arxiv: 2604.14158 · v1 · submitted 2026-03-23 · 💻 cs.CL · cs.AI

Recognition: 2 theorem links

· Lean Theorem

MemGround: Long-Term Memory Evaluation Kit for Large Language Models in Gamified Scenarios

Yihang Ding , Wanke Xia , Yiting Zhao , Jinbo Su , Jialiang Yang , Zhengbo Zhang , Ke Wang , Wenming Yang

Authors on Pith no claims yet

Pith reviewed 2026-05-15 01:25 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords long-term memorylarge language modelsbenchmarkgamified scenariosmemory evaluationdynamic trackinginteractive tasksmemory agents

0 comments

The pith

State-of-the-art LLMs and memory agents still struggle with sustained dynamic tracking, temporal event association, and complex reasoning from long-term accumulated evidence in interactive environments.

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

The paper argues that existing evaluations of long-term memory in large language models rely on static retrieval and short-context tasks that overlook dynamic state changes and hierarchical reasoning during ongoing interactions. It introduces MemGround as a benchmark built on rich gamified scenarios that apply a three-tier framework to test Surface State Memory, Temporal Associative Memory, and Reasoning-Based Memory. Specialized metrics track overall question-answering accuracy, unlocked memory fragments, correctly ordered fragments, and exploration paths. Experiments on current top models and agents show persistent failures at maintaining coherent memory across extended interactive sessions. A reader would care because reliable long-term memory is required for any AI system meant to handle multi-turn tasks or real-world continuity.

Core claim

MemGround establishes a long-term memory evaluation kit grounded in gamified interactive scenarios. It deploys a three-tier hierarchical framework that separately measures Surface State Memory for basic state recall, Temporal Associative Memory for linking events across time, and Reasoning-Based Memory for drawing inferences from accumulated evidence. Performance is quantified through a multi-dimensional suite of Question-Answer Score, Memory Fragments Unlocked, Memory Fragments with Correct Order, and Exploration Trajectory Diagrams. Experiments demonstrate that state-of-the-art LLMs and memory agents continue to fail at sustained dynamic tracking, temporal event association, and complex ev

What carries the argument

The three-tier hierarchical framework that evaluates Surface State Memory, Temporal Associative Memory, and Reasoning-Based Memory through specialized interactive tasks in gamified scenarios.

If this is right

LLMs require stronger mechanisms for tracking changing states across continuous interactions.
Memory agents need improved methods to associate events in correct temporal order.
Complex reasoning over long-term accumulated evidence remains unsolved in current systems.
Static benchmarks miss critical failure modes that appear only in interactive settings.
Development of new memory architectures should be guided by performance on dynamic gamified tasks.

Where Pith is reading between the lines

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

Real-world agents for extended conversations or planning may fail without addressing the gaps shown here.
The metric suite could be adapted to evaluate memory in non-LLM systems such as robotic controllers.
If models improve on these tasks, it would indicate progress toward coherent long-horizon behavior.
The gamified approach suggests similar interactive tests could expose memory limits in other domains like code maintenance or scientific reasoning.

Load-bearing premise

The three-tier hierarchical framework and gamified scenarios provide a comprehensive and accurate assessment of long-term memory capabilities in LLMs.

What would settle it

A model achieving consistently high scores on all three memory tiers together with accurate dynamic tracking and reasoning across multiple extended game sessions would contradict the reported struggles.

Figures

Figures reproduced from arXiv: 2604.14158 by Jialiang Yang, Jinbo Su, Ke Wang, Wanke Xia, Wenming Yang, Yihang Ding, Yiting Zhao, Zhengbo Zhang.

**Figure 1.** Figure 1: Overview of MemGround. MemGround includes three-tier hierachical memory evaluation framework, consisting of Surface State Memory, Temporal Associative Memory, and Reasoning-Based Memory. Each is evaluated in a particular gamified scenario. acters (Wang et al., 2025a), and complex problemsolving environments (Ma et al., 2025), the demands on their memory capabilities have evolved significantly. Therefor… view at source ↗

**Figure 2.** Figure 2: Overview of Game Ground. Game Ground consists of two components, data collection pipeline as well as model evaluation pipeline. in-context baselines and agents equipped with external memory. These tools operate over a persistent store of interaction histories and environment observations collected during gameplay. Each tool exposes a simple inputoutput interface that enables models or agent frameworks to… view at source ↗

**Figure 3.** Figure 3: The progress chart of GPT-5.2 and its memory agents in No Case Should Remain Unsolved. The [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: The progress chart of GPT-5.2 and its memory agents in Type Help. The blue and yellow points stand [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Example ETD of both human and evaluated models in a selected subsection of Type Help. 4.6 Comparison of Exploration Trajectory Diagrams (ETD) [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: The progress chart of all evaluated models and memory agents in No Case Should Remain Unsolved. [PITH_FULL_IMAGE:figures/full_fig_p027_6.png] view at source ↗

**Figure 7.** Figure 7: The progress chart of all evaluated models and memory agents in Type Help. The blue and yellow points [PITH_FULL_IMAGE:figures/full_fig_p028_7.png] view at source ↗

read the original abstract

Current evaluations of long-term memory in LLMs are fundamentally static. By fixating on simple retrieval and short-context inference, they neglect the multifaceted nature of complex memory systems, such as dynamic state tracking and hierarchical reasoning in continuous interactions. To overcome these limitations, we propose MemGround, a rigorous long-term memory benchmark natively grounded in rich, gamified interactive scenarios. To systematically assess these capabilities, MemGround introduces a three-tier hierarchical framework that evaluates Surface State Memory, Temporal Associative Memory, and Reasoning-Based Memory through specialized interactive tasks. Furthermore, to comprehensively quantify both memory utilization and behavioral trajectories, we propose a multi-dimensional metric suite comprising Question-Answer Score (QA Overall), Memory Fragments Unlocked (MFU), Memory Fragments with Correct Order (MFCO), and Exploration Trajectory Diagrams (ETD). Extensive experiments reveal that state-of-the-art LLMs and memory agents still struggle with sustained dynamic tracking, temporal event association, and complex reasoning derived from long-term accumulated evidence in interactive environments.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note private letter to a colleague

MemGround proposes a gamified three-tier benchmark for LLM long-term memory but the results do not yet isolate memory failures from other task demands.

read the letter

MemGround proposes a gamified three-tier benchmark for LLM long-term memory but the results do not yet isolate memory failures from other task demands. The main new pieces are the Surface State Memory, Temporal Associative Memory, and Reasoning-Based Memory levels, plus the metrics for memory fragments unlocked, fragments in correct order, and exploration trajectories. These move the evaluation away from static retrieval questions toward ongoing interactive scenarios, which is a reasonable direction given how real use cases work. The paper does a clean job explaining why existing tests fall short on dynamic tracking and accumulated evidence. That framing is useful and direct. The soft spots sit in the experimental support. The abstract states that current models struggle with sustained tracking, temporal association, and reasoning from long-term evidence, yet there are no ablations shown, no full-context controls, and no human baselines to confirm that the failures are memory-specific rather than tied to game complexity or planning load. Without those checks it is difficult to know whether the benchmark actually measures what it claims. This paper is aimed at researchers building memory mechanisms for agents or long-horizon conversational systems. Someone looking for benchmark ideas could pull useful structure from the tiers and metrics, but anyone wanting to treat the performance claims as established findings would need more data first. It deserves peer review so referees can ask for the missing controls and see whether the framework holds up once the results are fully reported.

Referee Report

2 major / 2 minor

Summary. The paper introduces MemGround, a benchmark for evaluating long-term memory in LLMs using gamified interactive scenarios. It defines a three-tier hierarchical framework (Surface State Memory, Temporal Associative Memory, Reasoning-Based Memory) evaluated via specialized tasks, along with a metric suite (QA Overall, MFU, MFCO, ETD), and reports that state-of-the-art LLMs and memory agents struggle with sustained dynamic tracking, temporal event association, and complex reasoning from accumulated long-term evidence.

Significance. If the benchmark tasks and metrics are shown to isolate long-term memory demands, MemGround could provide a valuable dynamic evaluation framework that addresses limitations of static retrieval benchmarks, enabling more targeted assessment of memory utilization and behavioral trajectories in interactive settings.

major comments (2)

[Experiments] The experimental section does not include ablations (e.g., full-context controls or memory-erasure variants) or human baselines to establish that observed performance deficits are attributable to long-term memory limitations rather than short-term inference, planning, or language comprehension; this is load-bearing for the central claim that models 'struggle specifically with sustained dynamic tracking, temporal event association, and complex reasoning derived from long-term accumulated evidence.'
[Evaluation Framework] The three-tier framework is presented as isolating distinct memory capabilities, but no validation (e.g., task difficulty controls or correlation analysis with memory-specific vs. general-reasoning metrics) is provided to confirm the tiers are not confounded by task complexity or recency cues; this directly affects the interpretability of the reported struggles.

minor comments (2)

[Abstract] The abstract references 'extensive experiments' and specific quantitative struggles but provides no numerical results, error bars, or sample sizes; these details should be summarized for readers.
[Metrics] Notation for the proposed metrics (QA Overall, MFU, MFCO, ETD) is introduced without explicit formulas or computation details in the main text; add a dedicated subsection or table for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments highlight important aspects for strengthening the claims regarding long-term memory isolation. We provide point-by-point responses below, indicating revisions where we can incorporate the suggestions.

read point-by-point responses

Referee: [Experiments] The experimental section does not include ablations (e.g., full-context controls or memory-erasure variants) or human baselines to establish that observed performance deficits are attributable to long-term memory limitations rather than short-term inference, planning, or language comprehension; this is load-bearing for the central claim that models 'struggle specifically with sustained dynamic tracking, temporal event association, and complex reasoning derived from long-term accumulated evidence.'

Authors: We agree that ablations are essential to isolate long-term memory effects from short-term inference or planning. In the revised manuscript, we have added full-context controls (providing the entire history in the prompt) and memory-erasure variants (resetting memory modules at intervals). These show clear performance drops attributable to memory demands, bolstering the central claim. Comprehensive human baselines for extended interactive sessions are resource-intensive and not fully feasible within this study; we have added a limitations discussion referencing cognitive literature on human long-term memory performance in similar scenarios as a partial response. revision: partial
Referee: [Evaluation Framework] The three-tier framework is presented as isolating distinct memory capabilities, but no validation (e.g., task difficulty controls or correlation analysis with memory-specific vs. general-reasoning metrics) is provided to confirm the tiers are not confounded by task complexity or recency cues; this directly affects the interpretability of the reported struggles.

Authors: We acknowledge the need for explicit validation of the tier distinctions. The revised manuscript now includes task difficulty controls via expert-rated complexity normalization across tiers and correlation analyses with general reasoning benchmarks (e.g., MMLU, GSM8K) as well as memory-specific probes. Results indicate low correlation with general reasoning and robustness to recency cues (via event-order randomization controls), supporting that the tiers isolate distinct capabilities. These additions are detailed in the updated Section 3. revision: yes

Circularity Check

0 steps flagged

No circularity: new benchmark and metrics defined independently without reduction to inputs or self-citations.

full rationale

The paper introduces MemGround as a new benchmark with a three-tier framework (Surface State Memory, Temporal Associative Memory, Reasoning-Based Memory) and metrics (QA Overall, MFU, MFCO, ETD) for gamified scenarios. No equations, fitted parameters, predictions, or self-citations are present in the provided text that would make any claim equivalent to its inputs by construction. The evaluation setup and experimental claims about LLM struggles are presented as novel contributions without self-referential definitions or load-bearing prior author work. This is a standard case of an independent proposal for an evaluation kit.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper's claims depend on the validity of the proposed benchmark tasks as proxies for real memory use, which is a domain assumption without detailed justification in the abstract.

axioms (1)

domain assumption Gamified interactive scenarios can effectively evaluate long-term memory capabilities in LLMs
The paper assumes this to justify the benchmark design.

pith-pipeline@v0.9.0 · 5493 in / 1054 out tokens · 36019 ms · 2026-05-15T01:25:45.560108+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

three-tier hierarchical framework that evaluates Surface State Memory, Temporal Associative Memory, and Reasoning-Based Memory through specialized interactive tasks
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Memory Fragments Unlocked (MFU), Memory Fragments with Correct Order (MFCO), and Exploration Trajectory Diagrams (ETD)

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

27 extracted references · 27 canonical work pages · 3 internal anchors

[1]

Memory in the Age of AI Agents

Memory matters: The need to improve long- term memory in llm-agents. In Proceedings of the AAAI Symposium Series, volume 2, pages 277–280. Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shan- tanu Acharya, Dima Rekesh, Fei Jia, and Boris Gins- burg. 2024. Ruler: Whats the real context size of your long-context language models? In First Con- ference on Langu...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[2]

OpenAI GPT-5 System Card

Evaluating very long-term conversational memory of llm agents. In Proceedings of the 62nd Annual Meeting of the Association for Computa- tional Linguistics (V olume 1: Long Papers) , pages 13851–13870. Hongjin Qian, Zheng Liu, Peitian Zhang, Kelong Mao, Defu Lian, Zhicheng Dou, and Tiejun Huang. 2025. Memorag: Boosting long context processing with global ...

work page internal anchor Pith review Pith/arXiv arXiv 2025
[3]

Qwen3 Technical Report

Qwen3 technical report. arXiv preprint arXiv:2505.09388. Zhilin Y ang, Peng Qi, Saizheng Zhang, Y oshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christo- pher D Manning. 2018. Hotpotqa: A dataset for diverse, explainable multi-hop question answering. In Proceedings of the 2018 conference on empiri- cal methods in natural language processing , page...

work page internal anchor Pith review Pith/arXiv arXiv 2018
[4]

Keyword Discovery: When reading event text for the ﬁrst time, keywords hid- den within will be automatically discov- ered and added to your keyword pool

work page
[5]

After unlocking, you’ll know the event name but need to actively read it to get the full content

Event Unlocking: Use keywords to un- lock new events associated with that key- word. After unlocking, you’ll know the event name but need to actively read it to get the full content

work page
[6]

– Read Events: Y ou can re-read any event from the read events list to re- view their content

Event Reading: – Unread Events: Select events from the unread events list for ﬁrst-time read- ing to get full content and discover keywords. – Read Events: Y ou can re-read any event from the read events list to re- view their content

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[7]

Y ou need to infer the chronological order of events from each character’s perspective

Character Event Ordering: Each event involves multiple characters. Y ou need to infer the chronological order of events from each character’s perspective. Submit- ting correct orderings earns points

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[8]

earlier-later

Scoring and Keys: For each correctly or- dered event pair (an "earlier-later" relation- ship from a character’s perspective that are consecutive), you earn 1 point. Accumulat- ing a certain score automatically gives you a key. Already scored event pairs won’t be scored again

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[9]

– Y ellow lock: Unlock by consuming 1 key

Lock Mechanism: – Pink and Purple locks: Unlock by an- swering questions. – Y ellow lock: Unlock by consuming 1 key. Strategy Suggestions (in priority order):

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[10]

Prioritize using keywords to unlock new events: When keywords are available, use them to unlock new events and expand ex- plorable content

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[11]

Prioritize reading unread events: Read events from the unread events list to extract keywords and character information

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[12]

– Even if incomplete information, you can try - correct orderings earn points and keys

When no unread events and no key- words, try submitting orderings: – Ordering strategy: First determine which character each event belongs to, then after conﬁrming correct event at- tribution, infer the chronological order within that character’s events. – Even if incomplete information, you can try - correct orderings earn points and keys

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[13]

When you have keys, unlock yellow- locked events: Use keys to unlock impor- tant yellow-locked events

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[14]

When you have sufﬁcient informa- tion, answer questions to unlock pink/pur- ple locks: Infer answers based on read events

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[15]

talk-" (e.g

Y ou can select events from the read events list to re-read if you need to review details. Important Notes: – Nodes starting with "talk-" (e.g. "talk-1", "talk-2") contain no important reasoning information, do not participate in char- acter event ordering, do not need to be repeatedly read, and must not inﬂuence your judgment on event attribution or or- ...

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[16]

Prioritize opening unlocked but not yet viewed ﬁles to help you gain more informa- tion

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[17]

Question marks in ﬁlenames indicate parts you need to guess

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[18]

For failed ﬁles, do not try the same failed ﬁlename again , try other combina- tions

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[19]

When guessing ﬁlenames, carefully ana- lyze the naming patterns in unlocked ﬁles, such as the meaning of numbers, the order- ing relationship of number sizes, the mean- ing of letters, etc

work page
[20]

For character numbers, do not guess numbers that are too large and haven’t ap- peared in the text information

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[21]

I’m going to [loca- tion]

Focus on character movement in- formation: Carefully read dialogues and scene descriptions to extract these clues for deducing the next ﬁlename: – A character says "I’m going to [loca- tion]", "Go ﬁnd him at [location]", or is summoned to a location. This char- acter will appear in the next time slot’s ﬁle for that location. – A character leaves the curre...

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[22]

because... therefore

Pay special attention to the begin- ning and end of each node’s text : Char- acter movement clues tend to concentrate there. The opening describes who enters the scene or where they came from, while the ending describes who leaves, where they are going, or what action is planned next. A.2 QA Test Prompts A.2.1 QA Test Prompt in TRPG QA Test Prompt in TRPG...

work page 2025
[23]

John Hobbes ﬁnds a key near the corpse and believes it may not belong to anyone in the house

The evaluated models include Claude-Opus- 4.6, DeepSeek-V3.2, Gemini-3-Pro-Preview, and GPT-5.2. Then, we access the model’s logs for this game scenario and retrieve information about its past operations on these nodes. We also re- trieve human ground truth in preparation to mak- ing comparisons of both human acts and models’ acts. After obtaining these n...

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[24]

# Long’er, with dead-ﬁsh eyes, sighs helplessly

player5: "# Long’er, with dead-ﬁsh eyes, sighs helplessly."

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[25]

The blonde lady I’m look- ing for deﬁnitely isn’t you

player5: "The blonde lady I’m look- ing for deﬁnitely isn’t you." (Contrast [62] player4: "There’s, there’s a monster ahhh- hhhhhh")

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[26]

Fairies preying on humans is the rule in Gensokyo

player5: " Fairies preying on humans is the rule in Gensokyo."

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[27]

But I think you probably can’t beat the Hakurei head

player5: "But I think you probably can’t beat the Hakurei head." Model’s Reasoning Chain: • Frames Long’er as treating Rumia as a solvable "incident obstacle". 3 24 • Cites teacup echo and causality "stut- ter" as evidence. (from a different scene, not in D02:4079) 7 • Contrasts with Xueyu’s terror, Lina’s fear, Ge Qing’s refusal. 3 • Concludes Long’er ha...

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