NeurIPS 2026 Papers

Time series anomaly detection (TSAD) remains challenging not only because anomaly labels are scarce, but also because temporal anomalies are highly context-dependent. Existing methods often rely on unsupervised or surrogate objectives, producing anomaly scores indirectly rather than learning explicit normal--anomalous distinctions. We propose Counterfactual Pairing with Anomaly Semantics (CAPS), a supervision-recovery framework for TSAD. CAPS formulates temporal anomalies as mechanism-induced effects on normal temporal evolution and recovers matched supervision without target-domain anomaly labels. It learns transferable anomaly semantics from simulated normal--anomalous pairs, disentangles them from normal temporal structure, and organizes them into family-wise modes. Instead of directly using simulated anomalies as target positives, CAPS instantiates the learned semantics as residual-form anomaly effects on target-domain reference trajectories via residual MoE generation. The resulting matched counterparts provide supervision-aligned signals for boundary-oriented detector learning. Experiments on nine benchmark datasets show that CAPS outperforms unsupervised and injection-based baselines, is competitive with supervised-reference baselines, and provides interpretable evidence through anomaly-effect generation and expert association.

Many machine-learning quantities are straightforward to compute once a finite data set is fixed, but their population versions can encode much harder computational problems. We study this gap for population risk, attribution, counterfactual explanation, and calibration certification, asking which of these quantities can be approximated in time polynomial in the requested number of bits. The formal basis is the Kawamura-Cook second-order complexity theory, instantiated here with compact-domain real functions carrying explicit evaluation, continuity, and range information. In this framework, expected risk has exactly the difficulty of real integration. Integrated gradients behave differently by dimension: in one dimension they reduce to evaluating endpoints, while in two dimensions even one coordinate can already encode one-dimensional integration when the relevant derivative is provided. The same lens separates the costs in related explanation and certification methods. Interventional SHAP is integration-hard even with two features, succinct coalitional SHAP is #P-hard, nearest-threshold counterfactual search is NP-hard, and continuous calibration certificates combine integration with maximization. These results give a taxonomy of when population-level explanation and certification admit polynomial-time bit approximations, and what additional structure is needed for tractability.

On-policy training plays an important role in modern machine learning, especially in reinforcement learning (RL) for language models. However, quantifying how training data influences the policy's behavior is difficult in on-policy settings: one must account for the cascading effect by which a training event affects its immediate policy update, which in turn affects future action samples, which further affects future policy updates, and so on. Attribution methods developed for off-policy and supervised learning (e.g. influence functions) do not account for such \emph{policy-data interaction}, and can therefore fail in on-policy settings. In this work, we formalize the problem of attributing behaviors to individual training events in the on-policy setting. We derive an influence measure that accounts for policy-data interaction while also being tractable to compute in practical training setups (e.g. LM finetuning) and for realistic optimizers (e.g. AdamW) at a cost that scales linearly in the total number of training events. Empirically, our influence measure accurately predicts the true effect of ablating specific events during RL, while traditional methods fail to do so. Further, we show that this influence accurately identifies helpful and harmful RL training events, allowing us to improve performance via influence-weighted retraining. Overall, we show that accounting for policy-data interaction is both necessary and tractable, allowing for principled training intervention in modern RL pipelines.

AI tools are increasingly integrated into real-world workflows. However, existing measures of reliance on these tools focus on AI output adoption or on self-reported indicators, rather than how task effort is distributed between users and tools. Here, we introduce offloading score, a measure of reliance that quantifies the fraction of cognitive effort offloaded to an AI tool. Offloading score is simulation-based---we construct a counterfactual workflow by estimating how the user would have completed the task without the tool, and then computing the fraction of steps saved by using the tool. We validate offloading score through intrinsic evaluations of metric validity, and a controlled user study ($n=40$) with developers performing programming tasks using AI tools. We vary time pressure to test whether reliance measures capture the known increase in reliance under time pressure. We show that offloading score detects significantly higher reliance in time-constrained settings ($+43\%$, $p=0.018$), while usage-based and self-reported baseline measures do not distinguish the conditions. We complement this with descriptive insights showing that higher reliance manifests as greater delegation and more direct reuse of AI outputs. Finally, we demonstrate an approach of using offloading score in combination with target outcomes of reliance (e.g., task understanding) to identify when reliance may be (in)appropriate. Our framework offers two contributions: an instrument users can apply to measure and reflect on their own reliance, and a quantitative signal that agent designers can utilize to mitigate overreliance.

Understanding how neural activity encodes behavioral and sensory variables is a key challenge in systems neuroscience. This requires models that go beyond predicting activity from labels and instead support source-conditioned counterfactual editing: given a recorded source response, we should be able to edit one behavioral or stimulus variable and predict how neural activity would change while preserving other observed factors and source-specific variability not explained by the labels. Here, we introduce **Neural-DISCO**, a disentangled representation learning framework for counterfactual modeling of neural activity. Neural-DISCO partitions the latent space into label-specific components, each associated with an observed behavioral or stimulus variable, together with a residual latent that captures variability not supplied through observed labels. Across simulated and real neural recording datasets, we show that this structure enables selective manipulation of individual labels to generate counterfactual neural responses while preserving the remaining content of the original activity. Further, to gain insight into how behavioral and stimulus variables are encoded at single-neuron resolution, we pair this disentangled framework with feature attribution methods that identify which neurons are most important for each label. We find that disentanglement enhances the recovery of known functional cell roles in both synthetic and real data. Together, these results support Neural-DISCO as a framework for controlled counterfactual modeling of neural activity and provide a practical interface for probing how neural populations encode behavior and sensory information.

Proteolysis-targeting chimeras (PROTACs) induce protein degradation through coordinated interactions among a degrader molecule, a target protein, and an E3 ubiquitin ligase. Yet computational prediction is often framed as if degradation were an intrinsic property of the degrader alone, overlooking the tuple-level context that determines activity. This mismatch is especially consequential in public PROTAC datasets, where degradation labels are sparse but molecule–target–E3 records are abundant. We introduce DegradeQuery, a tuple-conditioned framework that uses unlabeled records as structured relational evidence rather than incomplete labeled examples. DegradeQuery first performs counterfactual tuple pretraining, learning to distinguish observed molecule–target–E3 tuples from alternatives generated by replacing the target, the E3 ligase, or both. This objective induces a conditional compatibility representation before degradation labels are used, enabling the model to capture how degrader activity depends on biological context. The pretrained representation is then fine-tuned for high/low degradation prediction. On the official PROTAC-8K benchmark, DegradeQuery achieves 0.9065 AUROC and 0.8500 accuracy, outperforming reported semi-supervised results without pseudo-labeling, teacher models, distillation, or ensembles. Ablations, scaffold holdout, target–E3 holdout, and target-wise few-shot adaptation further support PROTAC degradation prediction as a tuple-conditioned rather than molecule-only problem.

Reinforcement learning has rapidly become the dominant paradigm for training software engineering (SWE) agents on long-horizon, multi-turn tasks such as resolving real-world GitHub issues. Yet existing pipelines suffer from a fundamental credit-assignment bottleneck: rewards arrive only at the end of trajectories that may span dozens of file edits, shell commands, and test invocations, leaving every intermediate decision indistinguishable from luck. We observe that containerised SWE tasks provide a particularly useful form of environment forkability: a commit hash plus a container image specifies a real executable software state that can be restored cheaply and exactly, with auditable provenance and without a simulator-to-reality gap. We exploit this property by introducing Counterfactual Rollout Replay (CRR), a training-time procedure that re-executes the environment from selected decision points with alternative actions and assigns each selected step an advantage equal to the sampled on-policy versus counterfactual return differential. CRR requires no learned process reward model, no human rubric annotation, and no oracle hindsight signal; it is free of auxiliary process labels and reward-model training, while still incurring extra environment-replay compute. Applied to a 14B base model on SWE-Gym and evaluated on SWE-bench Verified and SWE-rebench, CRR-trained agents reach higher resolution rates with substantially fewer trajectories than outcome-only GRPO and complement orthogonal advances such as PRM-based scoring and trajectory search.

We study finite-horizon two-player zero-sum perfect-recall partial-information games and ask for the minimal finite-dimensional state sufficient to answer all rooted observation-measurable unilateral continuation queries. For each player $i$ and information set $I$, we consider the span $\mathcal L_i(I)$ of feasible counterfactual beliefs and the effective local query space $\mathcal E_i(I)$ generated by rooted continuation queries restricted to $\mathcal L_i(I)$. We prove that its dimension $d_i(I)$ is simultaneously the rank of the local counterfactual query operator, the minimum core-query basis size, the minimum exact local realization dimension, and the dimension of the canonical quotient $\mathcal L_i(I)/\mathcal N_i(I)$, yielding reduced counterfactual predictive-state representations, unique up to similarity, with exact first-return update operators. Aggregating the local ranks defines the deviation-conditioned predictive-state dimensions $d_t^i$, $d_t$, $d_\star$, and $D_\star$. We then prove that local rooted payoff-query control transfers to global exploitability, and under local design richness and bounded one-step parameter norms obtain a rollout-based upper bound in these minimal coordinates. We complement this with a minimax lower bound showing that $\Omega(d/\gamma^2)$ local rollouts are necessary even when the peak DCPSD equals $d$, and with an exponential separation theorem exhibiting games with $d_\star=m$ but observable continuation-trace complexity $2^m$. Finally, we show that DCPSD recovers finite-rank predictive-state, observable-equivalence, and linearly parameterized continuation-law models as special cases.

Counterfactual explanations for time-series classification generate synthetic instances that flip a prediction to a desired class while remaining plausible and making small, local changes. Existing methods often rely on classifier gradients, and many do not naturally extend to one-class settings. To bridge this gap, we propose CTCF, a model-agnostic method for hard-decision classifiers in supervised and one-class settings. CTCF decouples generation from classifier-specific control by reusing an unconditional Flow Matching generator and learning a flow-time-dependent desired-class region from interpolation states labeled by endpoint hard decisions, without classifier gradients or class probabilities. At inference time, CTCF uses a dual-control mechanism: fused-lasso endpoint-objective steering encourages small, segment-local changes, while linearized projection correction reduces violations of a conservative desired-class region. Theoretically, under ideal marginal matching, we show that the surrogate risk on interpolation states equals the corresponding ODE-trajectory risk. Experiments on UCR datasets demonstrate CTCF's effectiveness across quantitative metrics and qualitative case studies.

Estimating counterfactual outcomes when multiple treatments are applied simultaneously, the composite treatment setting, is fundamentally harder than the single-cause case: the treatment space grows combinatorially, most combinations are unobserved, and confounding is entangled across dimensions. We propose PACE (Progressive Alignment for Composite Effects), which exploits a known causal DAG over treatment variables to progressively transform the observational data distribution into the target interventional distribution. PACE performs K sequential augmentation steps in reverse topological order, each isolating a single treatment component and conditioning only on its parents in the DAG. We derive the closed-form optimal augmentation density minimizing KL divergence at each step, establish finite-sample bounds prescribing the augmentation budget via DAG-aware selection bias ratios, and show that the DAG provides a deterministic processing order that eliminates the need for random permutation averaging. On semi-synthetic benchmarks with real covariates (IHDP, Twins, Hillstrom) and controlled synthetic environments, PACE substantially outperforms seven baselines on both continuous and binary outcomes, with the largest gains on rare treatment combinations where confounding is most severe. Ablations confirm consistent improvements over the closest prior method across varying DAG topologies, confounding strengths, and under DAG misspecification.

Policies achieving strong performance in simulators or learned world models can fail when deployed into the real environment. While there must be environmental differences that account for these failures, not all differences are equally responsible. A benign error of irrelevant visual details may co-exist with a failure to predict a single critical transition causing an inevitable catastrophic failure. In this paper, we introduce \emph{counterfactual debugging} as an approach for \emph{world model transfer gap attribution} to identify the time steps whose transition or reward errors are \emph{causally responsible} for a performance degradation exhibited in a real trajectory, rather than only visually or statistically different. We develop a scalable algorithm for computing these attributions that exploits the sparsity of causal errors by recursively divide-and-conquer, significantly reducing computational costs and achieving an exponential speedup. The resulting ranked attribution report can better explain the performance gap in the real trajectory. Experiments across several distinct environments with a variety of injected world-model failures (observation corruption, reward misprediction, and physics violations) demonstrate that counterfactual debugging correctly identifies the errors that are responsible for performance gap, providing theoretically grounded, actionable insights for model improvement.

Counterfactual explanations (CFs) provide human-interpretable insights into model's predictions by identifying minimal changes to input features that would alter the model's output. However, existing methods struggle to generate multiple high-quality explanations that (1) affect only a small portion of the features, (2) can be applied to tabular data with heterogeneous features, and (3) are consistent with the user-defined constraints. We proposeCounterFlowNet, a generative approach that formulates CF generation as sequential feature modification using conditional Generative Flow Networks (GFlowNet). CounterFlowNet is trained to sample CFs proportionally to a user-specified reward function that can encode key CF desiderata: validity, sparsity, proximity and plausibility, encouraging high-quality explanations. The sequential formulation yields highly sparse edits, while a unified action space seamlessly supports continuous and categorical features. Moreover, actionability constraints, such as immutability and monotonicity of features, can be enforced at inference time via action masking, without retraining. Experiments on eight datasets under two evaluation protocols demonstrate that CounterFlowNet achieves superior trade-offs between validity, sparsity, plausibility, and diversity with full satisfaction of the given constraints.

Relational out-of-distribution errors are easy to describe and hard to evaluate cleanly. An image may be natural, a caption may be fluent, and all mentioned objects may be common, yet the caption may describe the wrong relation in the image. Many evaluations intended to test this behavior can be solved without checking the pairing at all, because negatives differ in caption style, template, length, object frequency, or image statistics. We propose counterfactual pairing cycles, a blocked evaluation primitive that reuses the same two images and the same two captions on both sides of the comparison, changing only which image is paired with which caption. This makes image-only, text-only, and additive component shortcuts cancel exactly in finite samples. The same contrast also gives a training loss for relational outlier exposure and a diagnostic for bilinear vision-language scores. Across synthetic controls, GQA, and COCO, cycles separate coarse image-caption matching from stricter role-binding failures. In a shortcut-contaminated COCO outlier-exposure stress test, a model selected by a perfect shortcut validation AUROC transfers poorly to exact relational cycles, while cycle training improves CycleAcc from \(0.576\) to \(0.911\). The main lesson is simple: to test whether a model understands a pairing, the benchmark should keep the parts fixed, change the pairing, and ask only about coupling.

In cooperative teams where agents act in a fixed order and share a single team-level reward (multi-agent language systems, sequential robotic tasks), per-agent credit assignment is under-determined. Critic-based approaches scale poorly as the number of agents grows owing to the costly maintenance of joint/factored critic(s), whereas the existing critic-free alternatives have other issues: common credit across agents that couples every agent's signal to teammate noise, importance-sampling corrections for upstream-update staleness that incur variance exponential in team size, or per-agent counterfactual replay that isolates each agent's effect at the price of extra environment or reward calls. We propose COSAC, a critic-free per-agent policy gradient for sequential cooperative teams. COSAC fits an additive per-agent decomposition of the team reward by a single ridge regression on the rollout batch (giving each agent a learning signal decoupled from teammate noise), and computes each agent's counterfactual advantage from fictitious continuations of the current policy (policy forward passes that replace both importance-sampling reweighting and per-agent environment replay, at no extra environment or reward cost). The estimator instantiates the **Sequential Aristocrat Utility** (SeqAU), our extension of Wolpert and Tumer's (2001) aristocrat utility to sequential teams. We prove bias and variance bounds on SeqAU credits that stay controlled as the team grows. Our controlled study on sequential bandits demonstrates that COSAC attains the lowest advantage MSE and consistently low learning regret across team sizes up to $K = 16$. On the AI2 Reasoning Challenge (ARC) task, where four Qwen3-0.6B agents reason in turn about a grade-school science question, COSAC attains faster convergence than the other critic-free baselines.