NeurIPS 2026 Papers

In the animal kingdom, mirror self-recognition is a canonical probe of higher-order cognition, emerging only in some species. We ask whether an analogous functional capability emerges in embodied vision-language model (VLM) agents: can they recognize themselves in a mirror? We introduce a controlled 3D benchmark where a first-person VLM agent must infer a hidden body attribute from its reflection and select the matching target, while avoiding self–other misattribution. To separate mirror-grounded self-identification from shortcuts, we test mirror removal, misleading cues, and occluded reflections. We also evaluate the decision process through mirror seeking, temporal ordering, self-attribution, and reasoning–action consistency. Our experiments show that mirror-based self-identification emerges mainly in stronger VLMs. These models can use reflected evidence for action, whereas weaker models often inspect the mirror but fail to extract self-relevant information or misattribute their reflection. Language–vision conflict further shows that self-referential language alone is not evidence of grounded self-identification. Overall, mirror-based evaluation provides a diagnostic for whether embodied self-grounding is causally rooted in perception and action rather than priors, prompt compliance, or confabulation.

Despite rapid progress in artificial intelligence, current systems struggle with the interconnected challenges that define real-world decision making. Practical domains such as business management require open-ended optimization, actively learning environment dynamics from sparse experience, planning over long horizons in stochastic settings, and reasoning over spatial information. Yet no existing human-AI benchmarks assess how well agents integrate these challenges in a grounded decision-making context. To this end, we introduce Mini Amusement Parks (MAPs), an amusement-park simulator designed to evaluate an agent’s ability to model its environment, anticipate long-term consequences under uncertainty, and strategically operate a complex business. We provide expert human performance and a comprehensive evaluation of state-of-the-art agents, finding experts outperform these systems by 11.4× on easy mode and 15.3× on medium mode. Our analysis reveals persistent weaknesses in long-horizon planning, sample-efficient learning, spatial reasoning, and modelling uncertainty. By unifying these challenges within a single environment, MAPs offers a new foundation for benchmarking agents capable of adaptable decision making.

Although LLMs have made substantial progress in reasoning, systematically producing frontier-level reasoning data remains difficult. Existing synthesis methods often have limited visibility into the structural factors that govern problem difficulty, which can result in narrow diversity and unstable difficulty control. In this work, we view the difficulty of a reasoning problem as arising from the accumulation of atomic knowledge-reasoning transformations, which we term *thought modes*. Building on this perspective, we propose MindLoom, a framework for synthesizing frontier-level reasoning data through compositional thought mode engineering. Given a collection of hard problems with verified solutions, MindLoom first decomposes those solutions into thought mode chains that reveal each problem's construction logic. It then trains a retrieval model that matches problem states to compatible thought modes, providing guidance on which reasoning challenges to introduce during synthesis. New problems are composed by iteratively applying retrieved thought modes to seed questions, with distribution-aligned sampling to encourage diverse reasoning coverage. Finally, a rollout-based judging stage labels generated questions by difficulty and supplies judged-correct responses for supervised fine-tuning. We evaluate MindLoom on nine benchmarks covering five STEM disciplines and four mathematical reasoning tasks across multiple model families and sizes. Models fine-tuned on MindLoom generated data consistently improve over base models, distillation, and external-data baselines across the reported benchmarks. Ablation studies indicate the contribution of each component, and further analysis suggests that MindLoom covers a broad range of reasoning patterns while maintaining useful difficulty control. We have open-sourced our implementation at https://anonymous.4open.science/r/MindLoom-5B8E.

Large language models (LLMs) are increasingly deployed as interactive agents, yet their capacity for social and strategic reasoning over extended interaction remains poorly understood. Existing evaluations rely on static vignettes or single-game benchmarks that cannot capture the sustained, multi-faceted reasoning that real-world multi-agent settings demand. We introduce Mindgames, a multi-game arena and evaluation platform for LLM agents that operationalizes complementary reasoning demands relevant to ``theory of mind'': belief attribution under hidden information, opponent modeling through repeated strategic interaction, cooperative inference under knowledge asymmetries, and sustained deception in social deduction. Built on TextArena, Mindgames provides a unified interaction interface, TrueSkill-based rating, and full trajectory logging across four game environments. We instantiate Mindgames through a 2025 competition cycle hosted at a major AI conference, which assessed 944 submitted agents from 76 teams across four games: Colonel Blotto, Iterated Prisoner's Dilemma, Codenames, and Secret Mafia. Our analysis surfaces both agent-level and evaluation-level limitations: brittle rule adherence remains a major bottleneck, top-performing systems repeatedly rely on explicit structural scaffolding, and leaderboard validity differs sharply across environments. In particular, failure-heavy environments can reward robustness to opponent errors as much as strategic ability, with Secret Mafia exhibiting a pronounced error-survival confound in this cycle. We release a dataset of 29,571 multi-agent games (comprising 94,132 player trajectories and 243M tokens) with turn-level observations, actions, and rewards, together with MG-Ref, a deterministic offline tournament protocol that scores new agents against a frozen reference pool of top-ranked, low-error Stage~II submissions under the same error-attribution lens used in this analysis. By releasing the benchmark interface, dataset, MG-Ref, and starter kit, Mindgames provides a reproducible resource for studying progress in multi-agent social and strategic reasoning.

Visual decoding from brain signals is a key challenge at the intersection of computer vision and neuroscience, requiring methods that bridge neural representations and computational models of vision. A field-wide goal is to achieve accurate, generalizable decoding from non-invasive, temporally resolved signals, including electroencephalography (EEG). A major obstacle towards this goal is the low signal-to-noise ratio of EEG and the substantial inter-subject variability, which render direct end-to-end EEG–image supervision weak and unstable. To address this, we introduce a tri-modal contrastive framework for EEG-based visual decoding that aligns EEG, visual, and textual representations within a unified latent space. Our approach follows a two-stage design. First, we pre-train an EEG encoder via masked reconstruction on unlabeled trials, learning spatio-temporal regularities that transfer robustly downstream. Second, we jointly align EEG, image, and LLM-generated textual descriptions through contrastive learning, where text supervision acts as a semantic regularizer that injects linguistic structure into the shared space without overwhelming the primary EEG–image signal. The encoder integrates subject-specific adaptation, graph-attention over channels, and temporal-spatial convolutional embeddings. On the Things-EEG2 200-way zero-shot benchmark, our framework achieves 54.1\% Top-1 and 83.4\% Top-5 accuracy, substantially exceeding the strongest prior baseline (32.4\% / 64.0\%), with paired Wilcoxon tests confirming significance (p < 0.01) over all in-subject baselines. We validate generalization on Things-MEG. Analysis reveals that compact embedding geometries (CN-CLIP) outperform much larger backbones, and that decoding aligns with established neurophysiology of visual processing. This work is a critical step towards robust, semantically-grounded visual decoding from non-invasive temporal neural signals.

Representation alignment has emerged as an effective approach to improve Multimodal Large Language Models (MLLMs) by regularizing their internal representations toward those of an external vision encoder. However, existing methods typically align a fixed layer of the language backbone, overlooking the fine-grained structure of Transformer models. In this work, we propose **He**ad-Wise **R**epresentation **A**lignment (**HeRA**), a method that enforces cross-modal alignment at the level of individual attention heads. Our approach is grounded in the Platonic Representation Hypothesis, focusing on preserving the *topological structure* of representations (*i.e.*, their local neighborhood relationships) across modalities. Following the Mutual K-Nearest Neighbor (MKNN) alignment metric, we introduce a contrastive objective that acts as a differentiable proxy for matching local structures. HeRA applies this objective during multimodal training to specific attention heads in the LLM, selected by their alignment score according to the MKNN metric. Counterintuitively, we find that aligning the *least* aligned heads yields the largest gains. Extensive evaluations across multiple MLLMs and 18 benchmarks demonstrate that HeRA consistently improves performance on challenging vision-centric tasks and serves as an effective regularizer against visual hallucinations by naturally curbing the over-reliance on linguistic priors. Our code will be publicly released.

Audio descriptions (AD) are essential for making visual media accessible to blind and low-vision (BLV) users. While Multimodal Large Language Models (MLLMs) offer a scalable solution for generating audio descriptions on-demand, their performance on ``inline'' audio descriptions -- which must fit within existing silences in a video -- remains under-explored, particularly for diverse user-generated content (UGC). We present a comprehensive investigation into MLLM-generated inline audio descriptions. First, we introduce a professionally annotated dataset of 36 hours of videos, each averaging $\sim$5 mins. and total 7k+ descriptions, providing a high-quality benchmark for this task. Second, we propose an evaluation framework based on audio description expert guidelines that provides fine-grained actionable metrics for model improvement. Crucially, our framework evaluates the end-to-end task: it renders the generated scripts into audio tracks to assess their fit within the original video's natural silences, minimizing disruptive overlaps. Our analysis reveals that while frontier MLLMs accurately describe visual events, significant gaps persist in audio-visual timing, quality, and narrative flow. Finally, we validate the framework and conduct studies with BLV participants and sighted raters, finding that while users narrowly prefer human-authored audio descriptions, they still find MLLM-generated audio descriptions highly beneficial for comprehension. We provide guidance on the current capabilities of MLLMs for audio descriptions and release our dataset and evaluation suite to the community.

Automated discovery of governing equations remains challenging when models rely only on numerical tokens and ignore the phase-space and trajectory evidence that is informative in low-dimensional dynamics. We propose Visual Induction for Physics-based Equation Reasoning (VIPER-R1), a multimodal framework for visual formula discovery from phase portraits, trajectory plots, and aligned measurements. VIPER-R1 uses a supervised Motion Structure Induction (MSI) curriculum for joint rationale-and-equation generation, and is then refined by \textbf{Reward-Guided Symbolic Calibration (RGSC)}, a GRPO-based stage optimized with a structure-aware reward that emphasizes topological correctness over coefficient matching. At inference time, we optionally apply Symbolic Residual Realignment (SR$^2$), an external symbolic regression module that models only the residual numerical mismatch of the base hypothesis. We release PhysSymbol, a 10{,}000-instance two-part multimodal corpus; the quantitative evaluation in this paper uses its Part 1 mechanics split, which provides paired visualizations, trajectory data, and expert C-CoT traces. On this benchmark, VIPER-R1-7B achieves the best structural score ($S_{\mathrm{struct}}=0.812$), is effectively tied with VIPER-R1-3B on exact-match accuracy ($S_{\mathrm{acc}}=0.487$ vs.\ $0.488$), and, after optional SR$^2$ refinement, reaches a Post-SR$^2$ MSE of $0.032$, nearly $3\times$ lower than the best VLM baseline. These results show that VIPER-R1 improves symbolic structure induction in focused low-dimensional dynamics by combining paired visual evidence with structure-aware calibration to produce stronger first-order hypotheses for downstream refinement.

Multimodal large language models (MLLMs) show remarkable potential for scientific reasoning, yet their performance in specialized domains such as microscopy remains limited by the scarcity of domain-specific training data and the difficulty of encoding fine-grained expert knowledge into model parameters. To bridge the gap, we introduce MicroWorld, a framework that constructs a multimodal attributed property graph (MAPG) from large-scale scientific image-caption corpora and leverages it to augment MLLM reasoning at inference time without any domain-specific fine-tuning. MicroWorld extracts biomedical entities and relations via scispaCy or LLM-based triplet mining, aligns images and entities in a shared embedding space using Qwen3-VL-Embedding, and assembles a knowledge graph comprising approximately 111K nodes and 346K typed edges spanning eight relation categories. At inference time, a graph-augmented retrieval pipeline matches query entities to the MAPG and injects structured knowledge context into the MLLM prompt. On the MicroVQA benchmark, MicroWorld improves the reasoning performance of Qwen3-VL-8B-Instruct by 37.5%, outperforming GPT-5 by 13.0% to achieve a new state-of-the-art. Furthermore, it yields a 6.0% performance gain on the MicroBench benchmark. Extensive experiments demonstrate the enhanced generalization capability introduced by MicroWorld. A qualitative case study further reveals both the mechanisms through which structured knowledge improves reasoning and the failure modes that point to promising future directions. Code and data are available at anonymous GitHub.

We introduce MetaLoop, a seven-task benchmark that evaluates the full metacognitive loop in large language models: monitoring one's own uncertainty, translating that signal into action (abstaining, switching strategies, correcting errors), and updating from feedback. Existing evaluations test these components in isolation; MetaLoop produces integrated per-model profiles across all three. Evaluating 12 frontier models alongside 460 human participants, we find that (1) accuracy does not predict metacognitive ability (high-accuracy models routinely approve their own errors); (2) models universally produce calibration signals but fail to act on them; (3) forced self-explanation distinguishes genuine self-monitoring from brittle pattern-matching; and (4) humans and LLMs both show monitoring–control gaps but with different failure modes (humans are loss-averse, models overconfident), with clarification detection as the one clear human advantage. We release the benchmark, scoring code, model outputs, and human data.

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.

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.

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.

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.

Steering large language models (LLMs) is usually performed through either instruction prompting or activation steering. Prompting offers strong control, but repeatedly caches guidance tokens and can clutter long interactions. In contrast, activation steering ,while compact, typically offers weaker control and less fine-grained steering because it does not support large structured reminders. We introduce memory inception (MI), a training-free method that steers in latent attention space by inserting text-derived key-value (KV) banks only at selected layers. MI treats steering as selective KV allocation, where reminder content need not occupy the full-layer prompt cache in order to induce implicit behavioral guidance. We formulate MI as attention over prompt, target, reference, and auxiliary banks, with canonical pre-RoPE key storage for reuse across positions and architectures. On matched personality-steering tasks, MI offers a balance between prompting and contrastive activation addition (CAA), approaching or exceeding prompting in raw control strength and CAA in drift mitigation. MI also transfers to structured heuristic guidance on physical reasoning domains, such as the PHYSICS benchmark, where it outperforms visible prompting on average, wins 10/12 subject times mode cells, and cuts content-matched KV storage by up to 118 times. These results position MI as a powerful steering method when guidance is persistent, structured, or expensive to keep in the visible transcript.

Integrating external memory into Large Language Models (LLMs) typically faces a trade-off between flexibility and depth. Explicit text-based retrieval keeps memory editable, but often acts as a static prompt prefix, increasing context length and interacting with the reasoning process only indirectly. Conversely, parametric memory can influence internal computation more deeply, but updating memory content usually requires additional optimization and may couple domain-specific information with the model's general reasoning behavior. In this work, we introduce MemPilot, a decoupled latent memory framework that dynamically guides the hidden reasoning states of frozen LLMs. MemPilot constructs an external bank of compact latent representations from offline reasoning trajectories, retrieves candidate memory entries, and integrates their latent memory tokens into hidden states via cross-attention and gated residual fusion. By separating domain-specific memory content from the learned memory-use mechanism, MemPilot enables target-domain adaptation through offline memory-bank substitution without updating the base model. Experiments across QA, coding, and mathematical reasoning benchmarks show that MemPilot achieves strong in-domain performance and more robust cross-domain transfer than other memory-augmented baselines. These results suggest that LLMs can benefit from reusable latent memory-use mechanisms while keeping memory content external and replaceable.

In this paper, we introduce MegaStyle, a novel and scalable data curation pipeline that constructs an intra-style consistent, inter-style diverse and high-quality style dataset. We achieve this by leveraging the consistent text-to-image style mapping capability of current large generative models, which can generate images in the same style from a given style description. Building on this foundation, we curate a diverse and balanced prompt gallery with 170K style prompts and 400K content prompts, and generate a large-scale style dataset MegaStyle-1.4M via content–style prompt combinations. With MegaStyle-1.4M, we propose style-supervised contrastive learning to fine-tune a style encoder MegaStyle-Encoder for extracting expressive, style-specific representations, and we also train a FLUX-based style transfer model MegaStyle-FLUX. Extensive experiments demonstrate the importance of maintaining intra-style consistency, inter-style diversity and high-quality for style dataset, as well as the effectiveness of the proposed MegaStyle-1.4M. Moreover, when trained on MegaStyle-1.4M, MegaStyle-Encoder and MegaStyle-FLUX provide reliable style similarity measurement and generalizable style transfer, making a significant contribution to the style transfer community.

Clinical decision-making is intrinsically sequential: a clinician must propose a diagnosis and treatment plan from the pre-treatment state and anticipate the post-treatment outcome in order to revise the plan. Existing medical world models address this loop only partially, through image-to-image dynamics with separate diagnostic and scoring modules, text-only trajectory models without multimodal input, or reflection agents without a world model. We propose \textbf{MedWM}, a unified multimodal LLM in which policy and dynamics are two modes of a single autoregressive backbone with fully shared parameters; the dynamics mode emits a text-only \emph{structured} post-treatment state, free-text clinical description plus standardized key-value outcomes (pCR, RECIST, labs, survival), that admits objective evaluation without perceptual metrics. We post-train this backbone with joint policy-dynamics supervision and dynamics-grounded reinforcement learning, then wrap it in an inference-time agent harness that combines breadth-depth ($N \times K$) search using the model's own dynamics as an internal verifier with a self-evolving case memory. On MIMIC-IV, BreastDCEDL-ISPY2 and HCC-TACE-SEG, MedWM consistently improves over same-backbone controls, matches or surpasses a specialized three-module image-based world model, and outperforms reflection-only, multi-agent, and larger closed-source LLM baselines; a blinded clinician study corroborates the quantitative gains.

Large language models (LLMs) have shown promise in medical question answering and clinical reasoning, yet their improvement remains constrained by static parametric knowledge and costly expert supervision. Self-evolving agents offer a promising alternative by enabling models to improve through iterative task generation and problem-solving. However, most existing self-evolving methods are designed for easily verifiable domains such as mathematics and coding, where solutions can be checked by exact answers or executable programs. Medical reasoning is fundamentally different: it is open-ended, knowledge-intensive, and often only partially verifiable. We present \textbf{MedZERO}, a self-evolving framework for open-ended medical reasoning. MedZERO couples an \textit{Examiner} that generates frontier medical question-option pairs with a \textit{Reasoner} that solves them through evidence-grounded multi-turn reasoning with external knowledge tools. To support reliable, continual improvement, MedZERO introduces controlled knowledge accumulation, which maintains temporary exploratory knowledge and curated persistent knowledge in reasoning. We evaluate MedZERO on five public medical reasoning benchmarks using 4B- and 8B-scale base models under open-ended evaluation. Across all settings, MedZERO consistently outperforms the underlying base models and prior self-evolving baselines, achieving up to 13.7 average accuracy-point gains over the next-best self-evolving baseline.