REMem: Reasoning with Episodic Memory in Language Agent
Authors: Yiheng Shu
Saisri Padmaja Jonnalagedda
Xiang Gao
Bernal Jiménez Gutiérrez
Weijian Qi
Kamalika Das
Huan Sun
Yu Su
Venue: ICLR 2026 Poster (2026)
Keywords: language agent
episodic memory
long-term memory
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Humans excel at remembering concrete experiences along spatiotemporal contexts and performing reasoning across those events, i.e., the capacity for episodic memory. In contrast, memory in language agents remains mainly semantic, and current agents are not yet capable of effectively recollecting and reasoning over interaction histories. We identify and formalize the core challenges of episodic recollection and reasoning from this gap, and observe that existing work often overlooks episodicity, lacks explicit event modeling, or overemphasizes simple retrieval rather than complex reasoning. We present REMem, a two-phase framework for constructing and reasoning with episodic memory: 1) Offline indexing, where REMem converts experiences into a hybrid memory graph that flexibly links time-aware gists and facts. 2) Online inference, where REMem employs an agentic retriever with carefully curated tools for iterative retrieval over the memory graph. Comprehensive evaluation across four episodic memory benchmarks shows that REMem substantially outperforms state-of-the-art memory systems such as Mem0 and HippoRAG 2, showing 3.4% and 13.4% absolute improvements on episodic recollection and reasoning tasks, respectively. Moreover, REMem also demonstrates more robust refusal behavior for unanswerable questions.

Published as a conference paper at ICLR 2026
REASONING WITH EPISODIC MEMORY IN LANGUAGE AGENTS
REMEM:
Yiheng Shu', Saisri Padmaja Jonnalagedda', Xiang Gao², Bernal Jiménez Gutiérrez!
Weijian Qi', Kamalika Das", Huan Sun', Yu Su'
'Te Ohio State University Intuit AI Research (shu.251, su. (a.809)6osuu edu, (saisri.jonnalagee xiang gnttggatttttt..oom
ABSTRACT
Humans excel at remembering concrete experiences along spatiotemporal con- texts and performing reasoning across those events, i.e., the capacity for episodic memory. In contrast, memory in language agents remains mainly semantic, and current agents are not yet capable of effectively recollecting and reasoning over interaction histories. We identify and formalize the core challenges of episodic recollection and reasoning from this gap, and observe that existing work of- ten overlooks episodicity, lacks explicit event modeling, ple retrieval rather than complex reasoning. We present REMem, two-phase framework for constructing and reasoning with episodic memory: 1) indexing, where REMem converts experiences into a hybrid memory graph that flexibly links time-aware gists and facts. 2) agentic inference, where REMem employs agentic retriever with carefully curated tools for iterative retrieval over the mem- ory graph. Comprehensive evaluation across four episodic memory benchmarks shows that REMem substantially outperforms state-of-the-ee memory systems such MemO and HippoRAG 2, showing 3.4% and 13.4% absolute improve- ments on episodic recollection and reasoning tasks, respectively. Moreover, RE- Mem also demonstrates more robust refusal behavior for unanswerable questions
overemphasizes sim-
INTRODUCTION
The ability to intentionally and precisely re-experience events from our past one of the defining features of human intelligence. This "mental time travel" capacity, or episodic memory, allows us to access specific events along a spatiotemporal axis, specifying their order, duration, and even causality. Unlike semantic memory, which stores concepts and knowledge about the world, episodic memory shapes each individual's 's unique experiences and preferences, establishing a cornerstone for continual learning within one's me's environment.
Despite its importance for human cognition and rising interest in memory systems for language agents, achieving human-level episodic memory remains elusive, mainly due the dominance of semantic memory paradigms current approaches. Parametric memory, embedded in model weights during pre-training fine-tuning, lacks adaptability and contextual grounding in specific experiences. Model editing methods (Yao et al. 2023b) can update stored facts but remain lim- ited modifying static semantic knowledge. retrieval-augmentt
generation (RAG) systems with embedding models (Zhang et al.) 2025 Lee et al.) 2025a) enable dynamic knowledge access but still operate dd-contextualized manner, divorced from spatiotemporal context. Finally, more ad- vanced non-parametric systems, which use large language models (LL.Ms) to construct summaries or semantic graphs (Gutierrez et al., 2024, Gutiérrez et al. 2025 Edge et al., 2024), improve over RAG but still prioritize structured world knowledge over lived, interaction-specific experience.
Recently, few studies have targeted episodic settings more directly. These typically represent episodic memory using entity relationships in (temporal) knowledge graphs with loss of coherent event contexts (Rasmussen al. 2025) or by selectively inferring what seems important and offering it summary (Chhikara al. 2025 Tan al. 2025). which lacks explicit event modeling. Crit- ically, they fail to integrate situational dimensions, such as time, location, and participants, within
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Published as a conference paper at ICLR 2026
Melanie
I just took family camping in the mountains last week. It was really nice time together!
just signed for pottery class yesterday. It's like therapy for me.
took my kids to a park yesterday. They had fun exploring and playing.
27 Jun 2023
2023
28 Aug 2023
Episodic Recollection
Episodic Reasoning
Q: When did Melanie to the park?
A: 27 Aug
Q: How many days passed between Melanie signing up for the pottery class taking her park?
A: 56 days
Melanie enjoy family camping?
Yes
69.4
71.9
40.2
78.8
47.9
74.0
92.2
WV-Embed-v2
Mem0
Graphiti
HippoRAG 2
TISER
REMem
Figure I: Overview of evaluation on episodic memory. Utterances are grounded to a timeline (top). We evaluate two progressive capabilities and show average scores on each (bottom): 1) Episodic recollection: recollect temporal and other situational elements past experiences, measured by LLM-as-a-judge scores on LoCoMo and REALTALK. 2) Episodic reasoning: multi-step reason- ing across the timeline based on recollection, e.g., event-to-event relations, counting, and ordinal queries, measured by LLMdae-a-judge score on Complex- and the EM score on Test of Time.
interaction histories. Furthermore, when memory must support reasoning across multiple linked events, existing methods rely heavily on similarity-based retrieval, offering limited capacity to infer complex inter-event relationships. Given these observations, we believe that comprehensive event representation, combined with flexible retrieval and reasoning, deserves further exploration.
To advance episodic memory and reasoning for language agents, we first identify two key, pro- gressive challenges, as shown in Figure 1) Episodic recollection: reconstruct events and their situational dimensions based experiences, such as time, location, participant, emotion; i.e., the ability to bind situational elements to specific events. 2) Episodic reasoning: multi-step reasoning based on episodic recollection, such as inter-event relations, ordinal constraints, and superlatives.
Then, we introduce a new episodic memory framework, REMem (Reasoning with Episodic Memory), for language agents. We formalize episodic memory time-aware event representations and propose a hybrid memory graph that stores gists (concise, numan--redabbl event summaries with parsed timestamps) and facts (time-scoped triples). Unlike existing work that selectively extracts useful information, we clearly instruct the LLM to construct memories organized primarily along time and linked situational dimensions such as participants, locations, and emotions. We develop an agentic inference procedure with carefully curated tools for retrieval and graph exploration. As result, memory management goes beyond simply matching isolated text spans and enables complex logical composition, including time-range filtering, neighbor exploration, and ordinal constraints.
We conduct one of the most comprehensive evaluations on episodic memory to date, covering four benchmarks on conversational and temporal reading comprehension. REMem delivers consistent gains over the current state of the art, achieving 3.4% and 13.4% absolute improvement on episodic recollection and reasoning tasks, respectively. REMem also shows unparalleled reasoning capabili- ties, being the only method to exceed 90% exact match score on the Test of Time benchmark (Fatemi al. 2025), and behaves more robustly to refuse unanswerable questions. These strong results po- sition REMem as a solid step towards bringing effective episodic memory to language agents.
2 RELATED WORK
2.1
NON-PARAMETRE MEMORY FOR LLMS
broad line of work augments LLMs with non-parametric memory. ChatGPT (OpenAl 2025a) combines prior chats with user-controlled saved memories for personalization. Strong embedding
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Indexing
Gist Node
Phrase Node
Verbutim (message)
Gists
Facts
Alice
Date: 3:57
Jan, 2024
Allic fixed the fance last Sunday Jan, 2024).
completed task
purchased
Alice: About last week, let me see. fixed the fence last Sunday,
Allice bought cows from Peter on Jan 17th (17 Jan, 2024)
fence fixing
cows
bought cows
Agentic Inference
Think
Act semantic retrieve Alice buys cow')
Obs purchased on 17 lan 2024
Query: did fix
fence before buying cows?
(o o
Think
rentreei Alice fence"" start_tim="'t 11 Jan 2024", start_op="Itt)
Obs: completed fence
fixing on 14 lan 2024
User
Response: 2024
Language Agent
Think
outputLanswer(11 2024")
Obs: generated
Figure 2: Overview of REMem. The indexing phase turns utterances into time-awar memory by extracting event gists and time-scoped facts (triples) and organizing them as a hybrid graph. The agentic inference phase invokes carefully curated tools over this graph to surface the most relevant gists and facts for reasoning in an iterative manner.
models (Lee et al. 2025a Zhang et al. 2025) yield competitive retrieval, but their flat vector spaces do not explicitly encode episodic or temporal structure. Structure-augmeente approaches build graphs or memory layers to improve multi-hop and cross-session retrieval (Edge et al. 2024): Hip-
PoRAG 1&2 (Gutierrez et al., 2024) Gutiérrez et al., 2025) organizes knowledge for associative re- trieval and continual updates. Graphiti/Zep (Rasmussen et al.) (025) maintains temporal knowledge graphs and context-assembly pipelines. Memo (Chhikara et al. 2025) extracts and consolidates conversational memory in graph structure with knowledge update mechanism. Orthogonal to graph layering, MemGPT (Packer et context management. A-Mem (Xu et [ 2025) performs agentic, Zettelkasten-like dynamic link- ing among notes. MemoryBank (Zhong et al. 2024) introduces time-decay-basee consolidation and forgetting. Reflective Memory Management (Tan et al.) 2025) further refines what store and retrieve over time. Overall, most existing systems rely on LLM-based summarization or graph con- struction to extract key information, yet they overlook the crucial role of spatiotemporal context and situational elements in supporting episodic memory. These systems rarely propose an explicit de- sign for episodic memory and reasoning. In contrast, our approach explicitly represents event gists and temporal facts grounded to a timeline, links them to core situational dimensions, and integrates them with tool-augmented reasoning.
al.] (202)) implements OS Otyytyle hierarchical paging and virtual
2.2 EPISODIC MEMORY AND REASONING
Recent benchmarks increasingly stress long horizon interaction for language agents: LoCoMo (Ma-)
harana et al. 2024) and REALTALK (Lee et al. 2025b) evaluate multi-session conversational mem- ory in synthetic and real settings. Conversation understanding of LongMemEval (Wu et al. 2025) requires information extraction or multi-session reasoning, but most tasks can be seen as single- retrieval problems only requiring agents to retrieve the correct segments (Hu et al. 2025b). Tempo- ral reading comprehension benchmarks, such as TimeQA (Chen et al. 2021), MenatQA (Wei al.] 2023). Complex-TR (Tan al. 2024). and Test of Time (Fatemi et al. 2025). decompose tem- poral reasoning into diverse skills: event ordering, date resolution, counting. duration estimation, and timeline construction. Methodologically, timeline self-reflection method like TISER (Bazaga 2025) improves temporal reasoning only through better prompting, while tool-use agents (Ge al.
et al.
2025 Hu et al. 2025a) enable procedural interaction with non- parrmmemtt memory. Our work complements these efforts by 1) encoding episodes as time-aware gists and facts in hybrid graph, and 2) using tool-augmented retrieval and graph exploration to perform episodic recollection and reasoning during inference.
METHODOLOGY
Our framework. REMem, allows language agents to reason with episodic memory through a two- phase process: indexing and agentic inference. The indexing phase builds memory graph that
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Table 1: Curated tools and their signature, Both the retrieval and graph exploration tools output two sets of results: list of gists and list of facts. See Appendix D for prompts and demonstrations.
Type
Name
Arguments
semantic retrieve
query, start_time, end_time, start.operatoo, end_operator query, start.time, endet start opprrooperator, end end_operator
Retrieval
exical_retriew
Graph
find gist contexts Hid_eenity_contexx
gist id, start time, end time, start operator, operator subject, object, predicate, start.time, end_time, start_operator, end_operator, limit, ordering, offiet, aggregation
Exploration
Flow Control
output answer
answer
stores the gists and facts for episodes. Then, the agentic inference phase flexibly leverages carefully curated tools to reason over user queries and retrieve from the memory graph.
3.1 INDEXING
Cognitive science reminds us that humans rely more on gist than verbatim memory when making decisions [Reyna & Brainerd 1995), and our discourse memory is shaped around situation mod- els. which integrate temporal, spatial, causal, and situational dimensions beyond surface details (JohnsonLaird] 1983 Zwaan & Radvansky 1998). Besides, recent RAG studies show that hybrid memory structures combining concept-level and context-level information are more effective than either alone (Edge et al.) [024] Guttérrez et al. (022).. Motivated by these insights, our principles for constructing a memory graph (Figure 2 focus on comprehensiveness and flexibility, which jointly encode gists with multiple situational dimensions and facts with temporal contexts. Specifically, we perform the following steps.
For each event statement or chat session, we generate one or multiple gist statements in natural language. Each gist is prefixed with the episode's timestamp (reference time) applicable, and any relative temporal expressions are resolved to absolute dates. For each episode (e.g., a chat session), this yields gist list, where each gist is concise sentence capturing the episode's key details, including participants, actions, objects, locations, intentions, and quantities, in single atomic event description. 2) Fact Extraction: We further extract structured facts from each episode's text and the extracted gist list. These facts are represented as (subject, predicate, object) triples, where each field a schemaless phrase, mainly capturing who did what to whom. We also extract temporal contexts (dates/times) and ground each fact in a timeline by optionally attaching Wikidata-style qualifiers (Vrandecic Krötzsch 2014), point in rime, start time, and end time, to the corresponding triple. We preserve gists and facts as they are added over time, even when they are potentially contradictory, thereby maintaining record that can be revisited historically. 3) Graph Construction: Using the above outputs, we construct memory graph that integrates gist nodes and phrase nodes. Gist nodes serve as context-level episodic representations, each connected to phrase nodes extracted from the same chunk. At the concept-level, subject and object phrase nodes of each fact are directly linked by edges, encoding the extracted relationships between those phrases. Thus, hybrid memory graph combining concept and context levels is formed. To further enhance connectivity, we add synonymy edges between gist nodes whose embedding similarity exceeds a threshold, following the HippoRAG 2 approach (Gutiérrez et al., (0225.. This mechanism clusters semantically related gists (e.g., different phrasings of similar events) and enriches the graph with higeer-level semantic connections.
1) Gist Extraction:
3.2 AGENTIC INFERENCE
In contrast to the common RAG approach of simply performing a one-time matching with related text, our inference phase includes iterative retrieval to bandle complex logic, and mental time travel, which filters memory entries according to temporal conditions. We adopt a ReAct-style agent (Yao) et al.) 2023at Gu et al.) 2024) equipped with three categories of carefully curated tools to access the hybrid graph: 1) retrieval tools, 2) graph exploration tools, and 3) the flow control tool. The results returned by retrieval graph exploration tools contain both gists and facts provide
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Table 2: The statistics of sampled datasets.
Datasets
LoCoMo
REALTALK
Complex-TR
Test of Time
# of queries
186
728
1,000
800
# of chunks/messages
5, 882
8,914
1,095
121,919
# of graphs
10
560
comprehensive view. Specifically, the agent follows a three-stage protocol over the hybrid memory graph, shown in T Full tool specifications provided in Appendix [D]
1) Retrieval: The agent first invokes mmmantic_retrieve (using an embedding model) or lexi- rel-retrieve (using BM25) tool obtain truncated seed nodes and their contexts from the graph, e.g., candidate entity IDs, temporal windows, and coarse topical cues. The agent decomposes com- plex questions into simpler sub-queries that guide the next step. 2) Graph Exploration: Using the seed nodes retrieved from stage 1. the agent issues targeted calls to find-gist finddgist.contertt conteetss to obtain episode-level narratives and temporally grounded evidence, or uses find entity contexts when the query explicitly targets entities under known graph schema. find entity contexts not only supports specifying subjects, predicates, or objects, but also filters gists facts that meet specific tempo- ral conditions. 3) Flow Control: Once sufficient evidence has been gathered, the agent invokes to generate the final response upon reaching confidence. This procedure exploits the entire interaction history, incorporating both explored gists and facts, to conduct the concluding reasoning.
4 EXPERIMENTAL SETUP
4.1 DATASETS
For the episodic recollection task, we use conversational question answering (QA) benchmarks: LoCoMo (Maharana et al. (024)) is synthesized conversational benchmark, while REALTALK (Lee et al. 2025b) is collected from real human conversations. Both benchmarks include questions that cover temporal aspects and non-temporal situational aspects, and we use all samples from them.
For the episodic reasoning task, we use reading comprehension benchmarks Complex-TR (Tan al. 2024) and Test of Time (Fatemi et al. 2025). ) From Complex-TR, we randomly sample 1, 000 queries. From the Test of Time, we adopt all samples from its semantic part, since this part explicitly requires memory.
4.2 METHOOSS FOR COMPARISON
We select state-of-the-as methods from the following categories for comparison: 1) Strong em- bedding models from the MTEB benchmark (Muennighoff al., 2003) for RAG setting, including (Lee et al. 2025a)
(Zhang et al.) 2025) and
2) Structure-augmennt memory approaches. including Memo (Chhikara et al. 2025). Graphiti RRasmussen et al. 2025). and HippoRAG 2 (Gutiérrez et al. 2025). where the first methods are originally evaluated on episodic memory tasks. We reproduced the experiments using their open- source implementation rather than proprictary ones. 3) The prompt method TISER for temporal reasoning (Bazaga et al. 2025): give the query and retrieved contexts, we use its prompt for the final generation. The method orthogonal to any memory system. 4) Additional references: Oracle Message takes the oracle retrieval results and only performs generation. Full-Context uses the en- tire corpus and the query for generation. Due its limited context window, it should not be regarded as memory method, but we take it as reference rather than a comparative memory method.
4.3 METRICS
For Test of Time (Fatmii et al. 2025), we use the unique metric exact match (EM) score. For the remaining benchmarks, as-a-judge scores (denoted as LLM-J later) as metrics for QA tasks. We adopt the calculation from HippoRAG 2 (Gutiérrez al. 2025) BLEU-1 implementation from HuggingFace Evaluate
token-based F1, BLEU-1 (Papineni et al. 2002), and LLM-
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Table Performance (%) on episodic recollection task. The highest value and second second-highest value in each column are bold and underlined, respectively. Numbers means with 95% bootstrap confidence intervals as subscripts and superscripts. The same applies to the tables below.
LoCoMo (1, 986)
REALTALK (728)
Methods
F1
BLEU-1
LLM-J
F1
BLEU-1
LLM-J
Oracle Message
48.0
38.3
81.0
Full-Context
37.8
28.6
76.7
25.3
18.6
65, 1
Large Embedding Models
Qwen3-Embed-88
35.3+1.7
28.9
64.2 2.0
20.2
14.9
52.5 52.5+3.4
NV-Embed-v2 (7B)
39.6
1177
31.0
73.0 2.0
23.8 1.9
17.7
59.5 3.6
+3.33
1.8
Structure-Augnee Augmented Memory
MemO
25.1+1.7
18.0 1.4
19.7
9.8*1.
7.2
14.3*2.7
Graphiti
33.7 1.8
28.9 17
52.5 2.3
2.3
15.1 15.1 -13
11.5
35.3 3
HippoRAG
39.0
30.8
74.0
21.9116
16.2
55.8
Ours
REMem-I
42.4t1.
32.7
76.2+2.0
25.6+1.
18.1
63.7+34
REMem-S
41.3 1.6
31.514
77.5
26.2
19.2
65.3
1.3
Table 4: Performance (%) on episodic reasoning tasks.
Complex-TR (1, 000)
Test of Time (2, 800)
Methods
F1
BLEU-1
LLM-J
EM
Full- Pull-Context
74.2
68.0
81.6
79.7
Large Embedding Models
(wen3-Embedd-e
77.1
71.4
80.9 2.5
70.3
NV-Embed-v2 (7B)
77.5 -11
2.2
71.9 2.
80.4+26 2
68.9
2.5
w/ TISER
88.1
83.6
88.3+1.3
68.9
Structure-Aggmente
Memory
MemO Graphiti
43.1+28
35.1+2.
41.0t3.0
78.8+
76.6
71.4
HippoRAG2
78.21
72.7121
81.5+3.a
66.9
Ours
REMem-I
83.3118
77.613.
89.6+2.0
93.1 93.1
w/ TISER
90.6+1.2
86.0+17
92.0
90.6
REMem-S
78.5
72.7 2.4
82.6
72.5
(Hugging ace 2025). We adopt the same LLM evaluation prompts as Memo (Chhikara al., 2025). which account for both temporal and conversational contexts.
4.4 IMPLEMENTATION DETAILS
We use HPT-44.-mini-2025-044 OPenAl 2025b) the default LLM and nvidia/NV-Embed-v2 (Lee et al. 2025a) as the default embedding model for both extraction and QA tasks. in REMem as well as in comparison methods. For baselines using the embedding model, original passages (messages in conversation). For Mem0 and Graphiti, we retrieve the top-10 of their processed chunks. REMem applies the same retrieval scope for each step, operating over the top-10 gists and facts. We use the top-3 returned sessions from HippoRAG 2 for final generation.
retrieve the top-10
We explore two settings for our method: REMem-((terative) autonomously selects tools in a multi- step retrieval and reasoning process, following the protocol in $3.2 REMee--(ingle) only adopts a single-step embedding based retrieval and then generation. The maximum number agentic inference steps in REMem-I for each dataset is selected from 2 to based on small validation set. Then, we use 3 for episodic recollection tasks and 5 for episodic reasoning tasks. The similarity threshold for synonymy edges is set to 0.8, following HippoRAG 2.

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Table 5: Ablation study on LoCoMo and Complex- -Tppee--R,, regarding the graph structure and the usage of retrieval tools.
LoCoMo
Complex- TR
Methods
F1
BLEU-1
LLM-J
F1
BLEU-1
LLM-J
REMem-S
41.3
31.5
77.5
78.5
72.7
82.6
REMem-I
42.4
32.7
76.2
83.3
77.6
89.6
w/o Gists
31.7
28.7
48.9
80.3
75.9
80.9
w/o Facts
42.0
32.6
74.1
80.5
74.5
87.2
w/o Synonymy edges
37.6
28.7
76.4
81.6
75.6
89.2
w/o Tool semantic retrieve
41.7
33.3
72.8
82.4
76.4
88.1
w/o Tool lexical retrieve
40.6
31.2
76.8
81.7
75.8
87.5
RESULTS
5.1
EPISODIC RECOLLECTION
The results on episodic recollection tasks are shown T Overall, REALTALK collected from human utterances poses greater challenges than synthetic LoCoMo. As reference, Full-Context performs well, but the inference cost is notable: the average number of input tokens for each Lo- CoMo query 26k, while REMem-I and REMem-S consume 9k and 0.9k tokens, respectively, dur- ing the inference phase. The RAG method using large embedding models serves as a strong baseline, especially NV-Embed-v2 formance, particularly on REALTALK, where human utterances are more spontaneous, noisy, and less dense in information. Memo extracts many statements, but most are subsequently discarded from memory by its own decision, resulting in only few details being remembered. Graphiti con- structs temporal knowledge graph centered around entities, which results the loss of coherent contextual information related to various situational dimensions of events. HippoRAG lacks any modeling of the temporal dimension or events. Its overall performance is only comparable to the embedding baseline due its use of passage nodes with embedding-based retrieval. In contrast, REMem-I and REMem-S substantially outperform existing methods and even approach the oracle performance. More detailed results for LoCoMo and REALTALK provided in Appendix [.T and C.2 respectively, where we show REMem-I and REMem-S have their respective strengths across different metrics. Notably, cross-session questions LoCoMo account for only 14.2% of the bench- mark, and support messages for most questions originate from a single chat session, which explains why REMem-S single-step variant performs better in certain scenarios.
dtructure-augmenter memory methods demonstrate subpar overall per-
5.2 EPISODIC REASONING
The results on episodic reasoning tasks are shown Table 4 RAG using large embedding mod- els remains strong baseline. TISER, prompt guiding language agents to perform temporal reasoning, demonstrates a stronger reasoning capability on Complex-TR, compared to our straight- forward answer-generation prompts (Figure However, it remains fixed prompt that struggles to cover all episodic reasoning challenges, and excels at chronological questions, such as before/after or first/last types. The seuuttrr--uggentt memory methods mainly leverage embedding to retrieve at various granularities, c.g., entities or summaries. But this simple semantic matching is insufficient for capturing the full contextual information necessary for complex reasoning, and fails to support the required logical operations. REMem shows absolute superiority in complex reasoning tasks. In particular, REMem-I demonstrates clear advantage over REMem-S (LLM-J +7.0, EM + 20.66 and becomes the unique one exceeding 90% EM score, benefiting from multi-step retrieval and flexible tool use. Moreover, REMem-I obtains substantially larger improvements over Full-Context (LLM-J +8.0, EM +13.4) on these challenging episodic reasoning tasks, compared to that episodic rec- ollection tasks. More detailed results for Complex-TT TR and Test of Time are provided Appendix and C.4 respectively.

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Table 6: Performance of refusal on LoCoMo. Among 1, 986 queries, 446 are unanswerable. Preci- sion is the proportion of predicted unanswerable cases that are indeed unanswerable (correct refusals as "no information available"). Recall is the proportion of truly unanswerable cases correctly pre- dicted such. The F1 score is computed from these precision and recall values.
Methods
of Refusals
Precision (%)
Recall (%)
(%)
Graphiti
954
38.9
83.6
53.1
MemO
90
40.0
8.1
13.5
REMcm
344
73.3
56.8
64.0
DISCUSSIONS
6.1 ABLATION STUDY
We conducted ablation experiments on LoCoMo and Complex-TR (Table Both gists and facts important, but they contribute in different ways. Removing gists leads the largest degradation, especially on LoCoMo, where LLM-J drops from 76.2 to 48.9. This supports our design choice that gists carry the main situationna elements. Removing facts produces a smaller yet consistent drop, particularly on Complex-TR, where LLM-J decreases from 89.6 to 87.2. This suggests that facts play a supporting role for multi-hop reasoning. by providing concrete anchors that connect concepts across sessions (Appendix (C.)). Graph structure and retrieval tools also matter. Ablating synonymy edges reduces Fl and BLEU-1 on both datasets, indicating that modeling synonymic relationships improves lexical robustness and recall, while LLM-J remains almost unchanged, which implies that the core reasoning path is largely preserved. Finally, removing either the semantic or the lexical retrieval tool hurts performance. Without semantic retrieve, LLM-J Complex- - decreases from 89.6 to 88.1 and also drops on LoCoMo, showing that semantic retrieval is crucial for finding conceptually relevant memories. Without lexical retrieve, F1 and BLEU- decline and LLM-J on Complex- TR falls to 87.5, indicating that lexical retrieval complements semantic retrieval by improving coverage of surface forms.
6.2 REFUSAL PERFORMANCE
In real-world applications, users will always pose queries for which the system lacks sufficient context. Such adversarial or unanswerable questions are still valid for evaluation purposes, and we include them as part of the full LoCoMo benchmark. For these cases, LoCoMo labels them as "no information available". Therefore, if a method either fails to produce an answer or explicitly outputs this phrase as instructed, we take it as a refusal to answer. For these metrics of refusal behaviors (not QA metrics) as shown in Table 6 REMem achieves the highest Fl (63.96%) by coupling the best precision (73.3%) with competitive recall (56.8%). Compared to Graphiti, which produces 954 refusals with low precision (38.9%) despite the highest recall (83.6%), REMem improves precision by +34.4 points and Fl by +10.9 points, while producing roughly one-third as many refusals (344 vs. 954), indicating substantially fewer unnecessary rejections. Compared to Mem0, which overly permissive (only 90 refusals), REMem correctly flags more unanswerable cases. Overall, REMem achieves a better balance for refusal behavior on adversarial questions.
6.3 HUMAN EVALUATION
To demonstrate the validity of using LLM as judge in episodic memory, especially for reasoning tasks, we randomly selected 100 samples from LoCoMo and conducted a human evaluation (Table 7). Both the LLM judge and the human evaluation use binary scores. The LLM judge matched human scores in 93% of cases, with only 7 discrepancies. Five LLM-accepted answers were man- ually judged as incorrect due to incomplete lists or temporal reasoning errors. Conversely, two LLM-rejected answers nized. Though some limitations remain, these findings suggest that using LLM as a judge produces evaluations most closely aligned with human judgment compared to traditional metrics.
manually judged correct, because valid paraphrases were not recog-
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Table 7: Comparison between automatic metries and human evaluation. Mean denotes the mean value over 100 selected LoCoMo samples. The correlations are compared with human evaluation.
Metric
Mean
Pearson w/ Human
Spearman p w/ Human
Human Evaluation
0.710
Fl
0,410
0,551
0,603
BLEU-1
0,284
0,417
0.531
LLM Judge
0.740
0.827
0.827
6.4 ERROR ANALYSIS
We conduct an analysis for REMem LoCoMo and Complex-TR. In 100 sampled LoCoMo errors, the most common are selection or grounding errors (46%). where REMem locates the cor- rect similar slot but assigns the wrong value misinterprets the referent in details. For example, when asked "What is Nate's 's favorite video game?" REMem responded "Catan", but that was only one of Nate's ' interests rather than his favorite "Xenoblade Chronicles". Temporal or numerical reasoning mistakes (19%) include errors in relative dates or durations. For instance, to the question "When is Nate hosting gaming party?" the model answered " "18-19 June 2022" while the gold ref- erence was "the weekend after June 2022". Another 18% are abstentions despite the evidence being retrieved, where the model incorrectly claims that no information is available, even though the gold answer is present. In 100 sampled Complex-TR errors, the most frequent failure mode (42%) was temporal window mismatch, where correct entities are retrieved but misaligned with the specified time span. For instance, when asked "Which employers did Ott-Heinricl Keller work for from Aug 1941 to Mar 1945?" the gold answer included both "the Naval Academy at Mürwik" and "the University of Münster"" but prediction listed only the first. Roughly 21% of cases were due to incomplete or inconsistent multi-entity lists, where some retrieved items are missing or ex- trancous. About 18% are off'stt direction mistakes, such confusing before and after or skipping to later hop. For the question "Which employer did Karyn A. Temple work for after RIAA?" ?" where the gold answer was "the U.S. Department of Justice" but the model instead gave "the Copyright Office". A smaller fraction (= 5%) of ours incorrectly returns "no information available" despite gold facts being present.
6.5 COMPARATIVE ANALYSIS: REMEM VS. RAG
We provide a few qualitative examples to compare REMem with a RAG system using NV-Embed- v2. REMem outperforms NV-Embed-v2 on questions that require disambiguation across brand cat- egories and reconciliation of time-stamped events, while the embedding baseline shines straight- forward interval calculations. For Q1 in Table g REMem extracts and reasons over a normal- ized gist, surfacing an explicit dated summary (9719 Dec 2023] John got an amazing deal with renowned outdoor gear company"), and therefore matches the gold label exactly. NV-Embed-v2 selects a semantically similar but incorrect brand vertical (beverage). For Q2, REMem correctly chains events and compares timestamps (Susie was adopted around Aug 2021, while Seraphim was adopted last year). whereas the embedding method fixates on an earlier mention and ignores the newer adoption. Conversely, for the comparative Q3, NV-Embed-v2 retrieves both dates cleanly and computes the three-month interval, while REMem misaligns events in a longer context, yield- ing a one-month error. Overall, these examples indicate that time-aware gist extraction and agentic inference provide generally better robustness to distractors and temporal ambiguity, whereas a com- mon RAG pipeline can be reliable when the answer reduces to simple computation over accurately retrieved facts. An additional comparative analysis between REMem and TISER on Complex-TR can be found in Appendix F.I
CONCLUSION
Challenges like episodic recollection and reasoning are far from solved for language agents. We presented REMem, a time-aware episodic memory framework. The proposed hybrid memory graph unifies concept-level and context-level information with flexible temporal awareness, while the agentic retriever enables integration of retrieval and reasoning. REMem consistently demonstrates better performance on episodic recollection and reasoning tasks across four benchmarks, with better

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Table Comparison between NV-Embed-v2 and REMem on LoCoMo. Tor F indicates the LLM's judgment (True False).
Question
Gold Answer
NV-Embed-v2
REMem
QI: What kind of deal did John get Deal with December?
Endorsement deal with Endorsement deal with an beverage company outdoor gear company (T)
door gear company
Q2:
Which pet did Jolene adopt more recently, Susie Seraphim?
Seraphim
Susie (F)
Seraphim (T)
How many months passed be- tween Andrew adopting Toby and Buddy?
Q3:
Three months
months (T)
1 month (F)
refusal behavior for unanswerable queries and higher token efficiency. REMem indicates promis- ing step toward more reliable long-horizon language agents. Future work should consider long-term memory for language agents operating in more complex environments. Building memories in streaming format also poses an engineering challenge compared to offline batch indexing.
ACKNOWLEDGMENTS
The authors would like thank colleagues from the OSU NLP group and Intuit Al Research for constructive discussions. This material is based upon work supported by the National Science Foun- dation under Grant No. 2443149. Any opinions, findings, and conelusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This work is also supported by an Alfred P. Sloan Foundation Fellow- ship.
REPRODUCIBILITY STATEMENT
We have provided the implementation details for reproducing the method and experiments. All datasets used in this paper publicly available, and present dataset sampling in $4.1 The
metrics calculation is shown in $4.3 The implementation details of REMem and the methods for comparison presented in $4.4 The details Memo and Graphiti are further provided in Ap- pendix
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Appendix Contents
A The Use of Large Language Models
14
Formal Definition
14
C Detailed Results
15
LoCoMo
15
REALTALK
17
Complex-TR
17
Test of Time (Semantic)
18
C.5
Semantic Memory Capability
18
6
Ablation Study
19
Performance by Temporal Category
19
D Prompts
20
Implementation Details
20
Analysis
F1
Comparative Analysis: REMem TISER
21
Time and Space Efficiency
F3
Token Usage
22
Example of the Extraction
22
F.5
Graph Property
23
A
THE USE OF LARGE LANGUAGE MODELS
Large language models did not play a significant role in the ideation or writing of this paper. They were only used for grammar checking and minor polishing of individual sentences.
B FORMAL DEFINITION
Let M (V, E) be typed multigraph with V = Viist U Vphrase and E Erel Eax Esyn. The graph thus contains two node types and three edge types.
Each gist node E yiu stores natural-language episode summary text(g) and an optional time scope T(g) (point or interval), and represents a context-level, human-readable description of an event that jointly encodes its situational dimensions, such as participants, actions, objects, lo- cations, intentions, and quantities, anchored to that time scope. Each phrase node p E Vparase stores short string name(p) and represents a concept-level element extracted from factual triple (subject, predicate, object), typically denoting a participant, action, or object in the event. Phrase nodes can inherit temporal qualifiers such as point in t time, start time, end time from the underlying fact.
Node
Edge Each relation edge (p., r. r.. T())) E E links a subject phrase node p. and an object phrase node pa with predicate and validity interval r(e), encoding fact-level relations such as
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Algorithm Indexing (D; Piist Piact; Osyn)
Require: Event statements = {di} with timestamps when available; prompts for gist and fact extraction P Pgis, Placi synonymy threshold Byya-
Ensure: Typed memory graph M and retrieval indices.
Initialize Vgist: Vphrases Eret, Exxx Esyn 0.
2: for d DEDDE do
3: G + E*TRAACCTISSST Pgia)
LLM returns set of gists with text(g) and r(g)
4: for each g E 9 do add g to Viist
5:
6:
F + EXTRACTFACTT((L. Prac)
LLM returns set of (p.. T.Po: T)
7:
Pa 5-
b phrase nodes appearing in this
8:
for each (Pa, E F do
9:
add Ps.Po to Vphrase if new add (Per T. Po: T) to Exel
10:
11:
add P.. Po to Pa for each 9 E G do for each p E Pa do add (g.p) p) to Egix
12:
14:
b bind all gists to all phrases from the same d
15: for all pairs (u, 2) in Vgist do
16:
if SIM(U, v) 2 Osyn then
add (u, v) Esyn
18:
Build embedding/BM225 indices over V and T(-)
return . (V, E) with indices
"Alice -ooghht->> cows" together with their temporal scope derived from these qualifiers. Each context edge (g. P) Eex connects a gist node g to phrase node p extracted from the same source chunk that gave rise to g. thereby linking an abstract episode summary (e.g., "Alice bought cows Jan. 17th.") to its underlying factual triple (Alice, bought, cows) and associated temporal qualifier (c.g., point in time "Jan. 17th"). Each synonymy edge e = (gi+ gj) E Esyn links two gist nodes whose text embeddings exceed a similarity threshold, clustering semantically equivalent or highly related episodes and enriching the graph with associative links.
Index We maintain retrieval indices over this graph: an embedding index over text(g) and name(p), and lexical (BM25) index over the surface forms of both gists and facts.
Algorithm The algorithmic procedures for indexing and agentic inference are shown in Algorithm and respectively.
C DETAILED RESULTS
C.I
LoCoMo
The LoCoMo performance is presented in Table and Table 10 For Oracle Message, while using annotations avoids retrieval challenges, QA remains non-trivial task. Overall, large embedding models serve as strong baselines, and structure-augmenttd memory methods show inferior per- formance. Compared with NV-Embed-v2, HippoRAG achieves only comparable LLM-J scores (+1.0). In contrast, REMem-I outperforms NV-Embed-v2 on both Fl and J scores (F1 +2.8, J +3.2), achieving performance most closely aligned with the oracle messages among evaluated methods. REMem-S further improves the J score by 1.3 and attains the highest J scores on LoCoMo. We find that REMem-S outperforms REMem-I in many single-session settings, suggesting that most queries do not require complex multi-step retrieval, which can instead introduce contextual noise. A notable bias of this benchmark that multi-session queries account for only 14.2% of all queries. Importantly, unlike previous studies, also evaluate performance under adversarial settings, which we regard as essential for evaluating a system's 's refusal capability and mitigating hallucinations. A more detailed analysis of model refusal behavior is provided in $6. Full-Context achieves the
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Algorithm 2 Agenticlnferrncelg.. M, mmxx)
Require: Query q hybrid memory graph M with indices; iteration limit Tmax
1: & +-b 0
> evidence; accumulated gists and facts
2: H. +
interaction history (thoughts, actions, observations) iteration counter
3: & <-0
4: while k < max do
5:
t LLM L.LMPPLA((-- &, H)
D reasoning step
6:
<- LLM LLM.SELLECAACTION))
7:
if a'a.mame annswe then
8:
return LLM LL...SYNTHESZEIQ..C. H)
> final answer based on gathered evidence
9:
else if o.name semantic.retriewe then
10:
(5 < CEEMATTCRETRUEEEEMMMMS.I..
D embedding search; returns seed BM25 search
nodes S and observations O
11:
+ -U; H + H| a,
else n.mam lexical then
13:
(S,0) + LEXICALRETRIEVEL
a.subquery)
+ E 11 a)
15:
else if n.name =
httpllsttcoottttteeeeeeeeee then
O FINNDIGSTCOOOTEESSSMMM a.....a,.esseeeeeee seedss a. tememola constraints)
17:
E EU - a,,
else a. = findeent finddentiitycconteee then
19:
a.subject, a.predicate, a.object, a.temporal.constraiin >> filter by phrras/preedicute/eepprall constraints
O FINDENNITYCONEXIISS a.,,ssseect.t
20:
E H HIl i.
return L.M.SYYNHESSZEIQ....
best results on multi-session questions, which may suggest that this benchmark imposes only lim- ited demands on long-context reasoning ability. Additional results on LoCoMo are also reported in Appendix C.I
By default, use the top-10 chunks on LoCoMo for embedding models. Notably, also evaluate NV-Embed-v2 with only the top-3 chunks (messages) and find that constitutes a strong base- and some structure-
line. Even under this constrained setting, surpasses augmented memory systems, except for HippoRAG 2, which is configured to use the top-3 sessions.
Table 9: Performance (%)
LoCoMo (Part 1/2).
Avg (1,986)
Single-Hop (321)
Multi-Hop (282)
Methods
F1
BLEU-1 LLM-J
F1
BLEU-1
LLM-J
F1
BLEU-1 LLM-J
Oracle Message
48.0
38.3
81.0
36.5
28.2
85.7
41.6
26.9
84.8
Full-Context
37.8
28.6
76.7
29.1
20.6
84.4
34.2
18.8
75.2
Large Embedding Models
Swen3-Embed-88
35.3
28.9
64.2
24.8
18.1
64.2
24.6
15.1
57.1
NV-Embed-x2 (7B)
39.6
31.0
73.0
30.9
22.6
77.9
29.4
17.8
64.9
w/ Top-3 Messages
39.0
32.1
67.2
26.5
20.2
68.2
21.1
13.6
51.4
Structure-Augmentee
Memory
Mem0
25.1
18.0
49.7
10.2
6.3
32.7
29.9
15.9
52.1
Graphiti
33.7
28.9
52.5
6.4
3.4
30.8
25.1
16.1
53.6
HippoRAG 2
39.0
30.8
74.0
25.8
18.0
72.0
30.7
19.5
72.3
Ours
REMem-1 REMem-S
42.4
32.7
76.2
37.9
25.3 2
81.3
32.6
18.7
70.2
41.3
31.5
77.5
35.4
22.6
86.0
31.6
18.9
69.5
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Table 10: Performance (%) on LoCoMo (Part 2/2).
The adversarial number only represents refusal
performance partially (see 66. 2.226 for details).
(841)
Temporal (96)
Adversarial (446)
Methods
F1
BLEU-I
LLM-J
F1
BLEU-1
LLM-J
F1
BLEU-1
LLM-J
Oracle Message
43.8
30.5
86.0
36.7
23.7
59.4
70.6
70.7
70.6
Full-Context
36.0
23.9
88.3
25.6
15.9
54.2
52.2
52.1
55.0
Large Embedding Models
Qwen3-Embed--8
27.9
19.6
67.3
25.
15.3
45.8
65.9
66.0
66.8
NV-Embed-v2 (7B)
36.8
24.6
82.6
26.2
16.2
51.0
60.8
60.8
61.2
w/ Top-3 Messages
33.7
23.3
71.7
23.1
13.1
43.8
72.9
72.9
73.1
Structure-Angmenttr Memory
Mem0
38.2
27.5
57.2
24.1
15.7
46.9
8.3
10.4
46.6
Graphiti
21.9
15.6
45.0
23.0
14.6
45.8
83.2
83.4
83.0
HippoRAG 2
35.5
23.9
81.7
28.8
18.5
56.3
62.6
62.6
65.7
Ours
REMem-I REMem-S
39.1
26.4
83.5
25.5
17.5
56.3
61.7
62.0
66.8
37.3
24.6
85.1
28.2
18.5
63.5
62.1
61.8
65.3
C.2 REALTALK
Overall, REALTALK proves to more challenging than LoCoMo, as evidenced by the consistently lower performance of all methods. Constructed from real human interactions, it naturally contains more noise and casual expressions. The overall performance distribution is similar to that observed LoCoMo. NV-Embed-v2 remains strong baseline enhanced memory methods fall short in comparison. REMem is the only one that surpasses the Full-Context method, partieularly on tasks involving temporal reasoning.
embedding model, while structure-
Table 11: Performance (%) REALTALK.
Overall (728)
Multi-hop (301)
Commonsense (108)
Temporal (319)
Methods
F1
BLEU-I LLM-J
FI
BLEU-I LLM-J FI BLEU-1 LLM-J
FI
BLEU-I LLM-J
Full-Context
25.3
18.6
65.1
27.8
21.7
60.1
19.5
15.1
55.6
24.9
16.8
73.0
Large Embedding Models
Owen3-Embed
20.2
14.9
52.5
19.6
16.2
41.5
14.5
11.8
39.8
22.7
14.8
67.1
NV-Embed-v2 (7B)
23.8
17.7
59.5
24.5
19.4
51.2
16.6
13.0
48.2
25.6
17.7
71.2
Structure-ugmeented Memory
Mem0 Graphiti
9.8
7.2
14.3
14.0
9.6
20.9
12.0
9.0
22.2
5.1
4.3
5.3
11.5
35.3
19.6
15.5
39.5
16.7
13.2
44.4
10.5
7.1
28.2
HippoRAG
21.9
16.2
55.8
26.1
20.9
51.8
18.2
13.7
54.6
19.2
12.6
59.9
Ours
REMem-1 REMem-S
25.6
18.1
63.7
26.6
21.6
16.1
13.0
45.4
27.9
16.5
77.4
19.2
28.6
23.1
58.8 8
14.7
26.7
17.1
C.3 COMPLEX-TR
The performance on Complex-TK is shown Table 12 On Complex-Tr embedding models re- main strong baselines. Among dtructure-augment memory methods, HippoR AG 2 slightly out- performs the embedding model +11), likely due its alignment with the entity-centric nature of this dataset. Compared to REMem, the only competitive method is NV-Embed-v2 w/ TISER. which employs multi-step temporal reasoning prompts and even outperforms REMem-I on the time-to- event task (J +1.4), but falls short average (J 113).. Compared with the event-to-event type, the time-to-veen type requires more direct temporal resolution, which better suits TISER.
REMem primarily focuses information extraction and agentic retrieval. For the reasoning ponent, we employ straightforward prompts (Appendix D) that leverage the context to answer ques- tions. When adopt TISER as the reasoning prompt for the final step, REMem w/ TISER achieves
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further improvements and delivers the highest performance (F1 +7.3,J compared REMem) overall. Additionally, for such complex reasoning tasks, REMem-S lacks multi-hop reasoning ca- pabilities (J -6.0), which demonstrates the necessity of employing agentic retrieval for autonomous reasoning.
Table 12: Performance (%) on Complex- -T.
Avg
Time to Event (543)
Event to Event (457)
Methods
BLEU-1 LLM-J
F1
BLEU-1 LLM-J
F1
BLEU-1
LLM-J
Full-context
74.2
68.0
81.6
73.0
65.6
80.3
75.6
70.8
83.2
Large Embedding Models
Qwen3-Embed-8B
77.1
71.4
80.9
74.5
68.8
79.0
80.2
74.5
83.2
NV-Embed-v2 (7B)
77.5
71.9
80.4
77.0
70.8
79.2
78.1
73.2
81.8
w/ TISER
88.1
83.6
88.3
88.9
83.5
90.4
87.1
83.7
85.8
Structure-Angmennted
Memory
MemO
43.1
35.1
41.0
43.5
35.1
40.9
42.6
35.1
41.1
Graphiti
76.6
71.4
78.8
77.3
71.1
80.3
75.6
71.7
77.0
HippoRAG
78.2
72.7
81.5
78.0
71.4
81.8
78.5
74.4
81.2
Ours
REMem-I 83.3
77.6
89.6
80.3
73.2
89.0
86.9
82.8
90.4
w/ TISER
90.6
86.0
92.0
92.5
87.1
94.3
88.1
83.8
89.3
REMem-S
78.5
72.7
82.6
78.6
71.6
84.2
78.5
74.0
80.7
C.4 TEST TIME (SEMANTIC)
The performance the Test of Time is shown in Table 13 Here, Full-Context directly uses the original prompt from the dataset, which contains hundreds of facts per question on average. Since Test of Time employs anonymous entity and relation labels, we also use BM25 the retriever in addition to the embedding model. The results show that even the improved GPT-5-chat still falls short as a Full-Context method when compared with its predecessor, the economical GPT-4. 1-mini (EM +4.8). HippoRAG heavily relies on graph traversal, and due to its lack of understanding of complex logic, its performance falls short of even embedding models. TISER continues serve as a strong baseline, while REMem is the only method that surpasses 90%, achieving an +8.2 EM improvement over Full-Context with TISER. Beyond Table [3, we further observe that REMem- I, when paired with stronger GPT-5-chat, can address nearly all eight types of challenges in this benchmark (EM 99.0), underscoring the rationality the agentic retrieval design.
C.5 SEMANTIC MEMORY CAPABILITY
Although episodic memory is a crucial aspect of long-term memory and the main focus of this paper, semantic memory should not be overlooked as another aspect. We select MuSiQue and 2Wiki from the benchmarks used in HippoRAG to evaluate this capability. From Table 7, we observe that MemO performs substantially worse than both NV-Embed-v2 and REMem across all metries, with results nearly collapsing. This performance highlights limitations in MemO's ' memory construction process: a major part of the extracted facts not added its memory, and its use of fixed dialog extraction pipeline is poorly aligned with the structure of passages that incorporate world knowledge. Mem0 frequently extracts incomplete or decontextualized sentences which even the subject is ambiguous, e.g.. "Managed La Liga clubs including Barcelona, Atlético Bilbao, Atlético Madrid, and Real Zaragoza" is extracted from a paragraph titled "Ferdinand Daučík", and then this extracted sentence is not added updated the memory. For comparison, though method primarily focuses on episodic memory and reasoning, still achieves performance comparable to strong embedding model on this task, demonstrating its strong extensibility.
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Table 13: Exact match (%) on Test of Time (semantic). GPT-4.I-mini is the default LLM. While Full-Context adopts the full prompt from the original dataset, BM25 and NV-Embed-v2 provide shortened prompts by retrieving the top-k facts. BA: Before-after. ET: Event at time 1. EW: Event what time. FL: Farst-last. EA: Event at the time of another event. NE: Number of events in time interval. RD: Relation duration. TL: Timeline. Each category contains 350 samples. denotes
using 160-sample subset an approximation due the high cost of full-dataset evaluation.
Methods
Avg
BA
FT
EW
FL
EA
NE
RD
TT.
Full-Context (-PT-4.1-mimi
79.7
72.0
86.9
98.0
81.1
83.7
76.3
93.4
46.3
w/ TISER
84.9
88.3
91.4
97.7
89.1
90.6
64.0
94.6
63.4
Full-Context (GPT-5-chat)
84.5
80.0
90.0
99.4
84.9
91.1
90.0
82.3
58.0
BM25
80.8
71.1
77.7
98.6
92.3
64.3
65.1
98.3
79.1
Owen-Embed-8BB
70.3
53.1
88.3
100.0
72.6
42.9
71.7
98.0
35.7
NV-Embed-v2 (7B)
68.9
86.6
100.0
74.6
48.0
75.7
96.9
26.6
w/ TISER
68.9
48.0
86.0
100.0
74.6
51.7
67.4
96.9
26.3
HippoRAG 2*
66.9
60.0
90.0
100.0
75.0
45.0
50.0
100.0
15.0
REMem-1
98.1
97
99.4
100.0
92.0
97.4
71.4
94.0
92.9
w/ TISER REMem-S
90.6
98.6 6
99.4
98.6
95.4
93.7
68.3
85.1
85.4
72.5
74.6
78.0
100.0
90.9
64.3
52.0
99.7
20.9
Table 14: Performance (%)
QA tasks with semantic memory.
Avg
MuSiQue (1, 000)
2Wiki (1, 000)
Methods
F1
BLEU-1
LLM-J
F1
BLEU-1
LLM-J
F1
BLEU-1
LLM-J
NV-Embed-v2
38.2
30.5
57.5
37.5
31.9
56.3
38.8
29.1
58.6 6
Mem0
6.9
5.1
8.0
7.4
5.7
9.2
6.3
4.5
6.8
REMem
38.2
32.1
55.3
37.9
33.5
53.2
38.6
30.8
57.4
C.6 ABLATION STUDY
More detailed results of the ablation study are presented in Table 15 and Table 16 Overall, gists provide the primary context (J -7.33% LoCoMo and 8.77% on Complex-TR), while facts of- fer indispensable supplementary support. In particular, for LoCoMo, REMem-I w/o Facts achieved comparable performance to REMem across most tasks, but showed a clear drop in multi-hop ques- tions 1 7.1%). This highlights the crucial role of phrase nodes in bridging concepts across sessions and facilitating effective graph exploration.
Table 15: Ablation study on LoCoMo.
Avg
Single-Hop
Multi-Hop
Open-Domain
Temporal
Adversarial
Methods
F1
LLM-J
Fl
LLM-J
F1
LLM-J
Fl
LLM-J
F1
LLM-J
F1
LLM-J
REMem-1
42.4 4
76.2
37.9
81.3
32.6
70.2
39.1
83.5
25.5
56.3
61.7
66.8
w/o Gists
31.7
48.9
19.7
69.8
20.5
12.2
12.0
21.9
14.5
28.1
88.3
88.1
w/o Facts
42.0
74.1
45.7
81.9
28.2
63.1
41.7
84.0
25.6
54.2
52.2
61.0
REMem-S
41.3
77.5
35.4
86.0
31.6
69.5
37.3
85.1
28.2
63.5
62.1
65.3
C.7 PERFORMANCE TEMPORAL CATEGORY
We present performance metrics temporal category to provide analysis for temporal and non-temporal questions. We set a few temporal categories, as shown Table and instruct GPT- 4.1 mini classify each query into one of the categories.
Then, we report REMem-l's performance on LoCoMo [8 to the above categories, where 'temporal' is the average value of all temporal categories, and 'overall" is the average value
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Table 16: Ablation study on Complex-TR.
Avg
Time Event (543)
Event to Event (157)
Methods
F1
BLEU-1
LLM-J
F1
BLEU-1
LLM-J
F1
BLEU-1
LLM-J
REMem-I
83.3
77.6
89.6
80.3
73.2
89.0
86.9
82.8
90.4
w/o Gists
80.3
75.9
80.9
73.7
69.1
75.3
88.1
84.0
87.5
w/o Facts
80.5
74.5
87.2
79.3
72.7
86.6
81.9
76.6
88.0
REMem-S
78.5
72.7
82.6
78.6
71.6
84.2
78.5
74.0
80.7
Table 17: Our defined temporal categories LoCoMo.
Category
Count
Description
Query Example
Existence Check
14
Check for the presence of temporal facts, given specific time frame Determine the specific time points intervals when an event occurred Identify attributes or characteristics of event, e.g., location. partici- pants. etc.
Did Andrew have pet dog during March 2023?
Event Timing
298
When did Melanic paint sunrise?
Event Attrihutes
302
What are some changes Caroline has faced during her transition journey?
Order
22 Understand temporal relations tween events, c.g., sequence, con- currency, overlap Determine the duration of an event the time interval between events
What Melanie do after road trip to relax?
Duration
44
How long has Caroline had her cur- group of friends for?
Aggregation
30 Count occurrences within time frame, or count spe- cific attributes of events Other temporal reasoning tasks not covered by above categories
an event
How many times has Melanie gone to the beach in 2023?
Other
Would Caroline want move back home country soon?
Non-temporal
1270 No temporal reasoning is required, but other situational elements
Where did Oliver hide his bone once?
of 'temporal' and "none" categories. These results suggest that the performance of REMem-l on non-temporal questions on par with the temporal ones.
D PROMPTS
This section shows details REMem. The prompts used for gist and fact extraction are illustrated in Figure and Figure The prompts for tool selection, along with the corresponding tool descrip- tions, are presented in Figures The tool selector selects one of the available tools each step based on their descriptions.
E IMPLEMENTATION DETAILS
We replicated using open-source versions of Mem0 (Chhikara et al. 2025) and Graphiti (Rasmussen al. 2025) rather than proprietary ones, aligning settings as closely as feasible, including the back- bone LLM. embedding model, and the scale of contexts. Since we observed Mem0 frequently au- tonomously choosing to reject adding input text to memory, we opted for finer granularity: adding memory at the message level to encourage more information to be incorporated into memory. For Graphiti, we found that frequent LLM and embedding calls incurred excessive time and economic costs, and added memory at the session level. Subsequently. Graphiti indexes information within the session into multiple facts for subsequent retrieval. Thus, both Memo and Graphiti store information the fact/sentence level rather than the session level.
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Table 18: The performance on LoCoMo by temporal categories.
Temporal category
samples
F1
BLEU-1
LLM-J
Overall
1986
42.4
32.7
76.2
Non-temporal
1086
41.8
32.4
75.0
Temporal
900
43.2
33.1
77.7
Existence Check
15
59.5
11.9
86.7
Event Attributes
450
45.9
37.9
76.7
Event Timing
298
39.1
26.0
82.6
Aggregation
35
39.5
33.9
60.0
Duration
50
35.2
26.8
72.0
Order
49
48.4
45.0
71.4
Other
3
42.4
37.8
100.0
Table 19: The running time and memory usage.
REMem-1
Index Time (s)
Inference Time Query (s)
Max Memory Usage (GB)
LoCoMo
3, 604.2
4.3
2.6
Complex-TR
378.7
12.6
1.3
F ANALYSIS
F.1 COMPARATIVE ANALYSIS: REMEM VS. TISER
We compare REMem with NV-Embed-v2 w/ TISER (Bazaga et al. 2025) Complex-TR and show a few examples. NV-Embed-v2 w/ TISER is denoted as TISER in this paragraph. REMem outperformed TISER typically by handling multi-hop temporal reasoning more comprehensively and recovering the full set of required entities. For example, when asked "Where was Nancy I. Ross educated before ASU?", the gold answer was "BC Cancer Research Centre", and ours produced "Virginia Tech" and "BC Cancer Research Centre", which the judge accepted as correct, while TISER only returned Virginia Tech. Conversely, TISER surpassed REMem, usually by pinpointing the exact target within the specified time window, whereas ours produced over-verbose lists or drifted to the wrong temporal hop.
For instance, in the query "Where was Barack Obama educated after State Elementary School Menteng 01?", the gold was "Punahou School". TISER returned exactly that, while REMem stead listed later universities. This issue is most likely due to the ambiguity of the terms "before" and "after", which may introduce uncertainty in determining whether an incquality should include equality when using the provided tools in REMem, and the resulting tool calls do not always align with the intended meaning of the question. However, the precise relationship between "before" and "after" often be more reliably inferred from the retrieved context, since it can be directly inferred within the temporal scope of a sequence of events.
F.2 TIME AND SPACE EFFICIENCY
In the indexing phase. Graphiti is two orders of magnitude less efficient than Memo and REMem. This may be because its processing of each episode involves multiple rounds of calls both the gen- erative L.LM and the embedding model. In the inference phase, the single-step runtime of REMem comparable to Memō and Graphiti, while its multi-step runtime grows linearly with the number of steps.
We further report the runtime and memory usage REMem Table 19 where it reports the mem- ory consumption of REMem itself. If LLM or embedding model service is deployed locally, its main memory and GPU memory usage are counted separately. Experiments were conducted on a server with dual AMD EPYC 7643 48-Core CPUs (96 hardware threads), 4x NVIDIA A100-SXM4- 80GB GPUs, and I TB of system memory. The inference time for each query is affected by many factors, including the number of workers and the response time of the L.M service. The reported
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Table 20: Token usage on LoCoMo (1, 986 queries) using OpenAl bas0k_ encoding. The estimated cost is calculated using the standard GPT-4.I-mint pricing.
Indexing Phase
Inference Phase
Total
Est. Cost (USD)
Method
Input
Output
Input
Output
NV-Embed-v2
N/A
N/A
1.19M
0.17M
1.36M
0.75
REMem-I
0.90M
0.72M
18. 18.10M
0.79M
20.51M
$10.02
REMem-S
0.90M
0.72M
1.83M
0.18M
3.63M
$ 2.53
time excludes any LLM or embedding caching and does not use multithreading. With caching or multithreading enabled, the actual throughput would be higher.
F.3 TOKEN USAGE
The token usage of REMem on LoCoMo is shown in Table 20 REMem-S exhibits token con- sumption nearly equivalent to the embedding baseline during the inference phase, while the token consumption of REMem-I increases with the number of iterations.
F.4 EXAMPLE OF THE EXTRACTION
We present a passage from MuSiQue, along with the extraction results from Memo and REMem, in Table 21 In comparison, Mem0's extraction results exhibit a lack of factual coverage, which may hinder the subsequent comprehension and QA task. The extraction from REMem more comprehensive, especially as the last (14th) fact, even inferring that the season close of RFEF (2002- 2003) is June 2003, MemO stores memories prefixed with "user", assuming the working scenario always conversational messages, yet this is unnecessary for long passages that describe objective knowledge.
Table 21: A MuSiQue passage and its extraction results from Memo and REMem.
Passage: Lionel Messi
After a year at Barcelona's youth academy, La Masia, Messi was finally enrolled in the Royal Spanish Football Federation (RFEF) in February 2002. Now playing all competitions, he befriended his teammates, among whom were Cesc Fabregas and Gerard Piqué. After completing his growth hormone treatment aged 14, Messi became an integral part of the 'Baby Dream Team", Barcelona's 's greatest-ever youth side. During his first full season (2002-03) (002-03), he was top scorer with 36 goals and both the Spanish and Catalan cups. The Copa Catalunya final, a 4-1 victory over Espanyol, became known in club lore as the partido de la máscara, the final of the mask. A week after suffering broken checkbone during league match, Messi was allowed to start the game on the condition that he wear a plastic protector; soon hindered by the mask, he took it off and scored two goals in 10 minutes before his substitution. At the close of the season, he received offer to join Arsenal, his first from a foreign club, but while Fabregas and Piqué soon left for England, he chose remain in Barcelona.
games for the Cadetes A, who won an unprecedented treble of the league
Memo Extraction
User mentioned Lionel Messi's early football career details including his time at Barcelona's youth academy La Masia, enrollment in the Royal Spanish Football Federation in February 2002, friendship with teammates Cese Fábregas and Gerard Piqué, completion of growth hormone treat- ment age 14, being top scorer with 36 goals in 30 games during the 2002-03 season for Cadetes A, winning a treble of league and Spanish and Catalan cups, playing with plastic mask after a broken cheekbone and scoring two goals, and receiving an offer from Arsenal but choosing stay in Barcelona.
REMem Gists
Continued on next page
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Table 21: A MuSiQue passage and its extraction results from Memo and REMem. (Continued)
1. Lionel Messi was enrolled in the Royal Spanish Football Federation (RFEF) in February 2002 after a year at Barcelona's 's youth academy, La Masia. Messi befriended his teammates Cesc Fābregas and Gerard Piqué while playing in all compe- titions.
After completing his growth hormone treatment at age 14, Messi became an integral part of Barcelona's s greatest-ever youth side, the 'Baby Dream Team'.
During his first full season (2002-03), Messi was the top scorer with 36 goals in 30 games for the Cadetes A.
The Cadetes won an unprecedented treble of the league and both the Spanish and Catalan cups during the 2002-03 season.
The Copa Catalunya final, 4-1 victory over Espanyol, became known as the partido la máscara, the final of the mask. A week after suffering a broken cheekbone during a league match, Messi was allowed to start the Copa Catalunya final the condition that he wear a plastic protector.
8. Messi took off the plastic protector and scored two goals in 10 minutes before his substitution the Copa Catalunya final.
9. At the close of the 2002-03 season, Messi received an offer to join Arsenal, his first from a foreign club.
10. Messi chose to remain in Barcelona while Fàbregas and Piqué soon left for England.
REMem Facts
1. (Lionel Messi, was enrolled in, the Royal Spanish Football Federation (RFEF)) [point in time: 2002-02]
(Lionel Messi, befriended, Cesc Fàbregas and Gerard Piqué)
3. (Lionel Messi, became an integral part of, Barcelona's 's greatest-ever youth side, the 'Baby Dream Team') [point in time: 2002-03]
4. (Lionel Messi, was the top scorer with, 36 goals in 30 games for the Cadetes A) [point in time: 2002-03]
5. (Cadetes A, won, unprecedented treble of the league and both the Spanish and Catalan
cups) [point in time: 2002-03]
6. (Copa Catalunya final, was a victory over, Espanyol) [point in time: 2002-03]
(Copa Catalunya final, became known as, the partido de la máscara, the final of the mask)
8. (Lionel Messi, suffered, broken checkbone during league match) [point in time: 2002-03]
9. (Lionel Messi, was allowed to start, the Copa Catalunya final on the condition that he wear a plastic protector) [point in time: 2002-03]
10. (Lionel Messi, took off, the plastic protector) [point in time: 2002-03]
11. (Lionel Messi, scored, two goals in 10 minutes before his substitution in the Copa Catalunya
final) [point in time: 2002-03]
12. (Lionel Messi, received an offer to join, Arsenal, his first from a foreign club) [point in time:
2003-06]
13. (Lionel Messi, chose to remain in, Barcelona)
14. (Cese Fabregas and Gerard Piqué, left for, England) [point in time: 2003-06]
F.5 GRAPH PROPERTY
For the evaluated four benchmarks, 22blee22 the scale of the constructed memory graphs, in- cluding the numbers of phrase and gist nodes, edges, triples, and associated token counts. The statis- ties show that LoCoMo, REALTALK, and Complex-TR induce large, densely connected graphs with many context and synonymy edges. For Test of Time, the graph is much smaller and contains no gist-level annotations, since its statements involve anonymous entities and are already highly for- malized, so we extract only fact-level information.
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Table 22: The graph properties evaluated benchmarks: average values for each graph.
LoCoMo
REALTALK
Complex-TR
Test of Time
of phrase nodes
777.5
974.0
1, 066.0
16.0
# of gist nodes
730.1
889.0
1, 0,09000
# of relation edges
736.2
891.0
0,2.0.0
of context edges
25, 172.4
56, 332.0
190.0
of synonymy edges
1, 0..82.2
606.0
748.0
of triples
763.9
917.0
1, 0,095.0
275.4
of input tokens of phrase-node tokens
15, 965.8
19, 250.0
26, 158.0
4, 109.7
5, 105.8
8,350.0
536.0
32.1
# of gist-node tokens
21, 713.8
30, 106.0
26, 514.0
Node degree of phrase nodes
34.0
59.7
4.1
9.7
Node degree of gist nodes
34.4
63.4
2.0
24

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Published as a conference paper at ICLR 2026
Gist Extraction Instruction & Demonstration
You meticulous information extractor, Your purpose distill personal episodic memories messages structured ISON format.
For the message(s), identify every individual fact, claim. Restate concise, self-contained English sentence.
Input Format
The user will provide current time and the message text. MUST 'current.time' any relative temporal expressions
(e.g. "yesterday", "last week").
#1 Output Format
Your output MUST be single, valid JSON object.
The JSON object must contain one key: "gists"".
value "gists" list strings.
Do not add any explanstions, comments, trailing
**о Rules Gists
**Decompasiton:"* Decompose complex sentences into multiple gists. Each gist should represent single atomic
fact event.
2. *TTimestamp Prefix:** Begin every gist with the message's timestamp in square brackets, '[20 January 2025,
2:28 pm]'
*TTmmporal Resolution."* any temporal reference. fully-resodee resolved absolute date or date range in
parentheses.
*Time Point Example*: '..lasst Thursday (16 January 2025).*
Durration Example": '..last week (12 lanuary 2025 January 2025).'
Coompleteness.** Capture details each participants, actions, objects, quantities, locations, intentions, etc.
5. Infer reasonable details about the above dimensions as many as possible for later retrieval, but do NOT invent new information.
Input 1:
Date: 3:57 pm on January. 2024
Alice: fixed Monday, bought cows Peter Jan
Output 1:
"'json
"gists":
*[20 January 2024, 3:57 pm] Alice fixed the fence last Monday (15 January 2024).".
"(20 January 2024, 3:57 pm] Alice bought cows from Peter on Jan 15th (15 January 2024)."
Input 2:
Date: 2:28 pm on 20 January. 2025
Bob: met with my advisor last Thursday marning and submitted the proposal two days later.
Output 2: "Yjson
"gists":
[220 January 2025, 2:28 pm] Bob met with his advisor last Thursday morning January 2025),",
120 January 2025, 2:28 pm) Bob submitted the proposal two days later (18 January 2025).*
Figure 3: The prompts for gist extraction. The instructions and demonstrations are marked in differ- ent colors.
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Published as a conference paper at ICLR 2026
Fact Extraction Instruction & Demonstration
episodic__ystom """Extract structured facts from personal episodic memory messages.
Return ISON with the unique key 'fctt'' a list where each fact has:
"subject' [str]: entity
"predicatee (str): the perforningeixperieningg the action
relationship, or state
"object' (str): entitylcancept being acted upon
"qualifiers' (dict): format in '%d %6B %6, %6%% %6p preferred for each of the following properties
secord_time (str. required] when message was created
point_in iimeime (str, optional) only
indicate point time when event occurred: used, ignore
'star__ting tiiee and "end_timm
start_time': (str, optional) event start, used to indicate time range
'ndd, time:: (str, optional) event end, used to indicate time range
Use short "event handles" (e.g. "fence fixing") for reusability. Connect events through shared entities.
Rules
Capture factual quantities, temporal references, relationships
Include everything: err the side inclusion
Use text-supported Interpretations, not assumptions, to avoid hallucinations
Leverage additional gists when they are provided
Always include "record_ time' from provided date/time
Return valid JSON only, extra keys comments
...
Date: 3:57 pm on 20 Jan, 2024
Alice: fixed the fence last Sunday, then bought 3 cows from Peter on Jan 15th
Outout: "Jsoo
"facts":
"subject": "Alice",
"predicate": "completed task".
"object": *fence fixing".
"qualifiers:: {
"record_time": Jan 2024. 3:57 pm3 "point_in_ initt_in_time"" "14 Jan 2024"
"subject": "Alice".
"predicate"""puurchssee""" "purchased",
"object": '3 cows".
"qualifiers::
"racord_time": *20 Jan 2024, 3:57 pm", "point_in_time". "15 Jan 2024"
"subject": 'cow purchase".
"predicate": "source",
"object": "Peter".
"quatifiers": {
seccord_time", '20 Jan 2024, 3:57 pm". "paint_in_time" "15 Jan 2024"
Figure 4: The prompts for fact extraction. The instructions and demonstrations marked in dif- ferent colors.
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Published as a conference paper at ICLR 2026
Tool Selection: Instruction & Demonstration
You intelligent agent exploring large knowledge graph parameters given the current contexts.
query. Select the
""Instucttons.**
Understand the contexts for previous and current steps and consider whether need to explore the KG output the answer.
Pay attention observations from previous steps they tell you what was found and how much information was retrieved.
Plan ahead and select one best tool from the available tools and provide parameters for current step.
Tool Strategy:
1. Initial Retrieval: Begin using semantic_retrieee retrievee' texical_retriee to understand the data scherna and gather preliminary context. The goal is to identify the structure of the KG. For complex queries, break them down into simpler sub sub-queries to guide next steps. Prioritize semanttc__ettee retriiee over reexical_etrieeee unless the query specifically targets entity identifiers requires en exact match.
Note: These tools return truncated content. This initial scan for identifying key information (eg. entity IDs, time ranges) required for
detailed search.
2. Focused Exploration: Next, 'find, gist contexts' or 'find "find_entity coc.totttts"" to retrieve spesific, detailed information. Populate the parameters of these tools (e.g, subject, object, Limit, ordering) using the key information discovered in the retrieval step.
'find_entity coaittts Use latter ONLY when query specifically targets entities with a well-defined KG schema you've seen; otherwise, DO NOT try match the exact phrase using
Prioritize find_git gist_cantextts
Answer Generation: Once tool to provide the final result.
gathered information confident in the answer, 'output_answer"
Temporal Reasoning Example:
Find facts start after 1950: start Litt=iim start. wtt.sstttarttteeetett ooprrttor=ggt'
Find facts end before 1960: end 59960' end_ pod_oprrato="'t
facts exist specific point '1955: start time= 1955'; start operatoprat, end. Eime:tmmeeeee "1te=: end_ operator="gel
Find facts
time='1960', end. operator="le' Find facts that overlap with [1950, 1960]: start_time='196 start operator='le', lll, end time='1950,, end. ond_operttor=gge
within [1950, 1960]: 1950=1 part_oppratoo='ge
(note: special case where the start time is larger than the end time)
*Outpput *ormat
Respond with JSON object, e.g..
"reasoning": "Brief explanation
tool parameters for step".
"function": Dovact_ttoo_nan tww.ttooooooaaoooooooooooooo name_from__ist
"parameters": {
"name": "value".
)
For the query 'Find the entity that was the R11 of ES7 immediately after E95 was the R11 of E57.' each step like this:
was of E57', which start time the next event that 'was the R11 of E57 "function": "lexical retrieve", "reticel_retrievee" "parameters": (['query": "E95 the R11 E57')]3)
(""reasoning",
Observation: Retrieved gists and 3 triples via BM25 texical matching, showing gists and triples
Truncated top contents:
E95 appointed the R11 of ES7 January 15, 2020. This position involves managing technical.
operations and strategic planning for entity E57. The to the organization's infrastructure.
(E95, was the R11 of, ES7) [[ d'start_time time": "2020-01-11 15', 01-1",, "end_time": "2022-03-207
by
2020
during time significant improvements were made
Step (("reasoning" "Find the next event that was the R11 E57 after retrieved time t1", "function":
"find_en hond__enity_com contexts". "parameters": (["object": "E57", "relation": the R11 "start_time" [1')
Observation: Found 12 connected gists and connected triples for entity E57 (filtered by time: t1 any), showing 15
gists and 15 triples
Truncated top
IEO, was the R11 of, ES7) [['start time": 2022203-21",, "end_time" time": "2023-06-15"];
EO succeeded E95 as the R11 of E57 on March 21, 2022, immediately after E95's term ended. ED brought new perspectives and strategies the role. JE95, the R11 of. ES7) [[ (fsttartttmme" "2020 15",, "end_time": 20022-33-20']
Step (("reasonin"": "The entity that closest t1 and matches 'was the R11 E57' is EO', "function": coutput.answer"" "parameters": (f'answer*: "EO"!]))
Figure 5: The prompts for tool selection. The instructions and demonstrations marked in differ- ent colors.
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"fypee""funttin"" "function",
"name": "output_ans""" answer".
"description". "When you believe you information, analyze retrieved information corresponding questions meticulously, answer the original query and end the search process.".
"parameters": [
"type"": "object'.
"properties":
"answer': [
"type": "string".
concise, definitive response, devoid of additional elaborations, Can string. integer. format required by the original query."
"dscriitioo":
required": ["answer"]
"type": "function", "functinn",.
"name": "semmantc_retrivvee
"description": "Search the most semantically relevant "gists' 'fctt' (triples] KG using embedding
similarity. It returns the top results from both types at once. Use when you need semantic understanding and conceptusl matching.".
"parameters": {
"type": "object", "properties":
"query": {
"type": "string". "description": "Search query string"
timet [
"type": "string".
"description": "The start of time range to filter facts by their temporal qualifiers'
"end. time":
"type": "string".
"description": "The end of time range to filter facts by their temporal qualifiers"
Sttrrtopperttor:
"type": "string".
"description": "Operstor start time comparison [it, le, ge, gt, eq)."
end aad_ooptttrrt.
"type": "string". "description": "Operator end time comparison (It, le, ge, gt, eq)."
}
"required:: ['query"]
Figure 6: The tool descriptions for oulput answer and semantic retrieve.
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Published as a conference paper at ICLR 2026
"type": "function",
"name": Dexxicat_retreevee
"description": "Search for the most relevant 'gists" and "fatss (triples) in the KG based on BM25 scoring. It returns the top results from both types at once. Use when you need keyword-baad --ased or exact term matching, e.g, identifiers." "parameters":
"type": "object".
"properties"-
"query":
"type": "string".
"description": "Search query string"
h
"start.time time":
"type": "string", "description": "The start of a time range to filter facts by their temporal qualifiers'
}.
"end_ unn__time:: {
"type": "string". "description": "The end of a time range to filter facts by their temporal, qualifiers'
start opprr.oop
"type": 'string". "description:: "Operator for start time comparison (lt. le, ge, gt, eq)."
"nd_ _ddeoprator"= [
"type": "string". "description:: "Operator comparisan (It, ge, gt. eq)."
'required:: ['query"].
"fypee=""unction"
"function".
"name": "ftdd_/iittoooteeeeeee conttetttttttsss"
"description". "Explore related 'gists' (via synonym relationships) connected (triples) specific gist, with optional temporal filters."
"parameters":
"type": "object",
"propertios":
"gist_id":
"type": "number",
"description:: index (not content) gist from step explore, starting from
ittart.tmee!: {
"type": "string". "description": 'The start time range filter facts by their temporal, qualifiers*
"end_time"
"type": "string".
"description": "The end time range to filter facts their temporal qualifiers'
"start.oper" "
"type": "string",
"descriptiont:"Opee "Operator for start time comparison (it, le, eq)."
[ppertoor:
"type": "string".
"description": "Operator for end time comparison (It. le, gt. eq)."
"required": ['gist_id"]
Figure 7: The tool descriptions for Sexical_rettievv and finttggst_eooo cost_ccssttssss.
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"function", "functi""""t.""",
"name": hoad__entttyyccceeeeee
"description:: "This tool finds facts (triples) that match the given criteria. You must providee least of 'subject',
'abject', or "predicate!..
"parameters":
"type": "object",
"properties":
"subject": {
"type": "string". "description:: "The subject of fact, 'E1' in (E1, was born in, E2)"
"object": {
"type": "string". "description": "The object of fact, e.g., 'E2' in (E1, was born in, E2)
"predicate":
"type": "string". "description": "Fitter specific relations by exact name (e.g.. 'was born in')"
start "start.time:: {
"type": "string". "description":
based on their start time, e.g. '1952-01-01. Use with sterraop to control
comparisan"
}.
time::
"type": "string".
'description:: "Filter facts based on their end time, '1957-12-31. Use with end_ end_operator control
comparisan"
start operator": {
"type": 'string".
"description": "Operator for start_time comparison. Options: 'ge' [>=], 'gt' (+), 'te' (s=), Ite' (s). 'oq (=). Default:
end een__opprate ooperrtorr [
"type" "string".
description": "Operator for end_time comparison. Options: [==], (b). "te' (c=), It'' loq' (-). Default
1e
}.
"imit":
"type": "integer", "description": "Restrict the number of returned results, Use with 'ordering' to get the first/last items. Skip
setting this get contexts" }.
ordering": {
"type": "string", "description" "Order the results time. 'asc' for ascending start_time (earliest first), 'desc' descending by end. time (latest first]"
}.
offsett: {
"type": "integer",
"description": "Skip the first N results. Use with 'ordering' and 'Limit=1' to get the Nth item (e.g.. 'the 2nd time')"
}.
aggregation":
"type": "string".
"description:: "Perform an aggregation and return single number. Options: 'count', tcuut__niquc_subbictts cuunt__unqqueobjjctts
requiree": []
Figure 8: The tool descriptions for find entity contexts.
30


Reviews:
[{"review_id": "zDOrhCwSbO", "reviewer": "Reviewer_JsEd", "summary": "the paper presents REMem, a prompt-based approach for instructing LLMs encoding episodic memory as a graph of ``gists'' and facts; indexing is performed offline and is queried at inference with either a single-step or iterative procedure. (This decoupling raise serious concenrs on the usefulness of the approach)\n\nauthor compare on 4 datasets against a set of alternatives (essentially, \nMem0, Graphiti and HippoRAG) and naive full-context/oracle baselines.\n\nanalysis is quite detailed, with analysis of errors and comparison of 100 samples to LLM as a judge answers, though it lacks statistically relevant depth.\n\nsummarizing, the paper tackles an important problem. it offers a relatively simple  solution (some doubts remain concerning practical relevance), and performs some experimental evaluation, with good  (deep dive, error analysis, human comparison)  as well as bad (lack f statitical rigour) aspects -- overall, the paper is therefore in a borderline position", "questions": "the paper tackles an important problem. it offers a relatively simple  solution, and performs some experimental evaluation. \n\n\n## an apparent oximoron?\n\ntwo main comments \n\n- on the one hand,  the paper claims the contribution to be to \"directly instruct the LLM\" to create memories. I am not sure this is a real benefit, as at the end of the day, this is created through a prompt -- whereas an algorithm could be analyzed under some hypotesis on the distribution/properties graph of gists/facts, a prompt-algorithm through an LLM can only be observed through its output.\n\n- on the other hand, the paper claims this indexing is still an offline phase, which created in my view an oximoron: if there is an advantage of having the LLM creating memories through a prompt, then the advantage is that this is part of its instruction set. if however, such indexing need to be done in batch over time, then (1) the advantage disappears (2) it is far less clear how these episodic memories are created, as the whole content has to enter context for Gist extraction + Fact extraction + Memory graph construction\n\nif that's the case, then the whole construct seems a bit too artificial, as the system cannot be used to dynamically and naturally construct episodic memories (as it is presented), but can just be evaluated as an adademic toy \n\n\n## lightweight contribution \n\nirrespectively of the above conudrum, the paper contribution remains quite lightweight, as it essentiually leverages such offline-prompt-built-graph through an iterative (or signle shot, which in most cases work fine) retrieval procedure (akin to a walk into the offline-built graph)\n\nas details of the contributions are lacking, and anyway not completely new as per authors own admiossion (e.g.,  graph is conflated with synonymous edges whose similarity increase above a threshold as in HyppoRAG), the whole construct remains quite heuristic\n\nadditionally, no analysis of the basic properties of the  constructed graph (connectivity, betweenness centrality, diameter, degree, anything) is addressed (let alone mentionoing an analysis of how such graph would differ from similar grapical abstraction of  graphRAG hyppoRAG). \n\nwith such graph information, one could study properties of the random walk (from initial random seeds) as intermediate step before reporting results of the downstream task.\n\nlacking the above, the methodological contribution is akin to \"we have engineered an heuristic\"\n\n## lack of statistical rigour in the analysis\n\ntables report numbers that are often very close and would need a statistically relevant analysis to tell if they are apart.  \n\nfor instance, my gut feeling is that Rem-I and Rem-S are statistically equivalent, i.e., a single shot suffice for most cases as the margin is too thin. this may of course depend on the hardness of the task, as intuitively a one-shot retrieval cannot be sufficient for more involved retrieval; howerver, part of the lack of advantage on Rem-I may be rooted in a poorly designed inference/retrieval strategies (as the gap from oracle is wider and so possibly statistically relevant).\n\nthe lack of statistical rigor leaves these questions unaddressed  \n\n## doubtful numbers reported, with lower than publicly reported baselines\n\n Mem0 reports significantly higher than 25.1 F1 score reported in Tab 3.\n\n Mem0 reports significantly higher than 25.1 F1 score reported in Tab 3.\n\n\t\tMethod Single Hop Multi-Hop Open Domain Temporal\n\t\tMem0 \t38.72\t 28.64 \t 47.65 \t 48.93\n\t\tMem0g \t38.09\t 24.32 \t 49.27\t 40.28 \n\nhttps://arxiv.org/pdf/2504.19413", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- balanced comparison with 4 datasets and 3 non-naive baselines\n- sufficiently detailed analysis of results", "weaknesses": "(check details in the question section)\n\n- an apparent oximoron?\n- lightweigth contribution (with apparent oximoron?) \n- lack of statistical rigour in the analysis\n- doubtful numbers reported, with lower than publicly reported baselines", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "RkHEhIsyyh", "reviewer": "Reviewer_1gLe", "summary": "The authors introduce a framework named REMem for indexing and reasoning with episodic memory. A two step approach is proposed: first, an *offline indexing* convert experiences to a time-aware memory graph, then an agentic retriever (in a single step or in iterative steps) performs the *online inference*.\n\nIn the offline phase, a memory graph is created (from the \"event statements\") containing both: *gists*, that are event summaries with timestamp, and *facts*, that (subject, relation, object) triplets with additional time information (date or range). Example of gist \"Alice bought 3 cows from Peter on Jan 17th\" with the associated timestamp (17 Jan, 2024). Example of fact:  (subject Alice, relation purchased, object 3 cows)\n\nIn the online phase, the graph enables logic composition (e.g., time range filtering, neighbor exploration). An agent retriever uses retrieval, graph exploration and flow control tools to extract the answer of the question.\n\nThe framework is evaluated on 4 benchmarks including LoCoMo and Test of time, and compared against Mem0 and HippoRAG2 among others, on 2 tasks (recollection and reasoning). The experiments show that the methodology outperforms Mem0 and HippoRAG2 for both tasks, especially for refusal behavior and is on-par compared to full context performance.", "questions": "My current grading for the contributions is \"fair\". I may reassess this aspect after discussing the limitations of the experimental settings, that are currently not discussed by the authors. In particular, the limitations of the work are only discussed w.r.t. the current experiment (like error analysis). But the main limitations regarding \"more complex environments\" (l. 486) are never explained:\n\n- the chunking is simplistic (well segmented short chat sessions): the chunking of each event (event statement, chat session) is given by the benchmark, but in realistic scenario it might not be the case: the absolute date may not be located in the current chunk or linked to the current date (it may be implicit or not available); same for location, or other entities. Relative temporal expressions may not be resolved locally (while in the paper, the model seems to be forced to give an absolute date).\n\nQ. what are the assumptions needed on the input data (\"event statement\") for ensuring the correctness of the methodology? How the \"event statement\" have been obtained/defined?\n\nQ. the entities e.g. \"Alice\" can be recurring. How this is handled? Is there a risk of confusion for large graphs? Can the model grounds two different \"Alice\"? How to resolve conflicts? Another example can be for referring to a book and a movie sharing the same name but with slight adaptation.\n \n- in-context setting gives the same performance.\n\nQ. If I understood correctly, all the documents are integrated in a single large graph (that might be composed of multiple non connected subgraphs). Can you confirm (and highlight) that for building the graph, you consider one for the whole corpus of experiences (instead of one separated graph per chat session)?\n\nQ. Can you describe the graph obtained? How many entities, how many links? Is it mainly a single connected graph (recurring entities), or an union of many disconnected graphs? Is it related to Table 2 (present but not referenced in the paper)?\n\nQ. l.262: I don't get: \"Due to its limited context window\", what is it? If you are able to perform the experiment, why the context window is a limiting factor?\n\nQ. Can you show a table with the time taken for building the offline graph as a function of the dataset size, and the time/cost for inference?\n\n\n## Clarification questions and minor comments:\n\nQ. Can you show a more complex facts graph in the appendix? The Fig. 2 represents two (s, r, o) relations. Is it possible for an object (like \"3 cows\") to be the subject in a following session? Is it possible to retrieve information from the object instead of the subject?\n\nQ. l.200 what is \"phrase node\" there?\n\nQ. What is the impact of the synonymy edges? (there is no ablation with and without it)? Is there a risk of confusion between events when the synonymy edges parameter increases?\n\nI agree that on this experiment, the results are better overall, from the table, compared to Mem0 and HippoRAG2 for recollection.\n\nQ. Does TISER or -I strategies applicable for HippoRAG2, for which performance seems comparable in Tab. 4 (against REMem-S)", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Grounding the events in time by design (compared to e.g., Mem0)\n- Better performance over HippoRAG2 and Mem0 for most experiments\n- Error analysis explanation in Sec. 6.4 regarding the remaining gap of performance in this experiment\n- Correct evaluation metrics", "weaknesses": "- Limitations have not been highlighted\n- No conflict detection and delete/update, compared to Mem0. This is not discussed at all in the paper\n- Experimental settings can be discussed, in particular the chunking / short size of each session\n- Modeling of events with grounding in time, but other aspects like spatial location are not systematically modeled\n- No data/code provided at submission time", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FvihTQp5d5", "reviewer": "Reviewer_f9y5", "summary": "This paper proposes REMem, a two-phase episodic memory system for language agents: (1) offline indexing that converts experiences into a hybrid memory graph containing time-aware gists and structured facts, and (2) online agentic inference using carefully curated retrieval and graph exploration tools. The authors evaluate on four benchmarks spanning conversational QA (LoCoMo, RealTalk) and temporal reasoning (Complex-TR, Test of Time), showing 3.4% and 13.4% absolute improvements on episodic recollection and reasoning tasks respectively over three SoTA systems (Mem0, Graphiti, and HippoRAG 2).", "questions": "- A plethora of approaches mix knowledge graphs and RAG/memory retrieval in LLMs, including commercial products (graphrag, entigraph by memory but keyword \"Graph LLM\" \"Graph LLM memory|rag\" yieds much more), including temporal reasoning. Only a tiny subset is mentioned in the related work. Why were these methods omitted?  \n\n- How robust is extraction to real-world noise? What percentage of extractions are incorrect or incomplete? How does performance degrade with noisier inputs? Can you provide extraction accuracy metrics on held-out data? How does the extraction perform on regular text outside the 4 datasets you tested?\n\n- perhaps for the discussion section: LLMs succeeded where decades of knowledge graph methods failed, largely because their learned representations are more flexible than rigid symbolic structures. Why should we return to explicit graph extraction? The paper doesn't engage with this question. Is the implicit claim that episodic memory specifically benefits from explicit temporal structure?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 2, "strengths": "- Presentation: Very well motivated and very well written paper (clear problem framing, separating episodic recollection from episodic reasoning and evaluates both). Figures are also nice and helpful.\n\n- Good execution: solid engineering, reasonable baselines, ablations that show both gists and facts matter, and an efficiency comparison. Refusal analysis is an added bonus.", "weaknesses": "- (main concern) The paper evaluates on relatively controlled settings where proper knowledge extraction is assumed to work. The most critical and challenging aspect of the work (how robustly the gist/fact extraction generalizes to noisy, ambiguous, real-world text) receives minimal treatment in the paper. Once you have clean, well-structured graphs, the superior performance on episodic reasoning tasks becomes somewhat predictable rather than surprising. Unfortunately, the paper spent most of its efforts narrating a success story on this predictable part. As said earlier, the ablation studies are helpful but don't address extraction robustness. \n\n- The graph+LLM combination is now ubiquitous in LLM research. Numerous commercial and academic systems combine knowledge graphs with RAG/memory, including commercial products, and including for temporal reasoning. What makes REMem's specific formulation necessary or superior isn't sufficiently differentiated from this crowded landscape, leading to a feeling of : yet another LLM + graph paper.\n\n- The paper rightfully motivates the need for episodic reasoning, and uses four datasets. However, although they do have episodic characteristics, none of these datasets was originally designed for episodic memory. Other recent benchmarks could strengthen claims about the method's episodic capabilities and generalization. e.g. (keyword \"episodic memory benchmark\" yields:\nhttps://arxiv.org/abs/2501.13121\nhttps://huggingface.co/papers/2410.08133 )\n\n- The code is not available yet (written: upon acceptance)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "6DKWiXwvmH", "reviewer": "Reviewer_oAJd", "summary": "This paper examines the memory capabilities of language agents, focusing on the challenges of episodic recollection and reasoning. To tackle these issues, the authors propose REMem, a two-phase framework for memory construction and utilization: (1) offline indexing, and (2) online inference. The offline phase converts experienced events into a hybrid memory structure consisting of (a) gists — concise representations that capture the key contextual details of each episode, and (b) facts — structured triples in the form of (subject, predicate, object). During online inference, the system retrieves and composes relevant information from the memory graph to support reasoning. REMem achieves significant improvements across various episodic recollection and reasoning benchmarks, demonstrating the effectiveness of explicitly modeling when and how events occurred. Additionally, the method enhances refusal behavior by reducing hallucinations when the necessary information is missing.", "questions": "- **Q1.** How are synonymy edges between gist nodes utilized in retrieval or reasoning? Do they improve multi-hop recall or only expand local connectivity?\n- **Q2.** In the retrieval tools, are semantic_retrieve and lexical_retrieve applied to both gist and fact entities? If so, does this imply that gists function at the sentence level, while facts operate at the keyword/entity level?\n- **Q3.** During graph exploration, once a relevant node is identified, are both its associated gists and facts incorporated into the reasoning workflow?\n- **Q4.** Fact extraction employs a schemaless approach for predicate construction. How does the system handle inconsistency in predicate expressions (e.g., synonymy, phrasing differences)?\n- **Q5.** When gists or facts extracted by the LLM contain errors, how does REMem detect these issues, and what strategies could be used for correction or quality improvement?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **S1.** REMem provides a well-motivated and well-structured approach to episodic memory, explicitly incorporating temporal and contextual information that prior non-parametric memory systems typically lack.\n- **S2.** The hybrid structure of gists and facts, the method for constructing this structure, and the use of multiple retrieval tools to enable multi-step reasoning together represent a novel contribution.\n- **S3.** The paper evaluates the model on both episodic recollection and episodic reasoning tasks, demonstrating significant performance improvements. Additionally, the authors conduct a comprehensive analysis, including refusal behavior, error analysis, and human evaluation, which provides deeper insight into the strengths and limitations of the method.", "weaknesses": "- **W1.** Since gist and fact extraction rely heavily on LLMs, errors introduced during this process may propagate and negatively impact the entire REMem framework. In particular, gist extraction is an abstractive procedure that can be prone to hallucination, making error detection increasingly important as the memory store expands.\n- **W2.** The distinction from prior KG-based memory systems (e.g., HippoRAG’s offline indexing and online retrieval pipeline) is not clearly articulated. While the incorporation of temporal modeling is highlighted as a contribution, further elaboration would help readers better understand the core novelty and value of the proposed approach.\n- **W3.** The paper would benefit from more detailed descriptions and concrete examples of how its core components — such as retrieval tools and graph traversal — operate together during inference. Additional clarification could significantly enhance the reader’s understanding of the system’s practical workflow (see Q1–Q3).\n- **W4.** (Minor formatting issue) In lines 211–215, the numbering format repeats “1)” twice. Using alternative markers such as “(a)” and “(b)” would improve readability.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rbDcZ7R3P6", "reviewer": "Reviewer_7Ujh", "summary": "The paper proposes REMem, a memory + reasoning framework for LLM agents. Offline, it converts past interactions into a “hybrid memory graph” containing (i) timestamped natural-language event summaries (“gists”) and (ii) structured temporal fact triples (subject, relation, object + validity interval). Online, a controller LLM acts as an agent: it issues tool calls to retrieve and traverse this graph (semantic retrieval, temporal filtering, entity-centric queries), iteratively gathers evidence, and then answers. The system targets both episodic recollection (“what happened when X visited?”) and episodic reasoning (“who lived there first?”, “how long did that last?”). REMem is evaluated on four benchmarks (LoCoMo, REALTALK, Complex-TR, Test of Time) and outperforms strong RAG-style baselines, structured memory systems (Mem0, Graphiti, HippoRAG 2), and even full-context prompting. On temporal reasoning, REMem-I is the only reported method to surpass 90% exact match on Test of Time, and it does so with lower average token cost than feeding the entire history to the model. The system also shows better-calibrated refusals on unanswerable questions.", "questions": "* Can you provide a compact formal schema: node/edge types; where timestamps/intervals live; how contradiction and multi-interval states are represented?\n* Why are both semantic (embedding) and lexical (BM25) retrievers needed in Stage-1 recall, and how are they combined? What happens if one is removed?\n* Can you include one full ReAct trajectory on a representative complex query (tool calls, parameters, intermediate results, final answer)?\n* Could you add a small slice evaluating non-temporal questions to demonstrate breadth beyond time-centric tasks?\n* Please report storage and runtime characteristics—e.g., growth of gists/phrase nodes/edges with history length, index sizes, average node degree, effect of synonymy edges on sparsity/density, average tool-calls per query, and latency curves.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. Conceptual clarity of task focus. The paper explicitly distinguishes two abilities—episodic recollection vs. episodic reasoning—and builds the system to address both. This sharpens what “memory for agents” should mean.\n2. Consolidation-style memory construction. While your paper targets episodic memory, I would argue that what your methodology does is more akin to systems consolidation in the neuroscientific sense. Systems consolidation in the brain entails that raw episodic memories are gradually distilled is distilled into schematic structures, which can be recalled in a compositional manner that may or may not include temporal details. This is a compelling stance that links agent memory to well-studied ideas in neuroscience. I think the paper would benefit from explicitly linking to this - I have left it as a strength as I believe it is one of the more interesting ways to interpret your work.\n3. Agentic temporal reasoning. The agent can iteratively call tools that (a) pull semantically relevant gists and (b) query the graph with temporal/entity constraints (“who occupied Apt B before Casey moved in?”), enabling before/after, duration, and ordering queries that vanilla RAG typically fails at. This also ties in to compositional retrieval as seen in the brain.\n4. Empirical results. REMem variants consistently outperform baselines on four datasets, including challenging temporal QA. The gains over “full context” are large, suggesting real reasoning benefits, not just better recall. The approach is also more token-efficient and shows more calibrated abstention (“I can’t answer”) than prior memory systems.\n5. Ablations and human eval. The paper ablates removal of gists vs. fact triples and shows both are important, especially for multi-hop temporal reasoning. It also shows that its LLM-as-a-judge metric correlates better with human judgment than F1/BLEU, which strengthens trust in the reported numbers.", "weaknesses": "1. Connection to memory consolidation. As mentioned earlier, I would argue that your methodology specifically targets memory consolidation - reframing from this perspective would give a stronger theoretical connection to neuroscience.\n\n2. Lack or discussion on parametric memory stores. In the introduction you mention that “parametric memory, embedded in modelweights during pre-training or fine-tuning, lacks adaptability and contextual grounding in specific experiences”. A whole breadth of literature covers parametric episodic memory stores that exist outside of the model weights. Some of these methods focus on restructuring internal model states (see [1]), others focus on event compression into a fast weight matrix [2]. I would argue that such methods are relevant and should be commented on/compared to as baselines.\n\n3. Lack of mathematical/formal specification. The methodology reads descriptively but lacks a clear formalism. There is no precise definition of the memory graph schema (node/edge sets, attribute spaces, temporal operators), the retrieval operators as functions over that schema, or the agent loop as a constrained decision process. Even a lightweight formalization (typed multigraph with timestamped edge attributes and well-defined filter/composition operators) would greatly improve clarity and reproducibility.\n\n4. Graph construction is under-specified. Even with the figure, it remains unclear:\n    * whether gist nodes and phrase/concept nodes live in a single unified graph or in two stores queried separately;\n    * where timestamps/intervals reside (on gist nodes, on relation edges, or on event-instance nodes) and how overlaps/validity are represented;\n    * how entity canonicalization across mentions works (e.g., “I”→“Alice”; handling other arguments like “Peter”);\n    * how synonymy links between gists interact with temporal distinctness (risk of over-merging similar but distinct events). Since retrieval operates over this graph, a precise schema is important.\n\n5. Redundant first-stage retrieval functions. During initial recall the system exposes both a semantic retriever (embeddings) and a lexical retriever (BM25). Both serve the same purpose, yet the paper doesn’t justify why both are needed, how they’re combined, or quantify their individual contributions. If REMem benefits from simply having two parallel high-recall channels, that should be isolated and reported.\n\n6. No end-to-end example trajectories. Given the centrality of a ReAct-style loop, the paper should print at least one full trace on a hard temporal query (sequence of tool calls, parameters, intermediate results, final answer). Without this, it’s hard to see how multi-step reasoning actually unfolds.\n\n7. Breadth of evaluation. Most evaluation focuses on temporally framed questions (before/after, first/last, duration). It’s less clear how well REMem handles non-temporal personalization or causal/relational queries where “when” isn’t central, despite claims that gists capture richer situational context.\n\n8. Memory complexity and system cost. The paper does not analyse space/time complexity of the memory store or retrieval: expected growth of gist nodes, phrase nodes, and relational edges with conversation length; frequency/impact of synonymy links; and the asymptotic/empirical cost of Stage-1 retrieval and graph exploration as histories reach hundreds of thousands or millions of tokens. A brief complexity discussion (e.g., storage per event, index sizes, average degree) plus empirical reporting (graph size, tool-call count, latency) would strengthen deployability claims. \n\n[1] Z. Fountas, M. A. Benfeghoul, A. Oomerjee, F. Christopoulou, G. Lampouras, H. Bou-Ammar, and J. Wang, “Human-inspired Episodic Memory for Infinite Context LLMs,” arXiv:2407.09450 [cs.AI], 2025.\n\n[2] M. Zhang, S. Arora, R. Chalamala, A. Wu, B. Spector, A. Singhal, K. Ramesh, and C. Ré, “LoLCATs: On Low-Rank Linearizing of Large Language Models,” arXiv:2410.10254 [cs.LG], 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)