RaMem: Contextual Reinstatement for Long-term Agentic Memory

Bryce Kan; Jesse Thomason; Jiate Li; Li Li; Paul Bogdan; Shixuan Li; Wei Yang; Yuehan Qin

arxiv: 2606.22844 · v1 · pith:7RMNUMOInew · submitted 2026-06-22 · 💻 cs.AI · cs.MA

RaMem: Contextual Reinstatement for Long-term Agentic Memory

Wei Yang , Bryce Kan , Shixuan Li , Li Li , Yuehan Qin , Jiate Li , Paul Bogdan , Jesse Thomason This is my paper

Pith reviewed 2026-06-26 08:45 UTC · model grok-4.3

classification 💻 cs.AI cs.MA

keywords long-term memorycontext collapseagentic memoryvalidity-aware retrievalcontextual reinstatementLLM agentsmemory benchmarks

0 comments

The pith

RaMem restores surrounding context to memory fragments so agents can judge whether they supply valid evidence for the current query.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper identifies context collapse as the problem that arises when long-term memories are compressed into reusable fragments: memories involving the same entities or states can look equally relevant even when their original situations make them invalid for the present query. It proposes RaMem, a four-stage process that first anchors each memory to its original time, session, and participants, then derives the conditions the query requires, then retrieves with priority for context match, and finally keeps the structured context available during generation. This matters for LLM agents that must operate across many sessions without losing the ability to treat past experience as reliable evidence rather than raw content. Experiments on long-term memory benchmarks report average F1 gains above 10 percent across multiple backbones. If the claim holds, agent memory systems could move from simple retrieval toward verifiable reinstatement without requiring changes to the underlying model.

Core claim

RaMem turns retrieved memory fragments into contextually verifiable evidence by coordinating evidence anchoring that records original episodic conditions, recall condition induction that extracts the conditions implied by the query, validity-aware retrieval that favors context-compatible items while keeping content-relevant fallbacks, and context-preserved synthesis that supplies the selected memories' structured context to the generator, producing consistent performance gains over strong baselines.

What carries the argument

The RaMem framework, which coordinates evidence anchoring, recall condition induction, validity-aware retrieval, and context-preserved synthesis to reinstate contextual verifiability around compressed memories.

If this is right

Memory fragments become usable as evidence only when their original conditions match the query's implied conditions.
Agents gain a fallback path that retains content-similar memories when no fully context-compatible memory exists.
The same four-stage reinstatement applies across different LLM backbones without retraining.
Long-term agent performance on tasks that span multiple sessions improves by more than 10 percent F1 on average.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The reinstatement idea could be tested on retrieval-augmented generation pipelines outside explicit memory systems to see whether the same validity check reduces context mismatch errors.
Real-world agent deployments with continuously evolving user states would reveal whether the reported benchmark gains persist when session boundaries and participant sets are noisier than in the test sets.
If the anchoring stage proves costly, a lighter version that records only time and session identifiers might still capture most of the benefit.

Load-bearing premise

The four stages can be added to existing memory pipelines without introducing new errors or latency that cancel the reported gains, and the chosen benchmarks measure the intended context-collapse failures.

What would settle it

Running the same long-term memory benchmarks with and without the four stages and observing no F1 improvement or a net loss would falsify the claim of consistent gains.

Figures

Figures reproduced from arXiv: 2606.22844 by Bryce Kan, Jesse Thomason, Jiate Li, Li Li, Paul Bogdan, Shixuan Li, Wei Yang, Yuehan Qin.

**Figure 2.** Figure 2: Overview of RaMem. RaMem converts long-term interaction history into contextually verifiable memory evidence through four stages. (A) Interaction histories are converted into memories anchored with episodic evidence conditions. (B) A query is decomposed into an information need and recall conditions. (C) RaMem retrieves candidates through multiple paths and prioritizes contextcompatible evidence when grou… view at source ↗

**Figure 3.** Figure 3: Context collapse mitigation across backbones. Each subplot corresponds to one backbone and compares SimpleMem with our method on three diagnostic metrics: D@1, RankGap, and GT R@10. Lower is better for D@1 and RankGap, while higher is better for GT R@10 [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: Sensitivity to the temporal reinstatement window. Each subplot shows the effect of varying [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

**Figure 5.** Figure 5: Sensitivity to retrieval budget. Each subplot varies the number of retrieved memories passed [PITH_FULL_IMAGE:figures/full_fig_p030_5.png] view at source ↗

read the original abstract

Long-term memory has become increasingly important for LLM agents that operate across extended interactions and evolving task contexts. Recent memory systems have made past experiences more persistent, compact, and retrievable, but retrieval alone does not ensure that a memory provides valid evidence for the current query. When experiences are compressed into reusable fragments, memories from different situations may appear equally relevant if they involve recurring entities or user states. We refer to this failure as context collapse: memories lose the surrounding context needed to judge whether they provide valid evidence for the current query. To address this problem, we propose Contextual Reinstatement for Agentic Memory (RaMem), a framework that turns retrieved memory fragments into contextually verifiable evidence. RaMem operates through four coordinated stages: (i) evidence anchoring grounds each memory in its original episodic conditions, especially event time, mention time, session span, and participants; (ii) recall condition induction derives the evidence conditions implied by the query; (iii) validity-aware retrieval uses these conditions to prioritize context-compatible memories while retaining content-relevant candidates as fallback evidence; and (iv) context-preserved synthesis keeps the selected memories' structured context available to the generator. Experiments on long-term memory benchmarks show that RaMem consistently improves performance over strong memory baselines, with average F1 gains of more than 10% across several backbones.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

RaMem names context collapse as a distinct retrieval failure and offers a four-stage pipeline to fix it, but the abstract gives no evidence that the reported gains come from the validity stage rather than generic improvements.

read the letter

RaMem's main move is to label the problem where memory fragments match on entities or states but come from mismatched episodes, then propose four stages to reinstate the missing context: anchoring each memory to its original time, session, and participants; inducing the conditions the query actually needs; doing validity-aware retrieval that prefers compatible memories; and keeping the structured context through to the final synthesis. That framing is clear and points to a practical gap in current agent memory work.

The paper does a decent job spelling out why plain retrieval is not enough once experiences get compressed. The separation between content match and validity match is worth stating explicitly for people who build these systems.

The soft spot is the evaluation. The abstract claims average F1 gains above 10% over strong baselines but gives no benchmark names, no ablation that turns off the validity-aware step, and no indication that the test sets were chosen or filtered to contain measurable rates of context collapse. If the datasets mostly test forgetting or compression loss instead, the gains do not directly support the central mechanism. The stress-test note is on target here.

This paper is aimed at researchers and engineers working on long-running LLM agents who already have retrieval in place and are hitting cases where retrieved memories look right but are not. A reader in that group would get a usable pipeline description and a problem definition they can test against their own logs. It deserves a serious referee because the claim is testable and the proposed stages are concrete, even if the current evidence is still thin.

Referee Report

2 major / 2 minor

Summary. The paper claims that 'context collapse'—where compressed long-term memories lose episodic context and supply invalid evidence despite entity or state matches—is a key limitation in LLM agent memory systems. It proposes RaMem, a four-stage framework (evidence anchoring, recall condition induction, validity-aware retrieval, context-preserved synthesis) to reinstate contextual verifiability, and reports that it yields average F1 gains exceeding 10% over strong memory baselines across multiple backbones on long-term memory benchmarks.

Significance. If the performance gains are attributable to the validity-aware mechanism rather than generic retrieval improvements, the work could meaningfully advance reliable long-horizon agentic systems by distinguishing content relevance from episodic validity. The staged approach is conceptually coherent and targets a plausible failure mode not explicitly handled by prior memory compression or retrieval methods.

major comments (2)

[Experiments] Experiments section: the manuscript provides no indication that the chosen benchmarks were constructed, filtered, or analyzed to contain measurable rates of context-collapse cases (entity-matching memories differing in time, session span, or participants). Without this or ablations isolating validity-aware retrieval, the >10% F1 claim does not substantiate the central mechanism over alternative explanations.
[Method] Method section (four stages): no analysis or controls are reported on whether the additional processing steps introduce new errors, latency, or context-preservation failures that could offset the claimed gains; the weakest assumption that the stages can be implemented without net negative effects therefore remains untested.

minor comments (2)

[Abstract] Abstract: specific benchmark names, backbone models, and baseline systems are not named, reducing the ability to assess generality.
Notation: the distinction between 'evidence conditions' and 'recall conditions' is introduced without a formal definition or example that would clarify how they differ from standard query expansion.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting areas where the experimental validation of RaMem's central mechanism can be strengthened. We address each major comment below and will incorporate revisions to provide clearer substantiation.

read point-by-point responses

Referee: [Experiments] Experiments section: the manuscript provides no indication that the chosen benchmarks were constructed, filtered, or analyzed to contain measurable rates of context-collapse cases (entity-matching memories differing in time, session span, or participants). Without this or ablations isolating validity-aware retrieval, the >10% F1 claim does not substantiate the central mechanism over alternative explanations.

Authors: We agree that an explicit quantification of context-collapse prevalence in the benchmarks and targeted ablations would more directly link the gains to the validity-aware retrieval stage. The benchmarks used are established long-term agentic memory datasets involving multi-session interactions with recurring entities across varying times and participants, where context collapse is a documented challenge in prior work. In the revised manuscript, we will add (i) a post-hoc analysis measuring the proportion of entity-matching but context-mismatched memory pairs in the test sets and (ii) an ablation that disables only the validity-aware component while retaining the other three stages, to isolate its contribution beyond generic retrieval improvements. revision: yes
Referee: [Method] Method section (four stages): no analysis or controls are reported on whether the additional processing steps introduce new errors, latency, or context-preservation failures that could offset the claimed gains; the weakest assumption that the stages can be implemented without net negative effects therefore remains untested.

Authors: We acknowledge that the current manuscript does not report explicit controls or measurements for potential overhead or error introduction from the staged pipeline. The consistent F1 improvements across backbones indicate that any such effects are not dominant. In revision, we will add (i) latency profiling for each stage on representative queries, (ii) an error analysis categorizing cases where a stage introduces incorrect context or drops valid evidence, and (iii) discussion of any observed context-preservation failures, to directly test the assumption of net non-negative impact. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical framework with no derivations or fitted predictions

full rationale

The paper presents RaMem as a procedural four-stage framework (evidence anchoring, recall condition induction, validity-aware retrieval, context-preserved synthesis) to mitigate context collapse in agent memory. No equations, parameters, or mathematical derivations appear in the provided text; performance claims rest on reported F1 gains from experiments on external benchmarks rather than any reduction of outputs to inputs by construction. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes, and the method description does not rename known results or smuggle assumptions via prior author work. The central claims are therefore self-contained empirical proposals, not circular by the enumerated patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no identifiable free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5783 in / 940 out tokens · 19668 ms · 2026-06-26T08:45:40.911328+00:00 · methodology

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Memory Depth, Not Memory Access: Selective Parametric Consolidation for Long-Running Language Agents
cs.AI 2026-06 unverdicted novelty 6.0

EVAF, a surprise- and valence-gated LoRA mechanism, provides memory depth for goal persistence in language agents via the loop-drift protocol, complementary to retrieval.

Reference graph

Works this paper leans on

35 extracted references · 2 canonical work pages · cited by 1 Pith paper · 1 internal anchor

[1]

Yuehan Qin, Li Li, Linxin Song, Wei Yang, Jiate Li, Yuqing Yang, and Yue Zhao

URLhttps://arxiv.org/abs/2603.07670. Yuehan Qin, Li Li, Linxin Song, Wei Yang, Jiate Li, Yuqing Yang, and Yue Zhao. Memory retrieval for changing preferences.arXiv preprint arXiv:2606.02976, 2026. 13 Bing Wang, Xinnian Liang, Jian Yang, Hui Huang, Shuangzhi Wu, Peihao Wu, Lu Lu, Zejun Ma, and Zhoujun Li. Enhancing large language model with self-controlled...

work page doi:10.1609/aaai.v38i4.28098 2026
[2]

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Association for Computational Linguistics. doi: 10.18653/v1/P19-1285. URL https: //aclanthology.org/P19-1285/. Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces, 2024. URLhttps://arxiv.org/abs/2312.00752. Zhengbao Jiang, Frank F Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graha...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.18653/v1/p19-1285 2024
[3]

lost-in-the-middle

URLhttps://arxiv.org/abs/2511.14460. 15 Zihan Wang, Kangrui Wang, Qineng Wang, Pingyue Zhang, Linjie Li, Zhengyuan Yang, Xing Jin, Kefan Yu, Minh Nhat Nguyen, Licheng Liu, Eli Gottlieb, Yiping Lu, Kyunghyun Cho, Jiajun Wu, Li Fei-Fei, Lijuan Wang, Yejin Choi, and Manling Li. Ragen: Understanding self-evolution in llm agents via multi-turn reinforcement le...

Pith/arXiv arXiv 2025
[4]

Generate enough memory entries to capture all useful information in the dialogues
[5]

Do not use pronouns or unresolved relative time expressions
[6]

Eachlossless_restatementmust be complete, independent, and understandable
[7]

Extract structured fields:keywords,timestamp,location,persons,entities, andtopic
[8]

Create separate entries for separate facts, preferences, plans, events, locations, relationships, work details, and media references
[9]

memory_entries

Return valid JSON only. Output Format. { "memory_entries": [ { "lossless_restatement": "Complete unambiguous restatement.", "keywords": ["keyword1", "keyword2"], "timestamp": "YYYY-MM-DDTHH:MM:SS or null", "location": "location name or null", "persons": ["name1", "name2"], "entities": ["entity1", "entity2"], "topic": "topic phrase" } 23 ] } Constraint.Ret...
[10]

What type of question is this?
[11]

What entities, events, or concepts need to be identified?
[12]

What relationships need to be established?
[13]

question_type

What minimal information would be sufficient to answer the question? Output Format. { "question_type": "factual / temporal / relational / explanatory / other", "key_entities": ["entity1", "entity2"], "required_info": [ { "info_type": "type of information", "description": "specific information needed", 24 "priority": "high / medium / low" } ], "relationshi...
[14]

Think through the reasoning process
[15]

Provide a very concise answer
[16]

Answer only from the provided context
[17]

DD Month YYYY

Format dates as “DD Month YYYY” when dates are needed
[18]

reasoning

Return valid JSON. Output Format. { "reasoning": "Brief explanation of the reasoning.", "answer": "Concise answer in a short phrase." } Constraint.Return only JSON. D.2 Analysis of Main Results Table 1 presents the full LoCoMo results across four backbone models and four question categories. RaMem achieves the strongest average F1 on every backbone. On cl...

arXiv 1934
[19]

James created a game avatar and joined a new gaming platform

Invalid: 2022-03-20–2022-03-27, gaming platform participation. James created a game avatar and joined a new gaming platform

2022
[20]

James streamed a game and received encouraging comments

Invalid:2022-09-20–2022-10-03, game streaming feedback. James streamed a game and received encouraging comments

2022
[21]

James started streaming games

Invalid: 2022-09-18–2022-09-20, game streaming plans. James started streaming games. GT rank:44. RaMem Top-3:

2022
[22]

James became interested in extreme sports and did rope jumping

GT / valid:2022-07-09–2022-07-22, extreme sports interest. James became interested in extreme sports and did rope jumping

2022
[23]

James went surfing

Invalid:2022-06-19–2022-07-09, surfing experience. James went surfing

2022
[24]

James won an online gaming tournament

Related but topic-conflicting: 2022-07-09–2022-07-22, gaming tournament victory. James won an online gaming tournament. GT rank:1. Observation.SimpleMem retrieves memories that are related to James and activities, but all top-ranked evidence violates the query’s recall conditions. RaMem reinstates the July 9 34 Table 19: Online efficiency under the same o...

arXiv 2022
[26]

Sam discussed Evan’s partner and family support

Invalid:2024-01-06–2024-01-10, family and partner support. Sam discussed Evan’s partner and family support

2024
[27]

Evan mentioned support from extended family

Invalid:2024-01-06–2024-01-10, family support after marriage announcement. Evan mentioned support from extended family. GT rank:18. RaMem Top-3:

2024
[28]

Evan shared details about a recent vacation in Canada with his new partner

GT / valid: 2023-08-13–2023-08-15, vacation details. Evan shared details about a recent vacation in Canada with his new partner

2023
[29]

Evan confirmed that he and his friends were fine after an accident

Invalid:2023-12-31–2024-01-06, accident and marriage news. Evan confirmed that he and his friends were fine after an accident

2023
[30]

Evan thanked Sam for being there

Invalid:2023-11-21–2023-12-05, mutual support. Evan thanked Sam for being there. GT rank:1. Observation.The baseline retrieves partner-related memories, but they belong to later sessions. RaMem identifies the vacation episode that satisfies the query’s temporal and participant conditions. Case C: Gaming Topic vs. Correct Game Preference Episode Query:What...

2023
[31]

Nate shared a recent victory in a regional video game tournament

Invalid: 2022-06-03–2022-06-05, regional video game tournament. Nate shared a recent victory in a regional video game tournament

2022
[32]

Nate won an inter- national tournament and mentioned gaming as a career

Invalid:2022-08-22–2022-09-05, gaming tournament and career. Nate won an inter- national tournament and mentioned gaming as a career

2022
[33]

Nate mentioned an upcoming gaming tournament

Invalid: 2022-04-21–2022-05-02, gaming tournament mention. Nate mentioned an upcoming gaming tournament. 35 GT rank:32. RaMem Top-3:

2022
[34]

Nate met people playing the same board game

Same session / related: 2022-10-09–2022-10-21, board game meeting. Nate met people playing the same board game

2022
[35]

Nate attended a game convention and met new people

Same session / related: 2022-10-09–2022-10-21, game convention attendance. Nate attended a game convention and met new people

2022
[36]

Nate men- tioned playingCyberpunk 2077

GT / valid: 2022-10-09–2022-10-21, recent movie and game preferences. Nate men- tioned playingCyberpunk 2077. GT rank:3. Observation.The baseline retrieves memories from the broad gaming topic, but they belong to earlier episodes. RaMem first narrows retrieval to the correct October 9 session, then includes the verified game-preference memory in the top r...

2022

[1] [1]

Yuehan Qin, Li Li, Linxin Song, Wei Yang, Jiate Li, Yuqing Yang, and Yue Zhao

URLhttps://arxiv.org/abs/2603.07670. Yuehan Qin, Li Li, Linxin Song, Wei Yang, Jiate Li, Yuqing Yang, and Yue Zhao. Memory retrieval for changing preferences.arXiv preprint arXiv:2606.02976, 2026. 13 Bing Wang, Xinnian Liang, Jian Yang, Hui Huang, Shuangzhi Wu, Peihao Wu, Lu Lu, Zejun Ma, and Zhoujun Li. Enhancing large language model with self-controlled...

work page doi:10.1609/aaai.v38i4.28098 2026

[2] [2]

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Association for Computational Linguistics. doi: 10.18653/v1/P19-1285. URL https: //aclanthology.org/P19-1285/. Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces, 2024. URLhttps://arxiv.org/abs/2312.00752. Zhengbao Jiang, Frank F Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graha...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.18653/v1/p19-1285 2024

[3] [3]

lost-in-the-middle

URLhttps://arxiv.org/abs/2511.14460. 15 Zihan Wang, Kangrui Wang, Qineng Wang, Pingyue Zhang, Linjie Li, Zhengyuan Yang, Xing Jin, Kefan Yu, Minh Nhat Nguyen, Licheng Liu, Eli Gottlieb, Yiping Lu, Kyunghyun Cho, Jiajun Wu, Li Fei-Fei, Lijuan Wang, Yejin Choi, and Manling Li. Ragen: Understanding self-evolution in llm agents via multi-turn reinforcement le...

Pith/arXiv arXiv 2025

[4] [4]

Generate enough memory entries to capture all useful information in the dialogues

[5] [5]

Do not use pronouns or unresolved relative time expressions

[6] [6]

Eachlossless_restatementmust be complete, independent, and understandable

[7] [7]

Extract structured fields:keywords,timestamp,location,persons,entities, andtopic

[8] [8]

Create separate entries for separate facts, preferences, plans, events, locations, relationships, work details, and media references

[9] [9]

memory_entries

Return valid JSON only. Output Format. { "memory_entries": [ { "lossless_restatement": "Complete unambiguous restatement.", "keywords": ["keyword1", "keyword2"], "timestamp": "YYYY-MM-DDTHH:MM:SS or null", "location": "location name or null", "persons": ["name1", "name2"], "entities": ["entity1", "entity2"], "topic": "topic phrase" } 23 ] } Constraint.Ret...

[10] [10]

What type of question is this?

[11] [11]

What entities, events, or concepts need to be identified?

[12] [12]

What relationships need to be established?

[13] [13]

question_type

What minimal information would be sufficient to answer the question? Output Format. { "question_type": "factual / temporal / relational / explanatory / other", "key_entities": ["entity1", "entity2"], "required_info": [ { "info_type": "type of information", "description": "specific information needed", 24 "priority": "high / medium / low" } ], "relationshi...

[14] [14]

Think through the reasoning process

[15] [15]

Provide a very concise answer

[16] [16]

Answer only from the provided context

[17] [17]

DD Month YYYY

Format dates as “DD Month YYYY” when dates are needed

[18] [18]

reasoning

Return valid JSON. Output Format. { "reasoning": "Brief explanation of the reasoning.", "answer": "Concise answer in a short phrase." } Constraint.Return only JSON. D.2 Analysis of Main Results Table 1 presents the full LoCoMo results across four backbone models and four question categories. RaMem achieves the strongest average F1 on every backbone. On cl...

arXiv 1934

[19] [19]

James created a game avatar and joined a new gaming platform

Invalid: 2022-03-20–2022-03-27, gaming platform participation. James created a game avatar and joined a new gaming platform

2022

[20] [20]

James streamed a game and received encouraging comments

Invalid:2022-09-20–2022-10-03, game streaming feedback. James streamed a game and received encouraging comments

2022

[21] [21]

James started streaming games

Invalid: 2022-09-18–2022-09-20, game streaming plans. James started streaming games. GT rank:44. RaMem Top-3:

2022

[22] [22]

James became interested in extreme sports and did rope jumping

GT / valid:2022-07-09–2022-07-22, extreme sports interest. James became interested in extreme sports and did rope jumping

2022

[23] [23]

James went surfing

Invalid:2022-06-19–2022-07-09, surfing experience. James went surfing

2022

[24] [24]

James won an online gaming tournament

Related but topic-conflicting: 2022-07-09–2022-07-22, gaming tournament victory. James won an online gaming tournament. GT rank:1. Observation.SimpleMem retrieves memories that are related to James and activities, but all top-ranked evidence violates the query’s recall conditions. RaMem reinstates the July 9 34 Table 19: Online efficiency under the same o...

arXiv 2022

[25] [26]

Sam discussed Evan’s partner and family support

Invalid:2024-01-06–2024-01-10, family and partner support. Sam discussed Evan’s partner and family support

2024

[26] [27]

Evan mentioned support from extended family

Invalid:2024-01-06–2024-01-10, family support after marriage announcement. Evan mentioned support from extended family. GT rank:18. RaMem Top-3:

2024

[27] [28]

Evan shared details about a recent vacation in Canada with his new partner

GT / valid: 2023-08-13–2023-08-15, vacation details. Evan shared details about a recent vacation in Canada with his new partner

2023

[28] [29]

Evan confirmed that he and his friends were fine after an accident

Invalid:2023-12-31–2024-01-06, accident and marriage news. Evan confirmed that he and his friends were fine after an accident

2023

[29] [30]

Evan thanked Sam for being there

Invalid:2023-11-21–2023-12-05, mutual support. Evan thanked Sam for being there. GT rank:1. Observation.The baseline retrieves partner-related memories, but they belong to later sessions. RaMem identifies the vacation episode that satisfies the query’s temporal and participant conditions. Case C: Gaming Topic vs. Correct Game Preference Episode Query:What...

2023

[30] [31]

Nate shared a recent victory in a regional video game tournament

Invalid: 2022-06-03–2022-06-05, regional video game tournament. Nate shared a recent victory in a regional video game tournament

2022

[31] [32]

Nate won an inter- national tournament and mentioned gaming as a career

Invalid:2022-08-22–2022-09-05, gaming tournament and career. Nate won an inter- national tournament and mentioned gaming as a career

2022

[32] [33]

Nate mentioned an upcoming gaming tournament

Invalid: 2022-04-21–2022-05-02, gaming tournament mention. Nate mentioned an upcoming gaming tournament. 35 GT rank:32. RaMem Top-3:

2022

[33] [34]

Nate met people playing the same board game

Same session / related: 2022-10-09–2022-10-21, board game meeting. Nate met people playing the same board game

2022

[34] [35]

Nate attended a game convention and met new people

Same session / related: 2022-10-09–2022-10-21, game convention attendance. Nate attended a game convention and met new people

2022

[35] [36]

Nate men- tioned playingCyberpunk 2077

GT / valid: 2022-10-09–2022-10-21, recent movie and game preferences. Nate men- tioned playingCyberpunk 2077. GT rank:3. Observation.The baseline retrieves memories from the broad gaming topic, but they belong to earlier episodes. RaMem first narrows retrieval to the correct October 9 session, then includes the verified game-preference memory in the top r...

2022