NeurIPS 2026 论文列表

Printed circuit boards (PCBs) are the hardware foundation of modern electronic systems. However, automating PCB schematic design remains difficult. A valid schematic must respect real-IC pin-level constraints and cross-component topological relations, neither of which is captured by generic simulation, local rule checks, or direct LLM generation. To address this challenge, we present TopoScope, a training-free LLM agent that uses the schematic graph not only as the final output, but also to guide generation and verification at runtime. TopoScope couples two mechanisms: Topology-Guided Context Scoping, which computes a scoped view for each component-level wiring decision, and Pattern-Based Rule Verification, which expresses datasheet-derived electrical constraints as graph patterns and uses them as in-loop feedback. We also introduce a 20-design benchmark spanning three complexity tiers and six application domains, with functional assertions evaluated on the generated schematic graph and calibrated against expert judgments. Across seven recent LLMs, TopoScope improves functional correctness, reduces invalid graph references, and enables more reliable generation of medium and hard multi-IC schematics compared with knowledge-provided baselines.

Geometric data pruning methods, while practical for leveraging pretrained models, are fundamentally unstable. Their reliance on extrinsic geometry renders them highly sensitive to latent space perturbations, causing performance to degrade during cross-architecture transfer or in the presence of feature noise. We introduce TopoPrune, a framework which resolves this challenge by leveraging topology to capture the stable, intrinsic structure of data. TopoPrune operates at two scales, (1) utilizing a *topology-aware manifold approximation* to establish a global low-dimensional embedding of the dataset. Subsequently, (2) it employs *differentiable persistent homology* to perform a local topological optimization on the manifold embeddings, ranking samples by their structural complexity. We demonstrate that our *unified dual-scale topological approach* ensures high accuracy and precision, particularly at significant dataset pruning rates (e.g., 90%). Furthermore, through the inherent stability properties of topology, TopoPrune is (a) exceptionally robust to noise perturbations of latent feature embeddings and (b) demonstrates superior transferability across diverse network architectures. This study demonstrates a promising avenue towards stable and principled topology-based frameworks for robust data-efficient learning.

Real-world documents distribute evidence across text, tables, figures, captions, and page layouts. Answering complex questions over such documents therefore requires more than retrieving relevant passages: systems must recover the evidence topology that connects heterogeneous evidence units. Existing GraphRAG evaluations focus primarily on text-derived chunks, entities, and relations, while multimodal document RAG benchmarks evaluate cross-modal retrieval and generation without directly testing whether systems recover the intended reasoning structure. We introduce \textsc{TopoGraphRAG-Bench}, a layout-grounded benchmark for multimodal evidence reasoning in GraphRAG. The benchmark contains 2,024 questions over 201 long, visually rich documents, with evidence grounded in text, figures, and tables. Each instance is constructed bottom-up from layout-grounded evidence units and assigned one of three controlled topologies: single-hop retrieval, bridge-chain reasoning, or multi-source synthesis. To ensure that questions preserve their intended structure, we apply counterfactual validation for shortcut resistance, modality necessity, and evidence necessity. We evaluate text-only GraphRAG, page-level visual retrieval, and multimodal GraphRAG systems using retrieval, generation, and topology-aware reasoning metrics. Multimodal GraphRAG systems achieve the strongest overall performance, but still fail when visual-textual evidence alignment or multi-unit composition is incomplete. Text-only GraphRAG struggles when key dependencies are grounded in figures or tables, while page-level visual retrieval lacks the fine-grained structure needed for topology recovery. These results suggest that future GraphRAG systems must model not only textual entities and relations, but also layout structure, modality alignment, and the reasoning roles of evidence units. Code and data are available at \url{https://anonymous.4open.science/r/Benchmark-B1DE}.

Persistence diagrams provide stable, interpretable summaries of geometric and topological structure, and are useful for simulation-based inference when important information is not captured by low-order statistics. In practice, however, persistence-based pipelines require hand-chosen filtrations, vectorizations, and compressors, usually without an objective tied directly to parameter uncertainty. We introduce \textbf{TopoFisher}, a differentiable persistent-homology pipeline that learns topological summaries by maximizing local Gaussian Fisher information. From simulations near a fiducial parameter value, TopoFisher optimizes trainable filtrations, diagram vectorizations, and compressors without posterior samples or supervised regression targets, while preserving the inductive bias of stable topological descriptors. We also give sufficient regularity conditions under which the log-determinant Fisher loss is locally Lipschitz in the trainable parameters. Controlled experiments on noisy spirals and Gaussian random fields, where the total Fisher information is known, validate the pipeline: TopoFisher recovers a large fraction of the available information and improves over fixed topological vectorizations. Our main results are on weak gravitational lensing, a high-dimensional non-Gaussian field-inference problem from cosmology. There, both learned topological summaries, a fixed cubical filtration with a learned PersLay vectorization, and a learned CNN filtration with the same PersLay vectorization, reach $\log|F|\approx 21$, compared with $13.8$ for the power spectrum, $17.1$ for peak counts, and $19.3$ for wavelet scattering, approaching an unconstrained Information Maximising Neural Network baseline ($22.4$) with up to $\sim80\times$ fewer parameters. More importantly, the fixed-filtration variant generalizes better: under simulator shift from lognormal to LPT-based maps it retains $\log|F|=19.24$ while the neural baseline drops to $8.9$, and in neural posterior estimation it yields tighter constraints than the neural baseline, power spectrum, peak counts, and wavelet scattering. These results suggest Fisher-based topological optimizations as a robust, parameter-efficient front end for simulation-based inference.

Topology reasoning jointly detects 3D lanes and traffic elements from multi-view images and infers their structural connectivity. Current methods model lanes as discrete polylines, lacking smoothness, analytical tangent directions, and global spatial support for attention, while providing sparse topology supervision. We propose TopoCurve, a geometry-driven architecture for 3D topology reasoning grounded in a structured parametric lane representation. Lanes are modeled as endpoint-fixed cubic Bézier curves, enabling continuous geometry with exact endpoints and analytically defined directionality. We exploit this shared curve geometry across the entire pipeline. Endpoint distance and tangent alignment are encoded with multi-scale Fourier features and injected into the topology head. Sampled curve points serve as geometry-aligned references for deformable cross-attention spanning the full lane. Parallel curve-anchored attention branches provide diverse predictions for one-to-many topology supervision. These components form a tightly coupled cascade where representation enables geometric reasoning, guides feature aggregation, and supports denser supervision. TopoCurve achieves 50.6 OLS on the OpenLane-V2 benchmark without any post-processing, establishing a new state-of-the-art among end-to-end camera-only methods, and outperforms all existing approaches on endpoint detection (56.8 vs. 52.6 on DET$_p$).

We propose a contextual cascading bandit model in which the click probability at each position depends on both the displayed item and the topics of previously shown items. This captures how previously displayed topics affect later click probabilities, allowing different topic orders to induce different user responses. Unlike standard cascading bandits, the optimal cascade is no longer determined only by the selected set of items, since the ordering itself affects the expected reward. To address the resulting planning challenge, we reformulate cascade construction as a Markov decision process whose state summarizes the previously selected topics. Based on this formulation, we develop an optimism-based algorithm and prove a regret upper bound of $\widetilde{\mathcal O}(\bar p^{\frac{K-1}{2}}d\sqrt{T})$, where $d$ is the number of unknown parameters, $T$ is the horizon, $K$ is the cascade length, and $\bar p<1$ upper bounds the no-click probability of an examined item. A key implication is that the regret decreases with the cascade length $K$, a phenomenon that had not been established even in order-insensitive contextual cascading bandits. We further provide a local regret lower bound showing that this decreasing dependence on $K$ is intrinsic, and validate our theory through experiments with topic-order-dependent click probabilities.

Large Language Models (LLMs) have demonstrated remarkable capabilities in orchestrating tools for reasoning tasks. However, existing methods rely on a step-wise paradigm that lacks a global perspective, which causes error accumulation over long horizons and restricts generalization to unseen tools. To overcome these limitations, we propose Tools as Continuous Flow for Evolving Agentic Reasoning (FlowAgent), which reconceptualizes tool chaining as continuous trajectory generation within a semantic space. To systematically evaluate this paradigm, we introduce the first plan-level closed-loop benchmark dedicated to plan-level agentic reasoning in dynamic real-world environments. Specifically, the proposed FlowAgent leverages conditional flow matching to generate continuous latent trajectories, providing a global planning perspective to ensure coherent and robust tool execution. Theoretically, we establish formal bounds on utility convergence and prove that our continuous formulation fundamentally guarantees robust generalization and error attenuation. Empirical evaluations show that FlowAgent achieves superior robustness and adaptability in long-horizon reasoning tasks.

Large language models (LLMs) excel at natural language processing but struggle to interact with external environments. Tool learning provides a promising way to extend LLMs into actionable agents, where tool selection is a critical prerequisite for successful tool use. Existing work often assumes a small or predefined set of tools, leaving large-scale tool selection underexplored. Real-world repositories contain a vast and diverse array of tools, making it difficult for LLMs to effectively search, distinguish, and compose tools under context-length constraints. We identify large-scale tool selection as a new challenge for agentic reinforcement learning, highlighting that existing RL methods for knowledge-based question answering are inadequate for selecting tools while considering compatibility. To address this challenge, we propose ToolSearcher, a novel RL framework for effective multi-turn search and fine-grained optimization in large-scale tool selection. Specifically, we introduce category-constrained tool discrimination to improve the model's ability to distinguish functionally similar tools, event-level search modeling to explicitly optimize the discovery of target tools during multi-turn search, and trajectory-aligned credit allocation to provide fine-grained reward signals for different stages of the search-selection process. Extensive experiments on large-scale tool selection benchmarks demonstrate that ToolSearcher consistently outperforms a set of strong baselines in challenging settings involving iterative search and complex tool composition.

Computer Use Agents (CUAs) can act through both atomic GUI actions ($\textit{e.g., click, type}$) and high-level tool calls ($\textit{e.g., API-based file operations}$), but they are often confused by this hybrid action space: they do not know when to continue with GUI actions and when to switch to tools, and finally fail to select the optimal execution path. We view this orchestration problem as GUI-Tool path selection: deciding when the agent should continue with GUI actions and when it should switch to tool calls to form an effective execution trajectory. This difficulty stems from two issues. First, high-quality interleaved GUI-Tool trajectories are scarce, and collecting real tool trajectories is expensive and brittle. Second, existing supervision provides limited guidance for GUI-Tool path selection, as most methods focus on step-level action imitation or final task completion and offer little trajectory-level feedback on whether GUI-Tool switching leads to a more effective execution path. In this paper, we propose $\textbf{ToolCUA}$, an end-to-end agent designed to learn optimal GUI-Tool path selection through a staged training paradigm. We first introduce an $\textbf{Interleaved GUI-Tool Trajectory Scaling Pipeline}$ that repurposes abundant static GUI trajectories and synthesizes a grounded library of tools, making it possible to scale diverse GUI-Tool trajectories without manual engineering or real tool-trajectory collection.Based on this data, we perform Tool-Bootstrapped GUI RFT, which combines warmup SFT with single-turn RL to improve decisions at critical GUI-Tool switching points. Finally, we further optimize ToolCUA with $\textbf{Online Agentic RL}$ in a high-fidelity GUI-Tool environment, using a Tool-Efficient Path Reward that encourages both appropriate tool use and shorter execution paths. Experiments on OSWorld-MCP show that ToolCUA achieves 46.85% accuracy and outperforms the baseline by over 60% relatively, establishing a new state of the art among models of comparable scale. It also improves by 3.9% over GUI-only settings, demonstrating effective GUI-Tool orchestration. The results further suggest that training in a hybrid action space is a promising paradigm for real-world digital agents.

Vision-Language Models (VLMs) have been increasingly adopted for Image Quality Assessment (IQA). However, current methods typically employ a static one-shot scoring paradigm, despite the fact that humans assess image quality through dynamic visual inspection, e.g., selectively adjusting views to verify details and subtle artifacts. Specifically, relying solely on a single-pass observation introduces two primary limitations: first, perceiving the image only at a global scale restricts the assessment of finer local details; second, the original intensity distribution of the image may overwhelm the visibility, leading to insufficient inspection of image quality. To address these issues, we propose Tool-IQA, shifting the assessment mechanism from passive scoring to a tool-augmented workflow. In particular, we equip VLMs with simple yet effective view tools: a Magnifier to inspect local details, and a Gamma Corrector to uncover visibility and hidden artifacts. The assessment follows a structured pipeline that consists of an initial observation with rubric notes, a tool-augmented in-depth inspection, and a final quantification for calibrated quality score. Furthermore, to ensure efficient and purposeful tool callings, we introduce a batch-aware training strategy to reward tool interactions that can yield positive contributions rather than simply encouraging usage. Experiments on a variety of IQA benchmarks demonstrate that, with effective tool calling and calibrated assessment, our proposed Tool-IQA significantly outperforms existing state-of-the-art models, e.g., it achieves a PLCC of 0.854 on the challenging CLIVE dataset. The code and models will be released.

Test-time reinforcement learning (TTRL) has emerged as a promising paradigm for adapting large reasoning models (LRMs) on unlabeled test inputs, using self-consensus rewards derived from majority voting over sampled rollouts. However, majority voting can mistake popularity for correctness: a spurious yet high-frequency unverified consensus may become a biased reward signal, causing test-time RL to reinforce frequent but wrong answers and collapse into an incorrect mode. We address this false-popular failure mode with T^3RL (Tool-Verification for Test-Time Reinforcement Learning), a verification-aware test-time RL framework. T^3RL grounds pseudo-label construction in external tool evidence. Concretely, a verifier uses external tool evidence, such as code execution, to upweight verified rollouts during verification-aware voting, producing more reliable pseudo-labels for training. Across mathematical reasoning benchmarks of varying difficulty, including MATH-500, AMC, and AIME 2024, and diverse backbone families, T^3RL significantly improves over TTRL, with stronger performance on harder problems. More broadly, T^3RL is positioned as a verified online data synthesizer, highlighting the role of test-time tool verification in reliable online adaptation.

Larger language models solve harder problems, but do they also produce proportionally better explanations? We study in-context explanation transfer across 40 open-weight models (360M--72B) and 5 receivers (360M--7B), using problems drawn from 13 benchmark sources and evaluated with domain-specific metrics. Our central finding is an \emph{articulability ceiling}: under equal token budgets, mid-range models are better teachers than frontier models. Transfer peaks at 170--220 words and then drops sharply beyond 300 words, falling below even the shortest explanations. Large models partly compensate through verbosity: longer explanations yield higher total transfer on average, but each additional word carries less pedagogical value, so a 360M model delivers more transfer value per word than a 72B model. We also find that RLHF improves pedagogical clarity: instruct models score better on LLM-judge evaluation than base models despite lower lexical overlap, and teacher correctness dominates all other factors. Code and logs are released with the paper.

AI-generated image detection has become increasingly important as generative models produce highly realistic visual content. Existing detectors mainly rely on visual artifacts or visual features extracted from pretrained multimodal models, while largely overlooking the structural differences between real and generated images in the joint image--text space. In this paper, we reveal a systematic Cross-modal Alignment Shift (CAS): generated images tend to exhibit stronger and more concentrated alignment with text than semantically matched real images. We verify this phenomenon through retrieval preference, image--text residual structure, spectral concentration, and matching entropy. Motivated by this finding, we propose a CAS-based detection framework(CASNet) that learns an amplified multimodal model with a text-guided alignment-shift objective. The amplification-induced feature increment is extracted as an explicit cross-modal cue and coupled with amplified visual representations, enabling the detector to exploit both alignment-level structural evidence and visual discriminative cues. Extensive experiments across diffusion models, GANs, autoregressive models, and commercial generators demonstrate consistent improvements over state-of-the-art detectors. In particular, on recent commercial generators, our method improves ACC and AP by 5.48\% and 6.13\%, respectively.

Neural scaling laws approximate a language model's loss as a power-law function of parameter count $N$ and token count $D$. Following Chinchilla-style compute-optimal training, many studies fit scaling laws from runs performed under a fixed tokens-per-parameter (TPP) ratio $k$ and set $D = kN$. We show that this collinear design, combined with the empirically common near-equality of the exponents governing $N$ and $D$, induces an inherent ill-conditioning in the Gauss-Newton least-squares problem: the condition number of the design grows as the inverse square of the gap between the $N$ and $D$-exponents. The scale coefficients become practically unidentifiable, with confidence intervals inflating by an order of magnitude or more, yielding a ``sloppy'' model whose extrapolations degrade sharply off the training ray. We prove this for four scaling-law formalisms and derive a closed-form TPP-diversity threshold that is necessary and sufficient for well-conditioned estimation. Empirically, non-collinear designs outperform collinear ones on held-out splits with a 97.3\% win rate across four laws, five corpora, multiple floating point precision modes. We further show the degeneracy is rooted in Jacobian geometry and is not an artifact of the loss function: any smooth estimation objective whose curvature involves the Jacobian inherits the same ill-conditioning.

Latent medical image generators usually treat the tokenizer as fixed preprocessing. We test whether this separation is valid in a controlled ChestMNIST study at 64×64, crossing discrete tokenizers, generator families, and sampler settings under a shared latent grid, with continuous-latent reference cells. The results show that tokenizer, generator, and sampler cannot be ranked independently. LFQ is the strongest default discrete tokenizer in the matched VQ/LFQ/FSQ grid, but generator rankings change with quantizer family and categorical rate. On LFQ-1024, retuning D3PM and SEDD moves them from default FID-192 0.44/0.41 to 0.09/0.11 at lower NFE, approaching the strongest continuous references. Reconstruction quality is not a reliable selection criterion: the highest-PSNR tokenizers are not the best generation substrates, even when each tokenizer is paired with its best observed generator. We interpret these results as a rate-distortion-modelability tradeoff, where modelability is conditional on the generator, sampler, and inference budget. The study covers 54 discrete tokenizer-generator cells and 16 continuous reference cells, and its scope is deliberately limited: low resolution, one training seed, unconditional generation, and non-clinical FID-based evaluation.

Planning has traditionally been addressed with symbolic search guided by domain models and heuristics. Learning offers a complementary way to exploit structure shared across related problems rather than solving each instance from scratch, making generalization to unseen instances a central challenge for learned planners. Recent work has explored planning mainly through action-centric models that predict actions or full plans from problem descriptions. A parallel line of work instead learns goal-conditioned transition models over states (state-centric learning) and recovers actions by matching predicted successor states to symbolic successors. In this setting, however, prior work has relied mainly on Weisfeiler-Leman (WL) representations, leaving the role of tokenizer choice unclear. We present a controlled study of tokenizer choice in state-centric learned planning, comparing WL, shortest-path, GraphBPE, SimHash, and a deterministic baseline within a common pipeline. Across varied benchmark domains and multiple planner configurations, WL performs well overall, but no tokenizer is best everywhere: the leading representation changes by domain, and tokenizer differences are larger on extrapolation rather than on interpolation tasks. Across the studied configurations, tokenizer choice is the dominant factor in held-out generalization: it explains 57% of performance variance, more than predictor architecture or any other pipeline factor, and its effect is substantially amplified on out-of-distribution problems relative to in-distribution ones. We conclude that representation choice is a strong determinant of generalization in state-centric learned planning and that tokenizer choice is a first-order modeling decision rather than a fixed preprocessing step. Code: https://tinyurl.com/4hre257u

Time series transformers have achieved strong forecasting performance, yet fundamental questions about their internal mechanisms remain unanswered: among self-attention and feed-forward networks (FFN), which component constructs temporal features? Why does a simple linear model outperform transformers? Why does patching help? We propose a temporal probing framework consisting of 15 token-level diagnostic tasks to trace information flow across transformer components. Applied to 5 architectures across 8 benchmarks, we find that temporal feature construction is not inherently tied to any single component—it emerges from the interaction between tokenization strategy and architectural design. In PatchTST, FFN dominates feature construction across all 8 datasets, with a $3.2\times$ mean ratio over attention. Even when attention is completely removed, FFN's feature construction capability is preserved or enhanced, indicating that attention is not essential for temporal feature construction. Conversely, iTransformer's variate-level attention dominates over FFN, and CATS's cross-attention is responsible for nearly all feature transfer. Point-wise tokenization ($P=1$) structurally deactivates FFN, explaining why early transformers lagged behind linear models. These findings unify contradictory results across DLinear, PatchTST, attention-free models (TSMixer, PatchMLP), and CATS within a single framework.

Multimodal large language models (MLLMs) should generate consistent responses given semantically equivalent inputs across modalities. However, we observe a systematic discrepancy in model predictions under such cross-modal variations. To study this phenomenon, we define the modality gap as the difference in model performance under semantically equivalent textual and multimodal inputs. We introduce TokenSwap, a method that constructs such inputs by replacing textual concepts with semantically aligned images, resulting in sequences where visual tokens are interleaved with text tokens. Based on TokenSwap, we transform existing text-based benchmarks (e.g., MMLU) into image-interleaved counterparts, resulting in TOKENSWAP-BENCH. Across 42 MLLMs, we observe a pervasive modality gap, with performance decreasing by 4.2% to 47.4% when moving from text-only to image-interleaved inputs, averaging 19.6% ± 3.3% across models. Notably, we observe that reasoning models exhibit consistently smaller gaps, achieving an average gap of 10.1% compared to 25.5% for non-reasoning models. In contrast, neither prompting strategies nor scaling training compute alone reliably reduces the modality gap. Finally, we demonstrate that incorporating TokenSwap during training effectively mitigates this gap while preserving strong text-only and vision-language performance.

Large language model (LLM) routing is widely used to advance the cost-quality Pareto frontier of modern serving systems. While coarse-grained routing at the session or query level has been widely adopted in production systems, recent algorithm work shows substantial efficiency and quality advantages of finer token-level routing. However, efficiently serving token-level routed inference poses significant challenges to existing system design. Built on single-LLM assumptions, current systems struggle with step desynchronization and frequent batch admission delays under token-level routing, and they also impose high implementation complexity on developers. To address this, we propose TokenRouter, an efficient and user-friendly serving system for token-level routed LLM inference. TokenRouter shifts from the conventional request-centric paradigm to a model-centric one. Instead of treating each request as a synchronized decoding stream, TokenRouter launches a subserver for each candidate LLM and dispatches requests asynchronously according to routing decisions. Each subserver employs a delayed-batching scheduler, whose hyperparameters are derived mathematically from a throughput model of the system. Across diverse routing algorithms, workloads, and model pairs, TokenRouter achieves 1.83-64.29× higher decoding throughput than existing systems, substantially advancing the serving efficiency of token-level LLM routing.