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Temporal Evidence Forests for Long-Horizon Multi-Party Conversational Memory

Junyou Guo, Chongyang Tao, Yan He, Yong Wang, Shuai Ma

arXiv:2610.12874v1cs.CL

TL;DR

Long-term multi-party conversations contain evolving decisions whose relevant evidence may be concentrated in episodes and difficult to interpret from similarity-ranked messages alone. T-MEM discovers episode evidence and organizes it into a temporally structured forest; on two benchmarks, it outperforms evaluated baselines across LLM backbones.

  • Problem

    Long-term multi-party conversations interleave participants and evolving decisions, so systems must locate temporally coherent evidence and trace the state evolution that determines an answer.

    Source: §1 · p. 1

  • Method

    T-MEM retrieves anchors, searches their episodes for evidence, structures messages into chronologically constrained state-change and support relations, then linearizes the forest for answer generation.

    Source: §1 · p. 2, §4.1 · p. 4, §4.4 · p. 6

  • Results

    T-MEM achieves the highest overall accuracy across GroupMemBench and EverMemBench under two LLM backbones, beating the strongest evaluated baseline by 6.50–8.59 percentage points.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The reported results support T-MEM’s temporal approach across benchmarks and question types, with favorable memory-processing efficiency.

    Source: §6 · p. 10

    Temporal Evidence Discovery depends partly on the quality and coverage of its initial Top-k anchors, limiting recovery when they miss the relevant episode.

    Source: H LIMITATIONS · p. 29

Abstract

from arXiv · show

Long-term memory has been widely adopted in LLM-based agents to support persistent interaction and personalized assistance. Long-term multi-party interactions, however, accumulate substantially more detailed information, with decisions repeatedly updated and states evolving across participants and interaction stages. Temporal structure is therefore crucial for tracing such memories: relevant information often appears densely within short intervals, while transitions among different states are anchored by a strict temporal order. Existing temporally aware memory frameworks primarily store time as metadata or use temporal signals to guide retrieval over preconstructed memory structures, without using temporal structure to both recover locally concentrated evidence and reconstruct its complete event progression. To address these limitations, we propose T-Mem, a temporal-structure-based memory framework that preserves interactions and organizes query-relevant evidence through two temporal mechanisms. Temporal Evidence Discovery exploits the local concentration of related messages to recover overlooked evidence, while Temporal Memory Structuring models state transitions and supporting relations over time and compresses low-information spans within the resulting forest. Evidence Forest Linearization then serializes the structured evidence for answer generation. Experiments on two long-term multi-party memory benchmarks demonstrate that T-Mem consistently outperforms competitive memory frameworks across LLMs, parameter settings, domains, and query types.

1 INTRODUCTION

Long-term multi-party conversations interleave evolving decisions, so relevant messages must be found within their episodes and interpreted in temporal order. T-MEM uses temporal discovery and structuring to organize that evidence, and reports higher overall accuracy than evaluated baselines on both benchmarks.

1 INTRODUCTION

  • Interleaved speakers and stages can make semantically similar messages describe incompatible states, while decisive updates may share few words with the eventual query.A useful memory context must identify the temporally coherent episode and expose the state evolution that determines the current answer.Source: §1 · p. 1
  • Temporal locality can help recover concentrated episode evidence missed by global similarity ranking, whereas simply increasing Top-k can introduce unrelated episodes and topics.A retrieved message can act as an anchor for finding its surrounding episode.Source: §1 · pp. 1–2
  • A flat set of relevant messages may leave chronology and functional roles unresolved, allowing superseded states to compete with the final decision.State-changing updates and supporting elaborations make the event progression interpretable.Source: §1 · pp. 1–2
  • T-MEM discovers episode evidence, organizes recovered messages into a query-conditioned forest of chronologically constrained UPDATE and EXPAND relations, compresses low-information spans, and linearizes the result.UPDATE captures state progression; EXPAND captures answers, explanations, and supporting evidence.Source: §1 · p. 2
  • T-MEM achieves the highest overall accuracy across two benchmarks and two LLM backbones, beating the strongest evaluated baseline by 6.50–8.59 percentage points.The reported comparison covers GroupMemBench and EverMemBench.Source: §1 · p. 2

2 RELATED WORK

Prior memory systems retrieve or organize related information through semantic, entity, and temporal structures. The paper positions T-MEM as addressing the additional need to locate episode-level evidence and represent its progression.

2 RELATED WORK

  • Structured retrieval and agent-memory systems broaden available information and relations, but mainly determine which memory units are relevant.Examples include retrieval trees, entity graphs, associative retrieval, hierarchical memory tiers, and multi-granularity associations.Source: §2 · p. 3
  • Temporally aware systems incorporate time in evolving notes, validity intervals, retrieval signals, or preconstructed graphs, but do not explicitly organize retrieved memories into a complete event progression.The paper also identifies temporal-locality-based recovery of episode evidence as unaddressed by these systems.Source: §2 · p. 3
  • Multi-party benchmarks interleave speakers, roles, topics, and decisions, requiring systems to identify when an interaction occurred and organize its temporal and evidential relations.T-MEM discovers localized evidence and structures original messages into query-conditioned evidence forests.Source: §2 · p. 3

3 PROBLEM FORMULATION

The formulation treats long-term multi-party question answering as deriving a compact evidence context from conversation memory under a bounded input budget. The context supports answer generation, while reference answers and evidence annotations remain evaluation-only.

3 PROBLEM FORMULATION

  • Each conversation message is represented by text, timestamp, and metadata such as author, role, channel, topic, and phase.A query contains query text and the requesting user.Source: §3 · p. 3
  • For each query, the system derives a compact evidence context from the conversation memory and generates an answer supported by the conversation.The reference answers and evidence annotations are unavailable at inference time.Source: §3 · p. 3
  • The objective evaluates benchmark correctness while bounding the evidence context by a maximum permitted input-token budget, without prescribing how that context is obtained.The objective is defined over an evaluation set with reference answers.Source: §3 · pp. 3–4

4 T-MEM

T-MEM preserves the conversation and constructs a query-conditioned evidence forest from original messages. It uses temporal locality to recover evidence, organizes state changes and support chronologically, compresses low-information context when needed, and serializes the forest for answer generation.

4.1 OVERVIEW

  • T-MEM leaves stored conversations unchanged and builds a query-conditioned forest of original messages for the answer model.Metadata-expanded seed retrieval supplies anchors; the resulting forest is serialized as answer context.Source: §4.1 · p. 4

4.2 TEMPORAL EVIDENCE DISCOVERY

  • Topic-specific temporal clusters of seed messages identify candidate intervals, which are padded to include nearby confirmations, corrections, and updates.Intervals must exceed a seed-density threshold, with the span penalizing diffuse activity.Source: §4.2 · pp. 4–5
  • Within each interval, similarity filters messages before a query-conditioned LLM keeps KEY messages establishing answer claims and SUPPORT messages providing bridges or constraints.Acceptance depends on query-specific evidence utility, rather than copying a discussion based only on shared keywords or topic.Source: §4.2 · p. 5

4.3 TEMPORAL MEMORY STRUCTURING

  • The forest links earlier messages within the same topic and valid stage order as UPDATE, EXPAND, or ROOT relations.UPDATE marks state progression or replacement; EXPAND adds support without superseding its parent.Source: §4.3 · p. 5
  • When the forest exceeds the answer budget, T-MEM omits low-scoring spans while retaining nodes and edges and protecting query entities, timestamps, values, and state changes.Span scores reward query-relevant content not already conveyed by the parent; every node retains metadata, a relation label, and at least one text span.Source: §4.3 · p. 5

4.4 EVIDENCE FOREST LINEARIZATION

  • Evidence Forest Linearization orders trees by root time and nodes by chronological preorder, encoding each node with its parent, relation, depth, timestamp, author, and retained text.This produces a model-readable sequence that preserves forest structure and temporal order.Source: §4.4 · p. 6

5 EXPERIMENTS

T-MEM is evaluated on two long-term multi-party memory benchmarks with multiple answer-model backbones and compared with retrieval and agent-memory baselines. It leads overall on both benchmarks, while controlled evaluations examine evidence recovery, forest organization, component effects, and robustness to revision noise and missing metadata.

5.1 EXPERIMENTAL SETUP

  • The evaluation follows both benchmarks’ official protocols, reports answer accuracy, and compares T-MEM with lexical, dense, retrieval-augmented, and structured agent-memory baselines.Experiments use GPT-5.5 and Deepseek-V4-Flash as answer and judge models, with within-backbone prompts and decoding settings held constant across methods.Source: §5.1 · p. 6

5.2 EXP-1: MAIN EVALUATION

  • 8.59 and 8.05 percentage points: T-MEM exceeds the strongest baseline on GroupMemBench with GPT-5.5 and Deepseek-V4-Flash, respectively.It has the highest overall accuracy under both answer models on both benchmarks; on EverMemBench, it exceeds the strongest completed baseline by 8.42 and 6.50 points. Category-level results favor T-MEM on multi-hop, knowledge-update, and temporal questions under both backbones, though other systems lead on several isolated categories.Source: §5.2 · pp. 6–7, Table 1 · p. 7

5.3 EXP-2: EVIDENCE RECOVERY

  • At Top-36, key-evidence recall is 72.8% for T-MEM, versus 56.6% for BM25 (Expand) and 61.5% for HippoRAG2.At Top-48, HippoRAG2 reaches 62.5%, versus 75.6% for T-MEM; the evaluation excludes forest structuring and linearization, isolating retrieval into temporally localized regions.Source: Figure 2 · p. 7, §5.3 · p. 7

5.5 EXP-4: ABLATION STUDY

  • 6.04 and 6.31 percentage points: removing Temporal Evidence Discovery yields the largest accuracy losses under GPT-5.5 and Deepseek-V4-Flash, respectively.Every ablation lowers accuracy, with losses from 0.94 to 6.31 points; removing temporal memory structuring yields the next-largest losses, while metadata expansion and compression provide smaller consistent improvements.Source: Table 2 · p. 8, §5.5 · p. 8

5.4 EXP-3: MEMORY ORGANIZATION ANALYSIS

  • 3.09 points at Top-24, 4.44 at Top-36, and 7.60 at Top-48: forest organization improves accuracy over a flat list at each depth.Both conditions use the same retrieved messages; flat BM25 (Expand) reaches 50.74% at Top-36 and then degrades, while the organized condition peaks at 55.70% with Top-48.Source: Figure 3 · p. 8, §5.4 · p. 8

5.6 EXP-5: ROBUSTNESS TO CONTEXT NOISE

  • 47.66%, 41.12%, and 38.32%: T-MEM’s accuracy declines gradually as injected revision chains grow, while it maintains stronger complete-chain recovery and fewer revision-only contexts than other methods.The chains insert intermediate revisions before a final rollback, testing recovery of the full state trajectory and retrieval of revisions without the rollback.Source: Figure 4 · p. 9, §5.6 · p. 9

5.7 EXP-6: ROBUSTNESS TO METADATA AVAILABILITY

  • 60.40%: with topic and phase metadata removed from both modules, T-MEM remains 5.77 points above HippoRAG2.Removing topic from evidence discovery reduces accuracy by 0.80 points, while withholding topic and phase from structuring lowers it by 1.20 points.Source: Figure 5 · p. 9, §5.7 · p. 9

6 CONCLUSION

T-MEM uses temporal locality and evolution to recover evidence from relevant discussion episodes and organize state transitions and supporting information. The paper reports consistent performance gains across benchmarks, LLMs, and question types, alongside deployment cautions and research-use boundaries.

6 CONCLUSION

  • T-MEM uses temporal locality to recover episode-level evidence and temporal evolution to organize relations among successive states and supporting information.The paper reports that it outperforms conventional retrieval and advanced memory frameworks across LLMs and question types, with favorable memory-processing efficiency.Source: §6 · p. 10
  • The authors report using generative AI for manuscript revision and controlled-experiment data processing, while retaining responsibility for methodological choices and final claims.GPT-5.6 Sol generated synthetic revision-and-rollback messages under the experiment’s data-construction protocol.Source: AI USE STATEMENT · p. 10
  • Reported results are for research evaluation, not automated decision-making in high-stakes settings; deployment should address sensitive histories and superseded decisions.The paper recommends informed consent, access control, data minimization, retention and deletion policies, and audits of retrieved evidence.Source: ETHICS STATEMENT · p. 10
  • Implementation details, metadata adaptation, model configurations, evidence annotations, and prompt templates are documented in the appendices.Source: REPRODUCIBILITY STATEMENT · p. 10

A DATASET AND EVIDENCE STATISTICS

The appendix describes the benchmark data, metadata adaptation, evidence annotation for Exp-2, synthetic revision chains for Exp-5, and the rationale for baseline selection.

A.1 GROUPMEMBENCH

  • GroupMemBench covers four domains with 120,000 messages and evaluates 745 questions across six capabilities using the official questions, answers, boundaries, and protocol.Each domain contains 30,000 messages.Source: §A.1 · p. 13

A.2 EVERMEMBENCH

  • EverMemBench contains 2,400 questions across Fine-grained Recall, Memory Awareness, and Profile Understanding, each comprising three diagnostic tasks.The tasks cover retrieval and temporal reasoning, rule application and updates, and behavioral personalization.Source: §A.2 · p. 13

A.3 METADATA SOURCES AND ADAPTATION

  • For EverMemBench, profiles are joined by speaker name and only source-provided fields are mapped; absent phase and reply-parent annotations remain empty.Gold answers and EverMemBench references are used only for evaluation, not retrieval or memory construction.Source: §A.3 · p. 13

A.4 EVIDENCE ANNOTATION AND QUALITY FOR EXP-2

  • Exp-2 annotates required evidence units for 606 non-abstention questions, using reverse retrieval, model filtering for direct support, and manual audits of low-confidence cases.Required cross-message bridges are represented as separate anchor and answer-bearing units; audited alternatives count only when they independently establish the same claim.Source: §A.4 · p. 14

A.5 NOISE CONSTRUCTION FOR EXP-5

  • Exp-5 tests final-state recovery on 107 Knowledge Update questions using synthetic chains of one to three revisions followed by a rollback to the original state.Three conditions average 2.0, 2.5, and 3.0 injected messages per question; hidden revision and rollback labels are not exposed to the systems.Source: §A.5 · p. 15

B COMPARISON BASELINES

  • Baselines span lexical and dense retrieval, linked notes, graph retrieval, temporal- and entity-aware networks, event consolidation, and multi-granularity association.The selection retains simple retrieval controls because sophisticated architectures do not consistently dominate them in GroupMemBench; some services were excluded because their interfaces could not meet the shared comparison contract.Source: B COMPARISON BASELINES · p. 15

C DETAILED EXPERIMENTAL SETTINGS

The experimental settings specify model versions, T-MEM and baseline configurations, shared answer and judging procedures, and staged execution with restart handling.

C DETAILED EXPERIMENTAL SETTINGS

  • DeepSeek-V4-Flash was used for main experiments unless specified; after its official API discontinuation, subsequent experiments used DeepSeek-V4.1-Flash.Within each controlled comparison, all conditions used the same answer and judge models.Source: C DETAILED EXPERIMENTAL SETTINGS · p. 15

C.1 T-MEM DETAILED CONFIGURATIONS

  • T-MEM uses Deepseek-V4-Flash for evidence selection and temporal structuring; its structuring stage identifies UPDATE and EXPAND relations under temporal-order constraints.Top-36 coarse anchors guide temporal clustering; selected intervals contribute up to 100 messages for evidence selection, and low-density spans are compressed when the 8,192-token evidence budget is exceeded.Source: §C.1 · pp. 15–16

C.2 BASELINE CONFIGURATIONS

  • Baselines use distinct memory units and retrieval procedures, including linked notes, graph retrieval, memory banks, event memories, and multi-level memories.All share an 8,192-token evidence-context cap; returned contexts are truncated only when they exceed it.Source: §C.2 · p. 16

C.3 ANSWER AND JUDGE MODEL SETTINGS

  • GPT-5.5 and Deepseek-V4-Flash serve as answer and judge backbones, with the same model performing those roles in separate calls.Controlled comparisons share questions, answer requirements, and judging protocol, while evidence presentation remains method-specific; references are not supplied to retrieval or answer generation.Source: §C.3 · pp. 16–17

D.1 STAGE-WISE EXECUTION

  • The implementation runs coarse retrieval, temporal expansion, forest organization, compression, answering, and judging as explicit stages, recording append-only JSONL outputs at each stage.Intermediate evidence sets and forests can be inspected independently of downstream accuracy.Source: §D.1 · p. 17

D.2 RESUME AND FAILURE HANDLING

  • On restart, successful records are skipped and failed or incomplete records retried, with embeddings and completed intermediate selections cached separately.Structured LLM calls use bounded retries, while persistent failures remain visible as error records.Source: §D.2 · p. 17

D.3 CONFIGURATION AND RESULT INTEGRITY

T-MEM uses fewer tokens than the evaluated LLM-based memory frameworks, while consuming moderately more tokens than conventional retrieval methods. The appendix also describes efficiency accounting and integrity checks.

D.3 CONFIGURATION AND RESULT INTEGRITY

  • Run contracts record configurations and prompt hashes, while validators check question counts, temporal ordering, forest structure, and exclusion of gold answers from retrieval and answer prompts.Reported summaries use the latest successful record per stable key, with raw JSONL histories retained for audit.Source: §D.3 · p. 17

E.1 EFFICIENCY MEASUREMENTS

  • 31.58M tokens—23.2%, 76.3%, 76.6%, 92.0%, and 93.5% fewer than MemGAS, StructMem, HippoRAG2, A-Mem, and Hindsight, respectively; total cost is USD 44.04.The comparison reports USD 46.84, USD 148.67, USD 65.50, USD 76.70, and USD 122.59 for those baselines, respectively.Source: Table 6 · p. 18, §E.1 · p. 18
  • Efficiency is measured primarily by total token consumption, with monetary cost as a complementary measure that depends partly on model prices.Memory-processing tokens cover construction or retrieval, while answer-and-judge tokens cover downstream evaluation.Source: §E.1 · p. 18
  • T-MEM uses 31.58M total tokens versus 14.87M for Embedding and 5.66M for BM25, with a total cost of USD 44.04.That cost is USD 4.40 more than Embedding and USD 5.36 more than BM25.Source: §E.1 · p. 18

E.2 EVIDENCE DISCOVERY ANALYSIS

Fixed-budget comparisons test whether temporal localization improves evidence recovery, while a separate analysis distinguishes forest topology from typed temporal relations. Both analyses find benefits beyond flat or global retrieval.

E.2 EVIDENCE DISCOVERY ANALYSIS

  • T-Mem Reranked and Global Evidence Retrieval both retain 36 messages, but the latter searches the whole topic rather than temporally localized intervals.The comparison uses the same seed results, evidence selection, reranker, and other retrieval parameters.Source: §E.2 · pp. 18–19, Figure 6 · p. 19
  • At initial depth 36, recall rises from 64.2% with Global Evidence Retrieval to 72.3% with T-Mem Reranked, just 0.5 percentage points below unrestricted T-MEM.The controlled variants share the final Top-36 budget, retrieval models, and decoding settings; only the candidate search region differs.Source: Figure 6 · p. 19, §E.2 · p. 19

E.3 RELATION SEMANTICS ANALYSIS

  • The untyped forest tests topology while replacing UPDATE and EXPAND labels with RELATED, separating parent–child organization from relation semantics.All three representations use the same retrieved messages and DeepSeek-V4.1-Flash as answer and judge model.Source: §E.3 · p. 19
  • At Top-36, the typed temporal forest reaches 58.79% accuracy, versus 58.39% for the untyped forest and 54.36% for the flat list.Both forest representations outperform the flat list at every tested retrieval depth, and the typed forest is best at each depth.Source: §E.3 · pp. 19–20, Figure 6 · p. 19

F.1 RELIABILITY OF DENSITY-BASED EPISODE SELECTION

The appendix analyzes density-based episode selection under separated anchor rates and justifies compression under a token budget. Its guarantees are conditional, and its qualitative cases are illustrative rather than causal evidence.

F.1 RELIABILITY OF DENSITY-BASED EPISODE SELECTION

  • Under separated anchor rates, density-threshold false-positive and false-negative probabilities both decrease exponentially with observable interval length.The result applies to intervals of duration ΔC ≥ δ and thresholds between the background and relevant-episode rates.Source: §F.1 · p. 21
  • The density decision is reliable only under the stated fixed-interval assumption and a threshold strictly between the two anchor rates.It does not guarantee recovery of every relevant episode, homogeneous Poisson arrivals in real data, or correct use of selected evidence by the answer model.Source: §F.1 · p. 21

F.2 JUSTIFICATION OF COMPRESSION IN TEMPORAL MEMORY STRUCTURING

  • For the continuous relaxation, compressing nonmandatory spans from low to high information density is optimal, but whole-span selection is only an approximation to the discrete problem.Node metadata, edges, mandatory evidence, and at least one span per node remain unchanged; the budget guarantee assumes mandatory content fits.Source: §F.2 · p. 22

G EXAMPLES AND CASE STUDIES

  • The appendix’s case studies illustrate individual operations of T-MEM’s temporal mechanisms and are not additional causal evidence.They complement the controlled mechanism analyses in the main paper.Source: G EXAMPLES AND CASE STUDIES · p. 23

G.1 RECOVERING AN OVERLOOKED STATE UPDATE

The examples show how temporal locality recovers state-changing messages that global retrieval misses, while temporal relations distinguish successive revisions from later supporting details. Together, the cases illustrate evidence recovery and state organization across distinct query-relevant discussions.

G.1 RECOVERING AN OVERLOOKED STATE UPDATE

  • Temporal Evidence Discovery recovers Msg_6416, which BM25 and HippoRAG2 miss, revealing that assisted, web, email, and in-app channels are reviewed together in the first pass.The recovered update supersedes the earlier “core first, assisted later” policy.Source: §G.1 · pp. 23–24, Figure 7 · p. 23

G.2 ORGANIZING SUCCESSIVE STATES WITH TEMPORAL RELATIONS

  • An UPDATE chain links the LMS-export approach, its challenge, and the proposed append-only completion log, making the decision’s state precedence visible.The sequence is represented as Msg_3433 → Msg_5244 → Msg_6194; all three messages concern the topic but represent different decision states.Source: Figure 8 · p. 24, §G.2 · p. 25

G.3 RECOVERING A NEWLY ADDED FIELD CONSTRAINT

  • Temporal Evidence Discovery recovers Msg_6579, adding fallback eligibility to the three-state field-tagging scheme and restoring the current two-part rule.Global retrieval reaches the field-tagging discussion but misses the low-ranked update.Source: §G.3 · pp. 25, 28, Figure 9 · p. 26

G.4 SEPARATING POLICY REVISION FROM EXECUTION CONSTRAINTS

  • An UPDATE from Msg_5666 to Msg_7186 revises the policy to a clause-specific gate; an EXPAND to Msg_9253 adds execution constraints without superseding it.The later message adds ownership, timestamps, change summaries, and Legal review.Source: Figure 10 · p. 27, §G.4 · p. 28

H LIMITATIONS

The limitations concern dependence on coarse retrieval anchors, coarse event relations, benchmark scope, and LLM-based evidence analysis and evaluation. The appended prompt figures clarify how evidence is selected and presented, but do not remove those boundaries.

H LIMITATIONS

  • Temporal Evidence Discovery may miss a relevant episode when the initial Top-k anchors are too sparse or fail to cover it.Its recovery remains partly dependent on the quality and coverage of coarse anchors.Source: H LIMITATIONS · p. 29
  • UPDATE and EXPAND capture coarse event progression, but can conflate distinct discourse functions such as questioning, justification, and elaboration.The resulting forest is not a complete, fine-grained discourse representation.Source: H LIMITATIONS · p. 29
  • Both benchmarks use cleaner metadata and more controlled histories than many deployed group chats, leaving generalization to noisy conversations for further study.Source: H LIMITATIONS · p. 29
  • Exp-2 evidence recall is a diagnostic estimate, not an exhaustive measure, because valid alternatives and paraphrases may remain unannotated.Correct answers may also rely on semantically similar messages or parametric knowledge when canonical evidence is absent.Source: H LIMITATIONS · p. 29
  • Final accuracy depends on LLM answer generation and judging, whose stochasticity and response variability can introduce noise and small run-to-run differences.Consistent protocols and decoding settings reduce but do not eliminate this model-induced variance.Source: H LIMITATIONS · p. 29

I ANSWER AND JUDGE PROMPTS

  • The answer-interface comparison holds evidence-only constraints and the judge prompt constant, varying flat retrieval-rank order against forest boundaries and relation labels.Figure 11 marks forest-specific instructions in blue.Source: I ANSWER AND JUDGE PROMPTS · p. 30, Figure 11 · p. 30

J TEMPORAL EVIDENCE DISCOVERY PROMPT

K TEMPORAL MEMORY STRUCTURING PROMPT

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