MEMIG: Self-evolving Memory Construction for Antibody Optimization

Yunhui Jang; Zach Wang; Jai Doshi; Kangway Chuang; Sidney Lisanza; Taylor Joren

cs.AIOct 30, 2026

MEMIG: Self-evolving Memory Construction for Antibody Optimization

Authors: Yunhui Jang, Zach Wang, Jai Doshi, Kangway Chuang, Sidney Lisanza, Taylor Joren

Organizations: KAIST · Genentech

Abstract

Memory has become central to self-evolving LLM agents, which store and retrieve experience from past rollouts to guide future decisions. Existing systems represent this experience as free-form natural language and maintain it by rewriting, which fits tasks whose experience is narratable. However, in scientific discovery and optimization, experience is often a quantity measured by an oracle under a specific context. Rewriting it as text leaves no index for where a record should be, replaces a measurement with a description, and cannot express how interventions interact. To address this, we argue that agent memory for biomolecule optimization should instead be indexable, quantitative, and structured, and we introduce MEMIG. This memory construction framework realizes these three properties. In detail, MEMIG addresses each insight with a domain-grounded identifier and accumulates the quantitative effect measured by a property oracle, so that updates remain local and retrieval becomes an exact lookup. Next, MEMIG connects insights whose interventions are applied together and measures their epistasis by decomposing each evaluated set. Our results show that MEMIG improves performance in most settings across in silico antibody optimization tasks.

Figures & tables

Figure 1: Overview of MemIG . MemIG constructs an epistasis graph with antibody-grounded identifiers. Directed edges organize property

\rightarrow

germline

\rightarrow

region

\rightarrow

mutation hierarchies, while undirected edges connect co-occurring mutations and store their epistatic effects.

Figure 3: Memory retrieval of MemIG . Given a starting antibody, MemIG identifies the matching identifiers, retrieves the corresponding insights with their neighbors, and curates them into natural language. Blue denotes the antibody-grounded identifier and the corresponding node, green its quantitative record, and yellow the epistasis graph neighbors and the curation based on them.

Figure 4: Multi property optimization results. Pass@1 and pass@3 (left) and the distribution of improvement over the starting sequence (right), where each colored dot is one rollout and the black diamond marks the mean.

Figure 5: In silico antibody binding optimization. Each numeric label corresponds to an antibody-antigen cluster, clustered by CDR sequence identity. ∗ denotes the structural baselines (dashed), which have access to the complex structure.

Component	Pass@1	Pass@3	Imp.
MemIG (Ours)	0.149	0.278	0.125
Identifier	0.074	0.153	0.117
Epistasis	0.049	0.130	0.142
Vanilla LLM	0.015	0.047	0.067

Table 1: Ablation study on component .

Adoption	Helpful	Harmful	$\Delta$
0.931	0.909	0.004	0.905

Table 2: Memory faithfulness. MemIG raises both adoption and net directional alignment.

Figure 6: Cumulative performance progression. Vertical lines mark memory updates, where the rollouts between two lines are generated concurrently under the same memory. The curves report the centered rolling mean with a half-width of 30 rollouts, computed within each memory step. Notably, the ordering of rollouts within the same memory is random, so a downward slope does not imply performance decrease. Each dot marks the mean over all rollouts of that memory step.

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

Property	Objective	Oracle	Why it matters
Immunogenicity	Minimize	Pep2Vec ( Thrift et al., 2024 )	Predicted MHC class II epitope cores that can provoke an anti-drug antibody response.
Liabilities	Minimize	Biopython ( Cock et al., 2009 )	Motifs that degrade during manufacture and storage, costing potency and shelf life.
GRAVY	Range	Biopython ( Kyte & Doolittle, 1982 )	Exposed hydrophobicity drives aggregation, but an overly hydrophilic domain loses stability.
Charge	Range	Biopython ( Cock et al., 2009 )	Endosomal charge at pH 5.5 governs clearance rate and formulation viscosity at both extremes.
pTM	Range	Protenix ( Zhang et al., 2026b )	Predicted TM-score between the predicted and the true complex.
AbAg iPTM	Range	Protenix ( Zhang et al., 2026b )	Accuracy of the predicted relative placement of the antibody and the antigen.

Appendix

Table A1: Optimized properties. Six antibody properties the agent optimizes, the oracle or function measuring each, and why each are significant.

Cluster	# Complexes	PDBs
993	5	7l7e, 8d8q, 8ekd, 8suo
114	6	6kyz, 8f2j
147	7	7ben, 7beo, 7orb, 7tbf
148	4	7jmw, 7lq7, 7s5q, 7s5r
193	6	4fp8, 5umn, 6d0u
234	4	4mwf, 6bkb, 6uyd, 6uym

Appendix

Table A2: Details of the in silico binding optimization dataset. Antibodies were clustered by CDR sequence identity. Each antibody’s Heavy and Light chain CDR loops are concatenated into one string. Mmseqs2 ( Steinegger & Söding, 2017 ) is run with a 70% sequence identity cutoff.

Task	Hyperparameter	Value
Multiple property	Rollouts per example	10
	Number of examples	100
	Maximum turns per rollout	15
In silico binding	Rollouts per example	30
	Number of examples	# complexes per cluster
	Maximum turns per rollout	10

Appendix

Table A3: Hyperparameters used in all experiments.

Figure 7: Cumulative performance progression without a rolling average. Vertical lines mark memory updates, where the rollouts between two lines are generated concurrently under the same memory. Each point is the mean improvement over all rollouts sharing a memory step, and error bars indicate the standard error across those rollouts.

Cluster	Model	Pass@1	Pass@3	Avg
148	DC-Cu*	0.067	0.182	0.109
	ACE*	0.100	0.215	0.169
	MemIG (Ours)	0.258	0.456	0.144
193	DC-Cu*	0.056	0.159	0.110
	ACE*	0.028	0.081	0.107
	MemIG (Ours)	0.194	0.374	0.141

Appendix

Table A4: Comparison under the same oracle calls. Bold highlights the best score in a cluster and * denotes additional decomposition oracle call mode.

Method	Input	Output	Pass@1	Tokens / pass
Vanilla LLM	98.1	6.8	0.022	4.719M
Obs.	204.3	7.2	0.000	–
ACE	103.8	6.6	0.017	6.623M
DC-Cu	120.3	7.7	0.006	23.028M
MemIG	270.1	9.2	0.194	1.436M

Appendix

Table A5: Token cost analysis. Average token consumption per rollout. Input and output tokens are reported in thousands. Tokens per pass include tokens consumed by unsuccessful rollouts.

Method	Ref.	Candidate	Single	Pair	Total
Vanilla LLM	1.00	6.03	0	0	0 7.03
Obs.	1.00	5.88	0	0	0 6.88
ACE	1.00	5.44	0	0	0 6.44
DC-Cu	1.00	5.76	0	0	0 6.76
MemIG	1.00	4.95	8.46	22.97	37.38

Appendix

Table A6: Oracle evaluation cost. Average number of oracle evaluations per rollout. Single and pair evaluations are additional evaluations used by MemIG to construct the epistasis graph.

		Pass		Improvement
Backbone	Model	@1	@3	Avg.	Charge	GRAVY	Immu.	Liab.
Random		0.003	0.009	-0.419	-0.071	0.000	-1.243	-0.363
MCMC		0.004	0.012	-0.043	0.308	0.008	-0.465	-0.115
Haiku-4.5	Vanilla	0.077	0.212	0.431	1.223	0.004	-0.037	0.535
	Obs.	0.073	0.168	0.477	1.225	0.019	0.167	0.499
	DC-Cu	0.188	0.423	0.602	1.240	0.040	0.431	0.696

Appendix

Table A7: Multiple property optimization. Teal indicates improvement over all baselines and bold indicates the best results.

		993					114					147
		Pass		Improvement			Pass		Improvement			Pass		Improvement
Category	Model	@1	@3	Avg	ipTM	pTM	@1	@3	Avg	ipTM	pTM	@1	@3	Avg	ipTM	pTM
Statistical	Random	0.000	0.000	0.028	0.043	0.013	0.089	0.206	0.016	0.036	-0.003	0.024	0.070	-0.035	-0.041	-0.030
Statistical	MCMC	0.013	0.040	0.075	0.110	0.039	0.233	0.368	0.069	0.115	0.024	0.171	0.383	0.064	0.101	0.027
Structure-based	ProteinMPNN*	0.000	0.000	0.138	0.196	0.081	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
Structure-based	AbMPNN*	0.000	0.000	0.116	0.164	0.068	0.600	0.762	0.160	0.242	0.077	0.229	0.406	0.109	0.158	0.059

Appendix

Table A8: In silico antibody binindg optimization. Bold indicates the best per column across all methods and teal indicates improvement over all non-structural methods within the same backbone. * indicates structural baselines included as reference.

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In drug discovery, molecular optimization aims to iteratively refine a lead compound to improve molecular properties while preserving structural similarity to the original molecule. However, each oracle evaluation is expensive, making sample efficiency a key challenge for existing methods under a limited oracle budget. Trial-and-error approaches require many oracle calls, while methods that leverage external knowledge tend to reuse familiar templates and struggle on challenging objectives. A key missing piece is long-term memory that can ground decisions and provide reusable insights for future optimizations. To address this, we present MolMem (Molecular optimization with Memory), a multi-turn agentic reinforcement learning (RL) framework with a dual-memory system. Specifically, MolMem uses Static Exemplar Memory to retrieve relevant exemplars for cold-start grounding, and Evolving Skill Memory to distill successful trajectories into reusable strategies. Built on this memory-augmented formulation, we train the policy with dense step-wise rewards, turning costly rollouts into long-term knowledge that improves future optimization. Extensive experiments show that MolMem achieves 90% success on single-property tasks (1.5

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