NeurIPS 2026 Papers

Test-time scaling (TTS) improves text-to-image (T2I) generation in unified multimodal models (UMMs), but existing TTS methods typically rely on frozen-parameter search or sampling, yielding only ephemeral, instance-level improvements. We propose Meta-TTRL, a metacognitive test-time reinforcement learning (TTRL) framework that converts test-time generation experience into learning signals, enabling UMMs to self-improve during T2I generation without external reward models. Meta-TTRL formulates test-time learning as a closed-loop interaction between an object-level generator and a meta-level introspector. The introspector decomposes prompts into structured verification rubrics and produces confidence-enhanced intrinsic monitoring signals, which are used to optimize the generator policy through reinforcement learning (RL). Extensive experiments demonstrate that Meta-TTRL generalizes well across three representative UMMs, including Janus-Pro-7B, BAGEL, and Qwen-Image, achieving significant gains on compositional reasoning tasks and multiple T2I benchmarks with limited data. Further analyses with external introspectors, RL leakage, and alternative monitoring signals reveal a key principle for effective TTRL: metacognitive synergy, where monitoring signals must align with the model's own optimization regime.

Meta-reinforcement learning (meta-RL) agents adapt to novel tasks from test-time experience, but require diverse training sets of environments and reward functions that are expensive to construct. Behavior Foundation Models (BFMs) learn policies from reward-free data that are capable of zero-shot RL (ZSRL), adapting to new objectives at test time when given a large reward-labeled dataset. Recent work has shown that BFM task inference can be performed online, making BFMs and meta-RL direct competitors for generalization in fixed environments. We ask whether BFMs can be extended to adapt to novel reward functions in novel environments, and identify four key limitations: environment identification, online data collection, test-time exploration-exploitation, and mixed reward supervision. We study these subproblems through toy domains, standard BFM benchmarks, and simulated humanoid locomotion, then propose a unified framework that addresses all four. The resulting method, MetaBFM, is a hybrid RL agent spanning meta-RL, ZSRL, and intrinsic exploration. During training, MetaBFM combines supervised reward-following with unsupervised reward-free learning; at test time, it interpolates between exploration, exploitation, and ZSRL-style reward inference. We evaluate MetaBFM on two toy meta-RL domains and at scale in MetaWorld, showing that hybrid meta-RL/ZSRL agents can learn more general behavior from the same set of reward functions and may reduce the need for future meta-RL domains to hand-design diverse training sets.

Unequal availability of human preference data across languages poses a significant challenge for aligning large language models in multilingual settings. To address the lack of sufficient data in low-resource language alignment, we propose a meta-learning framework for Reinforcement Learning from Human Feedback and Direct Preference Optimization. By leveraging preference data from other languages, our framework learns a transferable initialization that enables effective adaptation to a target language with minimal data. We provide theoretical guarantees for both the meta-reward modeling and meta-policy optimization settings, and empirically demonstrate the effectiveness of our approach on multilingual benchmarks. In an extremely low-resource setting with only 100 target-language preference samples, our approach achieves up to $28\%$ win-rate improvements over baseline methods, and consistently outperforms baselines across multiple target languages and model scales. Our approaches retain these advantages across different combinations of meta-training languages and varying linguistic distances from the target languages.

Memory-augmented LLM agents tackle complex long-horizon tasks by recursively summarizing interaction trajectories into compact memory. However, existing approaches typically train these memory policies using outcome-based reinforcement learning, failing to localize where intermediate memory quality degrades. As interactions unfold, ambiguous recursive summaries progressively discard or blur task-relevant information. This exacerbates belief deviation, obscuring the agent’s estimate of the latent task state and ultimately derailing long-horizon reasoning. We therefore argue that memory optimization should focus not merely on trajectory-level success, but on the clarity of the belief induced by intermediate summaries. To this end, we introduce Belief Entropy, a self-supervised proxy that probes how uncertain the model remains about the latent task state given its current memory. Based on this proxy, we propose Metacognitive Memory Policy Optimization (MMPO). Instead of relying only on sparse outcome-based signals, MMPO provides fine-grained, memory-specific supervision by explicitly penalizing summaries that induce high epistemic uncertainty. Experiments show that MMPO consistently outperforms existing methods on diverse long-horizon tasks, maintaining 97.1% performance even when scaled to 1.75M-token contexts.

Self-improving LLM agents typically improve answers, not the $\textit{process}$ by which answers are improved: their search loops and mutation rules are held fixed by design. We introduce $meta^n$, a framework for $\textit{recursive}$ self-improvement in which a single universal meta-operation $\Omega$ is applied repeatedly to build a hierarchy of meta-layers whose depth is sized by convergence rather than prescribed. Each layer observes execution traces from below, diagnoses failure patterns, and emits executable code (strategic guidance via pre-processing and reusable functions via a code library) whose effects cascade downward. Inter-layer conditioning permits up to $\prod_{d=2}^{n} k_d$ effective strategy-tactic combinations across $n$ layers rather than $\sum_d k_d$. An evolutionary orchestration mode maintains a growing archive of candidate chains with fitness-weighted parent selection and cross-candidate inspiration. Across seven benchmarks (combinatorial optimization, two text classification tasks, terminal agent tasks, mathematical discovery, symbolic regression, algorithm speedup), $meta^n$ beats prior self-improving agents on all five (of seven) benchmarks where they apply, with the largest gap on CO-Bench (0.849 test vs. Gödel Agent's 0.000 and OpenEvolve's 0.816). Layers specialize without prescription and their roles emerge at depth: generic primitives at depth 2 propagate as cross-task callables, task-aware routing emerges at depth 3, and deeper layers refine via specialization. Code and data will be released publicly.

Recent advances in video generative models have enabled flexible conditional generation and editing pipelines, such as text-to-video, image-to-video, and reference-guided synthesis. However, these workflows are inherently one-directional, mapping conditions to videos without providing a mechanism to invert this process. In complex media production settings, the ability to recover controllable textual conditions from a given video is highly desirable, yet remains underexplored. While inverse prompting has been studied for image generation, directly extending these approaches to video is challenging due to the need to capture temporal dynamics beyond static appearance, as well as the substantial computational cost of per-video optimization. In this work, we propose the first practical framework for efficient video inverse prompting. Specifically, we formulate inverse prompting as a meta-learning problem and introduce Meta Video Inverse Prompting (MVIP), which learns a meta-prompt optimized over temporal dimensions to enable fast and effective prompt inference. Our approach amortizes the inversion process, eliminating the need for costly iterative optimization at test time. Experimental results demonstrate that MVIP recovers higher-quality inverse prompts with significantly improved reconstruction fidelity compared to image-based inversion and iterative captioning baselines, while achieving substantial gains in efficiency.

Learning solution operators for partial differential equations (PDEs) requires models that can represent spatial structure across grids and scales. This becomes especially important in time-dependent problems, where the learned operator is often used autoregressively: each predicted state is fed back as input to predict future states. In this setting, small spatial errors can be repeatedly propagated through the learned dynamics, leading to instability and drift, vitiating long-term solution fidelity. We propose Mesh-Free Convolutions (MFCs), filters defined in continuous space and discretized only at evaluation time. This allows the same learned filters to be applied across resolutions. Rather than imposing a grid-dependent stencil or a fixed spectral cutoff, MFCs learn smooth scale-dependent filtering and include directional terms for transport-like behavior. Across fluid-dynamics and weather-forecasting benchmarks, MFC-based models produce stable long-horizon rollouts, transfer reliably across resolutions, and better preserve long-time statistics. These results suggest that continuous, mesh-free filtering is a useful inductive bias for PDE operator learning, especially in autoregressive prediction.

We introduce mesh-invariant adaptive Markov chain Monte Carlo methods for Gaussian-process posteriors arising in infinite-dimensional Bayesian inference. In function-space MCMC, posterior distributions are defined by a change of measure with respect to a Gaussian prior, making absolute continuity essential for valid proposal construction. Standard adaptive schemes that modify means or scales in the full discretized space can destroy this property, leading to proposal measures that become singular in the infinite-dimensional limit. To avoid this, we adapt only an active finite-dimensional subspace of the Gaussian-process representation while preserving the prior dynamics on inactive coordinates. This yields two adaptive proposals, pCNLV and pCNMV, which extend preconditioned Crank--Nicolson and Crank--Nicolson Langevin methods by learning posterior scale, and in pCNMV also posterior mean structure, on the data-informed subspace without introducing discretization-dependent Gaussian density ratios. The resulting samplers retain the mesh robustness of function-space methods while improving efficiency through local adaptation. Experiments on a Darcy-flow inverse problem and Bayesian logistic regression demonstrate consistent efficiency gains, including an approximately fourfold improvement in effective sampling efficiency for Darcy flow.

Autoregressive (AR) modeling has recently achieved remarkable progress in native 3D mesh generation, largely due to its natural ability to handle variable-length, discrete data structures. However, the inherent constraints of the AR paradigm severely restrict the generated meshes, leading to limited face counts, bounded vertex resolutions, and difficulties in supporting textures. To overcome these bottlenecks, we propose the Barycentric Dominance Field (BDF), a continuous representation defined on triangular mesh surfaces that elegantly encodes vertex topological connectivity. BDF bridges the fundamental gap between discrete mesh topology and continuous diffusion-based generative modeling by transforming connectivity into a continuous surface signal. As an intrinsic mesh property, BDF shares strong similarities with texture maps, enabling its seamless integration into existing 3D diffusion pipelines without requiring architectural modifications. Extensive experiments demonstrate that BDF empowers diffusion models to generate native meshes with significantly higher quality, greater scalability, and stronger robustness compared to state-of-the-art autoregressive methods.

We propose Policy-Shift-Guided Spectral Alignment (PSA), a retraining-free method for merging RLVR post-trained language-model experts by guiding spectral subspace selection with token-level expert-base policy shifts. We first show that existing SFT-oriented merging techniques under-preserve the sparse, directional changes in next-token probabilities that distinguish RLVR experts from the pretrained base. To address this challenge, PSA scores calibration tokens by expert--base probability difference under the same prefix, and converts probability-shift-weighted input activations into column weights. These weights define a weighted SVD objective that prioritizes update directions active on high-shift tokens. After low-rank truncation, PSA applies cross-expert polar alignment to the truncated task-vector bases and restores each expert's per-layer Frobenius scale before aggregation. Experiments on Qwen2.5-7B and Qwen3-1.7B across three RLVR tasks show that PSA consistently outperforms strong merging baselines while preserving expert-level capabilities in a unified model.

Large language models (LLMs) have shown strong performance on isolated psychiatric tasks, including dialogue, diagnosis, and treatment planning, yet existing benchmarks rarely simulate complete psychiatric clinical encounters. We introduce **MentalHospital**, a virtual evaluation environment for LLM-based psychiatric clinical encounters. MentalHospital instantiates the Subjective Interviewing, Objective Examination, Diagnostic Assessment, and Treatment Planning (S.O.A.P.) workflow, using skill-augmented standardized patients constructed from 1,193 de-identified psychiatric electronic health record (EHR) cases spanning all major ICD-11 categories and 76 disorders. Each encounter is assessed through a dual-track protocol that combines objective comparison against EHR-derived references with subjective assessment of clinical process quality. To scale specialist judgment, we develop **MentalEval**, five domain-specific evaluators covering communication empathy, interviewing professionalism, clinical-note quality, diagnostic rigor, and treatment appropriateness, trained with rubric-grounded SFT and expert-guided DPO. Survey responses from 22 clinicians support MentalHospital's clinical fidelity (3.88/5), while MentalEval achieves strong expert alignment with an average QWK of 0.944. Benchmarking shows that even the strongest LLM trails clinicians by 37.28 percentage points in objective psychiatric competence, with mental status assessment as a key bottleneck.

Large foundation models have accelerated progress toward general-purpose agents that interact with humans and other agents through language and multimodal signals. However, robust multi-agent decision-making requires reasoning about what other agents know, intend, and are likely to do under partial observability. Current agentic systems often operate through prompt design, memory, or end-to-end behavioral shaping, but typically do not learn an explicit partner-state representation that can be reused as a decision variable across tasks. We introduce mental-model-enabled agents, a framework that equips an agent with a latent mental model of its counterpart, allowing it to infer hidden beliefs, intentions, and likely reactions from the observed history and use these inferences to guide action selection. Our method learns an amortized recursive Theory-of-Mind representation, with first- and second-order mental-state structure, jointly with a belief-conditioned reward model that evaluates candidate actions relative to the inferred partner state. A policy is then learned under this belief-aware signal, yielding an agent that can act independently at inference time while retaining the benefits of explicit partner modeling. We evaluate the same framework on both language-only and multimodal benchmarks. Across these settings, explicit mental-state modeling consistently improves interaction quality and Theory-of-Mind performance over base agentic systems, showing that structured partner modeling is a useful inductive bias for general multi-agent systems.

Mental simulation, the ability to reason by constructing and updating internal visual states, is central to flexible human cognition, but remains elusive in current multimodal large language models. Latent multimodal reasoning offers a promising route toward this capability by interleaving text with continuous visual latent blocks, allowing models to maintain internal visual workspaces without decoding them into pixels. In this paper, we ask whether these latent visual states actually matter. Through a direct perturbation analysis across representative latent-reasoning MLLMs, we find that they often do not: dropping, replacing, or shuffling generated visual latents has only minor effects on the model's own final-answer probability, while equivalent perturbations to the linguistic reasoning trace are substantially more damaging. We call this failure mode Mental Simulation Collapse (MSC): the model appears to perform interleaved visual reasoning, but routes most answer-relevant computation through text. We further diagnose that MSC follows naturally from existing training objectives: visual alignment can make generated latents resemble helper-image embeddings, but it does not require the decoder to rely on them; subsequent text-only supervision further leaves the semantic role of the latent pathway unidentifiable. To address this, we introduce AnchorSim (Anchored Mental Simulation), a contrastive distillation framework that trains models to make their generated visual latents consequential. Across multiple visual reasoning benchmarks, AnchorSim improves performance, while diagnostic re-evaluation shows that visual perturbations now meaningfully affect predictions. Our results expose a fundamental failure mode of latent multimodal reasoning and demonstrate that mental simulation must be anchored to model behavior in order to matter.

This position paper argues that AI for mental health must move beyond two dominant paradigms. The first is diagnostic prediction, which frames mental healthcare as symptom detection, diagnosis, and decision-making. The second is chat-based support, which reduces therapeutic interaction to language-mediated information delivery. Both approaches neglect what therapeutic research shows to be central to healing: experiential, affective, and embodied processes. In many therapeutic traditions, insight emerges not through information-delivery but through emotionally resonant, multisensory experiences that allow individuals to feel, reflect, and reframe their inner world. We propose an alternative framework: co-experiential AI systems that identify emotionally salient moments across modalities, modulate sensory experience to facilitate reflection, and adapt over time to a user's evolving responses and interpretations. Using dreamwork therapy as a stress test for mental health AI, we define three key challenges for AI systems: identifying affective anchors in indeterminate multimodal interactions; modeling user perspective as a situated context; and enabling continual adaptation to individuals without repeated retraining. We posit that addressing these challenges requires rethinking AI's role not as an expert, but as a perspective-aware participant in human meaning-making.

Multimodal video fusion methods still suffer from limited performance due to the inadequate exploitation of historical frames and the simplified temporal consistency modeling. To address this, we propose MemoryFusion, a two-stage framework built upon cross-temporal memory learning, which leverages historical information across multiple temporal scales to enhance robustness and progressively refine temporal consistency. In the first stage, we introduce a Short-Term Memory Bank (STMB) to aggregate recent temporal features for enhancing the current frame, and a Long-Term Memory Bank (LTMB) to preserve representative features from extended temporal sequences through adaptive update rules. Meanwhile, we incorporate a Temporal Smoothing Module (TSM) to perform coarse temporal consistency modeling, suppressing abrupt background variations and establishing a stable temporal foundation. In the second stage, we design a Temporal Refinement Module (TRM) implemented as a lightweight 3D residual network, which conducts prior-guided fine-grained temporal refinement to recover subtle temporal dynamics and spatial details overlooked in the first stage. Extensive experiments demonstrate that MemoryFusion significantly improves both visual fidelity and temporal consistency by performing cross-temporal learning, outperforming the state-of-the-art in both frame-based and video-based fusion methods. The source code and pretrained models will be publicly released.

Memory-augmented LLM agents enable interactions that extend beyond finite context windows by storing, updating, and reusing information across sessions. However, training such agents with reinforcement learning in multi-session environments is challenging because memory turns the agent's past actions into part of its future environment. Once different rollouts write, update, or delete different memories, they no longer share the same intermediate memory state, making trajectory-level comparisons fundamentally unfair. This violates a key assumption behind group-relative methods such as GRPO, where rollouts are compared as if they were sampled from the same effective environment. Consequently, trajectory-level rewards provide noisy or biased credit signals for long-horizon memory operations. To address this challenge, we introduce Memory-R2, a training framework for long-horizon memory-augmented LLM agents. Its core algorithm, LoGo-GRPO, combines local and global group-relative optimization. The global objective preserves end-to-end learning from long-horizon trajectory-level rewards, while local rerollouts compare alternative continuations from identical intermediate memory states, yielding fairer group comparisons and more precise supervision for memory writing, updating, and retrieval. Beyond credit assignment, Memory-R2 jointly optimizes memory formation and memory evolution with a shared-parameter co-learning design, where a fact extractor and a memory manager are instantiated from the same LLM backbone through role-specific prompts. To make memory construction more controllable, we further formulate each session as a multi-step decision process by chunking the interaction and alternating the two memory roles. A progressive curriculum from 8 to 16 to 32 sessions stabilizes training as the memory horizon grows. Together, these components provide an effective training paradigm for memory-intensive LLM agents in long-horizon multi-session settings.

Existing VLM-based open-set semi-supervised learning (OSSL) methods primarily rely on coarse-grained class-level semantics, thereby hampering the accurate identification of in-distribution (ID) versus out-of-distribution (OOD) samples. To overcome this issue, we propose a novel memory-driven contrastive embedding enhancement approach by integrating fine-grained visual contrastive cues into class-level textual side. Concretely, we design a memory bank to construct and maintain a diverse collection of the most distinctive fine-grained visual embeddings. Guided by the memory bank, visual information is incorporated into the textual side through cross-modal contrastive learning, enabling more discriminative fine-grained separation between ID and OOD samples. Consequently, the learned OSSL model exhibits improved contrastive discriminability across known and unknown categories. Extensive experiments demonstrate that our method achieves state-of-the-art (SOTA) performance on multiple fine-grained datasets. The code is available at https://anonymous.4open.science/r/CodeForPaper.

Associative memory models classically describe retrieval as convergence to stored patterns, but the theory of sequential retrieval remains comparatively limited. We develop a two-timescale input-driven Hopfield network in which fast associative retrieval is modulated by a slow feedback variable evolving over a prescribed memory-transition graph. This yields autonomous, graph-constrained transitions while preserving analytical tractability. Using slow-fast and geometric singular perturbation theory, we characterize memory fixation via memory manifolds and identify the geometric regions that govern stability loss and escape from retrieved memories. We further derive reduced slow dynamics and explicit criteria for transition onset, self-sustained retrieval, and collapse, expressed through escape times, gain thresholds, and fixed points. Together, these results provide a tractable framework for analyzing feedback-driven sequential retrieval in Hopfield networks.

The memory capabilities of Large Language Models (LLMs) have garnered increasing attention recently. Despite great success achieved, existing retrieval-based memory approaches typically overlook the differences between memories and employ a unified strategy to process all memories, leading to suboptimal performance. Thus, an intuitive question arises: can we categorize memory into different types and select appropriate strategies? However, given the topic-rich, scenario-complex, and boundary-blurred nature of memory scenarios, achieving precise classification of memories is not easy. To address this challenge, we propose a memory multi-class dataset in this paper, termed TriMEM, which provides precise annotations for memory types across diverse scenarios. Building upon this foundation, we propose a novel memory framework, named MemoType, which can adaptively recognize each memory and query type with the learned router model. With the memory and query routing, MemoType can retrieve the memory with corresponding query types and design tailored retrieval strategies, thereby enhancing the retrieval performance. Moreover, we theoretically prove that any single retrieval strategy is subject to a fundamental upper bound on its expected retrieval precision in multi-class corpora, leading to systematic precision degradation. Extensive experiments on three datasets demonstrate that MemoType consistently outperforms existing methods, achieving up to 16.18% improvement in Recall@1.