NeurIPS 2026 论文列表

Modern large language models (LLMs) often exhibit overconfident errors, assigning high confidence to incorrect predictions and thereby undermining reliability in real-world use. Existing calibration methods typically rely on post-hoc adjustment or retraining, operating at the output level without addressing the underlying causes of miscalibration. We propose CoCHI (Class-oriented Causal Head Intervention), a framework that improves calibration by identifying and intervening on internal components responsible for distinct confidence behaviours. By analysing predictions through correctness–confidence patterns, CoCHI enables targeted intervention on model internals, reducing overconfident errors while preserving correct predictions. Our approach provides a mechanistic perspective on calibration, linking reliability to identifiable structures within the model rather than treating it as an output-level issue. Experiments across multiple LLMs show consistent improvements in calibration metrics, including Expected Calibration Error, Negative Log-Likelihood, and Brier Score, while maintaining accuracy on five multiple-choice question answering benchmarks. These results suggest that effective calibration requires not only adjusting outputs, but also addressing the underlying causal mechanisms within the model. Implementation details and code are provided in the supplementary material.

We study identifiability and estimation of the direct causes, that is, the parents, of a designated target $Y$ in a structural causal model using only observational data. Our main assumption is an additive-noise model for $Y$: the value of $Y$ equals some function of its parents plus noise that is independent of those parents. We allow for general functional relationships and hidden confounding among all other variables. We prove that under a `no-backward' additive-noise condition the true parent set, $\mathrm{PA}(Y)$, is identified from the observational distribution by a simple population principle: among all candidate variable sets that make the regression residual of $Y$ independent of the regressors, choose the one with the smallest residual variance, and—if several tie—the smallest set. To justify the ``no-backward'' condition, we propose a novel identifiability scheme based on identifiability witnesses: we prove that in finite-dimensional analytic model classes, for which we have an identifiability witness (that is, a single identifiable model), residual independence for sets containing descendants of \(Y\) holds only on Lebesgue-null exceptional parameter sets. This scheme is strong enough to recover known identifiability results. For finite data, we propose Independent Risk Minimization (IndRM), which offers a simple, local, and non-interventional route to isolating the direct causes of $Y$ in multivariate systems, avoiding global model assumptions and full-graph search.

Sign-aware recommender systems have recently been developed to leverage negative feedback for a deeper understanding of user preferences. However, our empirical diagnosis reveals that state-of-the-art graph-based sign-aware recommender systems are paradoxically *valence-blind*. Even though they explicitly incorporate sign information during training, they consistently fail to differentiate liked items from disliked ones at the ranking stage, frequently infiltrating top-$K$ recommendations with disliked content. Through linear probing, we show that while valence information exists in the learned embeddings, it remains inaccessible to the inner-product scoring function. This widespread failure remains entirely undetected because conventional evaluation metrics, such as Recall, HR, and NDCG, assign a uniform utility of zero to both negative and unobserved items, creating a systematic evaluation blind spot. To bridge this gap, we propose a family of signed metrics, Signed Recall, Signed HR, and Signed NDCG, that explicitly penalize the recommendation of disliked content. Systematic re-evaluation under our proposed metrics fundamentally reshapes the established performance landscape, revealing that methods ranked highly under conventional metrics often fail to protect users from disliked content. Finally, through a proof-of-concept auxiliary loss, we confirm that the proposed metrics provide actionable training signals, guiding models toward valence-aware behavior without sacrificing conventional relevance. For transparency, our source code is available at: https://anonymous.4open.science/r/signed-rec-benchmark-07E4

A central goal of science is to produce valid explanations of complex systems: high-level causal accounts that faithfully reflect the behavior of lower-level mechanisms. Yet no consensus exists on how to measure whether a proposed high-level explanation is actually valid. We introduce a benchmark of ten complex systems spanning discrete and continuous state spaces and static and dynamical regimes, each equipped with consensual ground-truth causal explanations and invalid contrastive conditions. Within a unified causal abstraction framework, we systematically evaluate over thirty candidate metrics drawn from observational, functional, information-theoretic, and causal families. Our results show that only the latter reliably discriminates valid from invalid abstractions, and only when incorporating faithfulness testing over unmapped variables. Building on these findings, we introduce the Causal Abstraction Error (CAE), a continuous validity metric with an explicit faithfulness test, which passes all discrimination tests across every system and converges with as few as 30 sampled interventions. We offer it as a general-purpose metric for the discovery and validation of high-level explanations of complex systems.

Continual recommendation captures dynamic user interests amid sequential interactions, enabling models to adapt to new observations while preserving prior valuable knowledge. Existing continual recommendation methods primarily preserve or reuse historical knowledge through replay, distillation, or regularization to mitigate forgetting during incremental updates. However, noisy or obsolete inherited representations render indiscriminate preservation detrimental by inducing memory conflicts with current stage preferences. Moreover, rapid shifts in user interests require timely and selective memory updates, while coarse global mechanisms fail to provide such fine-grained responsiveness. To address these issues, we propose VAMIRec, a value-aware memory intervention framework for continual recommendation. VAMIRec first constructs a memory state by separating inherited user memory into a slow anchor component and a fast-changing drift component. We further design a value-aware action evaluation module to generate three candidate operations, and estimate conservative action values by accounting for uncertainty and intervention cost. Finally, we develop a policy-guided memory intervention module that distills the training-time action supervision into an inference-safe policy and applies the selected memory action before all-item ranking. Extensive experiments on three real-world datasets demonstrate that VAMIRec consistently outperforms the state-of-the-art baselines.

We partially resolve Conjecture 1 of \textit{Karagodin et al., Clustering in Causal Attention Masking (NeurIPS 2024)} and extend it to time-dependent parameter regimes. The conjecture predicts that causal attention dynamics converge to two clusters aligned with the eigenvectors associated with the largest eigenvalue $\lambda_{\max}$ of the value transformation. We prove that this behavior holds when $\lambda_{\max}>0$ and is generally false when $\lambda_{\max}\le0$. The resulting two-cluster regime is common in practice and substantially more challenging than the classical single-cluster setting studied previously. A central contribution of our work is the identification of a rigorous dimension deduction phenomenon: despite evolving in a high-dimensional embedding space, token trajectories collapse onto a two-dimensional structure governed by a small number of dominant directions. Building on this insight, we introduce a new analytical framework--instability within the flow--inspired by instability phenomena in physical systems. This framework enables the analysis of steady-regime behavior in non-monotone, non-autonomous attention dynamics. Our theoretical findings are illustrated through simulations and experimental studies using Group Query Attention (GQA) layers.

Generative recommendation (GR) aligns with advances in generative AI by casting next-item prediction as token-level generation rather than score-based ranking. Most GR methods adopt a two-stage pipeline: (i) \textit{item tokenization}, which maps each item to a sequence of discrete, hierarchically organized tokens; and (ii) \textit{autoregressive generation}, which predicts the next item's tokens conditioned on the tokens of user's interaction history. Although hierarchical tokenization induces a prefix tree (trie) over items, standard autoregressive modeling with conventional Transformers often flattens item tokens into a linear stream and overlooks the underlying topology. To address this, we propose TrieRec, a trie-aware generative recommendation method that augments Transformers with structural inductive biases via two positional encodings. First, a \textit{trie-aware absolute positional encoding} aggregates a token's (node's) local structural context (\eg depth, ancestors, and descendants) into the token representation. Second, a \textit{topology-aware relative positional encoding} injects pairwise structural relations into self-attention to capture topology-induced semantic relatedness. TrieRec is also model-agnostic, efficient, and hyperparameter-free. In our experiments, we implement TrieRec within three representative GR backbones, achieving notably improvements of 8.83\% on average across four real-world datasets.

Despite the importance of causal reasoning, training LLMs to perform causal reasoning remains underexplored. Existing data efforts mostly focus on benchmarking LLMs on specific aspects of causality, rendering them less suitable for training generalizable causal reasoners. To address this, we propose UniCo, a data generation framework that both (1) addresses 18 causal query types across Pearl's Causal Ladder and (2) translates symbolic samples into code and narrative domains to simulate real-world use cases where causal terms are not explicitly specified. To ensure data quality, UniCo grounds answers with exact causal inference and filter cases with reasoning shortcuts. Upon supervised-finetuning with 66.6K UniCo-generated instances, Qwen3-4B, Qwen3-8B and Olmo-3-7B-Instruct achieve an average of 22.9% improvements across all 18 in-distribution query types, and 8.1% over state-of-the-art causal data generation frameworks on 7 established causal benchmarks outside the training distribution. Furthermore, in real-world medical understanding, legal decision, and tabular reasoning, UniCo-trained models consistently display more faithful reasoning traces, outperforming the base models by an average of 20.2% in faithfulness metrics. These suggest that causality-centered training not only strengthens causal reasoning, but also equips LLMs with a causal mindset in general reasoning tasks.

Generative recommendation formulates next-item prediction as autoregressive generation over semantic ID (SID) sequences derived from users' historical interactions, making modern recommender systems structurally similar to large language models (LLMs). As privacy and safety concerns grow, these systems increasingly require concept unlearning to remove sensitive or harmful concepts associated with items. However, existing LLM unlearning methods cannot be directly applied to generative recommendation. Unlike word tokens with explicit semantics, SIDs are abstract identifiers that are often shared by both forget and retain items, leading to severe conflicts between concept removal and recommendation utility preservation. To address this challenge, we propose TRACER, an end-to-end concept unlearning framework based on token reassignment. Rather than directly suppressing shared SIDs, TRACER reassigns concept-related items to alternative tokens that better facilitate forgetting while minimizing side effects on retained items. We further introduce a coherence regularizer to preserve semantic consistency among retain items during unlearning. Experiments on real-world recommendation datasets demonstrate that TRACER effectively removes target concepts while substantially better preserving recommendation utility than existing unlearning baselines.

Tourism is a high-stakes setting for conversational recommender systems (CRS): a plausible-sounding suggestion can waste real money and trip time once a traveler acts on it. Existing CRS benchmarks primarily evaluate systems with a single Recall@$k$ score over entity mentions, and tourism-specific resources add spatial or knowledge-graph context, yet none of them couple multi-turn recommendation with verbatim review-span evidence and rejection recovery. This leaves an evaluation gap for tourism recommendation that is simultaneously *trustworthy*, *verifiable*, and *adaptive*: recommend the right point of interest (POI) for multi-aspect preferences (such as cuisine, price, atmosphere, walking distance), justify each suggestion with verifiable evidence from prior visitors so the traveler can act without trial and error, and recover when the first recommendation is rejected mid-dialogue. We introduce **TRACE**, where each item is *a multi-turn tourism recommendation dialogue with review-span citations and explicit rejection turns*: 10,000 dialogues over 2,400 Yelp POIs and 34,208 reviews across eight U.S. cities, paired with 14 retrieval, planning, and LLM baselines, along with 25 metrics organized under *Accuracy*, *Grounding*, and *Recovery*. Across these baselines, TRACE reveals the **Three-Competency Gap**: LLM Zero-Shot leads in closed-set Recall@1 and rejection recovery but cites less densely than retrievers; non-LLM retrievers achieve surface-verbatim grounding but with low accuracy; Multi-Review Synthesis fails on Recovery. The Grounding Score agrees with human citation precision (Spearman $\rho = +0.80$, $p < 10^{-20}$), and paired $t$-tests reproduce the per-baseline ranking ($p < 0.01$ on the dominant contrasts). TRACE reframes accountable tourism recommendation as a joint target (right POI, verifiable evidence, adaptive repair) rather than a single-axis leaderboard.

We introduce structural causal bottleneck models (SCBMs), a novel class of structural causal models in which causal effects between high-dimensional variables are mediated by low-dimensional summary statistics, or *bottlenecks*. SCBMs provide a flexible framework for mechanism-specific, target-dependent dimension reduction while remaining estimable via standard learning algorithms. We prove that bottleneck variables are identifiable up to bijection from observational data, and validate this experimentally. We then study causal effect estimation in linear SCBMs under three standard adjustment strategies. For instrumental variables, we identify a population-level non-identifiability of naïve two-stage least squares when all variables are high-dimensional, and show that bottleneck representations restore identification. For mediation and backdoor adjustment, we prove that conditioning on bottleneck variables yields valid effect estimates with explicit finite-sample efficiency gains over conditioning on full high-dimensional variables. We argue that SCBMs provide a principled alternative to existing causal dimension reduction frameworks such as causal representation learning and causal abstractions.

Many causal quantities are only partially identifiable due to the inherent missingness of potential outcomes, and the associated partial identification (PI) sets can be obtained by solving an optimal transport (OT) problem. Covariates often provide additional information about the potential outcomes and thus yield tighter PI sets, which can be obtained via conditional optimal transport (COT). However, COT-based PI set estimators are susceptible to the curse of dimensionality in the covariates, which precludes the asymptotic normality and hinders statistical inference. In this paper, we exploit the smoothness in the marginal densities of covariates and potential outcomes, and develop a wavelet-based primal approach for COT that attains a faster convergence rate. Moreover, for quadratic cost functions, we establish a stability result for COT and prove asymptotic normality of the proposed estimator, enabling valid statistical inference for the PI set. Empirically, we validate the estimation and inference performance of our approach through numerical experiments in comparison with existing benchmarks.

Temporal causal structure learning aims to recover lag-aware causal relations from multivariate time series. While prior knowledge has been widely used to improve static structure learning, its integration into lag-aware structure learning remains limited, because available priors are often lag-agnostic. Recent studies have explored lag-agnostic edge presence priors, but partial order priors, which can prune the ordering space and improve structure learning, remain underexplored. A key challenge is that variables may influence each other at different lags, leading to cyclic variable-level relations where ordering is no longer naturally defined. Moreover, directly applying static differentiable formulations of partial order constraints can be unstable due to repeated-walk accumulation on cycles. To address this gap, we represent partial order priors on the summary graph and follow the natural idea of the order relation that only allows a single direction of possible causality between two variables, which is equivalent to forbidding all reversed causality, direct or indirect. This converts partial order constraints into path prohibition constraints that can be extended to cyclic summary structures. For stable differentiable characterization, we propose a decoupled partial order constraint method. The method preserves the original lag-aware structure for data fitting, imposes partial order constraints through a normalized summary representation, and captures higher-order reachability with a closed-form connectivity characterization. Experiments further demonstrate its effectiveness in nonstationary settings.

Medication recommendation is a multi-label prediction task, yet existing prediction-set evaluation and calibration methods rely mainly on exact-overlap metrics and therefore treat all missed medications as equally severe. However, missing a medication but predicting a therapeutically close drug is less severe than matching it only with distant predictions. To address this, we introduce **Conformal Severity Control (CSC)**, a post-hoc framework for calibrating medication prediction sets under hierarchy-aware mismatch severity. CSC uses the Anatomical Therapeutic Chemical (ATC) hierarchy to quantify mismatch severity and controls patient-level severity without retraining the predictor, supporting both average severity and upper-quantile severity for the highest-severity mismatches. Experiments on MIMIC-IV show that CSC tracks user-specified severity tolerances and shifts prediction sets away from distant mismatches toward exact or close matches.

Multimodal Large Language Models (MLLMs) suffer from hallucinations, creating a critical need for Uncertainty Quantification (UQ) to ensure reliable deployment. However, existing approaches struggle to detect uncertainty caused by superficial associations, especially when the query-relevant signal is weak. We mainly attribute this issue to their bias toward aleatoric uncertainty arising from data ambiguity, overlooking epistemic uncertainty stemming from model limitations. To further decompose uncertainty types for a comprehensive UQ, we propose Causal-Invariant Masking (CIM), which measures the semantic shift between the original predictions and those conditioned on a causally-focused view. Based on this framework, we introduce Semantic Divergence as our core metric for UQ and provide theoretical evidence that it converges to the variance of model's sensitivity to non-causal correlations, establishing its ability to capture MLLM's limitation. To accelerate UQ in MLLMs, we further propose Expected Embedding Drift (EED), a fast geometric proxy metric that estimates semantic shift directly within the hyperspherical embedding space. Experiments show that our method achieves state-of-the-art performance on various benchmarks, while the proposed EED accelerates by nearly 50\% with comparable performance.

Semantic ID-based generative recommendation represents each item as a sequence of discrete tokens, enabling structured modeling of item semantics. A critical challenge is constructing semantic IDs that are both semantically expressive and computationally efficient. While recent approaches favor complex learned quantization, simple hashing-based methods such as SimHash are widely regarded as fundamentally inferior. In this work, we challenge this consensus by showing that the apparent performance gap does not stem from inherent limitations of hashing, but rather from a structural mismatch with autoregressive decoding, coupled with the inevitable information loss during rigid discretization. Based on this insight, we propose FLASH, a two-stage framework that revitalizes training-free SimHash tokenization through parallel decoding and explicit semantic alignment. Despite its simplicity, FLASH achieves state-of-the-art performance across multiple datasets without requiring any tokenizer training, while exhibiting stronger generalization in cold-start scenarios. Notably, we demonstrate that semantic alignment acts as a universally effective mechanism across diverse paradigms. Our findings suggest that, with compatible decoding and semantic grounding, simple and efficient tokenizers can outperform complex learned counterparts. Our code and data are available: https://anonymous.4open.science/r/Flash-014A/.

Causal modeling of physical temporal phenomena must handle interventions that act along trajectories, nonstationary induced laws, path-dependent effects, and feedback mediated by dynamics, all challenging in standard causal models. We introduce \emph{Hamiltonian Causal Models} (HCMs), a trajectory-level framework in which observed variables interact with local environments and interventions act as controls of Hamiltonian mechanisms. HCMs separate immutable equations of motion from intervenable mechanisms and define causal effects as discrepancies between interventional path laws. A key motivation for HCMs is their natural interface with non-equilibrium thermodynamics. Entropy production quantifies the irreversibility of a process and is a central causal observable: it is estimable from data and witnesses causal effects along the system's evolution that are invisible to endpoint and cumulative versions of the standard average treatment effect. As in physics, cause-and effect are not primitives of the relation between two random variables but arise from the non-invertibility of the thermodynamic arrow. With this, our paper reconciles the language of statistical causal models and non-stationary thermodynamics, offering new tools to describe causality in a wide range of physical systems.

Sequential recommenders need long histories, but full-prefix attention scales with user lifetime. We argue that an effective memory mechanism should provide space invariance, semantic fidelity, and lifelong evolvability, which existing fixed-window or capped-memory methods do not jointly satisfy. We propose RecMem, a recurrent compression framework that folds history segments into M forget-gated memory slots. RecMem has a history-length-independent rollout error bound, and its fixed-capacity memory is trained by next-item prediction to preserve useful signals. On MerRec, RecMem uses 5.6x fewer decoder-side tokens than full attention while retaining 88–97% of Recall@10–200. Compared with a compressed truncation baseline at similar decoder cost, RecMem improves Recall@50+ by 4–6 percentage points by compressing the full history rather than discarding old interactions. It also remains stable over 1,800 test steps.

Generative recommendation has recently emerged as a promising paradigm for large-scale recommendation by reformulating next-item prediction as discrete sequence generation. However, current SID-based models rely mainly on history-to-next-item supervision, leaving item--item collaborative topology only indirectly captured through sparse user sequences. To address this gap, we propose *R-GRec*, a graph-derived structural supervision framework for SID-based generative recommendation. From user interactions, *R-GRec* builds an item--item graph and derives three complementary auxiliary objectives---Neighbor Prediction, Topological Contrast, and Link Prediction --- to make collaborative topology explicit during generative training. Extensive experiments on the two public Amazon datasets demonstrate that R-GRec achieves state-of-the-art performance over representative traditional, generative, and LLM-based recommenders. Further ablation and analytical studies verify the contribution of each graph-derived objective and show that collaborative topology acts as a complementary supervision signal to Semantic ID generation, improving generative recommendation without adding inference-time graph computation.