Use and Disuse: Intent-Structured Experience Consolidation for Memory and Learning in LLM Agents

Xiangyi Zeng; Baihang Liu; Xutong Wang; Ze Jin; Yunpeng Li; Qixu Liu

cs.AIOct 8, 2026

Use and Disuse: Intent-Structured Experience Consolidation for Memory and Learning in LLM Agents

Authors: Xiangyi Zeng, Baihang Liu, Xutong Wang, Ze Jin, Yunpeng Li, Qixu Liu

Organizations: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

Abstract

The evolution of Large Language Model agents from single-task execution to long-term autonomous operation highlights the critical challenge of transforming continuous experiences into reusable knowledge. To address this, we propose Hippocam, a hierarchical memory and continual learning architecture. Hippocam draws inspiration from two characteristics of human memory: cognitive processes selectively maintain information relevant to current goals, while long-term memories form gradually through repeated consolidation. Accordingly, Hippocam structures an agent's ongoing work as nested intents. The active context remains centered on the current intent, while completed intents are consolidated into the task-relevant outcomes and state needed for subsequent work, rather than carrying forward their full working details. Concurrently, a recursive prefix consolidation mechanism repeatedly consolidates earlier history, causing long-unused experiences to become increasingly abstract. Original interactions are preserved, allowing the agent to progressively recover finer-grained details through the hierarchy and stop once sufficient information is available. Crucially, when past experiences are recalled and reintegrated into active work, they undergo subsequent consolidation alongside new experiences, thereby being reinforced, supplemented, and updated. Through this memory dynamic of use and disuse, Hippocam connects working context, long-term memory, knowledge accumulation, and skill learning within a single continuously evolving experiential process. This enables agents to learn and evolve capabilities through their own experiences without parameter updates.

Figures & tables

Figure 1: Hippocam unifies memory, knowledge use, and learning. Performance (%) with DeepSeek-V4.1-Flash as the task backbone. Evaluation details appear in § 4 .

Figure 3: How Hippocam learns. Knowledge: reused ; new ; fading .

Method	ALF.	Sci.	Stream.
Plain	78.4	66.9	69.0
HiAgent	85.1	56.7	69.0
mem0	87.3	58.6	69.3
A-Mem	95.5	87.2	79.0
Letta	85.1	71.2	81.3
ReasoningBank	85.1	66.9	68.3

Table 1: Task performance (%) on ALF World , Sci enceWorld , and Stream Bench .

Figure 4: Performance as experience accumulates. Cumulative accuracy on StreamBench. Each stream’s top three methods at task 150 continue to task 300. Shading marks this extension.

Panel of Table 2 — Table 2: Cross-domain knowledge generalization and integration. ScienceWorld mean progress and FEVER accuracy (%). $\Delta_{\mathrm{stream}}$ reports After minus Fresh in percentage points (pp).

Figure 5: Maintaining usable experience. Left: GoodAI LTM scores under isolated and 32k-span interleaved conditions. Right: MemoryAgentBench multi-hop conflict-resolution accuracy across input lengths. Lines connect separate length conditions.

Method	Overall	Single-hop	Multi-hop	Temporal	Open-domain	Adversarial
HiAgent	39.8	37.1	24.8	16.2	45.8	70.2
mem0	58.0	67.2	37.6	8.1	77.1	85.2
A-Mem	67.5	74.3	34.4	67.6	61.5	76.7
Letta	51.7	48.5	22.0	35.2	45.8	89.5
ReasoningBank	18.3	2.9	0.0	2.8	7.3	72.4
ACE	22.5	0.0	0.0	0.0	0.0	100.0

Table 3: What the retained conversation supports. Binary judge accuracy (%) on LoCoMo. Evaluation scope is specified in § A.3 .

Intent conditioning
Setting	Metric	Hippocam	w/o
GoodAI isolated	Score $\uparrow$	85.1	77.8
interleaved	Score $\uparrow$	88.7	65.6
Prefix consolidation
ToolBench	Acc. $\uparrow$	78.7	65.7
ALFWorld	Success $\uparrow$	97.8	100.0

Table 4: Component ablations and consolidation comparison. Scores, success rates, and accuracy are percentages. Context size is measured in tokens.

Appendix figures & tables13 assets

Supplementary material from the paper’s appendix.

Appendix

Parameter	Default	Role
$K$	2	Minimum working-tail interaction rounds
$B_{\min}$	4,096 tokens	Working-tail token floor
$\alpha$	0.20	Working-tail token cap as a fraction of $W$
$q$	0.95	Action-round length quantile
$E_{\min}$	2	New intent consolidations in a stable prefix
$T_{\min}$	16,384 tokens	Stable-prefix size for ordinary consolidation

Appendix

Table 5: Default consolidation configuration. Token quantities are implementation budgets;

W

is the configured context capacity.

	First 150 tasks per stream			First 300 tasks per stream
Method	DDXPlus	ToolBench	Mean	DDXPlus	ToolBench	Mean
Plain	74.7	63.3	69.0	—	—	—
HiAgent	76.7	61.3	69.0	—	61.3	—
mem0	70.0	68.7	69.3	—	69.0	—
A-Mem	84.0	74.0	79.0	91.3	74.3	82.8
Letta	86.0	76.7	81.3	91.7	77.3	84.5

Appendix

Table 6: StreamBench accuracy at two evaluation horizons. Mean gives equal weight to the two streams and is reported only when both reach the stated horizon.

	ScienceWorld		FEVER
Method	Progress	Cost	Accuracy	Cost
HiAgent	42.7	2.62	61.8	1.76
mem0	51.7	0.06	59.9	0.25
A-Mem	42.1	0.26	55.8	0.35
Letta	40.8	2.53	59.9	0.98
ReasoningBank	53.3	0.10	55.8	0.19

Appendix

Table 7: Frozen-source performance and recorded target-evaluation cost. Performance is reported in percentages; cost is the total USD expenditure for the reported target evaluation, including within-task memory operations and excluding source experience acquisition.

Method	6k	32k	64k	Overall	DetectiveQA
HiAgent	97.0	0.0	11.0	36.0	—
Hippocam	97.0	77.0	41.0	71.7	85.9
Letta	77.0	37.0	19.0	44.3	82.4
A-Mem	45.0	17.0	11.0	24.3	78.9
mem0	19.0	25.0	17.0	20.3	—
ACE	0.0	0.0	0.0	0.0	—

Appendix

Table 8: MemoryAgentBench conflict resolution and long-range understanding (LRU). Accuracy (%); Overall averages the three multi-hop input lengths.

Configuration	Success (%)	Mean steps
Hippocam	100.0	8.12
Empty placeholder	94.0	9.92
ACE playbook	97.8	9.00
Flat summary	100.0	9.68

Appendix

Table 9: Prefix consolidation and alternative knowledge configurations. ALFWorld success and mean actions. The ACE variant replaces Prefix Consolidation with its playbook-generation process.

Panel of Figure 6 — Figure 6: Retained detail for a later action. Local archive subtrees redrawn in the style of the Hippocam viewer, with verbatim memory and response excerpts. The left intent trace shows the completed shopping-list intent and the new customer intent, with labels shortened for display; node IDs identify saved records and ellipses mark omissions. The selected memories retain different levels of menu detail.

Intervening use	Unchanged	Supplemented	Updated	Rewritten	Dropped
No detected use	95.8	0.9	0.1	3.1	0.1
No API, unclassified	94.1	2.1	0.3	3.4	0.1
Used, call matches reference	57.0	28.4	0.3	14.3	0.0
Used, reference call differs	68.3	11.7	3.4	16.6	0.0

Appendix

Table 10: Knowledge-item transitions in ToolBench grouped by API-linked use between consecutive consolidations. Outcome columns report percentages within each group.

Environment	Textual reuse	Unchanged (%)
ALFWorld	Detected	41.6
	Not detected	63.2
ScienceWorld	Detected	57.7
	Not detected	59.1

Appendix

Table 11: Knowledge-item transitions grouped by four-word textual reuse in main-agent messages. Unchanged proportions are percentages.

Panel of Figure 7 — Figure 7: Experience changes through use and disuse. Knowledge gains supporting evidence as episode-specific narrative detail recedes. Abridged and paraphrased from ALFWorld traces.

Panel of Figure 8 — Figure 8: Progressive recall in two MemoryAgentBench conflict-resolution cases. Blue boxes are opened memory; dashed gray boxes are references or children left unopened. The second case preserves the observed reconsolidation and completion check between recall and answer.

Figure 9: Performance, reuse, and the cost of continued work. Top: accuracy versus average cost per task on each stream’s first 150 tasks; upper-left is preferable. The blue arrow connects Hippocam’s first 150 tasks (open marker) to its next 150 (filled marker); each endpoint reports its own interval. Bottom: the first-150 cost decomposed into cached input, uncached input, output, and embeddings. All components include memory maintenance.

Stream	Tasks	Acc. (%)	Input (k)	Uncached (k)	USD/task	USD/correct
DDXPlus	First 150	87.3	207.8	8.0	0.0095	0.0109
	Next 150	98.7	252.3	8.3	0.0085	0.0087
ToolBench	First 150	76.0	232.5	30.4	0.0132	0.0173
	Next 150	81.3	259.5	26.8	0.0118	0.0145

Appendix

Table 12: Later work improves without a higher mean cost per task. Hippocam’s consecutive StreamBench intervals. Input counts sum all agent calls per task, in thousands of tokens; they are not single-request context lengths. Cost per correct answer divides total cost, including incorrect answers, by the number correct.

Stream	Method	Acc. (%)	Input (k)	Uncached (k)	USD/task	USD/correct
DDXPlus	Hippocam	93.0	230.1	8.1	0.0090	0.0097
	A-Mem	91.3	20.1	19.1	0.0083	0.0091
	Letta	91.7	77.1	—	—	—
ToolBench	Hippocam	78.7	246.0	28.6	0.0125	0.0159
	A-Mem	74.3	61.9	51.6	0.0193	0.0260
	mem0	69.0	48.3	38.0	0.0470	0.0681

Appendix

Table 13: Cumulative cost–performance after the next 150 tasks. Scores and costs cover both intervals and include memory maintenance. Letta’s total input is recorded, but its uncached input and exact repriced cost cannot be recovered.

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