NeurIPS 2026 Papers

We study stochastic multi-objective optimization when each objective may be non-smooth and non-convex. We introduce the notion of $(\delta,\epsilon)$-Pareto Goldstein stationary points (PGSP) to characterize the convergence of solving multi objective optimization. The $(\delta,\epsilon)$ generalizes the definition of $(\delta,\epsilon)$-Goldstein stationary point for single objective non-smooth non-convex optimization and the $\epsilon$-Pareto stationary point for solving multi objective optimization with smooth objectives. We propose the multi-gradient-free method (MGFM), which finds $(\delta,\epsilon)$-PGSP with $\tilde{\mathcal{O}}(m^2d^{3/2}\delta^{-1}\epsilon^{-4})$ oracle complexity to the stochastic function values, where $m$ is the number of objectives and $d$ is the problem dimension. We further propose MGFM+, a faster multi-gradient-free method based on variance reduction, which improves the complexity to $\tilde{\mathcal{O}}(m^2d^{3/2}\delta^{-1}\epsilon^{-3})$. These provide the first non-asymptotic analysis for multi-nonsmooth-nonconvex-objective optimization. \end{abstract}

Partially Observable Markov Decision Processes (POMDPs) are systems in which one agent interacts with a stochastic environment, and receives only partial information about the current state. In a multi-environment POMDP (MEPOMDP), the initial state is unknown, and assumed to be adversarially chosen. In this work we focus on computing the optimal value and policy in MEPOMDPs with finite-horizon objectives. That problem is known to be PSPACE-complete in POMDPs. Our main results are as follows: (1) we establish that it is also PSPACE-complete in the more general setting of MEPOMDPs; (2) we present a practical algorithm and evaluate it on classical benchmarks, significantly outperforming the only previously known algorithm.

Randomized Smoothing (RS) is a principled and widely adopted approach for certifying the robustness of black-box models, such as neural networks, against adversarial perturbations. We propose an optimization perspective that unifies several prior randomized smoothing certificates. We then show how to reduce the underlying high-dimensional worst-case optimization problem to an equivalent two-dimensional formulation under a double- or multi-sampling scheme, enabling efficient solutions via convex optimization. By exploiting additional information about the variance of the smoothed classifier across different smoothing distributions, we derive certified bounds that improve upon standard certificates. We show results on both classification and regression tasks.

We study multi-armed bandits (MABs) augmented with best-action queries, in which the learner may additionally query an oracle that reveals the best arm in the current round. This setting was recently characterized by Russo et al. [2024] in the full-feedback model, where the learner observes the rewards of all arms after each round. They show that, in both stochastic and adversarial environments, $k$ best-action queries reduce the optimal $\widetilde{\mathcal{O}}(\sqrt{T})$ regret to $\widetilde{\mathcal{O}}(\min\{T/k,\sqrt{T}\})$. Whether this improvement extends to the more realistic bandit-feedback model---where the learner observes only the reward of the played arm---was left as an open problem. We fully resolve this question. When rewards are stochastic but correlated among arms, we show that the full-feedback result does not carry over: any algorithm must incur regret at least $\Omega(\sqrt{T-k})$. This lower bound directly extends to adversarial environments. On the positive side, we show that $\widetilde{\mathcal{O}}(\min\{T/k,\sqrt{T-k}\})$ regret is still achievable when rewards are stochastic and i.i.d., and establish a matching lower bound, up to logarithmic factors. Together, these results provide a complete characterization of the benefits of best-action queries in the bandit-feedback model.

Offline MARL increasingly relies on generative policies to model multimodal joint behavior, typically by distilling a centralized teacher into decentralized one-step actors under the CTDE. We identify a failure mode at the teacher training stage: standard flow-based teachers pair noise with replay targets independently, so nearby noise samples can be routed toward conflicting coordination modes. The teacher then produces samples between valid modes, and because the distillation loss regresses each local actor onto the conditional mean of the teacher's output given local input, this error is not absorbed but propagated to the student. To remove this teacher-side artifact, we propose Mode-Support Semi-Discrete Optimal Transport (MoSDOT), which summarizes multimodal replay into a finite mode support with prescribed capacities and uses conditional semi-discrete optimal transport to assign each noise sample to a single mode before teacher training. We additionally study a common-randomness variant that uses a shared noise component at execution to expose the residual gap intrinsic to strict-product execution. On controlled diagnostics and offline MARL benchmarks, MoSDOT improves endpoint quality and routing consistency, particularly on datasets exhibiting multimodal joint behavior.

Motion transfer enables controllable video generation by transferring temporal dynamics from a reference video to synthesize a new video conditioned on a target caption. However, existing Diffusion Transformer (DiT)–based methods are limited to single-object videos, restricting fine-grained control in real-world scenes with multiple objects. In this work, we introduce MotionGrounder, a DiT-based framework that $\textit{firstly}$ handles motion transfer with $\textit{multi-object controllability}$. Our Flow-based Motion Signal (FMS) in MotionGrounder provides a stable motion prior for target video generation, while our Object-Caption Alignment Loss (OCAL) grounds object captions to their corresponding spatial regions. We further propose a $\textit{new}$ Object Grounding Score (OGS), which jointly evaluates (i) spatial alignment between source video objects and their generated counterparts and (ii) semantic consistency between each generated object and its target caption. Our experiments show that MotionGrounder consistently outperforms recent baselines across quantitative, qualitative, and human evaluations.

We revisit the problem of computing mean-field equilibria (MFEs) in discrete-time, monotone, finite-horizon mean-field games (MFGs). We show that, when the transition kernel is independent of the state-measure term and the reward function satisfies the usual weak monotonicity condition and is Lipschitz continuous, anchored proximal gradient descent methods can be used to compute a monotone MFE. We also establish last-iterate convergence results for these methods. Our approach relies on formulating the computation problem as an optimization problem over the space of occupation measures. Using this formulation, we show that the problem is equivalent to a class of constrained Lipschitz monotone inclusion problems. We then apply iterative methods for this monotone inclusion formulation to derive a tractable algorithm. The resulting algorithm achieves a convergence rate of $O(1/\sqrt{T})$ after $T$ iterations, without requiring any regularization. This rate holds even in the absence of a uniqueness assumption for the corresponding MFE.

Modelling opinion dynamics typically relies on hand-crafted local interaction rules to study emergent macroscopic phenomena such as consensus and polarisation. In contrast, multi-agent reinforcement learning (MARL) enables agents to learn such behaviours directly by optimising simple rewards. To explore the potential of MARL for opinion dynamics, we introduce a GPU-accelerated *consensus and truth-finding game* that scales to populations of up to 1000 agents, comparable to many real-world social sub-networks. To prevent unrealistic conventions, we extend other-play to general-sum social interactions. We next validate our model on a subset of the Bluesky network by recovering agent importance structures from graph topology alone via a learned attention layer, finding that *highly conforming* populations most closely match human data. In large social media networks such high levels of conforming significantly reduce collective accuracy and promote dishonest agents that lie to fit in. By contrast, small, dynamic hunter-gatherer networks are less affected; here, conformity can even improve collective agreement. This suggests a mismatch between evolved human conformity heuristics and modern social media environments as a potential contributor to misinformation.

Reinforcement learning agents trained to maximize their own reward in repeated interactions can converge to supra-competitive outcomes resembling explicit collusion, without communication or shared design. Existing mitigation approaches are largely tied to specific economic settings, like two-sided platforms and auctions, leaving open how to design interventions for general repeated games. We address this gap by formalizing the connection between empirical observations from prior work on Q-learning collusion and classical theory of Simple Penal Codes (SPCs). We show any non-trivial SPC induces a quantifiable conditional dependence in agents' policies, detectable via the total variation distance between an agent's action distributions across cooperation and defection histories. Building on this connection, we propose CURB (Collusion Unwinding via Reward shaping and Belief injection), a reward-shaping framework that penalizes this Total Variation (TV) distance signal during Q-learning and is guaranteed to convert any SPC fixed point of the dynamics into a trivial one, thus precluding collusive equilibria sustained by punishment threats. Empirically, CURB substantially reduces collusion by Q-learning agents in both Bertrand and Cournot Competition Repeated Games. We further demonstrate that CURB extends to deep Q-network agents in Bertrand competition, suggesting the mechanism generalizes beyond tabular Q-learning.

Large language models are increasingly used for mental health support, yet their conversational coherence alone does not ensure clinical appropriateness. Existing general-purpose safeguards often fail to distinguish therapeutic disclosures from genuine clinical crises, and progress on developing better ones is bottlenecked by a lack of clinically grounded evaluation resources. To address this gap, we introduce a risk taxonomy, developed in collaboration with PhD-level clinical psychologists, that identifies actionable harm (self-harm and harm to others) while preserving space for safe, non-crisis therapeutic content. We release MindGuard-testset, a dataset of multi-turn conversations annotated at the turn level by clinical experts, which is, to our knowledge, the first public benchmark with turn-level clinical risk labels for multi-turn mental health support. We also release an automated red-teaming (ART) framework designed to measure how safety classifiers affect downstream model behavior in adversarial multi-turn interactions. Using synthetic dialogues generated via a controlled two-agent setup, we train MindGuard, a family of lightweight safety classifiers (with 4B and 8B parameters). Our classifiers reduce false positives at high-recall operating points and, when paired with clinician language models, help achieve lower attack success and harmful engagement rates compared to general-purpose safeguards. We release all models, human evaluation data, and the ART framework.

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.

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.

Agent memory systems have demonstrated significant potential in tasks such as long-term dialogue, personalized assistants, and video understanding. However, as inference progresses, continuously accumulated memory imposes substantial storage and retrieval burdens. To address this issue, we propose MemForest, a general memory compression framework adaptable to various agent memory systems. Specifically, MemForest leverages both global semantic similarity and local temporal continuity of memory events to partition the historical memory into a set of event-centric independent units. For each independent unit, the framework constructs a maximum spanning tree structure, referred to as an EventTree, and performs progressive merging by iteratively selecting high-weight edges, thereby effectively compressing redundant memory nodes and reducing storage overhead. In addition, we introduce an anchor-guided propagation retrieval mechanism, which retrieves more relevant memory nodes from the temporal neighborhoods of key memory nodes, thereby enabling more accurate memory retrieval. Extensive experiments demonstrate the effectiveness of MemForest. Under the unimodal Mem0 framework, across three benchmarks (LoCoMo, LongMemEval, and PersonaMem), MemForest preserves 97.1% of the original performance while compressing 50% of historical memory, achieving a 1.89× retrieval speedup. Under the multimodal M3-Agent framework, across two benchmarks (M3-Bench-robot and M3-Bench-web), MemForest retains 99.7% of the original performance under a 50% compression ratio, while achieving a 2.24× retrieval speedup. Our code is available in the supplementary materials, and all data will be released on GitHub.

Many agent-memory evaluations collapse state revision, deprecation, erasure, and provenance into retrieve-heavy scores. We introduce MemContract, a diagnostic benchmark of 1 , 080 multi-session tasks over store, retrieve, revise, deprecate, erase, and prove-origin, with formal pre/post-conditions, mandatory distractor sessions plus alias rotation on every family, blinded/stale/delayed controls, a fixed 25-probe residue audit, and benchmark-validity checks via a 180-task human contract-fit audit and a 120-task human-assisted upper bound. Under matched prompts, compute, storage, and latency budgets on GPT-4o across seven architectures, Graph Memory reaches 67.6% macro average (95% paired-bootstrap CI [65.8, 69.4]) versus 62.4% for the strongest hybrid baseline and 40.6% for RAG, with most separation concentrated in revise, deprecate, erase, and prove-origin while store/retrieve remain comparatively compressed. Claude 3.5 Sonnet and Gemini 1.5 Pro 002 preserve the same top-three ordering on a matched 360-task rerun, while the manual audit finds 96.7% agreement that the harness label matches the intended contract and the human-assisted upper bound reaches 93.8% macro, placing the best-system score in a solvable regime rather than a floor of label noise. Reviewer-critical calibration checks tell a narrower but durable story: removing prove-origin leaves the Graph - Hybrid gap unchanged at 5.2 pp, best-effort prompt/schema tuning narrows it to 2.6 pp, and fully external LongMemEval wu2024longmemeval and LoCoMo maharana2024locomo narrow it to 1.8 and 2.8 pp while preserving the top-three ordering Graph > Hybrid > Editable KV. The stable claim is therefore not universal backend superiority: contract-sensitive evaluation reveals separation that broader or retrieval-heavier workloads attenuate but do not erase. The 25-probe audit bounds catchable residue leakage at 3.9% for Graph Memory and 27.4% for RAG, but does not certify deletion.

LLM multi-agent systems rely on division of labor to solve complex tasks, but their execution can be blocked by contention for shared resources such as GPU test environments, CPU execution slots, and exclusive tool sessions. Existing workflows, task allocation methods, and runtime schedulers usually arbitrate such access indirectly through workflow position, dependency structure, queue state, or resource metrics. These criteria do not fully capture the value of satisfying a specific request, because request value is distributed across agents' local execution states and changes with tests, retries, and downstream results. We propose a market-based protocol for access arbitration in LLM multi-agent systems. Each agent converts its current plan and local execution state into a structured, budget-constrained bid that signals relative urgency. For asynchronous requests, an online clearing method based on optimal stopping decides when to close the clearing window, allocate access rights, settle payments, and track resource occupation until release. Across three contention scenarios, Market reduces makespan by 5.2\%, 2.5\%, and 4.9\% relative to the strongest primary baselines, while improving task score by about two percentage points.

Multi-agent LLM decision systems for portfolio management still lack a principled way to assign credit across specialist agents, remain vulnerable to cold-start dominance under regime shifts, and offer limited transparency into how final allocations are formed. We propose Market Regime Council (MRC), a cooperative multi-agent decision system that computes exact Shapley credits across all single, pairwise, and Grand-coalition outputs for online agent weighting. Instantiated with $N{=}3$ specialist agents, at each trading period, MRC recomputes coalition-based Shapley weights from exponentially weighted performance histories, uses a Bayesian adaptive mixture to stabilize early periods, applies regime-dependent multipliers to adjust agent authority, and records each rebalance through a five-layer causal trace. Over 1,037 trading days across 13 crypto assets and five seeds, MRC achieves a Sharpe ratio of 1.51 and a cumulative return of 440.1\%, ranking first on CR, SR, and IR among active baselines and attaining the lowest MDD among active methods. Ablation results show that the gains come from Shapley-weighted integration across coalition outputs rather than from any single stage in isolation. Code and demo data are included in the supplementary material.

Multimodal Large Language Models (MLLMs) show strong visual perception capabilities, but existing evaluations largely focus on single-view or multi-view settings with shared camera parameters, leaving the challenge of {multi-agent collaborative} scenarios underexplored. In such settings, multiple agents observe a shared environment from independent viewpoints, and reliable decisions require reasoning across these independent observations of the same environment. To study this problem, we introduce **MaRiO**, a benchmark for evaluating multi-agent collaborative reasoning, and evaluate 23 state-of-the-art MLLMs on it. Our analysis reveals three key findings: (1) spatial tasks are generally easier than geometric reasoning tasks; (2) most models struggle to resolve cross-agent correspondences, with errors increasing with scene complexity; and (3) models show limited ability to leverage geometric cues present in the scene. As an initial step toward addressing these challenges, we explore geometry-guided synthetic view augmentation that generates intermediate transition frames between agent observations, providing auxiliary spatial context that improves performance in certain categories. Overall, our results highlight that multi-agent collaborative reasoning remains an open challenge and an important direction for multimodal embodied systems.

Modeling gene regulation in single cells requires jointly representing DNA sequence, chromatin accessibility, and gene expression across regulatory scales. Sequence-based models capture DNA-level regulatory grammar but typically lack cell-level resolution, whereas single-cell omics models capture cell-state semantics but treat genes and peaks as fixed vocabulary tokens, and thus lack a unified sequence-based view of regulation. Here, we present MUSS, a sequence-based multi-scale model for single-cell gene regulation that preserves both DNA-level and cell-level resolution. MUSS derives peak- and gene-level representations from DNA using a frozen pre-trained sequence encoder, integrates them with a multi-scale encoder combining peak-peak self-attention and distance-biased peak-gene interaction, and trains on paired scATAC-seq and scRNA-seq to align sequence-derived representations with cell-specific regulatory states. Across gene expression prediction, enhancer-gene linking, TF-target gene recovery, and zero-shot cell clustering, MUSS outperforms both sequence-based and single-cell omics baselines, and further generalizes to genes and peaks unseen during training. Together, these results demonstrate that MUSS learns biologically meaningful and broadly generalizable regulatory representations for decoding cell-type-specific regulatory programs from DNA sequence.

3DGS-based open-vocabulary 3D scene understanding requires identifying reconstructed 3D instances that correspond to a natural-language query. Existing methods typically compress multi-view observations of an instance into a single semantic descriptor, which is insufficient to represent a complete 3D instance. To retain rich semantic information across diverse viewpoints, we propose MSR-3D, a framework built around a Multi-Mode Semantic Representation. Specifically, MSR-3D derives multi-view observations from both 2D segmentation masks and rendered masks of each segmented 3D instance. These observations are organized into groups. Each group is summarized as a semantic mode through a representative feature and associated statistical attributes. Based on the multi-mode semantic representation, we further introduce a lightweight learnable mode-aware matcher for querying. The matcher employs linear scoring modules to project the multi-mode representation of each query-instance pair into a three-dimensional score space. Calibrated decision boundaries are then learned in the resulting score space to separate true query-instance matches from semantically related distractors. This structured and decomposable design further improves the interpretability and generalization of MSR-3D. Experiments on open-vocabulary scene-understanding benchmarks show that MSR-3D achieves state-of-the-art results, improving mIoU on LERF-OVS by 7.52% over the baseline and achieving the best overall performance on 3DOVS.