NeurIPS 2026 论文列表

Using offline datasets to evaluate conversational agents often fails to cover rare scenarios or to support testing new policies. This has motivated the use of \emph{controllable user simulators} for targeted, counterfactual evaluation, typically implemented by prompting or fine-tuning large language models. In this work, we formalize controllable simulation as a causal inference problem. By bridging natural language evaluation with off-policy evaluation methodology, we show that the standard practice of training simulators via supervised fine-tuning on post-hoc trajectory labels yields a structurally biased model. Specifically, these labels are inextricably coupled to the data-generating behavior policy, injecting a \emph{look-ahead bias} that breaks causal consistency. Furthermore, we prove that under policy shift this failure causes the variance of evaluation metrics to explode geometrically, a phenomenon we term \emph{controllability collapse}. To restore causal consistency, we establish theoretical conditions for accurate simulation and propose practical training mitigations: \textit{a priori} controls, step-wise dynamic controls, and direct policy-conditioned learning. Empirical evaluation confirms that while standard global controls distort conversational distributions and collapse behavioral diversity, our causally grounded simulators eliminate look-ahead bias, preserve natural variance, and exhibit robust zero-shot generalization to unseen agent behaviors.

Competition among LLM providers hinges not only on model accuracy, but also on the latency of serving user requests. However, user demand leads to congestion which increases latency, creating a feedback loop between provider decisions and user choices. In this work, we study a stylized game between two providers who strategically balance allocating fixed compute between training better models and reducing latency. A population of users then chooses between the providers. Our equilibrium analysis reveals that a lower-budget provider can still attract users by taking advantage of the congestion faced by the dominant provider. Relative to a competitive baseline where providers have equal compute resources, compute asymmetry leads the higher-resource provider to divert a greater fraction of their compute from improving accuracy to reducing latency. Moreover, this compute asymmetry strictly harms users. Altogether, our results illustrate how congestion distorts competition between model-providers, leading to subtle implications for market competitiveness, model accuracy, and user utility.

Incremental learning aims to continuously learn from dynamic data streams without suffering from catastrophic forgetting. However, existing methods may fail in challenging scenarios where class and domain spaces expand simultaneously, struggling to learn continually and stably from fragmented data streams. To address this challenge, we propose EMCift (Extensible Multi-Center Modeling with Dual-Path Drift Harmonization), a novel model that treats heterogeneous data across domains and semantic spaces as co-existing multi-view observations, enabling the construction of global knowledge from local observations. Our approach organizes extensible multi-center prototypes as a tree hierarchy, allowing the global class representation across different domains to grow organically from fragmented local data. To sustain this dynamic topology, we introduce a dual-path drift harmonization strategy that balances structural stability with adaptive plasticity. Specifically, one path captures essential updates to enable prototype evolution, while the other path leverages a conditional adversarial mechanism to robustly initialize domains, effectively preventing catastrophic forgetting caused by class confusion. Extensive experiments on three datasets demonstrate the state-of-the-art performance of our proposed model in the class-domain incremental learning scenario. Code will be released upon acceptance.

Cellular perturbation experiments are essential for probing biological mechanisms and guiding therapeutic discovery. While existing methods often treat perturbations as monolithic distributional shifts, they leave the underlying structural variations implicit. Rather than attempting to infer independent global networks for every condition, we propose scPertCRL, a causally structured generative framework for single-cell perturbation prediction. scPertCRL decomposes perturbation effects into a shared latent structural backbone, condition-specific structural modulations, and exogenous shifts. By leveraging biologically informed embeddings and mechanism-aware supervision, Differential Network Learner parameterizes these localized structural changes, enabling robust generalization to unseen interventions. Extensive experiments on genetic and pharmacological benchmarks demonstrate that scPertCRL significantly improves predictive accuracy and out-of-distribution generalization, while yielding model-based insights into perturbation mechanisms.

Personalized systems rely on user representations to connect behavioral history with downstream recommendation applications. Existing methods typically employ either supervised latent user embeddings, which are effective for retrieval but difficult to interpret, or textual user profiles, which are interpretable but challenging to optimize for downstream utility due to lack of direct supervision. To bridge this gap, we present BLUE, a reinforcement learning framework that unifies these two forms of user representation by aligning language-based user profiles with embedding-based recommendation objectives. Given a user’s interaction history, BLUE leverages a profiler Large Language Model (LLM) to generate textual profiles, while an embedding model provides reward signals. This encourages the resulting textual representations to move closer to positive items and farther from negative ones in the embedding space. We further introduce a text-space supervision signal based on next-item prediction, ensuring the learned profiles remain both semantically meaningful and highly effective for downstream retrieval. Experiments on Amazon Reviews 2023 and Google Local Reviews in zero-shot sequential recommendation settings demonstrate that BLUE consistently outperforms strong baselines under both frozen and trainable embedding conditions. Notably, BLUE achieves clear gains in cross-domain transfer, highlighting the strong generalization ability of the learned user profiles. Furthermore, these generated profiles provide superior personalized context for question answering compared to raw user histories or alternative profile optimization methods. Overall, these results show that BLUE provides an effective way to unify interpretable textual profiling with discriminative latent embeddings for personalization.

The increasing prevalence of preference-based learning, particularly for machine learning models interacting with human users, gives rise to scenarios where the model's decision should cater to the preferences of a number of users. Due to the inherent misalignments in such preferences, it is natural to seek a fair decision-making paradigm. Motivated by that, we pose the problem of fair multi-user dueling bandit, where each user's preferences over pairs of actions are encoded by a preference matrix unknown to the agent. We design a Nash social welfare objective based on the Borda scores of the individual user preferences. The notion of the Borda score measures the average likelihood of an action being preferred over the other actions, and importantly, it does not require the existence of a completely dominant action. Considering online learning in this general setting, we construct hard instances and establish a minimax lower bound on the achievable regret. We also design an explore-then-commit algorithm and derive an upper bound on its worst-case regret. Furthermore, we formulate a fair multi-user generalized linear dueling bandit to enable modeling large action spaces, which typically necessitate a structured representation. In this setting, too, we establish a lower bound on regret and an upper bound on regret for a proposed explore-then-commit algorithm.

Many decision-support systems require adapting to individual users, yet evaluating this capability remains challenging because user preferences and beliefs are rarely stated explicitly. Instead, they are revealed through sequences of real-world behavior. Existing benchmarks for user understanding often rely on textual personas, constructed preferences, or simulated users, which may not faithfully reflect how people actually decide.We introduce \textsc{BehaviorBench}, a benchmark for modeling user decisions from real-world behavioral traces. Built from public prediction-market and on-chain records, the benchmark reconstructs longitudinal decision histories and evaluates whether models can infer how a particular user believes and acts from prior behavior. It defines two complementary task layers: \emph{Belief prediction}, which infers a user's final revealed stance and confidence, and \emph{Trade prediction}, which predicts the direction and magnitude of individual actions. We evaluate frontier and open-weight generative models under multiple history representations, including direct behavioral history, generated user profiles, and retrieved cross-user evidence. Results show that personalization is not a single capability: models benefit differently from different representations depending on the task, and performance remains limited when reasoning over implicit behavioral signals. \textsc{BehaviorBench} provides an evaluation setting for studying personalized decision modeling grounded in real-world behavioral evidence rather than simulated users alone.

Understanding the dynamic behavior of biomolecules is fundamental to elucidating biological function and facilitating drug discovery. While Molecular Dynamics (MD) simulations provide a rigorous physical basis for studying these dynamics, they remain computationally expensive for long timescales. Recent deep generative models accelerate conformation generation but often either discard temporal correlations entirely or struggle to condition on the extended history required for faithful kinetics, a consequence of the effectively non-Markovian nature of partially observed subsystem coordinates. To bridge this gap, we introduce ATMOS, a novel generative framework based on State Space Models (SSM) designed to generate atom-level MD trajectories for biomolecular systems. ATMOS integrates a Pairformer-based state transition mechanism to capture temporal dependencies, with a diffusion-based module to decode trajectory frames autoregressively. We demonstrate that ATMOS achieves state-of-the-art performance in generating conformation trajectories for both protein monomers and protein-ligand systems. This work provides a unified and computationally efficient framework for biomolecular trajectory generation, taking a step toward dynamics foundation models.

Typical LLM responses tend to follow a default style, even though users often have distinct preferences regarding tone, verbosity, and formality that they do not explicitly state in their prompts. Evaluating whether personalization methods can adapt to these implicit preferences is challenging, since users typically provide prompts rather than reference responses, style preferences are not factually verifiable, and reference-free LLM judges may conflate personalization with general response quality. To address these challenges, we introduce the Arbitrary Preference Mapping (APM) benchmark, which decouples user attributes (e.g. enthusiastic) from response principles (e.g. persuasive) via a hidden, randomized mapping $\mathbf{C}$ that maps user attributes to preferences about response traits. Because $\mathbf{C}$ carries no semantic content and is resampled across runs, models cannot exploit stereotypical associations and must infer preferences from conversation history. Using this unbiased evaluation methodology, we adapt retrieval-augmented, prompt-optimization, and routing personalization methods and evaluate them on Llama-3.1-8B and Qwen-3.5-27B. Our results show that routing is the most reliable approach, while RAG benefits only from the stronger base LLM, and soft prompt optimization fails to improve significantly over a non-personalized baseline. Our extensive evaluation reveals that in this realistic setting, personalization remains challenging, but our adapted methods show promise.

Multimodal Large Language Models (MLLMs) often exhibit significant modality preference, which is a tendency to favor one modality over another. Depending on the input, they may over-rely on linguistic priors relative to visual evidence, or conversely over-attend to visually salient cues over textual facts. Prior work has applied a uniform steering intensity to adjust the modality preference of MLLMs. However, strong steering can impair standard inference and increase error rates, whereas weak steering is often ineffective. Since steering sensitivity varies substantially across instances, a single global strength is difficult to calibrate. To address this, we introduce an instance-aware diagnostic metric Modality Contribution Ratio (MCR) that quantifies each modality’s information contribution and reveals sample-specific susceptibility to steering. Building on this signal, we propose a scaling strategy and a learnable module for instance-aware control of modality preference. Experiments show that AMPS outperforms conventional steering on $MC^2$ by improving preference control with lower generation collapse, and improves MLLM performance on general multimodal QA dataset such as MM-Vet v2.