Towards end-to-end LLM-based censoring-aware survival analysis
Yishu Wei, Hexin Dong, Yi Lin, Jiahe Qian, Yi Liu, Yifan Peng
Abstract
Objective: Survival analysis is central to medical prediction, yet large language models (LLMs) are rarely used as end-to-end survival models because censoring prevents straightforward supervised fine-tuning. Here we present LLMSurvival, a framework that enables censoring-aware survival analysis with unmodified LLMs operating directly on tabular clinical data. Materials and Methods: LLMSurvival reformulates time-to-event prediction as pairwise ranking among comparable subjects, and derives test-time risk by aggregating comparisons against anchor individuals from the training cohort. Results: Across two clinical tasks (ICU mortality prediction in MIMIC-IV and fragility fracture prediction in a NewYork-Presbyterian/Weill Cornell Medicine cohort), LLMSurvival improves overall concordance over Cox proportional hazards modeling by 3.1% for ICU mortality and 0.5% for fracture risk, 2.1% on average for ICU mortality and 2.8% for fracture risk over three established deep learning survival models. Discussion: The results show that survival modeling with censoring can be made compatible with LLM fine-tuning through comparison-based reformulation. The framework demonstrates high portability and superior performance over expert curated scores like SAPS-II and FRAX scores across diverse clinical context. Furthermore, the framework supports local deployment, as compact, publicly available base models provide sufficient performance. Conclusion: The LLMSurvival framework serves as a proof of concept for an integrated, censoring-conscious approach to survival analysis via LLMs.
AI Impact Assessments
(1 models)Scientific Impact Assessment: "Towards end-to-end LLM-based censoring-aware survival analysis"
1. Core Contribution
LLMSurvival proposes a framework that reformulates censored survival analysis as a pairwise ranking task compatible with standard LLM fine-tuning. The key insight is that by converting survival prediction into "which patient experiences the event first?" comparisons—restricted to comparable pairs where censoring does not obscure the ordering—the authors sidestep the fundamental problem that censored observations lack explicit labels. At inference, risk scores are derived by comparing each test subject against a set of anchor patients from the training cohort and aggregating binary outcomes.
This is a genuinely clever reformulation. While pairwise ranking for survival analysis is well-established in machine learning (the authors cite several prior works), and LLMs have been used as pairwise rankers in NLP, the specific contribution of bridging these two ideas to enable end-to-end LLM-based survival modeling is novel. The framework requires no architectural modifications to the LLM, which is an important practical advantage.
2. Methodological Rigor
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Weaknesses:
3. Potential Impact
The conceptual contribution—showing that LLMs can handle censored data through comparison-based reformulation—is more impactful than the empirical results. This opens a door for future work where LLMs could process mixed-modality inputs (clinical notes + tabular data) for survival analysis without custom architectures.
However, real-world impact is limited by several factors: (1) the computational overhead of pairwise inference is substantial; (2) the performance gains over much simpler methods are marginal; (3) the framework only produces risk rankings, not survival probability estimates needed for clinical decision-making. The practical case for deploying a 7-8B parameter model when Cox regression achieves nearly identical C-indices is weak.
The illustrative explanations (Figure 5) are interesting but use the base model conditioned on outcomes rather than the fine-tuned model's internal reasoning—this is post-hoc rationalization, not genuine interpretability.
4. Timeliness & Relevance
The paper addresses a timely intersection of LLMs and clinical prediction. The survival analysis community is actively exploring deep learning approaches, and the LLM community is pushing toward broader task coverage. The question of how to handle censoring in LLM-based frameworks is genuinely relevant and underexplored. The emphasis on local deployment with smaller models (7-8B) is also timely given privacy concerns in healthcare AI.
5. Strengths & Limitations
Key Strengths:
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Overall Assessment
LLMSurvival presents a creative and conceptually sound approach to an important problem. As a proof-of-concept, it succeeds in showing that censoring-aware survival analysis can be made compatible with LLM fine-tuning through pairwise reformulation. However, the empirical evidence for practical superiority is weak—improvements are marginal and the computational costs are substantially higher. The paper would be significantly strengthened by incorporating unstructured clinical text (where LLMs have a clear advantage), providing calibration analyses, and honestly confronting the cost-performance tradeoff. It is a solid methodological contribution but not yet a compelling case for deploying LLM-based survival models in practice.
Generated May 26, 2026
Comparison History (15)
Paper 2 has higher likely scientific impact: it introduces an end-to-end, censoring-aware reformulation that makes LLMs directly usable for survival analysis, a high-value and widely applicable clinical/statistical problem. The approach targets real-world deployment (local, compact models), shows improvements over Cox and multiple deep survival baselines on two cohorts, and could influence both medical ML and survival methodology. Paper 1 is a strong, timely benchmark with useful metrics, but its impact is more bounded to programmatic video/code-generation evaluation, whereas Paper 2’s applications and cross-field relevance (clinical prediction, biostatistics, ML) are broader.
Paper 2 introduces a novel framework (LLMSurvival) that bridges two major fields—LLMs and survival analysis—addressing a fundamental limitation (censoring) that has prevented LLM adoption in time-to-event prediction. It has broad real-world clinical applications, demonstrated on real patient cohorts, and opens a new research direction at the intersection of foundation models and medical statistics. Paper 1, while technically strong with impressive scalability improvements, addresses a narrower problem (sensor selection in POMDPs) with more limited cross-disciplinary appeal and real-world applicability.
Paper 1 addresses a fundamental and ubiquitous challenge in AI: optimizing the cost-performance trade-off in LLM deployment. Its novel routing framework and new benchmark have broad applicability across any domain utilizing LLMs. While Paper 2 offers a valuable clinical application, Paper 1's contribution to core LLM infrastructure and generalization provides a significantly wider potential impact across the entire field of artificial intelligence.
Paper 2 likely has higher impact: it proposes a broadly applicable, explicitly regulated intermediate-representation framework (cognitive graph + feedback loops) for research agents, addressing a timely, general failure mode (error propagation under uncertainty) across many domains. The reported gains on agent benchmarks and reproducible open-source results suggest practical adoption potential. Paper 1 is novel and clinically relevant, but its contribution is more domain-specific (censoring-aware survival modeling) and shows modest improvements over strong baselines on limited tasks, implying narrower cross-field impact.
Paper 1 addresses a fundamental and ubiquitous challenge in medical research—survival analysis with censoring—by innovatively reformulating it for LLMs. This methodological advance has broad applicability across countless clinical predictive tasks. In contrast, Paper 2 focuses on a narrower, highly specific NLP task of extracting communication behaviors from secure messages, making its overall scientific and real-world impact more limited.
CUA-Gym addresses a fundamental bottleneck in training computer-use agents via RLVR by creating a scalable pipeline for generating verified training data. It produces a large-scale dataset (32K tuples, 110 environments), demonstrates strong empirical results with transfer to held-out benchmarks, and promises full open-source release of pipeline, data, and models. Its breadth of impact spans RL, LLM agents, and software automation—a rapidly growing field. Paper 1, while novel in adapting LLMs for survival analysis, shows modest improvements over baselines and serves primarily as a proof of concept in a narrower domain.
LLMSurvival addresses a fundamental challenge in medical AI—integrating censoring-aware survival analysis with LLMs—opening a new paradigm for clinical prediction. Its novelty (pairwise ranking reformulation for censored data), broad clinical applicability (ICU mortality, fracture risk), and demonstration of outperforming established clinical scores (SAPS-II, FRAX) give it higher potential impact. Paper 2, while methodologically thorough, is narrowly focused on synthetic data for low-resource patent classification—a niche application with limited cross-field influence. Paper 1's framework is more generalizable and timely given the rapid adoption of LLMs in healthcare.
Paper 2 has higher likely impact due to a more novel, general efficiency-centric framework for multimodal web agents (online skill distillation with a skill library plus routing/compression/cache-aware prompting) and clearly demonstrated gains in both success and token cost on a widely used benchmark. Its methods and proposed efficiency metrics can transfer across many agent settings, making the breadth of impact larger and the work timely given the field’s focus on inference-time compute. Paper 1 is valuable clinically, but shows modest improvements and is narrower in scope.
IdleSpec addresses a broadly applicable problem in LLM agent inference—idle time during tool calls—with a novel speculative planning approach that is generalizable across diverse agentic scenarios. It demonstrates significant performance gains (5-9%) on established benchmarks. Paper 2 (LLMSurvival) presents an interesting but incremental contribution with modest improvements (0.5-3.1%) on a narrower domain (survival analysis), and serves more as a proof-of-concept. IdleSpec's broader applicability to the rapidly growing LLM agent ecosystem, combined with stronger empirical gains and methodological novelty, gives it higher potential impact.
Paper 2 has higher potential impact due to its more general, theory-driven contribution: it derives scaling laws and “exchange rates” for the value of brain data across regimes, and analyzes robustness under distribution shift. This yields broadly applicable principles relevant to NeuroAI, multimodal learning, sample efficiency, and experimental design, potentially guiding data-collection strategy across many domains. Paper 1 is timely and useful for clinical survival modeling with LLMs, but its empirical gains appear moderate and the contribution is more specialized to censoring-aware clinical prediction and a particular LLM-based framework.
Paper 2 introduces a novel technical framework (LLMSurvival) that solves a concrete, well-defined problem—enabling LLMs to perform censoring-aware survival analysis without architectural modifications. This has broad applicability across clinical medicine and potentially other fields using time-to-event data. The pairwise ranking reformulation is a creative methodological contribution with demonstrated improvements over established baselines on real clinical datasets. Paper 1 offers valuable conceptual insights about pluralistic alignment measurement but is more narrowly scoped to alignment evaluation methodology, with findings that are primarily diagnostic rather than providing a broadly adoptable new capability.
Paper 1 has higher potential impact due to its timely, innovative integration of LLMs with censoring-aware survival analysis, a core methodology in clinical research. The pairwise ranking reformulation and anchor-based aggregation provide a concrete path to end-to-end fine-tuning under censoring, with demonstrated gains over Cox and deep survival baselines on real clinical cohorts, suggesting near-term translational value. Its approach could generalize across healthcare tasks and potentially other time-to-event domains. Paper 2 is useful but more niche (interactive ontology tooling) with less clear methodological novelty and broader cross-field uptake.
Paper 1 offers a highly practical and novel methodological breakthrough by adapting LLMs for censoring-aware survival analysis, a foundational and notoriously challenging task in clinical machine learning. By demonstrating superior performance over established clinical scores and deep learning models on real-world medical datasets, it has immediate, high-impact applications in healthcare. Paper 2 provides a valuable cautionary finding for mechanistic interpretability, but Paper 1's direct improvements to clinical predictive modeling give it broader, more tangible cross-disciplinary impact.
Paper 1 presents a large-scale empirical audit of AI governance infrastructure across 2.1M+ repositories, formalizing the novel concept of a 'governance horizon' with rigorous quantitative analysis. It addresses a timely, high-stakes policy problem—traceability of ethical constraints in open-weight AI—with broad implications for AI regulation, supply-chain accountability, and platform governance. Paper 2, while methodologically sound, offers incremental improvements (0.5-3.1%) on survival analysis using LLMs, addressing a narrower clinical ML niche. Paper 1's cross-disciplinary relevance to AI policy, law, and open-source ecosystems gives it substantially broader impact potential.
Paper 2 addresses a fundamental methodological gap—enabling LLMs for censoring-aware survival analysis—with broad clinical applicability. Its novel pairwise ranking reformulation is a generalizable contribution that bridges LLMs and a core statistical method used across medicine, epidemiology, and beyond. Validated on real clinical datasets (MIMIC-IV, NYP/WCM), it demonstrates practical utility and portability. Paper 1, while technically interesting, addresses the narrower problem of scientific introduction writing with incremental improvements, and its impact is largely confined to AI-assisted writing tools rather than opening new methodological paradigms.