NeurIPS 2026 Papers

Estimating the number of clusters, $k$, is a fundamental prerequisite for most clustering algorithms. Traditionally, this estimation has relied on computationally expensive grid searches over internal cluster validity indices (CVIs) or adaptive clustering algorithms, both constrained by specific statistical assumptions. We introduce TabK, a zero-shot, permutation-invariant transformer architecture that frames the estimation of $k$ as an amortized Bayesian inference problem for tabular data. TabK leverages a novel Quantile Feature Encoder (QFE) to extract position-free, content-derived representations from empirical marginal distributions. By learning a generalized structural bias over a diverse synthetic prior, TabK estimates the number of clusters in a single forward pass. Evaluated across 50 diverse real-world benchmarks, TabK outperforms its closest competitor, TabClustPFN, reducing the Mean Absolute Error (MAE) of the number of clusters estimation by 1.2 (from 2.24 to 1.04). Furthermore, TabK empirically achieves the best position on the Pareto frontier among 12 evaluated methods, providing predictions of $k$ in an average time of less than one second, which is $2\times$ faster than TabClustPFN and $30\times$ faster than traditional CVIs such as Silhouette and Calinski-Harabasz. *Code and data are available at:* https://anonymous.4open.science/r/TabK.

Prior-data Fitted Networks (PFNs) have reframed supervised tabular learning as single-pass in-context inference without per-dataset optimization. Extending this paradigm to unsupervised clustering is appealing yet fundamentally more challenging, due to absent supervision, unknown cluster cardinality, and label switching inherent to partition outputs. Existing PFN-based clustering methods address these challenges only partially, either requiring known cardinality as input or relying on unstable label-ordering conventions and overly restrictive synthetic priors. We introduce TabClustPFN, a clustering PFN that resolves all challenges jointly through co-designed prior, objective, and architecture. Our hybrid pretraining prior captures heterogeneous real-tabular geometry; our decoupled partition inference network-cardinality inference network architecture jointly infers cluster assignments and cardinality in a single pass; and our SoftARI training objective is permutation-invariant by construction, eliminating the need for any label-ordering convention. On a 44 curated real-world tabular benchmark, TabClustPFN achieves state-of-the-art clustering performance against classical, deep, and amortized baselines, with runtime comparable to efficient baselines.

Recent advances in machine learning have accelerated biomedical prediction, yet evaluation on structured biomedical data remains fragmented, especially for datasets with high dimensionality, missingness, class imbalance, and small-sample, many-feature regimes. We introduce TabBioMed, a large-scale benchmark of 95 curated public biomedical tabular datasets spanning electronic health records \cite{data2016secondary}, drug response \cite{wu2018moleculenet}, genomics, transcriptomics, proteomics, metabolomics \cite{yang2025mlomics}, single-cell omics \cite{heumos2023best}, and systems biology. TabBioMed unifies these datasets through a standardized, dataset-aware preprocessing and evaluation framework, enabling reproducible comparison across classification, regression, and multi-target tasks. We benchmark 23 models across classical baselines, gradient-boosted trees, neural tabular architectures, and emerging tabular foundation models. Foundation models deliver the strongest family-level predictive performance. However, their gains are regime-dependent: tree-based models and tuned MLPs remain highly competitive, often dominating under inference-time or training-cost constraints. We further show that foundation-model advantages are most meaningful in low-noise biomedical tasks, where small absolute gains translate into large reductions in residual error. Overall, TabBioMed reveals that biomedical tabular learning is strongly context-dependent, with optimal model choice shaped by data regime, task type, and computational budget. By releasing curated datasets, preprocessing code, baselines, and results, TabBioMed provides a reproducible foundation for practical model selection and future methods development in biomedical machine learning, available at https://anonymous.4open.science/r/tabbiomed.

Log-likelihood is a standard metric for evaluating generative models. Unfortunately, in contrast to autoregressive models (ARMs), discrete diffusion models generally do not admit exact computation of this quantity. Existing evaluations, therefore, rely on the evidence lower bound (ELBO), leaving unclear how much higher the true value may be. We address this by introducing the **Tangent Upper Bound on Evidence** (**TUBE**), a variational upper bound on log-likelihood that admits an unbiased Monte Carlo estimator. Our TUBE extends across latent-variable models, including masked diffusion models (MDMs), any-order ARMs (AO-ARMs), and block variants of both. Applied to block MDMs and block AO-ARMs, TUBE reveals our key empirical finding that these models lie strictly below the exact ARM baseline, showing that ARMs still dominate in likelihood.

Comparisons in video self-supervised learning often evaluate complete training recipes rather than isolating the method itself: architecture, objective, data exposure, schedule, scale, and decoder capacity can all vary at once. This makes it hard to identify which choices yield motion-prioritized representations, whose gains concentrate on frame-to-frame change while retaining useful appearance. We address this with a matched $4 \times 6 = 24$ architecture--objective study at roughly 170M--190M encoder scale on $\sim$1.7M OpenVid and Moments-in-Time v2 clips for 8 epochs, and propose TT-VidT. TT-VidT combines a DINOv3-initialized ViT-B/16 per-frame spatial path with a compact Temporal Transfer Layer, trained by Diff Compression to reconstruct target frames from a first-frame appearance anchor and frame-specific motion tokens. The sweep shows that TT3D with Diff Compression, not either component alone, enters the strongest motion-sensitive regime, and decoder ablations favor a compact video-pretrained decoder. In final comparison, TT-VidT leads Jester, Something--Something\,V2, ARID, and Diving48 fine-tuning simultaneously, improving over the strongest non-TT row by 54\%--121\%, while using 48\% fewer encoder FLOPs than DisMo and 55\% fewer than VideoMAE or V-JEPA~2. HMDB51, IARD, and EPIC-Kitchens bound the claim.

Long-term time-series forecasting requires temporal dependency modeling that remains stable and generalizable under temporal distribution shifts. However, real-world time series are often noisy and non-stationary, making deep learning models prone to fitting incidental temporal interactions in the training data. A key question is whether there exist temporal relationship structures that recur across different temporal positions and variables. We analyze temporal relationship matrices on commonly used time-series datasets and find that samples from different temporal positions and different variables exhibit shared dominant temporal relationship structures. This indicates that temporal relationship structures contain patterns that recur stably across samples and variables. To learn such shared structures, we propose Temporal Structure Network (TStruct), a simple yet effective network for directly learning shared and generalizable temporal dependencies. Specifically, TStruct directly parameterizes a set of shared temporal dependency matrices and generates normalized combination weights using dynamic sample information and static variable priors. These weights represent a soft allocation of each sample-variable pair over shared temporal dependency structures, enabling the model to generate adaptive temporal dependency matrices under shared structural constraints, rather than fitting incidental interactions in a fully unconstrained manner. Notably, this simple structure-aware design consistently outperforms several strong baselines across all datasets, achieving superior accuracy while maintaining high computational efficiency. Our code is available at \url{https://anonymous.4open.science/r/TStruct-NeurIPS2026-1173}.

Textual information is ubiquitous in real-world time series datasets, appearing as news, reports, and annotations that hold key predictive signals not deducible from numerical history alone. Large Language Models (LLMs) interpret text but lack numerical precision, while Time Series Foundation Models (TSFMs) are quantitatively accurate but cannot reason about the effects of text. Moreover, the lack of large-scale paired multimodal datasets limits progress in bridging this gap. In this work, we address this challenge by providing a scalable approach for generating realistic multimodal datasets by annotating real-world time series with LLMs. Building on this, we propose Context as Covariates (CoCo), a forecasting framework in which an LLM distills its textual reasoning into numerically grounded forecast and confidence covariates that guide a TSFM backbone, thereby exploiting the strengths of both LLMs and TSFMs. We align the two models in stages, first fine-tuning the LLM via Group Relative Policy Optimization (GRPO) with a reward tied to covariate-induced improvement in forecasting performance, and then fine-tuning the TSFM on covariates generated by the aligned LLM. Our experiments show that models trained on our synthetic multimodal corpus generalize to real benchmarks spanning finance, economics, weather, traffic, and security. Notably, CoCo with Qwen3-4B-Instruct-2507 as the LLM and Chronos2 as the TSFM achieves up to 12% MAE improvement over state-of-the-art TSFMs and LLM forecasters, such as GPT-4o.

Time-series segmentation, studied as either \emph{Change Point Detection} (CPD) or \emph{State Detection} (SD), underpins a broad range of monitoring and diagnostic applications. However, the field suffers from a persistent divide between the statistical community (focusing on CPD) and the machine learning and data mining communities (focusing on SD). Consequently, the literature remains fragmented: evaluations typically cover narrow methodological families, datasets are often small or homogeneous, and CPD and SD are rarely evaluated together. We address these gaps with \texttt{TSB-SEG}, a unified benchmark comprising $592$ univariate and multivariate time series across $8$ heterogeneous domains and surveying $27$ algorithms spanning five decades of progress. To ensure a fair comparison, we introduce a two-pool design: a scalable \emph{main pool} of sub-quadratic detectors used for the aggregate ranking, and an \emph{extended-scope pool} for computationally intensive specialized methods. All methods are unified under tsseg, an open-source library to ensure full reproducibility. Our systematic evaluation yields three insights: (a) no single detector dominates across domains, and per-dataset hyperparameter tuning remains decisive for performance, (b) CPD accuracy is a strong proxy for SD accuracy, and (c) data characteristics should guide method selection. We provide actionable guidance based on data properties, error tolerances, and tuning budgets, identify impactful hyperparameters, and characterize algorithm robustness to these choices.

Foundation models mark a profound paradigm shift in time series modeling, with task-specific models being superseded by general-purpose zero-shot models. Yet, current approaches primarily focus on forecasting, while real-world time series are often irregularly and partially observed, requiring models that can jointly forecast, impute missing values, and handle degraded sampling conditions. To address these challenges, we introduce TS-ICL, a novel probabilistic In-Context Learning encoder-regressor Transformer that unifies forecasting and imputation. TS-ICL formulates time series tasks as timestamp-aligned regression and naturally incorporates covariates by training on synthetic dependency structures generated from a novel causal data prior. Empirically, TS-ICL achieves a new state-of-the-art in imputation, while remaining competitive with leading forecasting foundation models across both univariate and covariate-aware benchmarks. It shows particularly strong performance in forecasting with partially observed look-back windows. *Code page*: https://anonymous.4open.science/r/tsicl-D3F1/.

Verifiable tool-interactive agents produce rich trajectories that contain far more information than final success or failure. However, standard RL post-training with verifiable rewards often collapses these trajectories into scalar outcomes and trains over a largely fixed data stream, leaving open the question of which examples are most useful for improving the current policy. We introduce TReDS, a trajectory-grounded framework for capability-conditioned training distribution shaping. TReDS estimates multidimensional task requirements from offline probing trajectories and controlled tool-view evidence, maintains the policy capability state in the same semantic space, and uses reliability-gated online correction to form a policy-conditioned requirement representation. A scheduler then converts requirement--capability mismatch, recent utility, and stabilizing pressure into a time-varying training distribution for GRPO, without modifying the GRPO objective. We evaluate TReDS with a progressive stress-test suite spanning $\tau^2$-Bench official evaluation, $\tau$-Bench native-bridge evaluation, BFCL V3, and API Bank, using only the $\tau^2$-Bench training split for policy optimization. TReDS improves over the GRPO baseline under the same training data and backend, and shows stronger behavior retention under same-family and cross-protocol evaluations. These results suggest that RL post-training for tool-interactive agents should optimize not only how trajectories are rewarded, but also where trajectory-revealed requirements allocate training probability mass.

*Discrete text-trigger optimization*—searching for text sequences that, when ingested by a model, steer it toward a specified objective—underpins model red-teaming (e.g., LLM jailbreaks), as well as auditing and interpretability. However, the current state of discrete optimizers hinders their adoption and progress. First, existing optimizers, when open-sourced at all, are scattered across research codebases tied to specific problem domains. Second, optimizer variants proliferate, each requiring engineering overhead to use or extend, and hard to compare head-to-head. Together, these raise the bar to *adopting* optimizers in existing or new domains, and to *advancing* them via new strategies. We address these gaps with TROPT, the first open-source framework that unifies discrete optimizers' execution and standardizes their development under a single interface. TROPT currently ships with 30+ end-to-end optimization recipes—covering applications such as jailbreaking and probing model internals—built from 15+ optimizers (spanning white-box to black-box access) and 15+ losses, from foundational to state-of-the-art methods. Beyond democratizing established methods, TROPT makes it easy to *customize* optimization recipes by swapping any component—models, objectives, and optimizers—extending its reach across domains and new applications. Demonstrating its utility, we leverage TROPT in several controlled studies: *(i)* novel large-scale experiments comparing and enhancing optimization strategies for LLM jailbreaks, revealing potent-yet-underadopted techniques; and *(ii)* cross-domain demonstrations (e.g., benign prompt recovery and corpus poisoning). In all, TROPT significantly lowers the barrier to adopting and advancing discrete text optimization.

Tabular encoders are usually evaluated inside task-specific end-to-end pipelines, so models from different training paradigms are difficult to compare directly even when they operate on similar tabular signals. We introduce TRL-Bench, a multi-granular tabular representation learning (TRL) benchmark that standardizes cross-paradigm representation-level evaluation: each encoder exports row-, column-, or table embeddings through its supported wrapper, and shared lightweight heads probe them across three suites: TRL-CTbench (column/table), TRL-Rbench (row), and TRL-DLTE (compositional Data-Lake Table Enrichment spanning all three granularities). To support this standardized setting, we release curated benchmark assets and task reformulations, including 50 OpenML tables with 123 verified targets, 16 row-pair linkage rewrites, and a 47,772-table DLTE lake derived from 1,379 parent tables. Across 20 models and 16 tasks, TRL-Bench shows that once downstream conditions are standardized, encoder quality is capability-specific rather than captured by a single leaderboard. At the column/table level, generic text encoders often lead on tasks with strong surface-text signal, while tabular specialists win where their pretraining objective aligns with the task. At the row level, within-table prediction and cross-table linkage favor different training regimes, with the row-matching stage of DLTE pipelines correlating strongly with atomic linkage performance. In TRL-DLTE, the strongest pipelines combine capability-matched specialists rather than reuse a single encoder, and top end-to-end quality depends on non-additive compositional fit rather than per-stage marginal rank alone. TRL-Bench provides a common protocol for measuring reusable signal in exported tabular representations under shared downstream conditions.

Most theories of in-context learning (ICL) treat the prompt as fixed. This fixed-prompt view misses the key difficulty in memory-updated retrieval systems such as Memory Mosaics. An inference-time write can help later queries, but it can also shift softmax weights and create interference. The question is when the benefit exceeds the later cost. We introduce TRIM (Theory of Retrieval with Incremental Memory), a theorem-level theory of append-only softmax retrieval under a write-independence abstraction. The five-theorem spine links retrieval-weight concentration, local write-induced defect, recurrence-level accumulation, ideal squared-loss benefit, and a benefit-cost comparison with critical-horizon regimes. TRIM places helpful memory and harmful interference within a single frame, so the harmful length scale is derived rather than tuned. We record a first-order dynamic correction for state-dependent writes separately, and it is not part of this spine. We give a theorem-facing empirical protocol. Exact synthetic checks test the spine identities and inequalities. Learned base-MM analyses measure calibrated analogues of concentration, local defect, horizon worsening, and realized benefit. These analyses test whether TRIM observables organize learned systems along the same routes.

RAG systems often verify citations only after retrieval and shortlisting have selected which passages will be tested. A verifier calibrated on random passage--claim pairs is therefore applied to a different population: shortlisted pairs chosen by the retrieval policy. This makes support verification a post-selection inference problem. We introduce TRIDENT, a contract-scoped framework for post-selection evidence accountability. Safe-Cover emits structural-facet support certificates with query-level Bonferroni FWER control, or abstains when the logged contract cannot certify support. Pareto-Knapsack relaxes certification and uses the same frozen facet table as a token-efficient multi-hop QA selector. Across HotpotQA, 2Wiki, and MuSiQue, TRIDENT controls false certificates under deployed contracts, identifies higher-quality answer-linked slices when certificates are emitted, and improves the matched-budget multi-hop QA frontier. The accountable unit is therefore not a retrieved passage or generated citation, but a support claim calibrated under the policy that selected it.

Ambiguity is a central feature of natural communication: a sentence, image, or sound may remain underspecified until evidence from the other channels is considered. Yet current multimodal benchmarks mostly test perception, recognition, or reasoning over already determinate inputs, leaving unclear whether omni-modal large language models can resolve ambiguity distributed across text, vision, and audio. We introduce TRIAD, a benchmark for omni-modal ambiguity resolution. Each item is a text--image--audio triplet paired with a question and a set of answer options, and is constructed so that the full triplet determines a unique answer while removing any one modality makes the set of consistent options non-unique. TRIAD contains $327$ hand-written queries in $86$ scene groups and covers $18$ ambiguity categories across the three modalities. Evaluating $14$ omni-modal systems reveals that strong item-level performance does not translate into robust scene-level disambiguation: the best model still trails humans by over $20$ points in group accuracy. Removing answer options widens the gap further, driving model group accuracy to the single digits while humans remain robust. Leave-one-out and transcript/description counterfactuals further show that current systems often underuse audio cues when text and image are present. TRIAD reframes omni-modal evaluation around disambiguation rather than recognition, and provides a taxonomy, benchmark, and diagnostic protocol for measuring this capability.

Generative recommendation formulates next-item prediction as autoregressive generation over semantic ID (SID) sequences derived from users' historical interactions, making modern recommender systems structurally similar to large language models (LLMs). As privacy and safety concerns grow, these systems increasingly require concept unlearning to remove sensitive or harmful concepts associated with items. However, existing LLM unlearning methods cannot be directly applied to generative recommendation. Unlike word tokens with explicit semantics, SIDs are abstract identifiers that are often shared by both forget and retain items, leading to severe conflicts between concept removal and recommendation utility preservation. To address this challenge, we propose TRACER, an end-to-end concept unlearning framework based on token reassignment. Rather than directly suppressing shared SIDs, TRACER reassigns concept-related items to alternative tokens that better facilitate forgetting while minimizing side effects on retained items. We further introduce a coherence regularizer to preserve semantic consistency among retain items during unlearning. Experiments on real-world recommendation datasets demonstrate that TRACER effectively removes target concepts while substantially better preserving recommendation utility than existing unlearning baselines.

Video understanding models often rely on motion, but faithful explanations must reveal not only whether motion matters, but also which source of motion supports a prediction. We identify motion-source under-specification as a key limitation of existing video XAI: region-centric explanations are source-ambiguous, while concept-based explanations are source-incomplete when their motion vocabulary is restricted to single-person pose sequences. To bridge this gap, we propose TraCE-Trajectory-Aware Concepts for Explainable Video Understanding-a concept-based framework that elevates visual trajectories to first-class explanatory concepts. By clustering optical-flow trajectories, TraCE discovers source-flexible motion concepts that capture dynamic evidence from people, objects, and interactions, while object and scene concepts account for static context. TraCE applies the same typed trajectory/object/scene concept interface to both action recognition and video question answering (VQA), enabling concept-level explanations beyond a single task. We evaluate TraCE across six benchmarks: NTU RGB+D Mutual, Chiral SSv2, UCF-101, KTH, TOMATO, and STAR. Experiments show that TraCE provides trajectory-aware explanations of dynamic evidence with minimal performance trade-off, trailing non-interpretable baselines by only 0.9 points on action recognition and 0.1 points on VQA on average. User studies, faithfulness analyses, temporal sensitivity tests, and concept-intervention results further show that TraCE produces interpretable, faithful, and practically useful explanations.

Tourism is a high-stakes setting for conversational recommender systems (CRS): a plausible-sounding suggestion can waste real money and trip time once a traveler acts on it. Existing CRS benchmarks primarily evaluate systems with a single Recall@$k$ score over entity mentions, and tourism-specific resources add spatial or knowledge-graph context, yet none of them couple multi-turn recommendation with verbatim review-span evidence and rejection recovery. This leaves an evaluation gap for tourism recommendation that is simultaneously *trustworthy*, *verifiable*, and *adaptive*: recommend the right point of interest (POI) for multi-aspect preferences (such as cuisine, price, atmosphere, walking distance), justify each suggestion with verifiable evidence from prior visitors so the traveler can act without trial and error, and recover when the first recommendation is rejected mid-dialogue. We introduce **TRACE**, where each item is *a multi-turn tourism recommendation dialogue with review-span citations and explicit rejection turns*: 10,000 dialogues over 2,400 Yelp POIs and 34,208 reviews across eight U.S. cities, paired with 14 retrieval, planning, and LLM baselines, along with 25 metrics organized under *Accuracy*, *Grounding*, and *Recovery*. Across these baselines, TRACE reveals the **Three-Competency Gap**: LLM Zero-Shot leads in closed-set Recall@1 and rejection recovery but cites less densely than retrievers; non-LLM retrievers achieve surface-verbatim grounding but with low accuracy; Multi-Review Synthesis fails on Recovery. The Grounding Score agrees with human citation precision (Spearman $\rho = +0.80$, $p < 10^{-20}$), and paired $t$-tests reproduce the per-baseline ranking ($p < 0.01$ on the dominant contrasts). TRACE reframes accountable tourism recommendation as a joint target (right POI, verifiable evidence, adaptive repair) rather than a single-axis leaderboard.

We propose TRACE, a structure-aware framework leveraging diffusion models for localized character encoding to embed data. Unlike existing methods that rely on edge features or pre-defined codebooks, TRACE exploits character structures that provide inherent resistance to noise interference due to their stability and unified representation across diverse characters. Our framework comprises three key components: **(1)** adaptive diffusion initialization that automatically identifies handle points, target points, and editing regions through specialized algorithms including movement probability estimator (MPE), target point estimation (TPE) and mask drawing model (MDM), **(2)** guided diffusion encoding for precise movement of selected point, and **(3)** masked region replacement with a specialized loss function to minimize feature alterations after the diffusion process. Comprehensive experiments demonstrate TRACE's superior performance over state-of-the-art methods, achieving more than 5 dB improvement in PSNR and 5% higher extraction accuracy following cross-media transmission. TRACE achieves broad generalizability across multiple languages and fonts, making it particularly suitable for practical document security applications.