NeurIPS 2026 论文列表

LLMs are powerful but incur substantial inference cost, which can be exploited by malicious users to induce overly long outputs, increasing latency and operational expense. Prior resource consumption attacks either coerce repetition or suppress the end-of-sequence token by lowering its logit value. However, with autoregressive, samplingbased generation, the search space grows exponentially with length and small deviations or unstable trajectories can derail optimization, limiting attack effectiveness and stability. To address this, we introduce Chatter Attack, a novel class of resource consumption attacks that optimize adversarial prompts to induce predefined short target phrases, thereby avoiding search-space explosion while still eliciting long responses. Specifically, Chatter Attack triggers self-doubt using short doubt-inducing phrases as the target, and sustains this state throughout generation via inference attention loss and global entropy loss. To extend to black-box settings, we further propose a novel Bayesian Prompt Optimization (BPO) method, which models the objective function globally by constructing a discrete token kernel, efficiently exploring the entire search space using prior information. Extensive experiments across multiple models and datasets show that our method outperforms baselines, with maximum response length increases to 31.5×, and revealing practical resource-depletion risks for LLM services.

Charts convey dense quantitative and relational information, yet general chart parsing remains challenging due to fragmented evaluation and limited structural reasoning. Existing benchmarks focus on narrow sub-tasks with inconsistent output formats (e.g., Markdown, CSV, JSON, SVG, code), hindering fair comparison and often overlooking real-world scenarios such as printed or hand-drawn photos. Meanwhile, end-to-end multimodal models tend to learn shallow pixel-to-text mappings without explicit structure understanding, making them fragile under visual variations. To address these issues, we present a unified framework that connects evaluation and modeling. On the evaluation side, we introduce **ChartArena**, a comprehensive bilingual benchmark covering eight chart families, including both numeric charts (bar, line, pie, radar, box plot, combination) and diagrammatic structures (flowchart, mind map). The dataset is constructed via a human-agent collaborative annotation pipeline, where model-assisted drafts are refined through multi-stage human verification to ensure structural consistency and annotation reliability. Furthermore, we design a format-agnostic evaluation protocol that maps different outputs into two canonical semantic spaces: a normalized triple view and a directed graph view, and evaluates them with structure-aware metrics. On the modeling side, we propose **Chart Atomic Primitives (CAP)**, a lightweight semantic scaffold that introduces structural inductive bias during supervised fine-tuning. By guiding the model to infer structure before generating outputs, CAP leads to more consistent and robust parsing behavior. Extensive experiments show that models trained with CAP achieve reliable performance on both rendered charts and real-world images. Code, benchmark, and models will be made publicly available.

This paper aims at analyzing the regularization effect that data augmentation induces on supervised regression methods in the proportional regime, where the number of covariates grows proportionally to the number of samples. We provide a tight characterization of the test error, measured in mean squared error, in terms only of the population quantities of the true data, as well as first and second order statistics of the augmentation scheme. Our results are valid under misspecified feature maps, and for any network architecture where only the last readout layer is trained, and the rest of the network is either frozen or randomly initialized. We specify our results in the case of Gaussian data, and show that our asymptotic characterization is tight in this setting.

Deep neural networks trained with different architectures, objectives, and datasets have been reported to converge on similar visual representations. However, what remains unknown is which visual properties models actually converge on and which factors may underlie this convergence. To address this, we decompose the object similarity structure of 162 diverse vision models into a small set of non-negative dimensions. To determine universal versus model-specific dimensions, we then estimate how often each dimension reappears across models. In contrast to model-specific dimensions, universal dimensions are more interpretable and more strongly driven by conceptual image properties, indicating the relevance of interpretability and semantic content as implicit factors driving universality across models. Differences in architecture, objective function, training data, model size, and model performance do not explain the emergence of universal dimensions. However, models with more universal dimensions also better predict macaque IT activity and human similarity judgments, suggesting that universality reflects representations relevant to biological vision. These findings have important implications for understanding the emergent representations underlying deep neural network models and their alignment with biological vision.

Randomized trial findings are routinely generalized to broader target populations to inform decisions in medicine and public policy. When the trial sample is misaligned with the target population, generalized effect estimates can yield suboptimal decisions. We identify and characterize the target subgroups that are underrepresented in the trial cohort in time-to-event settings. Here, underrepresentation arises in two distinct ways: baseline underrepresentation, when a subgroup's covariate profile is rare in the trial relative to the target, and time-varying underrepresentation, when differential censoring erodes a subgroup over follow-up. Existing diagnostics either conflate these mechanisms or address only the baseline case. We show that the asymptotic variance of the efficient generalizability estimator admits a decomposition in which only two terms can diverge as representation weakens: a sampling-weight term capturing baseline misalignment and a censoring-weight term capturing differential loss to follow-up. Underrepresented subgroups are therefore precisely those the trial estimates with poor precision, and the decomposition reveals which channel is responsible.We exploit this operationally in $\textbf{TRACE}$ ($\textbf{T}$emporal $\textbf{R}$ashomon $\textbf{A}$nalysis of $\textbf{CE}$nsored data), which combines a self-normalized variance objective with a tree-based search to localize underrepresented subgroups, attribute each to baseline mismatch or censoring, and pinpoint the follow-up intervals where censoring drives the variance inflation; the joint problem is solved by a parametric dynamic program nested inside a Rashomon ensemble of near-optimal trees. On synthetic benchmarks and a case study generalizing the National Job Training Partnership Act (JTPA) Study to U.S. JTPA-eligible adults, the framework recovers underrepresented regions, correctly attributes each mechanism, and identifies the follow-up windows driving temporal underrepresentation; refining the trial and target samples to reliably generalizable subgroups substantially reduces generalized effect-estimate variance.

Instantaneous Quantum Polynomial Quantum Circuit Born Machines (IQP-QCBMs) have been proposed as quantum generative models that combine a classically tractable training objective—based on the maximum mean discrepancy (MMD)—with a potential quantum advantage motivated by sampling-complexity arguments. While recent works have explored this model across various application domains, fundamental questions remain: does the model suffer from exponentially vanishing loss gradients, known as the barren plateau problem—a pervasive obstacle in quantum machine learning—and how do regimes of trainability relate to regimes of possible quantum advantage? Here, we address both questions analytically. To study trainability, we derive closed-form expressions for the variances of the partial derivatives of the MMD loss function and establish general upper and lower bounds. We explicitly characterize how trainability depends on the generator set and the spectrum of the chosen kernel, identifying regimes in which low-weight kernels avoid exponential gradient suppression under structured topologies. Regarding potential quantum advantage, we reformulate the anti-concentration property in terms of the same generator-set quantities that govern trainability, enabling a unified analysis. We show that sparse IQP architectures can produce classically intractable output distributions while simultaneously remaining trainable, at least at lower-weight frequencies. Our analytical results corroborate the numerical observations of prior work and provides principled guidelines for designing scalable IQP-QCBM architectures.

Autoregressive language models (ARMs) have been shown to memorize and occasionally reproduce training data verbatim, raising concerns about privacy and copyright liability. Diffusion language models (DLMs) have recently emerged as a competitive alternative, yet their memorization behavior remains largely unexplored due to fundamental differences in generation dynamics. To address this gap, we present a systematic theoretical and empirical characterization of memorization in DLMs. We propose a generalized probabilistic extraction framework that unifies prefix-conditioned decoding and diffusion-based generation under arbitrary masking patterns and stochastic sampling trajectories. Theorem 4.3 establishes a monotonic relationship between sampling resolution and memorization: increasing resolution strictly increases the probability of exact training data extraction, implying that autoregressive decoding corresponds to a limiting case of diffusion-based generation by setting the sampling resolution maximal. Extensive experiments across model scales and sampling strategies validate our theoretical predictions. Under aligned prefix-conditioned evaluations, we further demonstrate that DLMs exhibit substantially lower memorization-based leakage of personally identifiable information (PII) compared to ARMs.

Algorithmic complexity measures the intrinsic structure (or randomness) of strings. Kolmogorov-Chaitin-Solomonoff (KCS) complexity is the length of the shortest program that can output a string and halt over all possible programs; due to this reason it is uncomputable. Estimating the algorithmic probability of strings using simulations of finite Turing machines is one way of obtaining tight estimations of KCS complexity of strings. For two dimensional objects, this is currently doable only for binary strings using the block decomposition method (BDM). In this work, we present the quantized block decomposition (QuBD) method to extend the estimation of algorithmic complexity to any $k$-ary objects such as the weights of deep neural networks. We show theoretically that the proposed QuBD method yields better KCS complexity estimations than BDM that relies on binarization. We further study how algorithmic complexity interacts with learning in deep neural networks by tracking the evolution of weights during training. Using a variety of experiments we show that algorithmic complexity decreases as models learn, it correlates with generalization performance and can be used as a diagnostic measure to perform model compression.

Imagine Mr. Bean stepping into Tom and Jerry--can a video model generate interactions that stay faithful to each character's identity, behavior, and style across worlds? We present MiMix, a unified framework for multi-character text-to-video generation that goes beyond visual appearance to capture character-specific personas generalizing to unseen scenes and partners. To evaluate this, we introduce the Cross-Character Generalization Benchmark, a social stress test placing each character in novel scenes with previously uncoexistent partners. MiMix combines Cross-Character Embedding (CCE), which disentangles identity and behavior via text-aligned annotations, with Cross-Character Augmentation (CCA), which synthesizes cross-style training data while preserving native appearance. Experiments show consistent gains in identity fidelity, interaction quality, and style consistency over prior personalized and general video models.Additional results and videos are available on our project page: https://mi-mi-x.github.io.

Channel-adaptive Vision Transformers (ViTs) struggle to scale to non-RGB images with many uncorrelated or weakly correlated channels. Existing approaches roll out each channel into its own patch embedding, so the token sequence length grows linearly with the number of channels and self-attention cost grows quadratically. We introduce \emph{ChannelMixer}, a multi-channel tokenizer that aggregates all channels within each spatial patch into a single channel-mixing token, decoupling the token sequence length from the number of channels. ChannelMixer is pretrained once with a lightweight autoencoder in a channel-agnostic manner and the resulting tokenizer can be easily integrated with ViTs trained under both supervised and self-supervised objectives. On multi-channel imaging benchmarks spanning 3 to 29 channels, ChannelMixer matches or outperforms channel-adaptive state-of-the-art methods while using approximately $20\times$ fewer FLOPs and $29\times$ fewer tokens than rolled-out tokenizers, measured on 29-channel images with a ViT-S/16.

Generative models excel at synthesizing high-fidelity samples from complex data distributions, but they often violate hard constraints arising from physical laws or task specifications. A common remedy is to project intermediate samples onto the feasible set at each sampling step. However, repeated projection can disrupt the learned sampling dynamics because this feasible set is defined for ''clean'' samples rather than noisy intermediate states. Thus, recent studies first estimate ''clean'' samples and then project them to the feasible set, but this increases algorithmic complexity and accumulates errors across steps. To address these issues, this paper proposes Chance-constrained Flow Matching (CCFM), a training-free method that formulates constraint enforcement during sampling as stochastic optimization while retaining the hard-constraint feasibility guarantee of projection-based methods. By avoiding direct projection of noisy intermediate states onto the clean-sample feasible set, CCFM mitigates the distributional distortion. Unlike approaches that first estimate and project ''clean'' samples, CCFM avoids complex multi-stage procedures. Experiments show that CCFM outperforms current state-of-the-art constrained generative models in modeling complex physical systems governed by partial differential equations, molecular docking problems, and physics-informed motion generation, delivering superior feasibility and fidelity.

High-content multichannel imaging, from Cell Painting assays to remote sensing, is central to modern scientific pipelines. Because these channels are highly heterogeneous and experimental configurations frequently evolve, a practical vision backbone must be fundamentally channel-agnostic. Current channel-adaptive vision transformers rely on global self-attention with learnable channel embeddings, which can be computed only for previously known channels. Attempts to overcome this limitation of new channels either discard the embeddings, losing channel identity, or process each channel independently, thereby eliminating critical cross-channel interactions. To address this, we introduce ChanSFormer, an efficient Vision Transformer that replaces rigid embeddings with disentangled spatial-channel attention and individual channel CLS tokens. This architecture natively preserves both distinct channel identities and cross-channel information flow. Furthermore, the individual channel class token design unlocks more informative feature representations for self-supervised learning and enables representation-based channel sampling while remaining channel agnostic. We evaluate ChanSFormer on two biology multi-channel datasets, CHAMMI, JUMP-CP and a satellite dataset, So2Sat. Experimental results show that ChanSFormer outperforms state-of-the-art methods by up to 4.27% in classification accuracy. It also demonstrates exceptional cross-dataset transferability under a self-supervised setting, outperforming previous methods by up to 2.72% in accuracy in the same classification tasks. Furthermore, the disentangled attention reduces the quadratic complexity of channel $\times$ spatial sequence length of the global attention baseline, thus improving throughput by 55%--313%.

For the combinatorial graph alignment problem (GAP) --- finding the node correspondence that maximizes the number of common edges between two unlabeled graphs --- properly initialized FAQ remains a strong classical baseline, while existing GNN approaches struggle in the purely structural setting. We introduce a chaining procedure: a sequence of Folklore-type (2-FWL) GNNs in which each network is trained with cross-entropy after decoding the previous network's similarity matrix and ranking nodes by their current alignment quality. This non-differentiable ranking step injects discrete combinatorial feedback at every link; at inference, we iterate the final network and keep the candidate with highest observed nce. On sparse Erdos--Renyi graphs at noise level 0.25, chained FGNNs with FAQ post-processing reach $85\%$ accuracy versus 13% for FAQ initialized from the convex relaxation. On correlated regular graphs, where constant-feature MPNNs collapse to uninformative similarities and FAQ's convex initialization is degenerate, chaining recovers non-trivial alignments among the methods we evaluate. On three real-world benchmarks (yeast PPI, coauthorship, and road networks), dataset-specific chained FGNNs provide modest gains over a properly initialized FAQ baseline.

Spatial intelligence requires foundation models to maintain coherent spatial state across interactions with the physical world. However, existing data-centric approaches typically treat spatial reasoning as independent question-answer instances, enabling shortcut-based answering and providing limited supervision for persistent spatial understanding. To address this, we introduce ChainSpace, a chained-reasoning paradigm that structures spatial reasoning as a state-preserving multi-round process. In this paradigm, spatial questions are organized into logically constrained and jointly consistent chains, where later questions depend on spatial constraints established in earlier rounds. Following this principle, we instantiate ChainSpace-Bench, a manually annotated real-world multi-round benchmark with a Chain-Aware Metric, and ChainSpace-Pipeline, a simulator-based chain-structured supervision generation framework for spatial intelligence training. Experiments show that ChainSpace-Bench exposes chain-level failures that are not captured by isolated question accuracy. Additionally, with a relatively small amount of simulator-generated chained data, models trained by ChainSpace-Pipeline achieve the best performance among open-source models on ChainSpace-Bench and transfer competitively to multiple external spatial intelligence benchmarks. These results establish ChainSpace as an effective paradigm for more faithful evaluation and more data-efficient learning of spatial intelligence.

*Chains‑of‑thought* (CoT) allow language models to verbalise multi‑step rationales before producing their final answer. While this technique often boosts task performance and offers an impression of transparency into the model's reasoning, this position paper argues that rationales generated by current CoT techniques can be misleading and are *neither necessary nor sufficient* for trustworthy interpretability. We analyze *faithfulness* as whether CoTs are not only human-interpretable, but also reflect the model’s underlying reasoning well enough to support responsible use. By synthesizing evidence from prior work, we show that verbalised chains are frequently *unfaithful*, diverging from the true hidden computations that drive a model's predictions. As CoT is increasingly relied upon in application domains, including high-stakes ones such as medicine, law, and autonomous systems, we further argue that this unfaithfulness is not taken seriously enough in these settings: our analysis of 1,000 recent CoT-centric papers finds that approximately 25\% explicitly treat CoT as an interpretability technique, including papers in high-stakes domains which heavily hinge on such interpretability claims. Building on prior work, we make three proposals: (i) avoid treating CoT as being sufficient for interpretability without additional verification, while continuing to use CoT for its communicative benefits, (ii) adopt rigorous methods that assess faithfulness for downstream decision-making, and (iii) develop causal validation methods to ground explanations in model internals.

LLM routing dispatches each user query to the most suitable model from a pool of candidates, balancing response quality against serving cost. As LLMs are increasingly deployed in conversational interfaces, routing decisions must now be made repeatedly within an ongoing session. Existing routers treat each query independently, implicitly assuming that the optimal model depends only on the current input. This assumption ignores two forms of session state that accumulate across turns: the dialogue state, which shapes query meaning and difficulty, and the \textit{serving state}, which determines each model's true \textit{serving cost} via KV-cache reuse. Neglecting dialogue state leads to misrouting when queries depend on prior turns, while ignoring serving state causes systematic cost overestimation that worsens as conversations grow longer. A practical multi-turn router must therefore capture dialogue state for accurate quality prediction, and track per-model serving state for accurate cost estimation. To address this gap, we introduce Chain-of-Route (CoR), the first state-aware routing framework for multi-turn conversations. CoR comprises two modules: a Dialogue State Propagator (DSP) that maintains a compact recurrent state to capture session-level context across turns, and a Serving State Tracker (SST) that tracks per-model KV-cache to compute true serving costs. Together with the framework, we construct a unified multi-turn routing benchmark from WildChat and LMSYS-Chat-1M with per-turn quality annotations across five LLMs. Experiments show that CoR achieves state-of-the-art quality-cost tradeoffs, reducing QNC by up to 20.3\% over the strongest baseline while improving response quality.

Compositional prompts in text-to-molecule generation often specify several structural constraints at once, such as scaffolds, functional groups, substituents, and finer modifiers. Existing molecular latent diffusion models typically inject the full prompt once and apply it uniformly throughout denoising, which can cause later constraints to overwrite or distort earlier structural commitments. We propose **Chain-of-Generation (COG)**, a training-free progressive latent diffusion strategy for text-guided molecular design. Technically, CoG realizes progressive generation as multi-stage latent denoising over cumulative molecular sub-prompts: it factorizes a prompt into chemistry-informed coarse-to-fine segments, conditions each stage on the cumulative sub-prompt, and continues denoising from the previous-stage latent using a fixed partial-denoising schedule. This turns one-shot conditional generation into a sequence of stage-wise denoising steps that help retain earlier structure while introducing later constraints. Across benchmark, out-of-distribution, and ablation settings, CoG improves structural prompt fidelity over one-shot conditioning on the tested latent graph-diffusion backbones.

Imitation learning policies for robotic manipulation inherently suffer from compounding errors. To mitigate this, inference-time policy steering introduces predictive reasoning to evaluate and refine action proposals before execution. However, existing methods typically anticipate consequences in unstructured pixel or latent spaces, leading to physically inconsistent predictions and unreliable error detection. Furthermore, their blindness to global progress often yields locally plausible corrections that fail to complete the overall task. To address these limitations, we propose Chain-of-Correction (CoC), an inference-time steering framework driven by a single VLM. Shifting away from unstructured state predictions, CoC explicitly leverages anchor-grounded scene graphs to track exact 3D physical relations. Built upon this representation, a progress-aware dual-check mechanism verifies historical execution and predicts future task advancement to systematically intercept myopic action proposals. Through a failure-augmented supervised fine-tuning pipeline, CoC seamlessly unifies scene graph extraction, progress evaluation, and precise kinematic correction, eliminating the need for task-specific world models. Extensive experiments across the COLOSSEUM benchmark, a newly introduced offline protocol for predictive reasoning, and real-world tasks demonstrate that CoC significantly outperforms state-of-the-art baselines in fine-grained error detection and robust error recovery.

The attention mechanism in Transformers links queries, keys, and values through a sequence of computations, while the principles that constrain its form remain unclear. This paper examines attention under natural requirements that formalize basic properties of attention computation as transformations between representation spaces, and asks which forms of attention computation are admissible. We show that similarity computation, score-to-weight mapping, and value aggregation are unified by dual structures induced by convex potential functions and their Legendre transforms. In particular, the standard inner-product score is recovered as a limiting case of a potential-based score, the score-to-weight map must take a gradient-map form under structural requirements on attention normalization, and the value-aggregation rule must take a dual-coordinate barycentric form under structural requirements on representative-point aggregation. These results show that the standard components of attention are structurally constrained by the underlying dual structure, while the choice of potentials provides explicit degrees of freedom for understanding and modifying attention mechanisms.