Learning to Learn from Multimodal Experience
Xingyu Sui, Weixiang Zhao, Yongxin Tang, Yanyan Zhao, Yang Wu, Dandan Tu, Bing Qin
Abstract
Experience-driven learning has emerged as a promising paradigm for enabling agents to improve from interaction trajectories by accumulating and reusing past experience. However, existing approaches are predominantly developed in textual settings and rely on manually designed memory schemas, limiting their applicability to multimodal environments. In real-world scenarios, experience is inherently multimodal, involving heterogeneous signals across perception, reasoning, and action, which makes effective memory design significantly more challenging. In particular, the optimal way to structure and utilize multimodal experience is highly task-dependent and evolves over time, rendering fixed memory designs insufficient. In this work, we propose a new paradigm, learning to learn from multimodal experience, which shifts memory design from a predefined component to an adaptive and learnable process. Our framework enables agents to dynamically construct, organize, and utilize memory based on task requirements and interaction history, effectively learning how to structure experience for improved performance. Experiments demonstrate that adaptive memory design substantially enhances agent performance and generalization across multimodal tasks, highlighting the critical role of learning memory mechanisms in experience-driven learning.
AI Impact Assessments
(1 models)Scientific Impact Assessment: "Learning to Learn from Multimodal Experience"
1. Core Contribution
This paper introduces AUTOMMEMO, a framework that treats memory design for multimodal agents as an optimization problem rather than a fixed engineering decision. The key insight is that memory mechanisms—how agents store, organize, and retrieve past multimodal experience—should be adaptive and learned rather than manually specified. The framework represents each memory mechanism as an executable "memo program" (Python code implementing `update` and `retrieve` operations) and searches over this program space using an iterative process of evaluation, reflection-guided mutation, and budget-aware tree search.
The problem addressed is genuine and important: existing experience-driven learning approaches rely on hand-designed memory schemas that are brittle across tasks and modalities. AUTOMMEMO shifts this from a design problem to a search/optimization problem, allowing the system to discover task-appropriate memory structures automatically.
2. Methodological Rigor
The methodology is generally well-structured:
Evaluation protocol: The update-then-retrieve protocol provides a clean, fair comparison mechanism. All candidates share the same execution agent, environment, and evaluator, isolating the effect of the memory program. The offline evaluation with disjoint train/test splits is appropriate.
Search mechanism: The budget-aware tree search using UCB-style scoring for both evaluation and generation actions is principled. The minimum-width constraint prevents collapse into single lineages, and the LCB-based final selection adds robustness. The design borrows intelligently from multi-armed bandit theory.
Baselines: The comparison includes text-based (TrajectoryRetrieval, ReasoningBank, G-Memory), multimodal (XSkill, M²), and automatic design (ALMA) baselines. The adaptation of all baselines to the same protocol strengthens comparability. However, ALMA is adapted from a text-only setting, which may disadvantage it.
Concerns: The reliance on GPT-5 as the meta agent and GPT-5.4-mini as judge introduces dependencies on specific commercial models. The 20-step search budget is modest but adequate for demonstrating the concept. Three evaluation runs per benchmark is minimal for statistical confidence—no confidence intervals or significance tests are reported. The LLM-as-judge evaluation, while standard, introduces its own biases.
3. Potential Impact
Direct applications: The framework is immediately applicable to GUI/web navigation assistants, multimodal search agents, and visual reasoning systems. The demonstrated improvements (e.g., +19.23 AVG.GUI and +4.60 AVG.VR on Qwen3-VL-32B) are substantial.
Broader implications: The meta-learning perspective on memory design could influence how the community thinks about agent architectures. Rather than engineering memory systems, researchers could focus on defining good search spaces and evaluation protocols. The executable program representation is flexible enough to encompass diverse memory strategies (playbooks, episodic stores, skill libraries, graph memories).
Transferability findings: The cross-benchmark and cross-model transfer results (Figures 2 and 3) are particularly valuable, showing that learned memory designs capture genuinely reusable patterns rather than benchmark-specific heuristics—though diagonal dominance confirms some task specificity remains.
Efficiency gains: The cost analysis (Table 2) reveals that AUTOMMEMO not only improves accuracy but reduces token consumption and interaction steps, suggesting the learned memories provide more targeted, less noisy context than fixed baselines.
4. Timeliness & Relevance
This work addresses a clear bottleneck at the intersection of two active research fronts: (1) the rapid scaling of multimodal agents interacting with real-world environments, and (2) the growing recognition that experience-driven learning requires more than fixed memory schemas. The 2025-2026 citation landscape shows intense activity in both areas, and this paper's contribution of bridging automatic memory design to multimodal settings fills a timely gap.
The "learning to learn" framing connects to well-established meta-learning principles while applying them in a novel context. As multimodal agents become more prevalent in production settings (web automation, digital assistants), adaptive memory will become increasingly important.
5. Strengths & Limitations
Key Strengths:
Notable Limitations:
6. Additional Observations
The appendix reveals substantial engineering effort (detailed prompts, baseline adaptations, search statistics), which aids reproducibility but also highlights the system's complexity. The learned memo programs (Appendix J) show surprisingly sophisticated domain-specific designs emerging from search, which is perhaps the most compelling qualitative evidence for the approach. The performance gap between AUTOMMEMO and baselines is notably larger on Qwen3-VL-32B than GPT-5.4-nano, suggesting stronger base models may benefit more from adaptive memory—an interesting finding for future investigation.
Generated May 19, 2026
Comparison History (21)
Paper 2 has higher potential impact due to a more fundamental, broadly applicable contribution: making multimodal experience/memory structure itself learnable rather than hand-designed. This targets a core bottleneck in multimodal agents and could influence RL, embodied AI, robotics, and multimodal LLM agent design. Its applications extend across many task domains and remain timely as multimodal interaction becomes central. Paper 1 is strong and pragmatic, but is more of a systems integration around autonomous research workflows and may be narrower and benchmark-dependent, with impact concentrated in AI-for-science tooling.
Paper 1 proposes a novel paradigm shift—making memory design itself learnable for multimodal experience-driven agents—addressing a fundamental limitation in AI agent design. This has broader impact across multiple fields (robotics, embodied AI, multimodal reasoning) and introduces a conceptually deeper contribution. Paper 2, while solid as a benchmark contribution (PRISM), is more incremental and narrowly focused on programmatic video generation evaluation. Benchmarks have impact but are typically surpassed quickly, whereas new learning paradigms can shape research directions for years.
Paper 1 offers a clearer methodological innovation: replacing scalar reward/test scores with formally defined properties plus counterexample-guided feedback, yielding strong efficiency gains and more reliable synthesis. This is timely for LLM-based code generation and bridges LLMs with formal methods/planning, with broader cross-field impact (verification, synthesis, RL/planning, agentic coding) and concrete, measurable benefits. Paper 2 targets an important direction (adaptive multimodal memory), but the abstract is higher-level and closer to an incremental reframing of learnable memory mechanisms without clear technical specificity or guarantees, making impact harder to assess and potentially less novel.
Paper 1 addresses a highly relevant and rapidly growing field (multimodal AI agents), tackling a critical bottleneck by making memory schemas adaptive rather than manually designed. Its focus on dynamic, multimodal experience learning has broader implications for general AI and robotics compared to Paper 2's focus on knowledge graphs, which represents a more niche advancement in the Semantic Web domain.
Paper 2 is likely higher impact due to broader novelty and reach: it proposes a general paradigm for adaptive, learnable memory construction from multimodal interaction experience, applicable across embodied AI, multimodal RL, and agentic systems. This has strong real-world relevance (robots, assistants, autonomous agents) and cross-field breadth. Paper 1 is timely and methodologically rigorous with clear guarantees, but is more narrowly scoped to LLM-as-a-judge routing under budget/distribution shift, making its impact comparatively more specialized.
Paper 1 likely has higher impact: it introduces a concrete, technically novel RL objective (self-supervised action ranking) addressing a well-known offline-to-online challenge (critic overestimation vs. pessimism) with clear methodological grounding and strong quantitative results on standard benchmarks plus vision-based robotics, including sim-to-real gains. Its applicability spans offline RL, online fine-tuning, and robot learning with immediate real-world relevance. Paper 2 is conceptually promising (adaptive multimodal memory) but the abstract is higher-level, with less specificity about mechanisms, rigor, and standardized evaluations, making near-term impact harder to assess.
Paper 2 addresses the highly relevant and rapidly expanding field of multimodal AI agents. By proposing a learnable, adaptive memory framework for multimodal experiences, it offers broader applicability to real-world scenarios and greater potential cross-disciplinary impact. While Paper 1 provides rigorous theoretical contributions to POMDP planning, Paper 2's focus on dynamic multimodal learning aligns more closely with current major trends in AI, suggesting a wider and more immediate scientific impact.
Paper 2 addresses a broader and more fundamental problem—adaptive memory design for multimodal experience-driven learning in agents—which has wider applicability across robotics, embodied AI, and general autonomous systems. Its paradigm of 'learning to learn from multimodal experience' is more novel and generalizable compared to Paper 1's domain-specific prompt optimization framework for argumentative essay understanding. Paper 2's contribution to meta-learning and multimodal agent architectures positions it to influence multiple research communities, while Paper 1's impact is more confined to NLP and education assessment.
Paper 1 addresses a concrete, high-impact problem—making large-scale optimization models adaptable by non-experts through LLM-guided re-optimization. It demonstrates practical value with real-world case studies (supply chain, exam scheduling), offers a complete framework with toolbox-driven architecture, and directly impacts industrial decision-support systems. Paper 2 proposes an interesting meta-learning paradigm for multimodal experience but remains more conceptual and incremental within the agent/memory design space. Paper 1's combination of immediate practical applicability, methodological rigor with large-scale experiments, and bridging OR expertise gaps gives it broader and more tangible impact.
Paper 1 proposes a more general and foundational paradigm—learning to learn from multimodal experience—with broader applicability across diverse AI domains. It addresses the fundamental challenge of adaptive memory design for multimodal agents, which is relevant to robotics, embodied AI, and general-purpose assistants. Paper 2, while impressive in competitive programming results, addresses a narrower domain. Paper 1's meta-learning approach to memory structures has greater potential to influence multiple research communities and inspire follow-up work across fields, giving it higher estimated long-term scientific impact.
Paper 2 has higher potential impact due to its broader novelty and applicability: adaptive, learnable multimodal memory design targets a central bottleneck in modern agentic/LLM and embodied AI systems, with clear relevance and timeliness. If rigorously validated, it could influence multiple fields (reinforcement learning, multimodal learning, robotics, HCI) and enable real-world agents that improve over time. Paper 1 is a useful algorithmic improvement to clustering via metaheuristics, but likely incremental within a mature area and narrower in cross-domain reach despite practical sensor-network relevance.
Paper 1 introduces a fundamental paradigm shift in how multimodal agents construct and utilize memory, addressing a critical bottleneck in embodied AI and autonomous agents. Its approach to dynamically learning memory structures has broad implications across robotics and multimodal reasoning. In contrast, while Paper 2 provides a valuable evaluation framework for social LLMs, its scope is more narrowly focused on conversational role-playing, giving Paper 1 greater potential for widespread, cross-disciplinary scientific impact.
Paper 2 likely has higher impact due to a clearer novel framing (LLM self-correction as closed-loop control), concrete components (detector/controller/judge), and new dynamic metrics that can generalize across many LLM settings. It provides a benchmark with annotated correction trajectories, enabling reproducible evaluation and follow-on work. The topic is timely and broadly relevant to reliability/safety of LLMs with direct real-world applications. Paper 1 is compelling but more conceptual and underspecified from the abstract (adaptive multimodal memory), making rigor and immediate adoption harder to gauge.
Paper 2 introduces a fundamental paradigm shift by conceptualizing multi-agent systems as trainable neural network architectures, moving away from hand-designed workflows. Its theoretical insights into parameter efficiency and empirical findings on organizational scaling offer a highly novel and scalable axis for LLM development, promising broader impact and real-world applicability compared to Paper 1's more specific focus on multimodal memory adaptation.
Paper 2 tackles a critical bottleneck in the rapidly expanding field of autonomous agents by introducing an adaptive memory framework for multimodal experiences. Its approach has broad, cross-disciplinary applicability in robotics, vision-language models, and embodied AI. In contrast, Paper 1 focuses on a more niche evaluation paradigm for RL in specific economic/pricing scenarios. Due to the explosive interest in multimodal agents and the fundamental nature of the memory bottleneck, Paper 2 is poised to have a wider and more significant scientific impact.
Paper 2 addresses the highly relevant and rapidly growing field of multimodal agents, offering an adaptive memory framework that shifts away from fixed schemas. Its focus on learning how to structure multimodal experience has broad applicability across embodied AI, robotics, and complex reasoning tasks. While Paper 1 is methodologically rigorous and advances neuro-symbolic counterfactual reasoning, Paper 2's flexible, learning-based approach to multimodal memory aligns better with current AI trends and promises a wider impact across multiple domains.
Paper 1 provides a well-defined theoretical contribution with tight approximation bounds (4/3-competitive ratio with matching lower bounds), a novel problem formulation (OSSA) relevant to critical real-world applications (humanitarian logistics, vaccine distribution), and a learning-augmented extension bridging theory and practice. The mathematical rigor, tight bounds, and practical relevance give it strong impact potential. Paper 2 proposes an interesting but less formally grounded paradigm for multimodal experience-driven learning; while timely given LLM/agent interest, it lacks the theoretical depth and specificity of contribution that Paper 1 offers.
Paper 1 addresses a fundamental technical challenge in AI—adaptive multimodal memory for autonomous agents—which has broad applicability across robotics, NLP, and computer vision. Its algorithmic innovation in shifting memory design from predefined to learnable offers higher potential for driving future technical breakthroughs and citations compared to Paper 2, which primarily provides a valuable, yet narrower, database for AI governance and policy research.
Paper 2 presents a concrete, implementable framework with experimental validation for adaptive memory design in multimodal agents—a timely problem given the rapid growth of multimodal AI systems. It addresses a specific technical gap (fixed memory schemas in experience-driven learning) with a novel solution. Paper 1, while intellectually ambitious in proposing a new taxonomy (AAI) and adaptivity index, is primarily a conceptual/organizational monograph that renames and reframes existing work (meta-learning, AutoML, NAS) rather than introducing fundamentally new methods. Taxonomic proposals rarely achieve high citation impact unless widely adopted, whereas Paper 2's practical contributions are more directly actionable.
PopuLoRA presents a more concrete and rigorously evaluated contribution: a novel population-based self-play framework combining LoRA weight-space evolution with asymmetric co-evolution for LLM reasoning. It demonstrates clear empirical gains across 10 benchmarks with specific architectural innovations (LoRA mutation/crossover operators). Paper 2 proposes an interesting but more conceptual paradigm ('learning to learn from multimodal experience') with adaptive memory design, but lacks the specificity and benchmark rigor of Paper 1. PopuLoRA's approach to overcoming self-calibration limitations in RLVR is timely and directly addresses a known failure mode in current LLM training.