NeurIPS 2026 论文列表

E-commerce image search often takes a cropped image as the query, while each candidate is represented by full item images and structured text. This image-to-multimodal retrieval setting presents two asymmetries: a modality disparity -- a visual query must match image--text items, and a granularity disparity -- a cropped query must be compared with full images containing background context and possible distractors. Detection-based pipelines handle the granularity disparity through explicit localization but incur extra cost and error propagation, whereas CLIP-style encoders avoid detection, but are vulnerable to backgrounds or irrelevant items. To address these limitations, we propose TIGER-FG, a text-guided implicit fine-grained grounding framework for image-to-multimodal e-commerce retrieval. TIGER-FG uses item text as semantic guidance to produce target-focused item representations without object detection for retrieval. We further introduce dual distillation objectives that preserve target-region spatial consistency and query--item similarity structure, yielding more stable and discriminative multimodal representations. In addition, we construct ECom-RF-IMMR, a realistic benchmark suite with a 10M-pair training set and two evaluation benchmarks covering standard and cluttered item layouts. TIGER-FG improves Recall@1 over the strongest baseline by 6.1 and 34.4 percentage points on the two evaluation benchmarks, respectively, with only 85.7M query-side parameters and 256-dim embeddings. Results on public e-commerce benchmarks further demonstrate its generalization to noisy and one-to-many retrieval scenarios. Code and data will be released.

Time series forecasting aims to predict future values from historical observations and auxiliary features. We propose \textbf{SteerCast}, a retrieval-based latent steering method that improves decoder-only forecaster at inference time, without updating its parameters. SteerCast constructs a database from the training set by storing a representation of each history window together with a \emph{steering vector} computed in the forecaster's latent space, defined as the difference between representations induced by the ground-truth continuation and by the model's own prediction. At test time, SteerCast retrieves nearest neighbors for a query history, aggregates their steering vectors, and injects the resulting signal into the forecaster's hidden states at every step of autoregressive generation, guiding predictions toward trajectories consistent with similar training cases. Experiments across diverse multivariate benchmarks and multiple horizons show that SteerCast consistently improves forecasting accuracy over the fine-tuned backbone and retrieval-based baselines, while requiring no additional training beyond the original fine-tuning and using only the training set as a retrieval corpus.

Energy-based models (EBMs) are flexible generative architectures inspired by statistical physics, but their learning and generative properties remain poorly understood. Here, we analyze a solvable EBM in the high-dimensional limit: the spherical Boltzmann machine (SBM). Combining tools from random matrix theory and dynamical mean-field theory, we: solve exact equations describing the training dynamics of the SBM; compute the Bayesian evidence, which acts as a partition function in parameter space and encodes global properties of the trained model; and uncover cascades of phase transitions that occur both during training and as a function of hyperparameters, related to successive alignment and condensation of the top modes of the coupling matrix to the data. We connect these transitions to sampling-time generative phenomena in a teacher-student scenario, including: sampling temperature tuning, double descent as a function of regularization strength, tempered posterior effects, and out-of-equilibrium effects during training that induce biases in the trained model. We provide numerical evidence demonstrating that all these phenomena appear in standard generative architectures, beyond the SBM.

Large language models increasingly use external tools such as web search and document retrieval to solve information-intensive tasks. However, multi-hop tool use in complex tasks introduces substantial latency, since the model must repeatedly wait for tool observations before continuing. We study how to accelerate such trajectories without changing the final trajectory the model would have taken without acceleration, assuming access to faster but less reliable speculator tools. We develop a theoretical framework for lossless speculation in multi-hop tool-use settings, characterizing the optimal achievable latency gain. We propose SpecHop, a continuous speculation framework that maintains multiple speculative threads, verifies predicted observations asynchronously as target tool outputs arrive, commits correct branches, and rolls back incorrect ones. This preserves task accuracy while reducing wall-clock latency. We show that SpecHop can approach the oracle latency gain with sufficient active threads. Empirically, we evaluate SpecHop on retrieval-augmented multi-hop tasks and find that its latency gains closely match theoretical predictions, reaching up to 40% latency reduction in some settings.

Longitudinal radiology report generation increasingly conditions on prior images and reports. This history is clinically valuable, but it creates a source-validity failure mode: a generator can carry prior-only findings into the current report. Weintroduce source-causal robustness, a stress-test and control framework that holds the current chest X-ray fixed while intervening on historical context. On a full eligible MIMIC-CXR longitudinal study (18,716 pairs; 93,580 generations across five source conditions), historical context consistently trades fewer missed findings for more unsupported positives. Matched wrong-history, the key negative control, increases proxy-defined false positives by +0.366 per report while reducing false negatives by −0.129; random wrong-history yields an even larger false positive stress signal (+0.465 FP/report). We propose the Source-Causal Finding Router (SCFR), a source-validity controller trained with within-evaluation subject exclusion. Across three held-out evaluations, SCFR repairs matched wrong-history false-positive inflation by −0.343, −0.550, and −0.457 per report, passes Holm corrected primary testing at the bootstrap resolution floor (Holm-adjusted p ≤ 0.0012), and satisfies fixed current-only parity margins. Cross-generator stress panels with RadInfer and Libra, together with ReXPref-Prior, RadGraph2, Chest ImaGenome, ablations, and case review, support the mechanism. These results establish a protocol-defined state-of-the-art source-causal robustness/control result for longitudinal radiology report generation under matched wrong-history stress tests.

Composed Image Retrieval (CIR) retrieves a target image given a reference image and a textual edit. Existing training-free methods prompt a frozen multimodal large language model (MLLM) to infer the user's edit intent and rewrite the composed query into a single retrieval artifact, such as a target caption, an edited image, or a modality-fused embedding. These methods can reason about the edit, but they have limited means to execute that reasoning during retrieval. A key remaining bottleneck for training-free CIR appears to be not only intent understanding, but intent execution. To address this challenge, we propose SkillCIR, a training-free framework that tackles the intent execution gap by routing the inferred constraints to specialized retrieval skills. Specifically, the same frozen MLLM that infers the intent also emits a structured plan of active constraints; each constraint activates a typed skill that scores gallery candidates along one evidence channel, and the per-skill scores are composed in score space with explicit signs and re-checked by a local verification step. Across three benchmarks and three CLIP backbones, SkillCIR improves the primary recall/mAP metrics by 2.8 to 7.9 points at the latency of typical training-free CIR pipelines and around 23x faster than the latest training-free state of the art. These gains hold with a fixed skill pool and no model-parameter updates, suggesting that improving the means to execute the inferred edit is a useful complement to stronger reasoning about it.

We propose a dense associative memory for empirical measures (weighted point clouds). Stored patterns and queries are finitely supported probability measures, and retrieval is defined by minimizing a Hopfield-style log-sum-exp energy built from the debiased Sinkhorn divergence. We derive retrieval dynamics as a spherical Hellinger Kantorovich (SHK) gradient flow, which updates both support locations and weights. Discretizing the flow yields a deterministic algorithm that uses Sinkhorn potentials to compute barycentric transport steps and a multiplicative simplex reweighting. Under local separation and PL-type conditions we prove basin invariance, geometric convergence to a local minimizer, and a bound showing the minimizer remains close to the corresponding stored pattern. Under a random pattern model, we further show that these Sinkhorn basins are disjoint with high probability, implying exponential capacity in the ambient dimension. Experiments on synthetic Gaussian point-cloud memories demonstrate robust recovery from perturbed queries versus a Euclidean Hopfield-type baseline.

3D subsurface geological models underpin hydrocarbon exploration, geothermal energy, underground gas storage, and geological carbon storage. A decade of deep generative facies modeling has yet to yield a model that generalizes across geological settings, scales to realistic reservoir extents, and conditions naturally on well data, bottlenecked by the absence of a large-scale, multi-environment, property-rich dataset. We release SiliciclasticReservoirs, the largest and most diverse public 3D dataset of siliciclastic (sandstone-bearing) subsurface reservoirs to date: $10^6$ voxelized reservoirs at $64{\times}64{\times}32$ resolution spanning eight depositional settings (deep-water turbidite lobes, deltas, and six fluvial channel architectures from the Alluvsim family), each annotated with binary and six-class facies labels, per-voxel porosity and permeability, and a natural-language caption describing the reservoir's geological setting and petrophysical attributes. To our knowledge, it is the first siliciclastic dataset to pair 3D geological models with petrophysical properties at this scale, the first to span this breadth of settings in a unified release, and the first to ship text captions, supporting future multimodal and language-conditioned training for text-to-reservoir generation and retrieval. Building on SiliciclasticReservoirs, we train ResFlow, a flow-matching foundation model that, from a single set of weights, generates any environment under continuous parametric control, conditions on arbitrary well configurations via classifier-free guidance, and assembles reservoirs at arbitrary spatial extent through overlapping-tile MultiDiffusion, with no retraining. SiliciclasticReservoirs (CC-BY 4.0), ResFlow (MIT), and the data-generation engine ResMill (MIT) are publicly released.

Despite advances in retrieval-augmented generation (RAG), suppressing hallucinations during complex reasoning remains a persistent challenge. We identify that existing RAG pipelines suffer from a textual bottleneck: by passing only raw text to the generator, they discard discriminative signals of the retriever. Furthermore, recent attempts to unify retrieval and generation within a single LLM induce severe objective conflict and exacerbate the rank collapse of decoder-only architectures, degrading performance on both tasks. To address these limitations, we introduce ShiftRAG, a decoupled RAG framework that operates on a single LLM backbone but isolates discriminative retrieval learning from autoregressive generation. To bridge these decoupled modes, we project retrieval alignments into continuous soft tokens, establishing a high-bandwidth interface that infuses the generator with retrieval-side signals. Concurrently, our soft orthogonality objective mitigates rank collapse, while layer-wise relevance dynamics preserve the fine-grained latent space required for highly discriminative evidence separation. Extensive evaluations demonstrate that ShiftRAG significantly improves retrieval and question answering performance over state-of-the-art baselines. ShiftRAG preserves foundational capabilities of the backbone while maintaining a favorable efficiency-performance trade-off on general text encoding and generative tasks. Our code is available.

Discrete diffusion models generate sequences by iteratively denoising samples corrupted by categorical noise, offering an appealing alternative to autoregressive decoding for structured and symbolic generation. However, standard training targets a likelihood-based objective that primarily matches the data distribution and provides no native mechanism for enforcing hard constraints or optimizing non-differentiable properties at inference time. This work addresses this limitation and introduces Search-Augmented Masked Diffusion (SearchDiff), a training-free neurosymbolic inference framework that integrates informed search directly into the reverse denoising process. At each denoising step, the model predictions define a proposal set that is optimized under a user-specified property satisfaction, yielding a modified reverse transition that steers sampling toward probable and feasible solutions. Experiments in biological design and symbolic reasoning illustrate that SearchDiff substantially improves constraint satisfaction and property adherence, while consistently outperforming discrete diffusion and autoregressive baselines.

Large language models (LLMs) increasingly support science, but they can also convert hazardous scientific knowledge into actionable misuse guidance. Existing benchmarks often rely on templated queries disconnected from real-world hazards, and employ LLM-as-a-Judge paradigms without domain grounding. To address this, we introduce SciHazard, a real-world-grounded benchmark for scientific risks and a dataset agnostic evaluation framework for measuring harmfulness. SciHazard contains 2400 hazardous questions and 600 oversafety questions across 12 disciplines, with both queries grounded in regulated entities and documented failure scenarios. To compute DeHarm-Score , we develop a decomposed evaluating procedure that combines query hazard severity, refusal behavior, and response-level risk. For non-refused responses, it further decomposes response-level harm into Executability, quantified via dynamic checklists with importance weighting, and Net-new risk, assessed through retrieval-augmented claim extraction and synthesis-barrier verification. An expert-validation study shows that DeHarm-Score improves agreement with expert annotations by 90.17% over the strongest baseline. We benchmark 31 frontier LLMs and deep research agents in an extensive scientific safety evaluation. Notably, Deep research agents yield 32.3% higher mean Deharm-Score than standard LLMs, exposing autonomous agents as a critical blind spot in current safety defenses. Code and dataset are available at https://anonymous.4open.science/r/DeharmScore-7B55.

Vision-Language Models for Earth Observation are bottlenecked at the visual encoder. Existing pipelines either compress multi-spectral signals into RGB pixels or collapse them into a single CLIP-style global vector, losing spectral information or spatial structure respectively. We show this is a consequence of coupling contrastive supervision to the dense feature stream, which degrades spectral fidelity. SPEXT resolves this through architectural decoupling. A wavelength-parameterized encoder produces multi-spectral patch tokens for dense tasks and VLM grounding, while a dedicated alignment token absorbs the contrastive gradient for retrieval. SPEXT simultaneously leads the PANGAEA segmentation benchmark (+2.51 mIoU), tops zero-shot retrieval among multi-spectral CLIP baselines (+4.6 mAP@100), and enables a frozen VLM, paired with a small projection adapter and LoRA, to answer free-form Earth-Observation questions directly from its tokens, substantially outperforming an RGB-pixel baseline on a downstream EO-RAG benchmark.

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.

Chunking is a hidden bottleneck in dense retrieval and retrieval-augmented generation: it determines the units that can be indexed, retrieved, and ultimately used as evidence. Yet most systems still segment documents using fixed token windows or globally thresholded semantic similarity drops, treating chunking as an engineering heuristic rather than a statistical decision. We propose **SEISMOS**, a statistical signal detection framework for semantic chunking. **SEISMOS** models consecutive sentence-embedding cosine similarities as a document-level signal and derives boundary decisions from the null hypothesis of no semantic transition. Across four development BEIR corpora, we establish three corpus-invariant properties: bimodal document structure, rapid survival decay of shallow local minima, and positive lag-1 autocorrelation. These properties show that semantic boundaries should be detected by variance-normalized deviations, not by document means or global similarity thresholds. Accounting for autocorrelation leads to a normalized discrete Laplacian detector that identifies significant semantic valleys through a single interpretable decision rule. Evaluated on five BEIR benchmarks, **SEISMOS** consistently improves over fixed-length, recursive, and production semantic chunking baselines under one fixed operating configuration. Without corpus-specific retuning, the same detector transfers to a held-out TREC-COVID corpus. Our results suggest that the boundaries needed for effective retrieval are already encoded in embedding-similarity signals, and that principled, efficient, LLM-free chunking can be obtained by detecting them statistically.

Retrieval-Augmented Generation (RAG) mitigates Large Language Model (LLM) hallucinations by grounding generation in external knowledge. Although structure-augmented RAG methods improve multi-hop reasoning, they incur high indexing costs and degrade on simple queries. We argue that this robustness gap is driven by capacity bottlenecks in fixed-dimensional inner-product scoring. As corpora grow, semantically close passages create local regions where a base scorer cannot maintain sufficient separability. Reorganizing documents into trees or graphs can expose or partially mitigate this issue, but it does not by itself provide a stronger local scoring function. We propose Semantic Ambiguity Guided Capacity Expansion (SAGE), a lightweight RAG framework that detects such regions using document-side Local Separability Deficit and expands capacity only where needed. SAGE builds a two-layer semantic index, derives atomic query views from corpus passages, and calibrates a parameter-efficient hypernetwork to generate the local Ambiguity-Conditioned Scorers for selected nodes. Extensive experiments show that SAGE improves retrieval and QA performance over dense retrievers and structure-augmented baselines, achieving a better balance between single-hop, multi-hop retrieval accuracy, and robust scalability. Anonymous Code: https://anonymous.4open.science/r/SAGE-1676.

Circuit design for analog integrated circuits (ICs) is challenging because circuit topology, device sizing, and circuit performance are tightly coupled. Existing large language model (LLM)-based methods either rely on inference-time correction around frozen models or train on limited circuit types without directly using simulation outcomes as training signals. A key bottleneck is that existing datasets do not provide sufficient task information for training LLMs on end-to-end specification-conditioned design. To fill this gap, we first construct a large-scale simulation-grounded dataset with 8,626 design tasks across 10 different types of analog circuits, where each task includes a textual description, labeled I/O ports, loading condition, target specifications, a testbench, and a reference netlist. Using this dataset, we present SAFTAC, a simulation-augmented fine-tuning framework that trains LLMs to generate and revise analog circuit design. SAFTAC combines three-stage supervised fine-tuning, which progressively builds the capabilities required for analog circuit design and feedback-guided correction, with simulation feedback-augmented reinforcement fine-tuning, which further optimizes models using simulation-grounded rewards. We evaluate SAFTAC under single-pass generation and two-pass generation with simulation feedback, showing improvements over the strongest closed-source LLM by 15.9\% and 7.8\%, respectively.

Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in multi-step reasoning and calling search engines at appropriate steps. However, existing retrieval-augmented reasoning approaches rely on separate retrieval models, limiting the LRM's role in retrieval to deciding when to retrieve and how to formulate queries, even with its inherent ability to handle vast knowledge spaces. This structural separation induces a representation bottleneck, as the retriever’s latent space often lacks the expressive granularity required to satisfy the generator’s sophisticated information needs. To address this, we shift our perspective on retrieval from sequence-to-sequence matching for all corpora to locating the answer-containing paths within the corpus, and propose a novel framework called FREESON (Retriever-FREE Retrieval-Augmented ReaSONing). This framework enables LRMs to directly access external knowledge by acting as both a generator and a retriever. To achieve this, we introduce a variant of the MCTS algorithm specialized for the retrieval task, which we call CT-MCT (Corpus-Traversing Monte Carlo Tree Search). Through this algorithm, the LRM selectively references specific segments identified during traversal, instead of fetching a fixed top-k set. Experiments on five open-domain QA benchmarks covering both single-hop and multi-hop questions demonstrate that FREESON achieves an average improvement of 14.4% in EM and F1 over four multi-step reasoning models with a separate retriever, and it also performs comparably to the strongest baseline, surpassing it by 3% on PopQA and 2WikiMultihopQA, and by 12% on the fact-checking benchmark FEVER.

Maintaining long-term geometric consistency remains a key challenge in long-horizon autoregressive video generation. Recent memory-augmented generative models address this issue by retrieving historical frames beyond the limited temporal context, but their effectiveness depends on two key design choices: what 3D-geometric evidence should represent past observations, and how memory frames should be selected from this evidence. Existing methods often rely on camera poses or field-of-view overlap, which are lightweight but too coarse to reason about pixel-wise visibility, or use explicit 3D reconstruction, which provides fine-grained evidence but is costly to maintain over long rollouts. We propose Coverage-Maximizing Retrieval-Augmented Generation (COVRAG), a depth-based memory retrieval framework that uses pretrained 3D priors to construct a target-view coverage map as lightweight 3D memory evidence. For frame selection, COVRAG maximizes residual coverage gain, iteratively retrieving frames that explain target-view regions not covered by the current context or previously selected memories. To improve scalability in long-video generation, we introduce a sliding-window depth caching for efficient geometry estimation. Experiments on RealEstate10K and DL3DV show that \modelname improves long-horizon geometric consistency while maintaining low latency compared to baselines.

We investigate a general-purpose layer for deep learning that, instead of compressing arbitrary-size training data into fixed-size weight matrices, stores a new pair of key-value representations for every data point during training, and retrieves and recombines these representations through an attention mechanism at inference time---resulting in a growing neural net (NN). We derive such a "retrieval-centric deep learning" (RCDL) paradigm from the classic duality of a linear layer trained by gradient descent as a type of linear attention (LA) over the training data points---which motivates us to replace the corresponding LA by a more powerful form of attention from recent work on sequence models, namely, kernelized attention using radial basis function (RBF) and softmax-like kernels, as well as more advanced linear attention variants such as MesaNet and DeltaNet. We theoretically derive learning algorithms for the growing layers with kernelized attention, and empirically demonstrate their promising performance and sample-efficiency on classic image classification tasks and synthetic teacher-student learning datasets. In the MesaNet/DeltaNet-inspired extensions, we show a formal connection to a recently proposed optimizer and derive highly sample-efficient optimizers for conventional fixed-size NNs. Despite open challenges, RCDL represents a promising paradigm for nonparametric machine learning.