Long-term memory is essential for LLM-based agents operating over extended interactions. Existing memory systems primarily update memory when new information arrives, treating retrieval as the endpoint of memory access rather than a driver of memory evolution. Consequently, retrieval feedback is rarely exploited to reorganize memory for future access continuously. Moreover, most existing approaches rely on predefined memory structures together with fixed retrieval pipelines, limiting the agent's ability to organize and evolve its own memory autonomously. Inspired by memory reconsolidation in cognitive neuroscience, we propose REALM, a reconsolidation-evolution agentic long-term memory framework. It models long-term memory as a continual lifecycle by autonomously organizing memories into a heterogeneous cognitive graph, retrieving evidence via adaptively composed graph-search atoms, and continually reconsolidating memories based on retrieval feedback. REALM achieves an average accuracy of 75.97% on LoCoMo and 65.11% on LongMemEval, outperforming the strongest baselines by 7.17 and 1.31 points respectively. Ablation studies confirm that memory reconsolidation consistently boosts performance, with further analyses revealing that it progressively reorganizes related memory units into more coherent local structures for collective evidence recall and utilization during reasoning. These results suggest that retrieval-driven memory reconsolidation provides an effective mechanism for continually evolving long-term memory in LLM agents.
Figures & tables
Figure 1: Motivation of retrieval-driven memory reconsolidation. Existing memory systems mainly update memories with new information using predefined data structures and a fixed retrieval pipeline, while REALM forms a closed-loop memory lifecycle, leveraging retrieval feedback to dynamically reorganize the memory topology upon utilization.
Figure 2: Overview of REALM. The framework organizes observations into an agentic cognitive graph memory, retrieves evidence through adaptive graph-search atoms, and reconsolidates the activated subgraph to evolve memory topology after use.
Method
LoCoMo-Category
Average
Multi Hop
Temporal
Open Domain
Single Hop
MIRIX ( Wang and Chen, 2025 )
54.26
68.54
46.88
68.22
64.33
Mem0 ( Chhikara et al., 2025 )
58.75
52.34
45.83
73.33
64.57
Zep ( Rasmussen et al., 2025 )
52.12
54.82
33.33
66.23
59.22
MAGMA ( Jiang et al., 2026a )
52.80
65.00
51.70
77.60
68.80
Nemori ( Nan et al., 2025 )
56.90
64.90
48.50
76.40
68.70
Table 1: Accuracy (%) on LoCoMo by question category using GPT-4o-mini as both the backbone model and the LLM judge. Bold and underlined values indicate the best and second-best results respectively.
Method
LongMemEval-Category
Average
single-session preference
single-session assistant
temporal reasoning
multi- session
knowledge update
single- session user
MIRIX
53.30
63.60
25.60
30.10
52.60
72.90
43.49
Zep
53.30
75.00
54.10
47.40
74.40
92.90
63.80
MAGMA
73.30
83.90
45.10
50.40
66.70
72.90
61.20
Nemori
62.70
73.20
43.00
51.40
52.60
77.70
56.20
REALM (Ours)
36.66
82.14
56.69
46.28
88.89
89.06
65.11
Table 2: Accuracy (%) on LongMemEval by question category using GPT-4o-mini as both the backbone model and the LLM judge. Bold and underlined values indicate the best and second-best results respectively.
Figure 3: Robustness to randomized question orders on LoCoMo. Accuracy remains stable across original and shuffled orders, suggesting that reconsolidation improves memory topology rather than overfitting to a particular query sequence. The shaded region denotes performance variation.
Category
LoCoMo
LongMemEval
w/o recon
w/ recon
w/o recon
w/ recon
Multiple
59.57
64.54 +4.97
43.80
46.28 +2.48
Temporal
74.14
76.64 +2.50
55.12
56.69 +1.57
Single(-P)
81.09
81.57 +0.48
30.00
36.66 +6.66
Open/Update
53.12
58.33 +5.21
84.72
88.89 +4.17
Average
73.96
75.97 +2.01
62.98
65.11 +2.13
Table 3: Effect of reconsolidation across different categories on LoCoMo and LongMemEval. Subscripts report absolute gains from enabling reconsolidation; “Single(-P)” denotes the single-session preference category in LongMemEval because the other single-session categories remain unchanged.
Figure 4: Deviation between graph composition and evidence retrieval. The left figure illustrates that heterogeneous memory node types remain unbiased in both graph structure and evidence utilization, while the right figure shows that different relationship types are selectively exploited according to downstream reasoning requirements.
Figure 5: Retrieval depth for correct answers on LoCoMo and LongMemEval. The proportion first decreases and then increases with expansion depth, indicating that straightforward queries are answered early, and more challenging ones require deeper graph exploration.
Strategy
Acc (%)
Correct (#)
path_search only
39.84
147
subgraph_beam only
43.09
159
adaptive selection
43.36
160
Table 4: Effect of expansion strategy selection on questions requiring graph expansion. Adaptive selection slightly improves over fixed path search and subgraph beam, showing the value and current limitation of strategy composition.
Figure 6: Evidence gains from topology evolution during the expansion stage. “Found” denotes cases where the supporting evidence is retrieved during expansion, and “Only” those where it is retrieved exclusively during expansion.