NeurIPS 2026 论文列表

Many survival problems are inherently time-dependent: a biomarker may be predictive soon after diagnosis but irrelevant years later, or a treatment effect may diminish, reverse, or interact with follow-up duration. However, tree-based survival models commonly used in practice typically assign each individual a fixed, time-independent risk score. Neural non-proportional-hazards models can capture dynamic effects, but are often more challenging to tune, scale, and interpret than boosted trees. We present CCTimeBoost, a gradient-boosted tree framework for learning continuous-time, time-varying relative risk. The method augments covariates with time features, trains boosted trees on sampled risk sets using a group-softmax objective, and estimates a full-risk-set baseline hazard to generate survival curves. We show that this objective has a likelihood interpretation: for finite sampled risk sets, it matches a nested case-control likelihood, and in the full-risk-set limit, it converges to the Cox partial likelihood. Across eight right-censored survival benchmarks, CCTimeBoost delivers state-of-the-art discrimination performance relative to tree and neural baselines and is competitive in calibration. In controlled simulations, it accurately recovers diminishing effects, crossing hazards, and rule-based temporal interactions, also behaving as a proportional model when proportional hazards are present. These results indicate that non-proportional survival modeling can be incorporated into standard boosted-tree methods.

Reinforcement learning (RL) is essential for adaptive decision-making, e.g., robust Meta-RL and domain randomization (DR), and efficient post-training of large language models (LLMs), where tasks are commonly structured as Markov decision processes or prompts. A central bottleneck in all these settings is the cost of agent-environment interactions required for task selection and policy optimization. Model predictive task sampling (MPTS) has emerged as a promising approach to improve sample efficiency by querying informative tasks via a risk predictive model (RPM) trained on optimization history. However, existing RPMs suffer from two failure modes: (i) sparse optimization histories, which limit RPMs' generalization across the task space, and (ii) a one-step lag in risk approximation, which causes biased difficulty estimation as the policy evolves. This work formalizes these failure modes, derives lagged-risk decompositions that expose the resulting temporal bias, and recasts risk prediction as an in-context sequence modeling problem. The developed RPM integrates a one-step look-ahead mechanism with a temporal-weighted sliding window to mitigate data non-stationarity and scarcity, without requiring additional rollouts. Empirically, our approach yields more reliable difficulty estimates and consistent performance gains across robust Meta-RL, DR, and prompt curriculum RL post-training of LLMs.

As AI systems are deployed in open-ended, high-stakes domains, a critical behavioral dimension remains unmeasured: whether models differ systematically in how they translate perceived risk into action. We introduce a cross-domain evaluation framework that isolates and quantifies \emph{risk attitude} in large language models (LLMs) by decoupling contextual belief ($B_C$, a model's perceived danger level given situational context) from categorical risk decision ($R_D$, the action chosen in response to that belief). Unlike existing capability benchmarks, which assess factual accuracy but do not probe the belief-to-decision mapping, this framework directly targets the disposition governing whether an agent acts cautiously or aggressively under identical levels of perceived danger. We applied the framework to six frontier LLMs and 100 human participants across spatial navigation, clinical triage, and financial allocation tasks, and identified two qualitatively distinct behavioral profiles. Five models satisfied both reliability criteria (Contextual Belief Consistency and Risk Decision Consistency) across all conditions and tasks. These models showed strong measurement reliability with stable belief formation and belief-to-decision mappings, and exhibited stable, trait-level cross-domain risk attitudes that were preserved across all three tasks (Kendall's $W=1.00$, $p=0.017$). One model, Grok~4, failed both criteria: its belief formation was unstable in Drone Navigation Control task and its decision mapping was inconsistent in Clinical Triage Decision task, yielding task-specific rather than trait-level risk behavior. Across all six LLMs, risk profiles converged toward a restricted region of the broader human risk-attitude distribution, suggesting that alignment training constrains models toward a narrow behavioral consensus. These results demonstrate that the framework reliably measures risk attitude as a stable, model-specific behavioral dimension and can serve as a diagnostic tool for pre-deployment assessment by distinguishing models with coherent risk personalities from those with domain-contingent risk postures.

Prediction sets provide a means of quantifying the uncertainty in predictive tasks. Using held out calibration data, conformal prediction and risk control can produce prediction sets that exhibit statistically valid error control in a computationally efficient manner. However, in the standard formulations, the error is only controlled on average over many possible calibration datasets of fixed size. In this paper, we extend the control to remain valid with high probability over a cumulatively growing calibration dataset at any time point. We derive such guarantees using quantile-based arguments and illustrate the applicability of the proposed framework to settings involving distribution shift. We further establish a matching lower bound and show that our guarantees are asymptotically tight. Finally, we demonstrate the practical performance of our methods through both simulations and real-world numerical examples.