NeurIPS 2026 论文列表

Retrieval is increasingly moving from one-shot matching toward interactive reasoning, where language agents iteratively inspect evidence, reformulate queries, and search again. Training such agents raises a credit-assignment challenge: executable actions such as queries or summaries can be directly evaluated by the retriever, while latent reasoning steps are not directly observable and only affect future executable actions. This asymmetry makes outcome-level reward assignment unreliable, as the same final reward may credit reasoning steps that did not actually shape retrieval success. We propose RICE-PO, a critic-free policy optimization framework that converts retrieval interactions into localized learning signals. RICE-PO selects high-uncertainty executable actions as anchors, evaluates local counterfactual branches using retrieval metrics, and propagates credit to latent reasoning steps only when reasoning-to-action influence is strong and future residual effects are stable. On BRIGHT and BEIR, RICE-PO consistently outperforms prompt-based agents and group-based RL baselines under the same retriever setting. These results show that the structure of agent-environment interaction itself can provide useful supervision for training reasoning-based retrieval.

The Maximal Independent Set (MIS), Dominating Set (DS), and Vertex Cover (VC) problems are fundamental combinatorial optimization problems with wide-ranging applications. We identify a key property of these problems: by finding a cut in the graph and determining the states of vertices associated with the cut, the graph can be decomposed into multiple subgraphs whose solutions are independent of each other. Therefore, we classify MIS, DS, and VC as Edge-Cut Separable Problems (ECSPs). This property enables a natural divide-and-conquer strategy, which significantly reduces the computational complexity and allows neural networks to scale to large-scale graphs that were previously intractable. Based on this property, we propose a recursive framework that leverages this property to approximately solve ECSPs, namely \ours. Our framework consists of two major components: a partitioner that identifies a cut in the graph, and a predictor that solves the cut-determination sub-problem (CDSP) to determine the states of vertices associated with the cut. By recursively applying these components, RGF generates a complete solution for the original ECSP instance. Additionally, through partial prediction, the memory consumption is significantly reduced, enabling RGF to handle real-world large graphs. Experimental results validate the advantages of RGF: the performance of our method is up to 2.13\% better than the state-of-the-art machine learning (ML)-based approach, and our approach can handle real-world large graphs with up to 11M vertices.

Image geo-localization aims to determine where a photograph was taken, a task that often requires more than recognizing visible landmarks. Human experts typically solve it through an iterative workflow: they inspect informative regions, form location hypotheses, seek external evidence, and revise their judgments as new clues appear. Existing methods only partially capture this process: direct prediction methods bypass evidence acquisition altogether, while retrieval-augmented methods introduce external evidence but usually provide limited supervision on the intermediate decisions of where to search, how to query, and how to filter noisy results. We present REVERSE, a framework that reinforces the interplay between evidence search and verification to enable multi-turn agentic reasoning. REVERSE teaches three intermediate decisions: where to look, what to query, and what evidence to trust. To support this, we construct tool-grounded trajectories with annotated region selections, search observations, and geo-informative evidence labels, and introduce process rewards for visual grounding, query utility, and evidence discrimination. An offline search cache makes retrieval observations stable and reusable during reinforcement learning, enabling dense supervision over noisy search results. With a 4B model, REVERSE outperforms strong retrieval-augmented baselines and rivals substantially larger models on Im2GPS3k and YFCC4k.

Robust 3D lane detection requires accurate metric road geometry recovery from visual inputs, yet conventional RGB-based approaches suffer from fragile lane evidence under adverse illumination, motion blur, and long-range perspective compression. Event cameras offer complementary high-temporal-resolution, high-dynamic-range structural cues that are robust to these challenges, but their sparse, motion-dependent responses cannot be directly fused into consistent 3D geometry. In this paper, we present the first attempt to introduce event cameras into 3D lane detection. To enable systematic research, we first build two multimodal benchmarks with metric 3D lane annotations: DSEC-3DLD (real-world sequences) and Ev-OpenLane (large-scale simulated sequences). We further propose RETR, a structure preserving transformer that converts complementary RGB and event observations into coherent 3D lane geometry. RETR first aligns modality consistent evidence through Reciprocal Context Flow Fusion, then preserves thin and uncertain lane structures with Uncertainty Aware Structural Consolidation, and finally decodes ordered lane hypotheses using a Geometry State Decoder with proposal conditioned initialization, reference conditioned query evolution, and diversified geometric inquiry. Extensive experiments show that RETR achieves state-of-the-art performance on both benchmarks, especially showing strong improvements in challenging lighting and complex road geometry scenarios.

Accurate short-term forecasting of residential energy load and indoor temperature is essential for home energy management systems, grid-level demand response participation, and community-level energy efficiency programs. Domain adaptation methods and transfer learning have shown promise in improving forecasting accuracy under data heterogeneity and scarcity scenarios commonly observed at the residential level. However, progress remains limited by the absence of comprehensive residential datasets: existing ones are narrow in target coverage and lack structured support for cross-domain evaluation. To address this, we introduce RESCAST-100K, a large-scale residential forecasting benchmark for studying cross-domain generalization. It exposes a configuration-driven interface that instantiates source and target domains along interpretable axes, such as geography, climate zone, wall construction, and heating equipment, supporting systematic evaluation of transfer learning, domain adaptation, and zero-shot generalization under controlled domain shifts. The benchmark covers approximately 100,000 EnergyPlus-simulated U.S. homes derived from ResStock, providing 15-minute time series of three coupled targets per home, namely total load, HVAC (heating, ventilation, and air conditioning) load, and indoor temperature, together with weather channels, HVAC setpoints, and over 40 static building covariates. Finally, it integrates five real-world residential datasets under a unified schema, supporting sim-to-real evaluation on the same tasks. We benchmark models from recurrent, attention, and MLP-mixer architectures for zero-shot performance across different domains, under missing input data conditions, for each task. Cross-attention and MLP-mixer based models consistently outperform recurrent and classical transformer-based baselines under domain shift. RESCAST-100K is intended to support the machine learning and building analytics communities in advancing cross-domain residential forecasting at the home, community, and grid levels.

Bird's-eye-view (BEV) perception has emerged as a cornerstone of autonomous driving systems, providing a structured, ego-centric representation critical for downstream planning and control. However, real-world deployment faces challenges from sensor degradation and adversarial attacks, which can cause severe perceptual anomalies and ultimately compromise the safety of autonomous driving systems. To address this, we propose a resilient and plug-and-play BEV perception method (RESBev), which can be easily applied to existing BEV perception methods to enhance their robustness to diverse disturbances. Specifically, we reframe perception robustness as a latent semantic prediction problem. A latent dynamic predictor is constructed to extract spatiotemporal correlations across sequential BEV observations, thereby learning the underlying BEV state transitions to predict clean BEV features for reconstructing corrupted observations. The proposed framework operates at the semantic feature level of the BEV perception pipeline, enabling recovery that generalizes across both natural disturbances and adversarial attacks without modifying the underlying backbone. Extensive nuScenes experiments show RESBev notably enhances BEV perception robustness against natural disturbances and adversarial attacks, with latency, parameter and memory analyses verifying its excellent robustness-efficiency trade-off.

Parkinson's disease and other gait-related neurological disorders manifest in subtle but biomechanically meaningful changes in how patients move, making computational gait phenotyping a valuable tool for clinical assessment. Yet existing latent representations of motion are typically learned from data alone and miss the biomechanical structure that gives clinical motion its diagnostic meaning, leaving them vulnerable when training cohorts are small or acquisition sites differ. We propose REINS (Riemannian Embedding for INertia-induced motion Spaces), an Euler-Lagrange-induced motion representation that uses the generalized inertia matrix of articulated systems to define a biomechanically grounded geometry on configuration space. Because kinetic energy is itself a quadratic form in velocity weighted by the inertia matrix, this metric is a mechanically determined Riemannian structure, and we learn a latent embedding of the resulting structured space. By defining the representation space itself through rigid-body dynamics rather than enforcing physics as a post-hoc penalty, REINS aligns the geometry of motion representation with the same physical laws clinicians rely on to interpret gait. We apply the framework on Parkinson's disease gait analysis, where deviation from healthy motion is measured as a geodesic distance under the inertia-induced metric, and report results on healthy-control vs.\ PD discrimination and UPDRS-gait severity correlation.

Agent harnesses—runtime scaffolds that wrap an LLM's reasoning loop with tool dispatch, verification, and persistent memory—have become the standard substrate for deploying LLMs as autonomous agents, as demonstrated by Claude Code and Codex in software engineering. In trajectory planning, however, this paradigm has not taken hold: existing LLM-based trajectory planning methods either keep the LLM in the decision loop, exceeding the millisecond-level control budget, or offload it to offline rules or training-time supervision—leaving the runtime planner non-evolving. Intermediate hybrids still lack the verification-and-consolidation loop that makes harnesses reliable. We ask: *what runtime harness would let an LLM planner meet real-time constraints while self-evolving into a deployable one?* We propose **REINS**, a harness built on three LLM–planner couplings: LLM outputs are (i) **grounded** to a learned variable-level dynamics model, (ii) **verified** via calibrated predicates and forward rollout, and (iii) **consolidated** into an indexed skill memory that serves future scenes in $\mathcal{O}(\log n)$ without re-invoking the LLM. The harness closes a self-evolution loop: familiar scenes resolve by memory lookup; novel scenes invoke the LLM, whose verified outputs enrich the memory. Without modifying LLM weights, REINS attains 98%+ compliance, 50%+ collision reduction, and 2–5 ms latency on simulated and real-world benchmarks, with LLM invocation rate monotonically declining as memory matures. Code: https://anonymous.4open.science/r/REINS-1FE3/

Risk assessment and anomaly detection in financial interaction networks often fail under regime shifts triggered by external events such as policy changes or enforcement actions—models may retain high AUC yet suffer sharp drops in average precision and calibration. Although news and regulatory filings contain actionable signals, naïvely injecting raw text or off-the-shelf LLM embeddings into temporal GNNs can introduce temporal leakage, misalignment, and unstable out-of-time ranking. We propose REGATE, a time-causal framework that integrates exogenous documents into dynamic graphs through three coupled components: (1) a schema-guided extractor that converts unstructured documents into auditable, time- and entity-aligned policy tokens with explicit confidence scores; (2) a bounded gating mechanism that fuses these tokens into temporal GNN states as a residual update, downweighting uncertain or stale evidence with a stability guarantee under direction consistency; and (3) a closed-loop retrieval adaptation module that distills the model's own routing attention into a lightweight document scorer without manual relevance labels. On established dynamic-graph benchmarks with documented regime shifts and six backbone architectures (TGN, TGAT, DyGFormer, GraphMixer, CTAN, GeneralDyG), REGATE yields up to +0.16 AP and up to 90% ECE reduction in post-shift windows on Elliptic; cross-domain pilots on credit and equity networks confirm consistent gains. Ablations show improvements stem from time-aligned semantic content rather than timestamps alone, and we report quality–cost trade-offs across open-source and commercial extractors.

RNA molecules often function through multiple experimentally observed conformational states. A central challenge is to capture ensemble diversity without sacrificing structural fidelity: samples may collapse toward averaged representative structures or drift into unsupported conformational regions. We introduce $\texttt{REFLEX}$ (\underline{R}NA \underline{E}nsemble generation calibrated by \underline{FLEX}ibility), a sequence-conditioned generator for RNA backbone-frame ensembles based on a heteroscedastic stochastic bridge on $\mathrm{SE}(3)$. During training, experimental $\mathrm{B}$-factors calibrate residue-wise bridge widths, exposing each observed conformer through local perturbation neighborhoods with residue-specific scales while keeping constrained regions sharper. A learned flexibility condition guides the bridge-field network to recover target conformer-specific frames from these calibrated noisy states. At inference, $\texttt{REFLEX}$ uses only the input sequence, requiring no MSA-subsampling heuristics. On held-out multi-conformer RNA clusters, $\texttt{REFLEX}$ achieves the best precision--recall trade-off for ensemble coverage across evaluation thresholds, while ablations indicate that the learned bridge field recovers conformer-specific basins more reliably.

Visual planning asks a model to generate the remaining steps of a procedure in natural language given a partial video context and a goal. Progress on this task is bottlenecked by annotation: clean labeled datasets are small, domain-narrow, and encode a single execution trajectory per example, even though many valid orderings often exist. Large-scale instructional video corpora such as HowTo100M offer orders of magnitude more procedural content, but supervised fine-tuning on pseudo-labels extracted from their noisy ASR narrations fails: segmentation and alignment errors propagate into training, and the resulting supervision is still single-trajectory. We identify a key asymmetry. Extracting clean step labels from noisy video is hard, but verifying whether a generated step sequence is temporally grounded in ASR transcripts is comparatively cheap and scales to millions of videos via precomputed text embeddings. We exploit this asymmetry in RECIPE, which uses grounding quality as a reward signal for Group Relative Policy Optimization (GRPO), turning the noisy corpus into a verification signal rather than a labeling source. The framework applies uniformly to two input configurations of the planner: a Socratic pipeline in which a frozen vision-language model rewrites the video into a textual history fed to the planner, and a Video configuration in which the planner consumes video tokens directly. It applies equally to annotated and weakly supervised training regimes. We evaluate on seven procedural benchmarks using a reference-based LLM-as-judge protocol that scores generated plans across six procedural-quality criteria. RECIPE-RL improves over the base checkpoint at every scale we test (0.5B, 3B, 7B) and on every benchmark, with macro-accuracy gains of +7 to +8 points in-domain at every scale and up to +16 points zero-shot. It substantially outperforms supervised fine-tuning on both annotated and pseudo-labeled continuations (the latter actually *degrades* the base checkpoint), and is robust to fully replacing human annotations with VLM-derived pseudo-traces. Plugging RECIPE-RL into the proposal stage of VidAssist [14] improves over the strongest zero-shot baseline in our comparison at every horizon on the Visual Planning for Assistance benchmark, and a diversity analysis on COIN shows that RECIPE-RL preserves the generation variety that supervised fine-tuning collapses.

Single-step inorganic retrosynthesis is evaluated by whether a complete precursor set is recovered, yet many data-driven systems first rank individual precursors and then rely on threshold or count heuristics to assemble sets. This mismatch is especially severe when the true set size is unknown: individually plausible precursors can combine into incomplete, over-complete, or otherwise wrong near-miss sets. We reformalize the task as variable-size set-level ranking and introduce RECIPE, a target-conditioned framework that separates precursor recall from final set-level scoring. The Precursor Candidate Generator learns formula-level compatibility to build a high-recall precursor pool, while the Complete-Set Reranker compares variable-size candidate precursor sets directly. On the Retrieval-Retro year-split benchmark, the generator improves Combo@20 from 69.00 to 72.75. Compared with Retrieval-Retro, the reranker improves Combo@1 by 11.30 points to 71.70, Combo@20 by 20.82 points to 89.82, and Combo MRR by 14.14 points to 77.43. A preliminary 20-case out-of-distribution evaluation shows the same direction of improvement. These results suggest that set-level prioritization remains important after strong precursor recall, and that optimizing ranked complete precursor sets can reduce the inspection burden in inorganic synthesis planning.

Long chain-of-thought reasoning improves vision-language models (VLMs), but it also exposes a temporal grounding failure: as generation unfolds, models may drift from image evidence while reinforcement learning still provides only a final-answer reward. We study this gap through visual revisit, a temporally localized reactivation of image evidence during reasoning. Across three VLM backbones, revisit peaks are predictive of correctness and causally linked to performance: masking them degrades accuracy more than masking non-peaks, while injecting revisit-like peaks into failed rollouts partially restores correct answers. Control analyses show that this signal is not explained by raw attention strength, uncertainty, logit margin, or hidden-state norm. We propose RECAP, a GRPO-compatible training method that turns detached revisit traces into a credit-assignment signal. RECAP uses revisit-conditioned advantage estimation, dynamic discounting, and gated value prediction to propagate final rewards through visually important reasoning steps, without extra rewards, additional rollouts, or inference-time changes. Across Qwen2.5-VL-7B, InternVL3-8B, and LLaVA-OV-7B, RECAP consistently improves over GRPO and VPPO, yielding $+6.1$--$+6.5$ HallusionBench gains over the SFT base while preserving reasoning and text-only performance. Results on larger LoRA-tuned models, Chinese zero-shot benchmarks, visual perturbations, and human evaluation further demonstrate robust improvements in image-grounded reasoning.

Latent diffusion models (LDMs) achieve high-fidelity generation by moving diffusion from pixels to a compact latent space, but standard tokenizers are optimized for compression, not robustness under diffusion-time corruption. We formulate latent tokenization as diffusion-aware joint source--channel coding and diagnose standard LDM pipelines as missing an explicit channel-coding step. A diffusion-aware rate--robustness functional admits an explicit spectral characterization in $G^\top G$, yielding the Redundancy-Balance Principle: optimal redundancy is not uniform, but aligns with semantic sensitivity and equalizes marginal integrated-risk reduction across active directions. The resulting optima take weighted tight-frame and water-filling forms; isotropic bottlenecks are minimax-suboptimal under heterogeneous sensitivity, and a group-conditioned extension identifies rare groups as capacity-limiting under a fixed latent budget. We instantiate the theory as RB-LDC (Redundancy-Balanced Latent-Diffusion Coding), a lightweight tokenizer modification that inserts a learned coding layer before diffusion. Six controlled experiments validate the theorem sequence up to $k{=}1024$, with a $53.5\times$ alignment gap, $659\times$ minimax gap, and real-image VAE learnability via Adam recovery of the KKT spectrum with relative error below 0.5 percent. On SD-VAE and VA-VAE at ImageNet-256, RB-LDC outperforms isotropic uniform redundancy in all 20 perturbed metric cells over a 15-SNR grid; VA-VAE block-local obtains $-15.40$ average pFID and $-105.79$ peak pFID at $\log_{10}\rho{=}+1$, while preserving clean rFID parity (gap $-0.0001$). RB-LDC turns latent tokenizer design into a reliability problem for diffusion models.

Camera sensor RAW data offers intrinsic advantages for object detection, including richer bit depth, preserved physical information, and freedom from image signal processor (ISP) distortions. However, varying exposure conditions, spectral sensitivities, and bit depths across devices introduce substantially larger domain gaps than sRGB, making sensor-agnostic generalization a fundamental challenge. In this study, we present a physics-guided global-local tone mapping framework for sensor-agnostic RAW object detection. By factoring sensor-induced variations into a global tonal correction and a spatially adaptive local color adjustment, both driven by RAW distribution priors, our framework enables a single network to train jointly across heterogeneous sensors. To further support cross-sensor generalization, we construct a physics-based RAW simulation pipeline that synthesizes realistic sensor outputs spanning diverse spectral sensitivities, illuminants, and sensor non-idealities. Extensive experiments across multiple RAW benchmarks, covering bit depths from 12 to 24, demonstrate state-of-the-art (SOTA) performance in single-dataset, mixed-dataset, and challenging robustness settings.

Multimodal large language models (MLLMs) have advanced rapidly on image and video understanding, but their ability to reason over paired aerial electro-optical and infrared (EO-IR) videos remains underexplored. Such videos contain small targets, sparse temporal evidence, changing viewpoints, and modality-dependent cues that are poorly captured by RGB-centric benchmarks. We introduce RAVEN-Bench (Reasoning over Aerial Videos with EO-IR aligNment), a paired EO-IR video QA benchmark for low-altitude aerial multimodal understanding. RAVEN-Bench contains 48 temporally aligned EO-IR video pairs and 576 human-verified four-way multiple-choice questions. It supports EO-only, IR-only, and paired EO+IR evaluation, and uses hierarchical capability levels and structured question groups to diagnose cross-modal gain, modality reliance, consistency, and reasoning coherence. We evaluate representative frontier and open-weight MLLMs, showing that aggregate performance can overestimate reliable EO-IR reasoning. RAVEN-Bench provides a focused testbed for assessing whether progress on RGB video benchmarks transfers to aerial cross-spectral understanding.

Despite rapid advances in text-to-image (T2I) generation, current models often struggle with rare, compositional, and culturally grounded concepts where training data is sparse and visual priors are weak or absent. Increasing model capacity or prompt detail alone does not consistently resolve these challenges, particularly in long-tail settings where attribute grounding remains unstable due to attention skew, semantic collapse, and needle-in-a-haystack retrieval dynamics. We introduce RAVEL, a training-free framework that improves rare concept generation, context-driven image editing, and self-correction by integrating graph-based retrieval-augmented generation (RAG) into T2I pipelines. Unlike prior RAG and LLM-enhanced approaches that rely on visual exemplars, static captions, or implicit model knowledge, RAVEL leverages structured knowledge graphs to retrieve targeted compositional, symbolic, and relational context, enabling more precise grounding even in the absence of visual priors. To further refine generation quality, we propose SRD, a self-correction module that iteratively updates prompts using multi-aspect alignment feedback, improving attribute accuracy, narrative coherence, and semantic fidelity. Our framework is model-agnostic and is compatible with leading diffusion and autoregressive models, including Stable Diffusion XL, Flux, DALL-E 3, GLM-Image, and Janus-Pro. We conduct extensive evaluations across three newly proposed benchmarks: MythoBench, Rare-Concept-1K, and NovelBench, and show consistent improvements over SOTA baselines across perceptual, alignment, and LLM-as-a-Judge metrics, while also demonstrating stable and interpretable refinement trajectories for rare concepts across diverse model families. These results position RAVEL as a simple and robust approach for controllable and interpretable T2I generation in long-tail domains. Our code is available at: https://anonymous.4open.science/r/ravel-54E5/

When humans see a bird, they recognize far more than just ``bird'' --- they see a head, wings, and talons, a structured assembly of reusable parts that can be identified across every bird they have ever seen. We ask whether a self-supervised visual model can discover the same compositional structure on its own. To this end, we propose $RATS$ ($R$egister $A$ttention $T$ransformer$s$), which decomposes the classification token into $N$ learnable register tokens that route patch information through an $L{\to}N{\to}N{\to}L$ bottleneck. The $N$ registers are hard-partitioned across $H$ attention heads, structurally isolating each subset in an independent projection subspace. Without auxiliary losses or part annotations, each register spontaneously specializes into a semantically coherent visual part. RATS surpasses all baselines by an average of +12 mIoU on five segmentation benchmarks, and demonstrates stronger dense prediction on ADE20K (+1.11 mIoU) and COCO (+0.2 AP$^{\text{m}}$). The visual dictionary extracted from the trained registers also shows signs of part-level consistency and semantic proximity across related categories. Our results suggest that RATS may provide a useful architectural prior for structured and interpretable visual representation learning.

Full-dataset NeRF evaluation can require training and evaluating thousands of scenes, making exhaustive comparison expensive even when the benchmark is fixed. We introduce RASS, a risk-audited protocol for constructing compact NeRF benchmark subsets with explicit fidelity criteria. Given full-dataset per-scene logs, RASS repeatedly samples candidate subsets, checks whether their global metric means and score distributions match the full benchmark within user-defined tolerances, and recommends the smallest evaluated subset size whose Wilson lower confidence bound exceeds a target pass probability. We instantiate RASS on 3,522 Nutrition5k-derived food scenes using Zip-NeRF logs for the formal audit and PSNR, SSIM, LPIPS, and Kolmogorov-Smirnov (KS) distributional tests as fidelity criteria. The released 48-scene balanced operating point, RASS-48, is the lowest-cost evaluated screening subset that passes our declared global-fidelity audit and is intended for low-cost early ablations and rapid screening. A stricter target pass probability, $p_{\min}=0.20$, selects RASS-96, which we recommend as the compact reporting subset: it also passes joint fidelity audits over three and four view-synthesis methods. We also evaluate alternative subset generators, including uniform random sampling, farthest-first selection, and facility-location diversity sampling, and find that the RASS audit can validate different generators under the same fidelity contract. On DL3DV, a re-declared contract certifies 64 of 140 scenes for four methods under proportional allocation. We release the RASS-48 and RASS-96 scenes, configurations, descriptors, regime labels, seeds, audit scripts, and permitted logs to enable reproducible, compact NeRF evaluation. Project page: https://amughrabi.github.io/RASS/.