NeurIPS 2026 Papers

Machine unlearning (MU) is essential for enforcing the right to be forgotten in machine learning systems. A key challenge of MU is how to reliably audit whether a model has truly forgotten specified training data. Membership Inference Attacks (MIAs) are widely used for unlearned model auditing, where samples that evade membership detection are regarded as successfully forgotten. We show this assumption is fundamentally flawed: failed membership inference does not imply true forgetting. We prove that unlearned samples occupy a fundamentally different positions in the feature space feature space than non-member samples, making this alignment bias unavoidable and unobservable, which leads to systematically optimistic evaluations of unlearning performance. Meanwhile, training shadow models for MIA incurs substantial computational overhead. To address both limitations, we propose Statistical Membership Inference (SMI), a training-free auditing framework that reformulates auditing as estimating the non-member mixture proportion in the unlearned feature distribution. Beyond estimating the forgetting rate, SMI also provides bootstrap reference ranges for quantified auditing reliability. Extensive experiments show that SMI consistently outperforms all MIA-based baselines, with no shadow model training required. Overall, SMI establishes a principled and efficient alternative to MIA-based auditing methods, with both theoretical guarantees and strong empirical performance.

Automatic speech recognition (ASR) systems are usually judged by aggregate transcript accuracy, but real failures often hinge on meaning: for example, a wrong dose, address, account number, or date can matter more than many harmless word errors. We introduce SMASH, a framework for finding targeted semantic failures caused by sparse INT8 weight-bit perturbations, a dominant model data type for ASR edge devices, in quantized sequence-to-sequence ASR models. Given an audio input and a meaning-critical span,SMASH searches for small weight changes that replace the intended meaning while keeping the transcript readable, plausible, and close to the original output. Each accepted fault is a certificate that a specific semantic substitution is reachable under a bounded perturbation budget. Empirical study focuses on numeric and scalar substitutions across three ASR backbones: Whisper-small.en (WS), Whisper-large-v3 (WLv3), and SeamlessM4T-v2-large (S-M4T); and three corpora: an LLM-assisted controlled corpus (LACC), MultiMed, and LibriSpeech. On LACC at a 20-bit budget ($B$), SMASH-hybrid accepts numeric substitutions on 24/45 WS targets, 19/44 WLv3 targets, and 5/34 S-M4T targets; reachability is lower on real-speech datasets. In matched LACC budget sweeps tested up to $B=40$, WS reaches its ceiling already at $B=20$, while WLv3 and S-M4T continue to gain accepted targeted substitutions through $B=40$. Human validation supports the gate-based labels: on a 105-item shared validation set, independent human-majority labels and a four-judge large- language-model ensemble agree on the composite clean_success label, with $\kappa=1.00$. The faults are sparse: the pooled median is three INT8 bit flips, with medians of two flips on WS, eleven on WLv3, and twelve on S-M4T. Entity and negation pivots are evaluated as secondary evidence in the appendix. SMASH shifts ASR robustness evaluation from "how much did the transcript change?'' to "which meanings can be changed, how plausibly, and with how few bit flips?''

Accurate time series forecasting with exogenous inputs is critical across domains, including energy and retail, yet modern deep learning models often overfit to correlations that do not generalize under shifts in these inputs. We propose SLiDE, a Koopman-inspired architecture for forecasting with exogenous inputs that imposes structure on latent temporal dynamics. SLiDE approximates nonlinear system evolution using a learned linear operator in a latent space, combining (i) a history encoder that reconstructs the current latent state from past targets and inputs with (ii) a shared linear rollout driven by future exogenous variables. By enforcing a common linear recurrence across encoding and prediction, SLiDE aligns how past information is encoded with how future states evolve, reducing parameterization and preventing reliance on spurious correlations in the input history. Empirically, SLiDE achieves state-of-the-art accuracy on real-world benchmarks, including electricity price forecasting and retail demand, while maintaining a computational footprint comparable to lightweight MLP architectures. SLiDE is especially effective at generalizing to shifted exogenous inputs, reducing MSE by 20% on electricity price samples with out-of-range exogenous values and MAE by 30% in a synthetic transfer setting where the same dynamical system is driven by an unseen exogenous process. Ablations confirm the importance of both the structured history encoder and the linear latent rollout, suggesting that structured latent linear dynamics provide a useful inductive bias for forecasting with exogenous inputs.

Multimodal large language models (MLLMs) often answer visual reasoning questions by relying on linguistic priors rather than task-relevant visual evidence. Textual chain-of-thought reasoning can partially mitigate this issue by encouraging models to decompose visual questions into intermediate evidence-seeking steps, but generating these steps autoregressively increases inference cost. Latent reasoning avoids explicit rationale generation, but existing approaches provide limited control over what intermediate states encode, making it difficult to impose separate supervision for planning, grounding, and evidence selection. We propose Structured Latent Visual Reasoning (SLVR), a training framework that bridges explicit chain-of-thought and latent reasoning by organizing multimodal reasoning into typed latent stages for planning, grounding, evidence selection, and reasoning integration. SLVR first trains the model to rely on the image by masking answer-revealing text and contrasting the correct answer with visually plausible distractors. It then organizes reasoning into latent stages for planning, grounding, evidence selection, and integration, supervising each stage with the corresponding signal: plans, boxes, visual evidence, and final rationales. This gives latent reasoning an explicit functional structure while avoiding generated textual chains at inference time. Built on Qwen2.5-VL-7B, SLVR improves consistently across multimodal reasoning benchmarks, with absolute gains of +9.4 on MMVP and +14.2 on BLINK Relation, as well as improvements on V*, MathVista, and ChartQA. These results suggest that structured latent supervision can improve fine-grained visual reasoning without the decoding overhead of textual CoT. Code is available \href{https://anonymous.4open.science/r/SLVR-681F/README.md}{here}.

We introduce Skill Learning from Video Memory (SLVMBench), the first benchmark that jointly evaluates whether video large language models (video-LLMs) can learn skills from long video memory and apply them to real-time tasks. SLVMBench presents models with 2–3 hour video streams that contain a tutorial video embedded in a stream of arbitrary irrelevant videos, resembling real-world human learning practices. Video-LLMs are asked to apply the acquired skill to answer real-time questions about an ongoing video. Unlike long-video understanding benchmarks that emphasize passive comprehension and skill-learning benchmarks that rely on short, immediate demonstrations, SLVMBench tests the full pipeline of memorizing and extracting procedural knowledge, as well as transferring it to real-time tasks. Moreover, rigorous human annotations feature sub-second-level temporal calibration, manually engineered questions eliminating common-sense guessing, and collated tutorials to ensure coverage of the required skills. Evaluations on state-of-the-art proprietary and open-source video LLMs show that video-LLMs struggle substantially with learning and applying skill knowledge from videos. Moreover, performance degrades markedly when the skill knowledge is placed within a long video memory. These results reveal a key limitation of existing video LLMs and position SLVMBench as the first benchmark for studying real-time skill acquisition and application from long-context video memory.

As IT systems grow in complexity, automated agent-driven incident response becomes increasingly critical. A key capability for these agents is summarizing large log streams into human-readable reports. Further progress requires meaningfully evaluating this function, yet existing benchmarks leave end-to-end summarization from large log streams underexplored due to the prohibitive cost and complexity of collecting real-world data. We introduce SLS-Bench, a benchmark for incident log summarization featuring 90 tasks grounded in real-world incident reports, each with a synthetic log stream and a reference summary. To construct SLS-Bench, we developed a data generation pipeline that converts incident reports into structured incident models, which guide the generation of synthetic logs and reference summaries. This pipeline combines narrative grounding, explicit incident modeling, generator-verifier loops, and statistical comparisons with real-world log streams to produce realistic and controllable observability data. Our evaluation of 14 LLMs on SLS-Bench shows that while frontier models can produce useful summaries, they do so at substantial cost and latency, and open-weight models lag significantly. SLS-Bench provides a framework for systematic evaluation of log analysis agents and a scalable blueprint for future synthetic observability benchmarks.

Infrared (IR) spectral unmixing recovers individual component spectra from a measured mixture, enabling chemical identification. Existing methods either assume linear superposition of components, which fails for liquid-phase mixtures where molecular interactions alter spectral shapes, or use deep networks that entangle all components in a shared representation. We introduce SLOT-IR, a slot-based architecture that decomposes mixture spectra into separate per-component embeddings and decodes each independently into a predicted spectrum. A two-stage training procedure first learns to map a single molecule from gas-phase spectra to liquid-phase, then trains the full unmixing model on mixtures. On the benchmark of annotated infrared spectra, SLOT-IR outperforms all baselines on both binary and ternary mixture identification. To verify that the model captures genuine chemical structure rather than statistical shortcuts, we apply a post-hoc BatchTopK Sparse Autoencoder (SAE) to frozen slot embeddings and test whether recovered features correspond to known functional groups. Statistical and causal analyses confirm model alignment with established chemistry.

Model-based reinforcement learning (MBRL) is sample-efficient but struggles in sparse reward settings. A critical bottleneck arises from the lack of informative gradients in sparse settings, where standard reward models often yield flat landscapes that struggle to guide planning. To address this challenge, we propose **S**haping **L**andscapes with **O**ptimistic **P**otential **E**stimates (**SLOPE**), a novel framework that shifts reward modeling from predicting sparse scalars to constructing informative potential landscapes. SLOPE employs optimistic distributional regression to estimate high-confidence upper bounds, which amplifies rare success signals and ensures sufficient exploration gradients. Evaluations on **30+** tasks across **5** benchmarks and **real-world robotic deployments**, demonstrate that SLOPE consistently outperforms leading baselines in **fully sparse**, **semi-sparse**, and **dense** rewards. The project code is available at https://anonymous.4open.science/r/SLOPE-CF13.

Systematic reviews -- which requires comprehensive evidence collection and synthesis from large document corpora in response to targeted research questions -- are foundational in finance, social sciences, and other technical fields. Manual construction of evidence tables is labor-intensive, and recent LLM-based assistants relying on embedding or keyword based search often fail to meet the coverage standards of systematic reviews. We introduce SLIDERS, a novel LLM-based methodology for systematic reviews, by automatically assembling evidence tables tailored to research questions. In addition to extracting structured data from documents, SLIDERS can extract full-text excerpts that serve as direct evidence or as provenance for structured data. Core to SLIDERS is an automated evidence reconciliation agent that writes code to analyze and reconcile extracted evidence, bringing together information fragmented across documents, resolving inconsistencies across excerpts, and synthesizing overlapping findings into a coherent evidence table. In addition, SLIDERS allows users to ask follow-up questions in natural language to further explore the assembled evidence. We evaluate SLIDERS on three systematic-review-style tasks over large document collections. SLIDERS outperforms the best-performing baseline across benchmarks, remains near 90\% accuracy across 6M-11M-token corpora. On two new follow-up analysis benchmarks \system{} can answer 77.9% and 58.3% followup questions accurately.

Fine-tuning-as-a-service enables users to adapt aligned large language models (LLMs) to specialized tasks, but malicious fine-tuning can erode refusal behavior while preserving task performance on legitimate inputs. We revisit recent layer-wise safety diagnostics and find that safety sensitivity is signed: scaling different layers can strengthen refusal, weaken it, or have little effect. Motivated by this observation, we propose SLDR, a post-fine-tuning defense based on Selective Layers Recovery and Dynamic Routing. SLDR trains a LoRA recovery adapter only on the layers with the maximum and minimum sensitivity scores in the signed spectrum, and uses representation-based dynamic routing inference to activate the adapter only for malicious queries. Across four model architectures, five downstream tasks, and four harmful benchmarks, SLDR substantially reduces harmful outputs while preserving downstream utility. On Llama3.1/SST2, SLDR reduces the average harmful score from 11.54 to 0.08 while maintaining downstream accuracy, and the harmful score remains near zero under poisoning ratios up to 0.9.

Large Language Model (LLM) agents are increasingly deployed under structured generation, where grammar constraints enforce output format at inference time. Whereas existing compression methods target unconstrained generation, we first identify a fundamental mismatch: under structured generation, grammar constraints reduce the model's output distribution to valid tokens, and the logits matter most at the small set of positions that decide the agent's action. Motivated by this finding, we propose SKIM (Selective Knowledge-Informed Masking), the first pruning and distillation framework for LLM agents under structured generation, with both stages guided by token role. SKIM partitions token positions into context, decision, and format categories that reflect the input the model reads, the constrained options it chooses among, and the format realized by the constraint mechanism. Specifically, this partition drives two complementary axes: (1) category-aware pruning saliency scores that preserves parameters most relevant for decision positions; and (2) an on-policy distillation objective that aligns student and teacher on valid tokens, paired with category-aware feature matching. We evaluate SKIM on tool-calling benchmarks and within multi-step agent loops, across multiple model scales and sparsity levels. SKIM consistently improves accuracy over baselines, with its superiority becoming most striking in high-sparsity regimes where conventional methods struggle, demonstrating the potential of category-aware compression for deployment-time grammar-constrained agents.

Reinforcement learning from verifiable rewards (RLVR) trains reasoning language models with group-relative objectives such as GRPO. However, it leaves two sources of signal underused: every token in a rollout receives the same response-level advantage, and groups in which all rollouts succeed or all fail have zero group-relative advantage and are typically discarded. We observe that same prompt rollouts are not independent reward labels but sibling attempts: they share scaffolding, diverge at uncertain decisions, and sometimes reconverge. In this paper, we propose Sibling-Relative Advantage Shaping (SIRAS), a lightweight advantage-shaping method that exploits this structure. SIRAS segments rollouts into chunks, aligns sibling chunks with banded dynamic time warping, and reshapes the flat GRPO advantage across tokens via soft-divergence weights that upweight chunks distinguishing a rollout from its siblings and downweight shared ones. This reshaping preserves each rollout’s token-averaged GRPO advantage exactly and reduces to GRPO when weights are constant. The same sibling structure produces calibrated residuals for all-correct and all-wrong groups, reclaiming training signal that group-relative baselines discard. Across six reasoning benchmarks, SIRAS yields the largest gains on AIME-style competition math, including +12.2 on AIME25 with Qwen3-8B and +23.3 on AIME26 in our backbone-scaling study, without extra rollouts, process reward models, or step annotations.

Lifelong cross-modal hashing demands incremental updates as new tasks arrive, yet existing regularization-based methods such as EWC and SI share a fundamental weakness: their effective regularization strength is entangled with task scale, as the empirical Fisher Information Matrix (FIM) scales linearly with sample size. This forces costly per-task hyperparameter tuning in streaming deployments to avoid over- or under-regularizing tasks of varying sizes. We propose \textsc{Sing-CH}, a lifelong hashing framework built on information geometry. At its core is a Gibbs probabilistic reconstruction of the pairwise objective, which gives the parameter space a statistical manifold structure and yields a natural $1/n_t^2$ normalization. We prove this normalization renders the FIM scale-agnostic: its spectral properties remain independent of task size even under diagonal and K-FAC approximations. Beyond this invariance, a Forgetting-Rigidity trade-off theorem shows that natural gradient applied to the composite objective reintroduces scale dependence. This makes our decoupled design, pairing the FIM as regularizer with Adam for optimization, a structural necessity rather than a convenience. Experiments on three benchmarks demonstrate that \textsc{Sing-CH} outperforms 11 baselines by 4.1\% average mAP without per-task tuning. Under extreme task-scale imbalance, degradation is only 2.0\% versus 3.9\% for the strongest baseline, providing the first empirically grounded solution to scale-agnostic lifelong retrieval.

Accurate traffic prediction is essential for urban planning but remains challenging due to the irregular patterns caused by unexpected events. While Spatio-Temporal Graph Neural Networks (STGNNs) effectively model periodic data, they often struggle with these aperiodic fluctuations. To address this, we propose SIMBAD, which improves generalization on irregular patterns via two key mechanisms: 1) adaptive regulation of periodic signals to prioritize recent signals, and 2) dynamic spatial modeling that adjusts influence between connected nodes based on contextual relations. Built upon simple similarity metrics to adapt to aperiodic patterns, SIMBAD outperforms existing methods on real-world benchmark datasets, demonstrating superior performance in predicting unseen and irregular traffic events.

High-resolution 3D generation increasingly relies on voxel latents and multi-stage pipelines that first predict active structure and then synthesize local geometry. While effective, this design fragments continuous surfaces into many local tokens, inflates generation cost, and often weakens topological consistency for thin or highly connected shapes. We introduce **SILSA**, a topology-aware 3D generation framework that represents shapes with compact sliding-window slice latents. Instead of generating expensive voxel tokens, SILSA uses a fixed set of overlapping slices along the three canonical axes, where each token summarizes a local depth window to preserve cross-sectional continuity and support single-stage rectified-flow generation. A **Slice VAE** encodes oriented surface samples into multi-axis slice latents and reconstructs them with a sparse volumetric decoder, while a **Volumetric Anchor Lattice** coordinates directional slice streams through a shared 3D workspace. To preserve structural correctness, we introduce slice-level topology supervision that matches persistence diagrams and aligns Betti transitions across neighboring slices. Experiments show that SILSA improves structural fidelity while substantially reducing generation cost. SILSA improves PSNR by 8.7%, coverage by 5.96 absolute points, and Betti error by 9.2% over the strongest baseline, while using 70.0% fewer tokens than the next-most compact baseline and over 98% fewer tokens than sparse or hierarchical tokenizers, reducing training memory by 40.4% and inference time by 58.5%. Qualitative results further show improved preservation of thin structures, repeated components, and long-range connectivity.

Autoregressive language models generate under a fixed left-to-right order, whereas diffusion language models (dLLMs) denoise masked tokens through flexible any-order trajectories that may commit multiple positions per step. This makes dLLMs a distinctive setting for test-time scaling: additional inference compute can diversify both token choices and the order in which positions are committed. Existing samplers do not explicitly allocate these two sources of diversity. Confidence-based decoding is reliable but often redundant, while global temperature sampling and random remasking inject stochasticity without distinguishing useful exploration from unreliable commitments. We propose SIGMA, a training-free sampler that formulates each denoising step as a constrained allocation problem over decoding efficiency, token-level diversity, trajectory-level diversity, and model-internal commitment uncertainty. The resulting objective decomposes joint sampling entropy into token and trajectory terms, yielding a sigmoid-form stochastic gate for position selection and an adaptive per-position temperature for token sampling. Experiments on LLaDA and Dream across math and code benchmarks show that SIGMA improves the accuracy--cost Pareto frontier and can be integrated into existing dLLM test-time scaling pipelines.

Frontier LLMs are increasingly capable of expert-level scientific reasoning, but using them as reliable scientific agents requires more than general reasoning ability. Scientific simulation is a central test case: modern simulators are configured through executable interfaces such as XML decks, input scripts, and namelists that function as domain-specific languages tied to the simulator's internal API. Translating a researcher's natural-language intent into a runnable configuration remains a recurring expert bottleneck. We study how much of this bottleneck a general-purpose LLM coding harness, Claude Code, can absorb when wrapped with a **Simulator-Interface Grounding Adapter (SIGA)**: a package of skills, tools, and workflow control flow that grounds the agent's outputs in simulator documentation, schema, and example libraries. We instantiate SIGA for GEOS, an open-source multiphysics simulator used in CO₂-storage and induced-seismicity research. A Resolution-IV factorial surfaces three benefits over vanilla Claude Code. First, SIGA improves **reliability**, reducing across-seed variance by roughly 40× by preventing unparseable or empty decks on a hard tail of compound multi-physics tasks. Second, it improves **quality**, raising mean structural similarity by about +7 percentage points on the same hard tail. Third, a self-evolved variant matches the best hand-designed cell with roughly 16% fewer tool calls, suggesting that automatic SIGA discovery is tractable. A preliminary human baseline finds that geoscience-domain-expert volunteers new to GEOS take between 8 and 36 times as long as the agent on a representative task. An explicit human-consultation tool is used in only about 3% of under-specified trials, with the agent instead relying on the on-disk example library as a cheaper retrieval substitute. A small OpenFOAM transfer study indicates that the recipe is not specific to GEOS XML: the same stop-hook component again dominates reliability, and the best SIGA cell outperforms both vanilla Claude Code and a constrained Foam-Agent lint-only baseline. We close with SIGA-design recommendations grounded in failure modes that our best configuration still does not fix.

Multimodal large language models (MLLMs) achieve ever-stronger performance on visual-language tasks. Even as traditional visual question answering benchmarks approach saturation, reliable deployment requires satisfying low error tolerances in real-world out-of-distribution (OOD) scenarios. Precisely, selective prediction aims to improve coverage, i.e.\ the share of inputs the system answers, while adhering to a user-defined risk level. This is typically achieved by assigning a confidence score to each answer and abstaining on those that fall below a certain threshold. To enable reliable generalization, we require reasoner models to produce localized visual evidence while answering, and design a selector that explicitly learns to estimate the quality of the localization provided by the reasoner. We show that SIEVES (Selective Prediction through Visual Evidence Scoring) improves coverage by up to three times on challenging OOD benchmarks (V* Bench, HR-Bench-8k, MME-RealWorld-Lite, VizWiz, and AdVQA), compared to non-grounding baselines. Beyond better generalization to OOD tasks, the design of the SIEVES selector enables transfer to proprietary reasoners without access to their weights or logits, such as o3 and Gemini-3-Pro, providing coverage boosts beyond those attributable to accuracy alone. We highlight that SIEVES generalizes across all five tested OOD datasets and reasoner models (Pixel-Reasoner, o3, and Gemini-3-Pro), without benchmark- or reasoner-specific training or adaptation.

We identify a theoretical incompatibility between 3D point cloud masked autoencoding and SE(3)-equivariant networks. We prove that standard masking triggers a topological collapse, effectively forcing the model to hallucinate orientation without a reference frame. To resolve this, we introduce SIEVE (Scalar Invariant Extraction Via Equivariance), an SE(3)-equivariant self-supervised framework that compresses point cloud geometry into invariant scalar embeddings. At its core is the spherical projection mask, which preserves global pose while obfuscating local semantics, sidestepping the obstruction by construction. Experiments confirm that SIEVE circumvents the predicted collapse where constant and noise masks fail. On downstream point cloud tasks, the resulting embeddings improve over coordinate-based baselines and hand-crafted geometric descriptors, with the largest gains on generalization to unseen shape categories.