospection-Guided Retrieval (PGR), which decouples retrieval from how memories are stored. Given a user query, PGR first expands the goal into a short Tree-of-Thought (ToT) or linear chain of plausible next steps, and uses these steps as retrieval probes rather than relying on the original query alone. The facts retrieved by these probes are then used to personalize the next round of prospection, enabling PGR to uncover additional memories that become relevant only after the simulation is grounded in the user's history. We also introduce MemoryQuest, a challenging multi-session benchmark in which each query is annotated with 3--5 dated reference facts subject to a low query-reference similarity constraint. Across 1,625 queries spanning 185 user profiles from 3 publicly available datasets, PGR-TOT substantially improves retrieval, including nearly 3x recall on MemoryQuest over the strongest baseline. In pairwise LLM-as-judge comparisons against baselines, PGR-generated responses are preferred on 89--98% of queries, with blinded human annotations on held-out subsets showing the same trend. Overall, the results demonstrate that explicit prospection yields large gains in long-horizon retrieval and response quality relative to similarity-only baselines.

Topics

dialogue graphrag keyword queries language model retrieval-augmented generation user profile user query semantic similarity random access memory

Keywords

prospection-guided retrieval language models memory retrieval retrieval-augmented generation memory consolidation agentic memory iterative retrieval recall win rate temporal relevance

1 Introduction
2 Related Work
3 Prospection-Guided Retrieval
3.1 System Architecture Overview
3.2 Long Term Memory
3.3 Prospection Guided Retrieval
4 Benchmark Dataset: MemoryQuest
4.1 Gap Analysis and Comparison
4.2 Dataset Construction Pipeline
5 Experiments
5.1 Baselines
5.2 Evaluation Metrics
6 Results and Discussion
7 Conclusion
References
A Appendix
A.1 PGR with Alternative Generation Models
A.2 Computational Cost.
A.3 End-to-End PGR Trace: MemoryQuest Example

Knowls

Knowl 1 — Prospection-Guided Retrieval (PGR) Framework
model/method
Prospection-Guided Retrieval (PGR) is a retrieval framework for personalized conversational agents that decouples the retrieval policy from the underlying memory substrate. Unlike conventional Retrieval-Augmented Generation (RAG) and graph traversal methods that match queries retrospectively against stored documents or static graph edges, PGR operationalizes mental simulation through an iterative loop:

$\text{Simulate Future} \longrightarrow \text{Retrieve Relevant Memories} \longrightarrow \text{Refine Trajectory}$

Given a user query $q$ , the framework operates across two main prospection phases:
1. Query Augmentation: Underspecified user queries are disambiguated with initial context to produce an augmented query $q_A$ : $q_A = \text{LLM}(P_A(q, \phi(q; \mathcal{M}, \Theta_o)))$ where $\mathcal{M}$ is the memory store, $\phi(x; \mathcal{M}, \Theta)$ is an abstract retrieval function returning facts relevant to a sub-query $x$ under retrieval parameters $\Theta$ , $\Theta_o$ denotes initial augmentation parameters, and $P_A$ is an augmentation prompt.
2. Phase 1 (Initial Prospection): A language model simulates a broad horizon of potential future subgoals or contextual actions, structured as a linear Chain-of-Thought (CoT) or branching Tree-of-Thought (ToT), denoted $S^0 = \{s^0_1, \dots, s^0_L\} = \text{LLM}(P_{\text{sim}}(q_A))$ . Each step $s^0_l$ acts as an independent sub-query probe against $\mathcal{M}$ . The accumulated fact set $R$ is initialized as: $R \leftarrow \phi(q; \mathcal{M}, \Theta) \cup \bigcup_{l=1}^L \phi(s^0_l; \mathcal{M}, \Theta)$
3. Phase 2 (Personalized Prospection): Using the accumulated memories $R$ , the prospection sequence is iteratively refined to incorporate user-specific constraints: $S^i = \text{LLM}(P_{\text{ref}}(q_A, R, S^{i-1}))$ For each refined sub-query $s^i_l \in S^i$ , new facts are retrieved: $\Delta^i = \bigcup_l \phi(s^i_l; \mathcal{M}, \Theta) \setminus R$ . The fact set is updated via $R \leftarrow R \cup \Delta^i$ . The process repeats until $|\Delta^i|$ drops below a convergence threshold $\delta$ or an iteration limit $I_{\text{max}}$ is reached, returning the final fact set $R^*$ and a prospection reasoning summary.
Knowl 2 — Prospection-Guided Retrieval Algorithm
algorithm
The complete execution flow of Prospection-Guided Retrieval (PGR) is defined as follows:
Input: User query $q$ , memory store $\mathcal{M}$ , abstract retrieval function $\phi$ , augmentation parameters $\Theta_o$ , retrieval parameters $\Theta$ , convergence threshold $\delta$ , max iterations $I_{\text{max}}$
Output: Final retrieved fact set $R^*$
$R \leftarrow \emptyset$
$q_A \leftarrow \text{LLM}(P_A(q, \phi(q; \mathcal{M}, \Theta_o)))$
$S^0 \leftarrow \text{LLM}(P_{\text{sim}}(q_A))$
$R \leftarrow \phi(q; \mathcal{M}, \Theta) \cup \bigcup_{l=1}^{|S^0|} \phi(s^0_l; \mathcal{M}, \Theta)$
for $i = 1, 2, \dots, I_{\text{max}}$ do
     $S^i \leftarrow \text{LLM}(P_{\text{ref}}(q_A, R, S^{i-1}))$
     $\Delta^i \leftarrow \bigcup_{l=1}^{|S^i|} \phi(s^i_l; \mathcal{M}, \Theta) \setminus R$
     $R \leftarrow R \cup \Delta^i$
    if $|\Delta^i| < \delta$ then
        break
    end if
end for
return $R^* \leftarrow R$
In standard implementations, $\Theta = \{K, \tau\}$ where $K$ is the number of facts retrieved per sub-query and $\tau$ is the cosine similarity threshold (e.g., $K=5$ , $\tau=0.3$ ). For the initial query augmentation step, a stricter threshold $\tau_o = 0.6$ is used with $\Theta_o = \{K_o, \tau_o\}$ . The convergence threshold is set to $\delta = 5$ newly retrieved facts, and $I_{\text{max}} = 10$ iterations.
Knowl 3 — MemoryQuest Benchmark for Long-Horizon Personalization
definition
MemoryQuest is a multi-session conversational benchmark designed to evaluate long-term memory retrieval under severe semantic divergence between queries and required evidence. Constructed on top of PersonaLens demographic profiles across 20 domains, it contains 50 user profiles and 535 distinct queries spanning an average of 77.6 dialogue sessions (6.2 exchanges per session) per user.

The benchmark construction follows a three-step synthetic pipeline:
1. Query Generation: Candidate queries requiring multi-step planning are generated with an explicit query date and 3–5 ground-truth reference facts. Candidates are filtered to enforce low query–reference cosine similarity (average similarity $\gamma \le 0.3$ ), specifically testing the agent's ability to bypass the semantic similarity bottleneck.
2. Timeline Synthesis: An LLM constructs a chronological timeline of 5–10 events starting before the earliest required-reference date. Each required reference is placed in exactly one event, with remaining events populated by persona-consistent filler conversations.
3. Dialogue Expansion: Timeline events are expanded into 5–30 turn conversations conditioned on persona demographics. These dialogues are then converted into atomic facts stored in the user's long-term memory.

Knowl 4 — Comparative Retrieval and Response Win Rates of PGR Across Benchmarks

data/table

PGR-TOT (PGR with Tree-of-Thought prospection) was evaluated against three state-of-the-art memory baselines—GraphRAG, TaciTree, and Mem0g—across three benchmarks: MemoryQuest (535 queries, multi-reference), ImplexConv (196 queries, opposed setting with semantically distant context), and PersonaMem (894 queries, 32k history length). Evaluated using GPT-4o for generation, text-embedding-3-small for retrieval, and GPT-5.2 alongside human annotators (Amazon MTurk) for pairwise response judging:

Dataset	MemoryQuest				ImplexConv			PersonaMem
Method	Recall	$\text{Recall}_{\text{Exact}}$	Win Rate $_{\text{PGR}}$ (%)		Recall	Win Rate $_{\text{PGR}}$ (%)		Recall	Win Rate $_{\text{PGR}}$ (%)
			LLM	Humans*		LLM	Humans*		LLM	Humans*
GraphRAG	0.160	0.003	96.6	90.0	0.072	96.2	70.0	0.011	98.4	80.0
TaciTree	0.235	0.024	94.3	75.0	0.122	89.9	60.0	0.073	88.7	65.0
Mem0g	0.256	0.015	94.7	95.0	0.050	93.5	70.0	0.121	92.9	65.0
PGR-TOT	0.723	0.326	–	–	0.286	–	–	0.229	–	–

Recall is the average fraction of ground-truth reference facts present in the retrieved context. $\text{Recall}_{\text{Exact}}$ is the percentage of queries where all required reference facts (3–5 facts) were retrieved. Win Rate $_{\text{PGR}}$ denotes the percentage of pairwise comparisons where PGR-TOT responses were preferred over the baseline. PGR-TOT nearly triples the recall of the best baseline on MemoryQuest (0.723 vs. 0.256) and achieves a 20-fold increase in $\text{Recall}_{\text{Exact}}$ (0.326 vs. 0.015), while securing 88.7%–98.4% win rates under LLM judges and 60.0%–95.0% under human judges.

Knowl 5 — Structured Long-Term Memory Representation, Upsert, and Periodic Consolidation
model/method
PGR operates over an underlying long-term memory store structured around atomic statements called memory facts. Each fact is represented as a structured dictionary containing:
- fid: A unique sequential fact identifier.
- info: Concise, atomic text extracted by an LLM from conversation logs.
- type: One of six taxonomy categories: Identity (demographics, profession), Preference (likes, habits), Goal (intentions, plans), Interest (hobbies, topics), Activity (recurring/past actions), or Event (milestones, situations).
- frequency: Occurrence count across conversations.
- related_entities: List of extracted topic/entity keywords.
- provenance: IDs of source conversation sessions.
Memory management uses two operational policies:
1. Upsert: When new dialogue occurs, the LLM determines whether extracted facts update existing items (incrementing frequency and editing info) or are added as new entries.
2. Consolidation: When 50 new facts accumulate since the last merge, facts are embedded and clustered by cosine similarity. Within each cluster, facts whose most recent timestamps fall within a 7-day time window are merged by an LLM into a single comprehensive fact, aggregating their provenance and identifiers.
Knowl 6 — Ablations on Iterative Refinement, Prospection Strategy, and Summary Conditioning
empirical result
Ablation studies on the MemoryQuest benchmark demonstrate the individual contributions of iterative prospection, branching structures, and prospection-augmented generation (PAG):
1. Iterative Prospection vs. Single-Phase Base:
- PGR-TOT Recall improves from 0.677 (Base) to 0.723 (Iterative), yielding a 70.13% win rate in generation over the single-phase base.
- PGR-COT Recall improves from 0.645 (Base) to 0.718 (Iterative), yielding a 75.41% win rate over the base.
1. Prospection Strategy (ToT vs. CoT):
- Branching Tree-of-Thought (PGR-TOT) achieves a Recall of 0.723 compared to 0.718 for linear Chain-of-Thought (PGR-COT), winning 55.67% of direct head-to-head pairwise comparisons.
1. Prospection-Augmented Generation (PAG):
- Conditioning final answer generation on both retrieved facts and the synthesized prospection summary improves PGR-TOT recall from 0.703 to 0.722 (75.73% win rate against unconditioned generation).
- For PGR-COT, summary conditioning increases recall from 0.722 to 0.735 (74.37% win rate).

Knowl 7 — Human Annotator and LLM-as-a-Judge Decision Agreement Matrix

data/table

Pairwise response quality between PGR-TOT and baseline models was evaluated using human annotators on Amazon MTurk and Azure OpenAI GPT-5.2 on held-out subsets of MemoryQuest (4%), ImplexConv (5%), and PersonaMem (2%). The aggregated agreement matrix across all dataset-baseline pairs is:

Humans and the LLM judge concurrently agree on preferring PGR in 70.0% of all evaluated cases. In total, human annotators selected PGR in 76.0% ( $70.0\% + 4.0\% + 2.0\%$ ) of evaluations, validating that LLM judge preference closely aligns with human assessments of anticipatory and proactive reasoning.

Knowl 8 — Cross-LLM Generalization with DeepSeek-V3.2

empirical result

To establish that PGR's retrieval gains are model-agnostic, experiments replacing the GPT-4o generation backbone with DeepSeek-V3.2 while maintaining identical embeddings (text-embedding-3-small) and retrieval parameters yielded the following recall results:

Dataset	Base Recall	Iterative Recall	Base $\text{Recall}_{\text{Exact}}$	Iterative $\text{Recall}_{\text{Exact}}$
MemoryQuest	0.671	0.748	0.245	0.348
ImplexConv	0.308	0.374	–	–
PersonaMem	0.210	0.229	–	–

Using DeepSeek-V3.2, iterative prospection matches or exceeds the performance observed with GPT-4o on MemoryQuest (0.748 Recall / 0.348 $\text{Recall}_{\text{Exact}}$ vs. GPT-4o's 0.723 / 0.326), confirming that the prospection-guided retrieval mechanism generalizes across distinct LLM architectures.

Knowl 9 — Query-Time LLM Call Overhead and Fact Retrieval Volume

data/table

The computational overhead of PGR-TOT at inference time was evaluated in terms of the average number of query-time LLM calls and unique facts retrieved across datasets:

Dataset	GraphRAG	Mem0g	TaciTree	PGR-TOT	PGR Ph-1 Facts	PGR Ph-2 New Facts
MemoryQuest	1.0	2.0	$\sim$ 13.2	4.79	26.78	8.41
ImplexConv	1.0	2.0	$\sim$ 15.9	4.32	22.41	3.85
PersonaMem	1.0	2.0	$\sim$ 9.7	4.41	13.04	4.83

PGR-TOT executes on average 4.32–4.79 LLM calls per query (composed of 1 Phase-1 initial prospection call, average Phase-2 refinement calls, 1 prospection summary call, and 1 final answer generation call). While higher than single-pass retrieval methods (GraphRAG: 1.0, Mem0g: $\sim$ 2.0), PGR-TOT uses fewer than half the inference LLM calls required by tree-pruning methods like TaciTree ( $\sim$ 9.7–15.9).

Knowl 10 — Computational Overhead and Real-Time Latency Trade-off in PGR
limitation
PGR achieves higher recall and proactive personalization by planning and exploring multiple hypothesized future paths, but this multi-phase simulation introduces additional query-time LLM invocations (averaging 4–5 calls per query compared to 1–2 for standard dense RAG or GraphRAG). While the embedding-based vector lookups for each sub-step can be executed in parallel with negligible latency, the sequential LLM generation calls for prospection and iterative refinement increase inference latency, creating computational overhead that may constrain real-time deployment without specialized lightweight retriever distillation.

Coverage note — None was omitted; all contributed methods (PGR, memory taxonomy/consolidation), benchmarks (MemoryQuest), algorithms, empirical tables (Table 1-7), ablations, and stated limitations were fully converted into self-contained knowls.

References

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Citation

MLA

Chopra, H., et al. “Thinking Ahead: Prospection-Guided Retrieval of Memory with Language Models”. arXiv, 2026, http://arxiv.org/abs/2605.14177v1.

APA

Chopra, H., Chintalapudi, K. K., Nath, S., White, R. W., & Shah, C. (2026). Thinking Ahead: Prospection-Guided Retrieval of Memory with Language Models. arXiv. http://arxiv.org/abs/2605.14177v1

Chicago

Chopra, H., K. K. Chintalapudi, S. Nath, R. W. White, and C. Shah. 2026. “Thinking Ahead: Prospection-Guided Retrieval of Memory with Language Models”. arXiv. http://arxiv.org/abs/2605.14177v1.

Harvard

Chopra, H. et al. (2026) “Thinking Ahead: Prospection-Guided Retrieval of Memory with Language Models”, arXiv [Preprint]. Available at: http://arxiv.org/abs/2605.14177v1.

Vancouver

1. Chopra H, Chintalapudi KK, Nath S, White RW, Shah C (2026) Thinking Ahead: Prospection-Guided Retrieval of Memory with Language Models. arXiv

BibTeX

@article{chopra2026thinking,
  title = {Thinking Ahead: Prospection-Guided Retrieval of Memory with Language Models},
  author = {Chopra, Harshita and Chintalapudi, Krishna Kant and Nath, Suman and White, Ryen W. and Shah, Chirag},
  year = {2026},
  journal = {arXiv},
  url = {http://arxiv.org/abs/2605.14177v1},
  eprint = {2605.14177}
}

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Human \ LM	PGR	Tie	Baseline
PGR	70.0%	4.0%	2.0%
Tie	8.0%	0.0%	0.7%
Baseline	12.0%	2.0%	1.3%

Thinking Ahead: Prospection-Guided Retrieval of Memory with Language Models

Abstract

Topics

Keywords

Table of Contents

Knowls

Knowl 1 — Prospection-Guided Retrieval (PGR) Framework

Knowl 2 — Prospection-Guided Retrieval Algorithm

Knowl 3 — MemoryQuest Benchmark for Long-Horizon Personalization

Knowl 4 — Comparative Retrieval and Response Win Rates of PGR Across Benchmarks

Knowl 5 — Structured Long-Term Memory Representation, Upsert, and Periodic Consolidation

Knowl 6 — Ablations on Iterative Refinement, Prospection Strategy, and Summary Conditioning

Knowl 7 — Human Annotator and LLM-as-a-Judge Decision Agreement Matrix

Knowl 8 — Cross-LLM Generalization with DeepSeek-V3.2

Knowl 9 — Query-Time LLM Call Overhead and Fact Retrieval Volume

Knowl 10 — Computational Overhead and Real-Time Latency Trade-off in PGR

References

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