NeurIPS 2026 Papers

Reconstructing patient-specific cyclic cardiac motion at arbitrary phases is central to dynamic cardiac assessment, underpinning measurements such as ejection fraction, stroke volume, and regional myocardial deformation. Existing methods---pair-wise registration, time-conditioned decoders, and coordinate-based implicit fields---share a common implicit assumption: the cardiac cycle is treated as a sequence of discrete phases, with temporal structure stitched together through learned mappings supervised at the observed frames. Yet the cycle is, by physical fact, a closed periodic process. We propose \textbf{CycleSpectra}, a spectral motion representation that takes the cyclic structure as a starting point of the representation rather than as a property to be learned. Given a pair of sparsely sampled phase observations, the model learns the spectral structure of the underlying cyclic cardiac motion in a low-dimensional latent space, and recovers the displacement at any phase through a fixed analytic synthesis layer---time never enters the network as a learnable signal, only as a coordinate the readout takes by construction. To support evaluation, we collect a thin-slice phase-resolved 4D cardiac CT cohort and benchmark under both canonical and non-canonical input protocols, reporting whole-cycle motion-fidelity metrics alongside standard image and anatomical metrics. Under arbitrary two-frame input, CycleSpectra achieves the lowest ejection-fraction error and highest ventricular volume--time curve correlation among recent baselines, while remaining competitive on image fidelity. Beyond two-frame inference, the proposed framework supports test-time multi-frame latent fusion for higher accuracy, validated on both the in-house cohort and a cross-modality 4D cardiac MR cohort. Code will be available.

Frontier language-model agents are rapidly becoming capable cybersecurity assistants, creating a dual-use cyber risk: the same capabilities that help defenders find, understand, and validate vulnerabilities can also help attackers weaponize them. Existing cyber benchmarks largely measure either raw offensive capability or generic refusal behavior, but they do not evaluate whether models can preserve benign security utility while refusing assistance that materially advances misuse. We introduce CyberDualEval, a benchmark for measuring this boundary on real vulnerabilities. Each task is decomposed into three phases of increasing operationalization: vulnerability analysis, proof-of-concept generation, and exploit execution. To capture the contested middle ground, we annotate each proof-of-concept task with a Task Weaponization Score that measures how much work remains to turn a minimally sufficient demonstration into unauthorized cyber misuse. CyberDualEval evaluates frontier language models with varied agentic scaffolds and reports refusal, defensive analysis accuracy, and optional exploit validation as distinct signals. Our evaluation reveals two complementary failure modes. Many models over-comply, assisting with high-weaponizability PoCs or exploit requests that should be refused. Conversely, recent safety-tuned models often over-refuse, declining low-risk or defensive requests and thereby reducing benign cyber utility. These results show that cyber safety cannot be assessed by capability or refusal rate alone: safe deployment requires calibrated refusal that tracks weaponizability while preserving useful defensive assistance.

We present CyCLeGen, an autoregressive framework that integrates layout understanding and layout-to-image generation through cycle consistency: predicted layouts must produce faithful images, and generated images must yield consistent layouts. We enforce this constraint via CycleGRPO, a bidirectional reinforcement learning strategy with complementary geometric and perceptual rewards, enabling self-introspective learning from only 8k RL samples. This creates a natural loop, generation helps understanding by rewarding only those layouts that lead to high-quality images, and understanding helps generation by rewarding only those images whose spatial structure can be faithfully recovered. Extensive experiments show that CyCLeGen achieves significant gains across diverse image understanding and generation benchmarks, with emergent gains on image captioning.

Large language models (LLMs) are increasingly deployed as autonomous agents in offensive cybersecurity. In this paper, we reveal an interesting phenomenon: different agents exhibit distinct attack patterns. Specifically, each agent exhibits an attack-selection bias, disproportionately concentrating its efforts on a narrow subset of attack families regardless of prompt variations. To systematically quantify this behavior, we introduce CyBiasBench, a comprehensive 630-session benchmark that evaluates five agents on three targets and four prompt conditions with ten attack families. We identify explicit bias across agents, with different dominant attack families and varying entropy levels in their attack-family allocation distributions. Such bias is better characterized as a trait of the agents, rather than a factor associated with the attack success rate. Furthermore, our experiments reveal a bias momentum effect, where agents resist explicit steering toward attack families that conflict with their bias. This forced distribution shift does not yield measurable improvements in attack performance. To ensure reproducibility and facilitate future research, we release an interactive result dashboard at \url{https://cy-bias-bench.vercel.app/} and a reproducibility artifact with aggregated session-level statistics and full evaluation scripts at \url{https://anonymous.4open.science/r/CyBiasBench-2026/}.

Long-context LLM inference is fundamentally a retrieval process, transitioning from diffuse to sharply focused attention at a few architecture-determined (CTLs). Existing acceleration methods leave significant costs unaddressed: sparse attention ignores FFNs, KV cache compression neglects prefill, and hidden-state pruning relies on fixed, heuristic rules. We introduce CutAttn, a training-free framework that exploits CTLs via inter-layer Jensen--Shannon divergence, pruning redundant context at chunk granularity while dynamically gated by a normalized-entropy criterion. Operating at the hidden-state level, CutAttn reduces both attention and FFN costs, composing orthogonally with existing acceleration techniques. Evaluated on RULER, InfiniteBench, and LongBench, CutAttn preserves full-attention accuracy while achieving up to a $2.02\times$ prefill speedup on 128K sequences (and $2.81\times$ when composed with sparse attention). Furthermore, our theoretical analysis provides approximation bounds, an entropy-compression limit justifying the gating, and a closed-form speedup ratio.

Context or prompt-level reweighting has emerged as a central algorithmic lever in Reinforcement Learning with Verified Rewards (RLVR) for improving the reasoning capability of large language models, yet the principle determining what constitutes an optimal weighting remains poorly understood. We address this gap by formulating prompt reweighting as a functional derivative of a utility functional defined in the pass-rate function space, yielding a unified optimality framework that accommodates existing schemes, including REINFORCE and GRPO. Building on this optimality framework, we propose a distribution-aware prompt reweighting approach, called CurveRL, based on a quantile coordinate transform, in which the weight assigned to each prompt depends not on the absolute value of pass rates but on its rank and density to reflect the distributional structure of the pass rates in the learning dynamics. Extensive experiments across multiple benchmarks demonstrate that our proposed CurveRL consistently outperforms GRPO and other RLVR baselines. Our study identifies context-distribution control as a principled axis for analyzing and designing prompt-reweighted RLVR algorithms.

We introduce CurveBench, a benchmark for hierarchical topological reasoning from visual input. CurveBench consists of $\textbf{756 images}$ of pairwise non-intersecting Jordan curves across easy, polygonal, topographic-inspired, maze-like, and dense counting configurations. Each image is annotated with a rooted tree encoding the containment relations between planar regions. We formulate the task as structured prediction: given an image, a model must recover the full rooted containment tree induced by the curves. Despite the visual simplicity of the task, the strongest evaluated model, $\texttt{Gemini-3.1-Pro-Preview}$, achieves only $\textbf{71.1\\%}$ tree-generation accuracy on CurveBench-Easy and $\textbf{19.1\\%}$ on CurveBench-Hard. We further demonstrate benchmark utility through RLVR-style fine-tuning of open-weight vision-language models. Our trained $\texttt{Qwen3-VL-8B}$ model improves over $\texttt{Qwen3-VL-8B-Thinking}$ from $\textbf{2.8\\%}$ to $\textbf{33.3\\%}$ tree-generation accuracy on CurveBench-Easy, exceeding $\texttt{GPT-5.4}$ and $\texttt{Claude-Opus-4.5}$ under our evaluation protocol. The remaining gap, especially on CurveBench-Hard, shows that exact topology-aware visual reasoning remains far from solved.

Multi-task learning (MTL) aims to jointly optimize multiple related tasks to obtain shared representations within a single model. Recent research has focused on developing MTL algorithms to alter optimization dynamics through task re-weighting or gradient manipulation. However, in this paper, we first empirically identify that current MTL approaches are highly sensitive to the model initialization, complicating empirical evaluation and limiting practical reliability, which has been overlooked but appears to be critical. To better understand this phenomenon, we provide a local theoretical analysis that motivates the connection between early-stage MTL optimization behavior and curvature around initialization. Motivated by this analysis, we propose a Curvature-guided Parameter Initialization (CPI) approach tailored for MTL. Specifically, CPI performs a short warm-up phase to probe local curvature around the initial parameters and heuristically biases the initialization toward empirically favorable curvature profiles for subsequent multi-task optimization. The proposed approach is optimizer-agnostic, requires no modification to existing MTL algorithms, and can be seamlessly integrated as a plug-in prior to standard training. Extensive experiments on standard MTL benchmarks show that CPI improves the aggregate multi-task trade-off while remaining compatible with mainstream MTL methods.

In Euclidean online convex optimization, first-order and full-information feedback share the minimax regret \(\Theta(DL\sqrt{T})\) over \(T\) rounds, as convex losses can be replaced by their linearizations. On Riemannian manifolds this reduction fails, because the corresponding first-order surrogate of a geodesically convex loss is generally not geodesically convex; we show that this failure has provable consequences. For online convex optimization on Hadamard manifolds with sectional curvature bounded below by \(\kappa<0\) and feasible diameter \(D\), we construct hard hyperbolic-space instances yielding first-order regret lower bounds at the scale \( DL\sqrt{\zeta\, T}, \) where \( \zeta=\sqrt{|\kappa|}D/\tanh(\sqrt{|\kappa|}D) \) is the curvature--diameter factor. Our first lower bound applies to all (possibly randomized) first-order algorithms satisfying a natural geometric-span condition, holding almost surely over the algorithm's internal randomness. Our second lower bound removes the geometric-span restriction for deterministic first-order algorithms, provided the feasible diameter satisfies \(D=\Omega(\log T)\); the proof rests on a new construction that confines the hidden comparator to a single horosphere in hyperbolic space. Combined with existing curvature-free full-information upper bounds, these results yield a feedback-model separation on Hadamard manifolds: full-information algorithms attain \(O(DL\sqrt{T})\) regret, yet the additional \(\sqrt{\zeta}\) factor is unavoidable for first-order feedback whenever either the geometric-span condition holds or the algorithm is deterministic.

Submodular functions---functions exhibiting diminishing returns---are central to machine learning. When the objective is monotone and non-negative, the greedy algorithm achieves a tight $63\%$ approximation. But many practical objectives incorporate costs that make them negative on some inputs, and all existing multiplicative guarantees require non-negativity. Prior work handles negativity through additive bounds for the special class of decomposable functions and non-monotonicity through partial-monotonicity parameters, but these address each difficulty in isolation and neither extends the classical structural theory. We extend curvature---a parameter measuring how far a function deviates from linearity---to all submodular functions, handling both non-monotonicity and negativity through a single classical concept. A greedy algorithm with pruning achieves a curvature-controlled multiplicative ratio for any submodular function, including those taking negative values---the first such guarantee beyond monotonicity and non-negativity. In the non-monotone regime $1 \le c_g < 2.2$, the bound strictly beats the best known uniform ratio of $0.401$ (for non-negative $f$), and it recovers the classical $(1-e^{-c_g})/c_g$ guarantee for monotone functions. A multilinear-extension variant extends the framework to general combinatorial constraints via multilinear relaxation. Experiments on cost-penalized experimental design, coverage, feature selection, and a curvature sweep on Multi-News passage selection support the theory.

Formal verification provides strong correctness guarantees, but proof development remains difficult because developers must diagnose coarse verifier errors and repair failed proof obligations. Recent approaches leverage language models (LMs) to improve proof automation through verifier-in-the-loop repair, but they often rely on hand-engineered prompts, retrieved examples, or sparse pass/fail feedback. We present \tool, an LM-based framework for Verus proof repair that internalizes counterexample-guided diagnosis as a model capability. Our key insight is to train counterexample generation not as a standalone validation task, but as a data-driven diagnostic objective: a counterexample is useful when conditioning on it helps the model produce a safe, verifier-passing proof. Given a failed Verus proof and verifier error, \tool generates a structured source-level counterexample, repair rationale, and repaired proof in one trajectory, optimized with reinforcement learning from Verus feedback and a proof-code safety gate. To address sparse and order-sensitive credit assignment, \tool introduces a topological graph reward that derives invariant dependencies from inductiveness tests and encourages counterexample generation following the same topological order as the invariant dependencies. Across seven Verus benchmarks with 471 problems, \tool achieves 61.6\%/72.2\% weighted-average Safe-Pass@1/Safe-Pass@3, outperforming the strongest baseline, Claude Sonnet 4.5, by 5.7/6.3 points. We also show that \tool's learned counterexample can directly augment the reasoning of existing LM-based proof repair approaches, improving their success rate by 64.3\% compared to using the chain-of-thought diagnostics.

Molecular conjugates, including PROTACs and peptide-drug conjugates (PDCs), derive their function from the joint behavior of multiple coupled components, yet most generative approaches design these components independently and combine them only after generation. Such staged pipelines ignore cross-component dependencies and often produce conjugates that are chemically invalid, fall outside empirical conjugate distributions, or lose function upon assembly. We introduce $\textbf{C}$o$\textbf{up}$led $\textbf{O}$bjective-Guided Discrete $\textbf{F}$lows for $\textbf{Mo}$lecular $\textbf{C}$onjugate $\textbf{A}$ssembly $(\textbf{CupOFMoCA})$, a discrete generative framework that formulates conjugate design as a constrained, coupled generation problem. CupOFMoCA restricts generative trajectories to a chemically feasible conjugate manifold and biases local transitions using target-specific activity predictors, ensuring all components remain mutually compatible throughout generation. We show that coupling constraints and objective guidance enable anticipatory design that preserves post-assembly predicted activity and produces structurally realistic conjugates across both PDC and PROTAC settings, outperforming staged baselines, including LinkerNet, DiffLinker, and DiffPROTACs for PROTACs, across assembly validity, predicted activity, and physicochemical property ranges. These results demonstrate that explicit coupling and constraint enforcement are sufficient to recover functional conjugates across conjugate classes, and provide a principled foundation for generative modeling where function emerges only at the level of the assembled system. Our anonymous code repository can be found at https://anonymous.4open.science/r/Cupofmoca-Neurips.

Crystal generative models mainly learn what stable crystals look like, with little explicit supervision for what makes them stable. We reveal a substantial representation gap between state-of-the-art crystal generative models and pretrained universal machine learning interatomic potentials (MLIPs) via energy probing, and show this gap can be closed by a simple training-time alignment. We propose Crystal REPresentation Alignment (CrystalREPA), a plug-and-play framework that aligns the atom-wise hidden states of generative encoders with frozen MLIP representations through an element-aware contrastive objective, transferring stability-aware atomistic priors with marginal training overhead and no additional inference cost. Across three generative frameworks, ten MLIP teachers, and two benchmark datasets, CrystalREPA consistently improves the thermodynamic stability, structural validity, and structural fidelity of generated crystals. Equally important, we find that an MLIP's transfer effectiveness is poorly predicted by its accuracy on standard leaderboards (e.g., Matbench Discovery) but strongly predicted by the distinguishability of its atom-wise representation space, yielding a practical, accuracy-independent criterion for selecting MLIP teachers for generative transfer.

Cryptanalysis---the task of finding attacks against cryptographic schemes---sits at the intersection of mathematical reasoning and programming, two areas where LLMs have made rapid progress. Just like math and programming, cryptanalytic attacks are formally defined and can be unambiguously verified. This raises the question if cryptanalysis might experience a similar acceleration in progress through LLMs. In this paper we introduce $\texttt{CryptanalysisBench}$, a benchmark of 113 tasks spanning six families of cryptographic primitives (block ciphers, hash functions, etc) drawn primarily from four NIST standardization competitions. Each task asks an agent to break an implementation of a cryptographic primitive by winning a formal security game. The benchmark has three tiers: (1) primitives with known practical breaks; (2) scaled-down variants of primitives without one; (3) a challenge set of unbroken production primitives. We evaluate Claude Opus 4.7 and GPT-5.5: both solve a majority of Tier 1 (71.4\% and 78.5\%, respectively), while Tier 2 remains largely out of reach. For Tier 1 successes, we provide a fine-grained analysis distinguishing paper recall from source-level rediscovery. We release $\texttt{CryptanalysisBench}$ both as a forecasting tool to track when AI cryptanalytic capability becomes a serious factor, and as scaffolding for subjecting candidate schemes to more attacks before they are deployed.

Deep learning has accelerated automated atomic model building from cryo-EM maps, yet progress remains limited by the lack of large, high-quality datasets and standardized evaluation benchmarks. We address this gap by introducing CryoAtlas, a rigorously curated dataset and unified benchmark for density-guided atomic model building. By integrating and filtering EMDB and PDB entries (up to March 2026), CryoAtlas provides 17,809 map--model--sequence entries, including a curated 16,657-entry training split. From this resource, we construct a 618-target benchmark with a shared evaluation protocol that jointly assesses structural accuracy, model-to-map fit, and stereochemical validity. Using this framework, we benchmark state-of-the-art learning-based methods against Phenix and AlphaFold~3, comprehensively analyzing the impact of map resolution, sequence length, protein category and sequence identity on modeling accuracy. As a case study, retraining the ModelAngelo CNN component on CryoAtlas improves performance on the benchmark. CryoAtlas provides an open, reproducible foundation for future cryo-EM atomic model building research.

Benchmarks for large language models typically evaluate the accuracy of answers against fixed reference labels. But a central step in many complex real-world tasks is identifying which questions are worth asking in the first place: decomposing a difficult problem into subquestions whose answers provide key steps on the path toward solving the larger problem. To evaluate this capability of information discovery, we introduce CruxBench, a benchmark that grades LLM-generated questions by their Value of Information (VOI): how much a model-proposed subquestion updates beliefs about a target outcome. To automate this measurement at scale, we target as outcomes the prices in continuously updating forward-looking prediction markets. Our benchmark has three rare properties: it is contamination-resistant by construction, since ground truth is generated by future world events; it is open-ended, admitting unbounded and complex text-based submissions rather than one correct numeric answer; and it is grounded, with informativeness measured against quantified changes in real-world beliefs (prices). We evaluate eight frontier and open-weight models on 336 target outcomes and find that VOI correlates highly with independent measures of model quality ($r=0.87$) and predicts downstream usefulness when the generated questions are passed to a separate forecasting agent.

Merging separately trained LoRA adapters is a practical alternative to joint multi-task training, but it often hurts performance. Existing methods usually treat the LoRA update $\Delta W = BA$ as a single object and do not distinguish the two LoRA matrices. We show that the main source of LoRA merge interference comes from the output-side matrix $B$. Across tasks, $B$ repeatedly uses a small set of shared directions, while $A$ remains much more task-specific. As a result, the merged adapter overemphasizes these shared directions, and task-specific information is lost. We propose Pico (Pre-merge interference calibration in output-space), a data-free method that calibrates $B$ before merge by downscaling over-shared directions and then rescaling the merged update. Pico plugs directly into existing merging methods such as Task Arithmetic, TIES, and TSV-M. Across eight different benchmarks from math, coding, finance, and medical domains, Pico improves average accuracy by 3.4-8.3 points over the corresponding base method and achieves the best overall average performance. Pico also enables merged adapters to outperform the LoRA trained with all task data. These results show that LoRA merging works better when the two LoRA matrices are treated separately.

Modern machine learning progresses through empirical work, benchmarking new methods to evaluate relative performance. However, the statistical variability inherent to evaluation -- exacerbated by the stochastic nature of many algorithms -- often makes performance estimation unreliable due to the limited test samples available, leading to a validation crisis in which genuine advances are difficult to discern. In this work, we show that cross-validation improves markedly confidence when evaluating and comparing learning algorithm performances. We introduce the concept of \emph{sample gain}, which quantifies the virtual data augmentation achieved by using multiple cross-validation splits to reduce benchmarking variance. Experiments on both synthetic and real-world datasets (histopathologic scans and NLP fine-tuning) demonstrate that multiple splits can substantially improve the reliability and stability of performance estimates, with diminishing returns often setting in later than expected. We also introduce a procedure to dynamically early-stop cross-validation by estimating from the first few folds if subsequent folds will bring large sample gains. Our findings highlight the value of pushing cross-validation on available samples to achieve robust and reliable benchmarking.

Dictionary learning methods - such as Sparse Autoencoders (SAEs) and crosscoders - decompose model activations into human-interpretable building blocks. We introduce _temporal crosscoders_, a simple and flexible framework for feature discovery in Large Language Models (LLMs). To properly evaluate temporal crosscoders we develop TempBench: a panel of synthetic and real-world tasks for evaluating temporal structures. Temporal crosscoders outperform both conventional and temporal architectures in both of our synthetic settings and on two out of four of the real world settings - more than any other current architecture. Most strikingly, they can detect backtracking - a key reasoning behavior - at a 40\% higher rate than conventional SAEs, and are 15\% more effective in inducing it. Our results establish temporal crosscoders as a simple and flexible framework for feature discovery, both local and temporal. We provide full code at the following anonymous repository: \url{https://anonymous.4open.science/r/temp-bench-anon/}.