NeurIPS 2026 论文列表

Sequential recommendation predicts which item a user will interact with next. A key property of this task is that user preferences are concentrated: only a small cluster of items is relevant at a given moment. Recent diffusion-based methods add noise to the item's embedding and score candidates by embedding similarity. Since similar items have similar embeddings, their score gaps are inherently small and easily overwhelmed by noise, causing the most relevant candidates to become indistinguishable first. We prove that moving diffusion to distribution space, where each item receives independent noise, preserves rankings better and makes the reverse denoising process easier. This shift, however, loses the semantic structure of embedding space and requires richer supervision than one-hot labels. We propose PreDiff (**Pre**ference **Diff**usion), which performs diffusion in distribution space and introduces two components to address these problems: Distribution-Guided Sparse Projection projects the preference scores into embedding space while filtering out low-relevance noise, and soft targets encode inter-item similarity to teach fine-grained ranking within the preference cluster. On four benchmarks, PreDiff achieves 8%--17% relative improvement over existing methods.

Autoregressive transformers with no positional embeddings (NoPE) can recover absolute position. A common explanation is that causal attention creates a position-dependent variance signal, but this account is incomplete because LayerNorm removes per-token scale, so a viable mechanism must encode position in *direction*, not magnitude. We describe the most compact mechanism with two-layer NoPE transformers that accurately extract position. In Layer1, prefix averaging creates a shared beginning of sequence (BOS)-direction component. In Layer2, the attention's output-value matrix maps the \bos{} and \nbos{} contributions into two distinct directions, and a position-dependent \bos{} attention weight acts as a mixing coefficient that interpolates between them. The resulting directional trajectory is linearly decodable for position. Interestingly, we show two transformer variants that converge to the same theoretical mechanism and instantiate it when trained to predict position.

Causal Abstraction can be used to define a notion of equivalence between a neural network and an algorithm, thus serving as a powerful framework for mechanistic interpretability. In this paper, however, we show the existence of a class of spurious features which straightforwardly pass such equivalence tests. Suppose there are two directions in a model's representation space where: (i) one discriminates the input based on some specific feature, and (ii) the other steers the model towards a certain output. Causal Abstraction techniques can create a pathway leveraging these unrelated directions to simulate their combined behavior in the model, which thus spuriously appears to implement a certain feature. In such cases, we say the method discovered a parasite feature. Experimentally, we show that Distributed Alignment Search---a Causal Abstraction-based method---finds parasite features using linear alignment maps in two tasks: syntactic agreement and two-digit addition. Our paper highlights an important consideration for Causal Abstraction and contributes to the ongoing debate about best practices for mechanistic interpretability.

Large-scale networked systems are increasingly controlled through a small number of global configuration variables while their local controllers remain black-box, adaptive, and mutually interfering. The operator observes only post-burn-in telemetry, so neither independent-unit causal estimators nor immediate-feedback bandits directly apply. We propose \emph{CAUSAL-UCB}, a causal-optimistic framework for online steady-state configuration. The framework turns raw network operation into a linked pipeline: an exposure interface maps neighborhood interference into an auditable context; measurement-level exploration creates overlap while the target remains the no-exploration steady-state value $\Winf(z;0)$; a clipped doubly robust evaluator converts exploratory logs into value certificates; and an OFU rule selects the next configuration. We prove a unified high-probability deviation bound that decomposes statistical fluctuation, nuisance product error, clipping bias, exposure approximation, cross-configuration mismatch, context shift, and residual dependence. We also give an identification--deployment frontier showing that reliable counterfactual identification and low deployment cost cannot be optimized independently. Controlled semi-realistic replay and real-data-driven semi-synthetic replay on METR-LA and FlockLab validate the resulting benefit--reliability trade-off across regret, optimality gap, ATE error, confidence width, deployment gap, sensitivity, ablation, and frontier visualizations.

Neural causal models can simulate interventions in complex, high-dimensional settings, but typically require a known causal graph. Observational data, however, generally identifies only a Markov equivalence class of graphs, represented by a CPDAG, and causal queries may vary across DAGs in that class. We introduce \textit{Masked Neural Causal Models }(NCMs), a provably expressive nonparametric framework for simulating and bounding interventional queries over all models compatible with a given CPDAG given observational data. We give an optimization objective in the space of Masked NCMs that asymptotically recovers the true bounds of the causal effect. To enable this optimization in practice, we introduce an attention-based architecture and a novel optimization strategy that recovers highly accurate bounds in discrete nonparametric settings.

Deep neural networks are known to rely on non-robust features that align poorly with human perception. Prior work has largely framed these features as opportunistic shortcuts that help standard models on clean data. We show instead that non-robust cues can be antagonistic: they can act as **Natural Jammers** that actively mislead standard models on clean, unmodified images. Using disagreement between a standard model and robust models as an analysis lens, we identify a Jammed Set on which robust models outperform standard models. We provide causal evidence for *Spectral--spatial Locking*: jamming arises only when high-frequency components are precisely aligned with specific spatial structures. Disrupting this lock through spectral filtering or small geometric shifts can restore correct predictions. Crucially, we uncover a *Substitution Paradox*: while individual jammers are fragile to geometric shifts, the aggregate prevalence of jamming remains stable and is redistributed rather than eliminated under such transformations. Our results reframe natural non-robustness as a structural property of the model-data manifold: one that redistributes rather than disappears under simple interventions.

Many decision-making problems involve multiple objectives, where actions affect several performance metrics, fairness criteria, or safety constraints. We study multi-objective causal bandits with multiple reward nodes, where actions correspond to interventions on subsets of variables in a causal graph and performance is evaluated through Pareto optimality. Existing approaches to multi-objective bandits typically define optimality at the level of individual actions, using pairwise dominance or scalarization. In contrast, randomized decision rules induce convex combinations of action-level reward vectors, so the relevant achievable set is the convex hull of these rewards. Thus, an intervention may be suboptimal even if no single intervention dominates it, because it can be dominated by a randomized policy over other interventions. We formalize this policy-level phenomenon and show that it has consequences for both causal action-space reduction and online learning. First, we provide a necessary-and-sufficient graphical characterization of possibly Pareto-optimal minimal intervention sets (PPOMISs). Our characterization yields a minimal, sound, and complete candidate intervention family from the graph alone, correcting prior formulations that may include intervention sets that are never Pareto-optimal under any compatible structural causal model. Second, over this reduced intervention space, we design an online UCB-style algorithm that eliminates actions using dominance tests against convex combinations of other actions, and prove logarithmic gap-dependent Pareto regret. Finally, we study constrained multi-objective causal bandits, where feasibility and optimality may be realized only with randomized policies, and develop an online UCB-style constrained bandit algorithm with sublinear regret and constraint-violation guarantees.

Modeling event sequences requires understanding when future events will occur and how event types causally influence one another over time. Existing temporal point process (TPP) models typically assume static or first-order dependence structures, which limits their ability to capture the dynamic and multi-order causal mechanisms commonly observed in real-world systems. We propose MOCHA, a Multi-Order Causal Hierarchical Architecture for multivariate TPP that jointly models time-varying causal structure and multi-hop influence propagation in continuous time. MOCHA learns latent dynamic weighted directed acyclic graphs (DAG) over event types, where acyclicity and sparsity constraints promote structurally valid causal graphs. Based on the learned graph, the model decomposes event dynamics into multi-order causal paths, and incorporate both direct influence and indirect propagation into the intensity function. The entire framework is end-to-end differentiable and optimized jointly for event prediction and causal structure discovery. Experiments on seven real-world datasets from different domains show that MOCHA consistently achieves superior negative log-likelihood, while also recovering dynamic causal patterns that align with domain knowledge. These results demonstrate that MOCHA provides an effective framework for dynamic causal structure learning in TPP.

Scaling sequential recommendation to long user histories requires compressing diverse behavioral evidence into memories that can be scored efficiently against many candidates. We first provide evidence that real user histories exhibit multi-scale semantic structure: short-lived intent, medium-term interests, and long-term preferences can coexist in the same sequence. However, existing summarization-based models mainly optimize compact history compression and do not explicitly organize cached user memory across temporal scales, which can lead to \textit{temporal aliasing} where distinct behavioral signals become entangled before prediction. We propose MARS, a multi-resolution user memory that writes the full history into recurrent state tracks initialized with different half-life priors. A sparse routing reader then materializes compact seed memories by selecting the relevant temporal resolutions for each seed, preserving fixed-size candidate scoring. Experiments across recommendation datasets show that MARS outperforms strong recommendation baselines, with gains most pronounced for users with long histories.

Learning a response law for continuous decisions from observational time series is central to many real-world decision systems, yet temporal non-stationarity poses a fundamental challenge: outcome levels drift over time, causing naive models that fit raw outcomes to conflate treatment effects with shifting baseline conditions. Even when the one-step response is identifiable, unrestricted cross-time generalization remains fundamentally ill-posed. Thus, we introduce a constrained decision target based on a causal action geometry with anchored link-scale morphologies. Our approach constructs anchored contrasts over a family of link functions, isolating the action-dependent component of the response that can be shared across periods while allowing flexible, period-specific calibration. This yields a morphology-restricted oracle target for continuous decision making under temporal drift. Building on this formulation, we develop a two-stage orthogonal estimator that first obtains cross-fitted pilot estimates of anchored responses and then performs honest profiled selection over morphology and representation classes. The resulting estimator admits oracle guarantees for the restricted target and, under approximate persistence, produces decision rules that remain near optimal in future periods. Experiments on synthetic data and a large-scale e-commerce pricing application support the proposed target and its decision benefits under temporal shift.

Efficient exploration in sparse-reward environments, where informative extrinsic feedback is scarce, remains a significant challenge in reinforcement learning. A key limitation of existing exploration strategies is that they often assume densely coupled environment dynamics, overlooking the underlying causal mechanisms of the environment, particularly the fact that causal relationships may vary across contexts. To address this issue, we propose contextual causal dynamics model-based intrinsic motivation (CIM), a causality-aware exploration framework that explicitly captures context-dependent sparse causal structures and derives intrinsic motivation signals, specifically causal action influence and curiosity. These intrinsic rewards encourage interventions on causally relevant state factors rather than merely promoting observation novelty-seeking behavior, thereby facilitating robust exploration and active skill acquisition. Moreover, by learning contextual causal mechanisms, CIM identifies and filters out task-irrelevant state variables, improving generalization under distribution shifts. Empirical results demonstrate that our method achieves superior sample efficiency, training robustness, and out-of-distribution generalization performance across multiple robotic manipulation tasks.

The rapid proliferation of Large Language Models (LLMs) has created a diverse ecosystem and motivated model routing as a way to navigate differences in architectures, capabilities, latency, and cost. Yet current routing methods still face bottlenecks in generalization, scalability, and interpretability. To solve these problems, we argue that model routing can be formulated as a recommendation problem. This formulation links routing bottlenecks to problems long studied in recommendation systems (RecSys), including cold start, large-scale recommendation, dynamic adaptation, and explainable decision-making. Building on this RecSys perspective, we organize a roadmap that maps major routing bottlenecks to established RecSys paradigms, providing a practical foundation for adapting mature recommendation methodologies to multi-LLM orchestration. We further present preliminary empirical case studies showing that recommendation-inspired routers can achieve strong accuracy-cost trade-offs while supporting model cold-start and interpretable routing behavior.

Current Large Language Models (LLMs) are gradually exploited in practically valuable agentic workflows such as Deep Research, E-commerce recommendation, job recruitment. In these applications, LLMs need to select some optimal solutions from massive candidates, which we term as \textit{LLM-as-a-Recommender} paradigm. However, the reliability of using LLM agents for recommendations is underexplored. In this work, we introduce a \textbf{Bias} \textbf{Rec}ommendation \textbf{Bench}mark (\textbf{BiasRecBench}) to highlight the critical vulnerability of such agents to biases in high-value real-world tasks. The benchmark includes three practical domains: paper review, e-commerce, and job recruitment. We construct a \textsc{Bias Synthesis Pipeline with Calibrated Quality Margins} that 1) synthesizes evaluation data by controlling the quality gap between optimal and sub-optimal options to provide a calibrated testbed to elicit the vulnerability to biases; 2) injects contextual biases that are logical and suitable for option contexts. Extensive experiments on both SOTA (Gemini-\{2.5,3\}-pro, GPT-4o, DeepSeek-R1) and small-scale LLMs reveal that agents frequently succumb to injected biases despite having sufficient reasoning capabilities to identify the ground truth. These findings expose a significant reliability bottleneck in current agentic workflows, calling for specialized alignment strategies for LLM-as-a-Recommender. The complete code and evaluation datasets will be made publicly available upon acceptance.

As intelligent assistants are increasingly deployed in real-world environments, recommendations move from passive preference matching to grounding user intent from underspecified multimodal evidence. In these settings, users often provide only a reference image and a fuzzy natural-language query, leaving crucial visual attributes, personalized preferences, and situational constraints implicit. Existing recommenders either rely on historical interactions enriched with multimodal item features or presuppose fully specified textual requests, which are insufficient to resolve such queries in a single-turn setting. We present \textbf{IntentLens}, a tool-augmented recommendation framework that grounds underspecified multimodal queries into explicit, query-relevant evidence before ranking. IntentLens adopts a \emph{ground-then-rank} design: a shared multimodal LLM orchestrates visual, user-memory, and item-attribute tools to (i) recover latent user intent from the image and language query, and (ii) enrich each candidate with fine-grained, query-conditioned evidence; a lightweight ranker then scores candidates over the grounded representations. To support this new setting, we further construct two benchmarks with underspecified multimodal queries, \textsc{GoogleReview-MMTool} and \textsc{Yelp-MMTool}. Comprehensive experiments show that IntentLens outperforms strong MLLM and retrieval baselines.

Explaining model predictions in high-stakes settings requires knowing *why* a prediction was made, a central challenge in modern AI. Yet existing methods lack a principled causal framework for explanation, leading to a disagreement problem: explanation methods produce conflicting attributions with no way to determine which is correct. Based on the literature in cognitive science, law, and philosophy, and using the language of causality, we formalize four desiderata for explanation methods that assign numerical credit to variables. We show that no prior explanation method including SHAP and LIME satisfies all desiderata, and prove the Explanatory Impossibility Theorem, which shows that even in principle, no method can satisfy all desiderata without causal assumptions. We introduce *counterfactual Shapley values*, an aggregation of a novel counterfactual quantity we call the *natural total effect*, and prove they satisfy all desiderata. We develop an algorithm for computing tight bounds on counterfactual Shapley values. Experiments corroborate our theory.

Causal discovery methods commonly assume that all data is independently and identically distributed (i.i.d.) and that there are no unmeasured variables affecting the system. In practice, these assumptions are often violated, leading to inaccurate inference. In this paper, we study how to identify hidden confounding and selection biases from causal mechanism shifts. In particular, we show that structural biases lead to dependent mechanism shifts. That is, by considering for which variables the mechanisms change given data from different environments, we can tell which variables are unbiased, which are subject to hidden confounding, and which are undergoing selection bias. We formalize this into an empirically testable criterion based on mutual information, and show under which conditions it identifies structural biases. To tell which nodes are subject to what kind of bias, we introduce the StruBI algorithm. Experiments on synthetic and real-world data show that StruBI works well in practice, accurately recovering affected variable sets and types of biases, outperforming the state-of-the-art by a wide margin.

In LLM-based recommendation, memory effectively plays the role of a user-level agent, typically built on top of a memory-augmented backbone. Yet under this paradigm users and items remain asymmetric participants: only the user is given a structured profile; that profile is consumed inside a bandwidth-limited reranker prompt; and it is frozen after warmup. Starting from this observation, we recast LLM-based recommendation as an agentic-communication process in four stages --- Build a self-description, Transmit it along the collaborative graph, Understand the other side, and Sync it from the next interaction. Analysing the dominant memory-augmented paradigm through this formulation, we identify three open communication challenges that current pipelines leave unaddressed: the item side never produces a comparable self-description (one-sided Build); profile-level signals are routed only into a bandwidth-limited reranker prompt rather than into the retrieval stage where they would not compete for context tokens (prompt-trapped Understand); and the user's description is frozen after warmup with no principled update rule (absent Sync). We instantiate the missing stages as HARP (Homophily-aware Agentic Recommendation via dual Profiles}), a training-free, three-operator addition to a frozen LLM backbone: a rule-extracted symmetric item profile (zero extra LLM calls), a channel-decomposable profile-level homophily $\mathcal{H}(\phi_u,\phi_i)$ consumed at retrieval time rather than inside the reranker prompt, and a critic-gated single-pass reflective update realising in-context profile evolution without any gradient step. On four public benchmarks (Yelp, Amazon Books, MovieTV, Goodreads), HARP lifts Hit@1 over the strongest published baseline by $6.9\%$--$24.0\%$, with family-wise Holm-corrected $p<10^{-9}$ on every dataset. Ablations and a per-case attribution study isolate each operator as a distinct, complementary source of gain; we also quantify and openly report the trade-off introduced by the reflective pass. Our contribution is a unified formulation of agentic communication for LLM-based recommendation, together with a training-free three-operator instantiation on top of it.

Uncertainty quantification is central to many applications of causal machine learning, yet principled Bayesian inference for causal effects remains challenging. Standard Bayesian approaches typically require specifying a probabilistic model for the data-generating process, including high-dimensional nuisance components such as propensity scores and outcome regressions. Standard posteriors are thus vulnerable to strong modeling choices, including complex prior elicitation. In this paper, we propose a \textit{generalized Bayesian framework for causal inference}. Our framework avoids explicit likelihood modeling; instead, we place priors directly on the causal estimands and update these using an identification-driven loss function, which yields generalized posteriors for causal effects. As a result, our framework turns existing loss-based causal estimators into estimators with full uncertainty quantification. Our framework is flexible and applicable to a broad range of causal estimands (e.g., ATE, CATE). Further, our framework can be applied on top of state-of-the-art causal machine learning pipelines (e.g., Neyman-orthogonal meta-learners). For Neyman-orthogonal losses, we show that the generalized posteriors converge to their oracle counterparts and remain robust to first-stage nuisance estimation error. With calibration, we thus obtain valid frequentist uncertainty even when nuisance estimators converge at slower-than-parametric rates. Empirically, we demonstrate that our proposed framework offers causal effect estimation with calibrated uncertainty across several causal inference settings. To the best of our knowledge, this is the first flexible framework for constructing generalized Bayesian posteriors for causal machine learning.

Autoregressive large language models have dominated generative recommendation by sequentially predicting the next item. However, this unidirectional paradigm suffers from error propagation, and limited ability to jointly optimize an entire recommendation list. In this paper, we introduce Full-Sequence Masked Diffusion for generative recommendation (FSMD), which extends masked diffusion models to enable global joint modeling over the entire user history and Top-$K$ recommendation list. We formulate recommendation as a unified sequence diffusion process, where the forward process randomly masks tokens across the full sequence and the reverse process simultaneously denoises all masked positions using bidirectional context. This formulation enables parallel generation of the entire recommendation list and naturally captures inter-item dependencies within a coherent global structure. To better align generation with ranking objectives, we further introduce a unified training objective that combines masked diffusion likelihood with a listwise ranking loss. Extensive experiments on multiple public benchmarks demonstrate that FSMD consistently outperforms state-of-the-art autoregressive and item-level diffusion-based generative recommendation methods across accuracy and list quality metrics.