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CLADE: Category-Level Answering from Deficient Evidence in Retrieval-Augmented Language Models
2026-11-10 17:16:06S M Rayeed, Charles V. Stewart, Yao MaAbstract
Retrieval-augmented systems often retrieve passages that are accurate and relevant but state the answer at a coarser level of granularity than the question requires. For example, a question may ask which specific disease a drug treats (familial hyperlipidemia), while the retrieved passage mentions the broader disease family (hyperlipidemia). Prior work on context sufficiency treats such coarse passages simply as insufficient. We argue that they form a distinct case, which we call resolvable insufficiency: text alone cannot settle the fine-grained answer, but the broader category it mentions maps to a short list of candidate answers in a domain ontology. When language models read these coarse, family-only passages, they exhibit a phenomenon we call Clade (Category-Level Answering from Deficient Evidence): they answer instead of abstaining or searching further. On biomedical questions from PrimeKG, family-only passages reduce abstention by 13.8 points compared to uninformative passages, and the resulting guesses are wrong about half the time. A counterfactual test confirms the cause: replacing an exact disease name with its broader family name lowers abstention by 17.4 points compared to deleting the sentence. Simple fixes like confidence filtering, scaling to a larger model, and explicitly instructing the model to abstain cannot resolve Clade, and this replicates on ecological species interactions in GloBI. Finally, we test whether an ontology can turn coarse passages into exact answers. Given the correct family, an ontology list includes the true answer in 98.5% of cases, but string matching finds that family on the page for only 19.8%. Letting the model choose the family raises this to 30.0% and end-to-end recovery from 10.0% to 26.8%. Overall, our results show when language models should stop guessing and consult an ontology, and highlight which steps of that process remain unsolved.
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URL
https://arxiv.org/abs/2611.14355
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Forward-Value Discretization: Faster Stochastic Diffusion Sampling Without High-Order Approximation
2026-11-10 16:53:27Yuchen Jiao, Na Li, Changxiao Cai, Yuting Wei, Gen LiAbstract
Stochastic diffusion samplers often require substantially more neural function evaluations (NFEs) than deterministic samplers to achieve comparable sample quality. Although higher-order discretization accelerates deterministic sampling, its benefits for stochastic sampling remains unclear. We establish matching upper and worst-case lower bounds showing that stochastic samplers with higher-order approximations remain first-order convergent, even with exact score functions. Thus, increasing the approximation order does not generally improve the convergence order. Motivated by this limitation, we introduce forward-value discretization, a training-free framework that evaluates the data/noise prediction model at a lookahead estimate constructed from previous predictions. For deterministic sampling, the resulting Forward DPMSolver (F-DPMSolver), improves sample quality at low NFE budgets through its DDIM-based variant, despite remaining first-order convergent. For stochastic sampling, we develop Forward DDPM (F-DDPM), which evaluates the model at a cleaner lookahead point adjusted for subsequent noise injection. This estimate reuses both the previous model prediction and the previously injected noise. Both variants admit first-order convergence guarantees with one model evaluation per iteration. Across CIFAR-10, ImageNet, FFHQ, and LSUN, F-DDPM consistently achieves lower FID than the evaluated stochastic baselines at matched NFE budgets. With 1.5 times as many NFEs as deterministic baselines, it achieves competitive sample quality, narrowing the practical efficiency gap between stochastic and deterministic sampling. Code is available at this https URL.
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URL
https://arxiv.org/abs/2611.14328
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UniFlow-RS: Unified Generative Prediction for Heterogeneous Remote-Sensing Tasks
2026-11-10 16:23:31Jianlong He, Liang Liao, Jing Xiao, Chaofeng Chen, Gui-Song XiaAbstract
Remote-sensing foundation models increasingly unify representations across sensing modalities and downstream tasks, yet their prediction mechanisms remain largely task-specific. Separate decoders, prediction heads, and objectives leave prediction fragmented across tasks. This naturally raises a question: can heterogeneous remote-sensing tasks share the prediction process? UniFlow-RS answers this question with a single latent flow-matching predictor shared across tasks. This requires addressing two key challenges: unifying heterogeneous prediction spaces and harmonizing joint optimization across tasks. First, we cast heterogeneous task outputs into structured visual targets in a common latent space, allowing them to be modeled by the same pretrained flow predictor. Task-dependent conditioning further accommodates differences in input configurations. Second, we optimize all tasks with a common Flow Matching objective, eliminating task-specific prediction losses. To mitigate optimization mismatch across tasks and modalities, we introduce Task-Modality Adaptive Timestep Sampling (TMAS), which dynamically allocates training timesteps according to the online learning state of each task-modality group. Using a single shared model, UniFlow-RS handles six tasks and thirteen datasets, surpassing recent task-specific state-of-the-art methods on several benchmarks while remaining competitive on the others. Beyond these benchmarks, the same checkpoint demonstrates strong zero-shot transfer across unseen datasets, task settings, occlusion conditions, and near-infrared false-color imagery, without target-specific adaptation.
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URL
https://arxiv.org/abs/2611.14281
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Breakdown of Autoregressive Multi-Step Reasoning Under Concentrated vs. Diffuse Attention Head Dropout
2026-11-10 15:29:57Karol StruniawskiAbstract
During extended wakefulness, mammalian neocortical networks experience localized periods of transient neuronal silence termed off-states. Here, we examine how analogous spatiotemporally concentrated disruptions affect sequential reasoning in autoregressive Large Language Models (LLMs). Evaluating 250 paired derivations on the GSM8K benchmark across two 8B models (Qwen3-8B and LLaMA-3.1-8B-Instruct), we contrast clustered bursts of attention head masking against budget-matched diffuse i.i.d. dropout. Concentrated bursts induce substantial reasoning degradation: at onset parameter $\mathcal{B}=0.10$ (scheduling $\sim 5.7\%$ of vulnerable head-token volume), Qwen3-8B accuracy drops from $91.2\%$ under diffuse noise to $78.8\%$ under burst dropout ($\Delta = -12.4$ percentage points [pp], exact McNemar $p = 5.5 \times 10^{-6}$), while LLaMA-3.1-8B-Instruct drops from $84.0\%$ to $76.8\%$ ($\Delta = -7.2$ pp, $p = 0.0198$). Conversely, dispersing the identical cumulative budget uniformly across generation steps via stratified quintile matching yields substantially milder degradation (retaining $91.2\%$ in Qwen and $84.0\%$ in LLaMA). In an exploratory factorial dissociation ($N=100$), single-step dropout density is associated with an acute drop in a single schedule draw ($-10.0$ pp on Qwen, $p_{\text{holm}} = 0.0425$), while multi-token continuity exhibits an inconclusive compounding trend ($-7.0$ pp, $p_{\text{holm}} = 0.1892$). Error analysis reveals that Qwen failures reflect derivation errors within formatted responses (conditional accuracy drop of $-11.4$ pp), whereas in LLaMA $22.2\%$ of discordant losses lack a final answer format line (conditional gap narrows to $-5.0$ pp). These findings demonstrate that autoregressive reasoning chains are particularly vulnerable to concentrated structural disruptions, providing empirical benchmarks for fault tolerance in transformer architectures.
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URL
https://arxiv.org/abs/2611.14209
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Cross-Border Fulfillment Center Selection via Doubly Robust Estimation and Linear Programming
Abstract
We introduce the cross-border (XB) fulfillment center (FC) Selection Model: a multi-stage optimization framework combining forecasting, doubly robust estimation, and linear programming to select optimal source FCs. For each source-destination shipping arc, the objective is to choose a subset of source-country FCs that minimize delivery time and cost while maximizing profit and shipped units under a fixed domestic network. We evaluate the model on three production-scale international shipping corridors for a large e-commerce company using 12 months of shipment data, employing both aggregate offline replay and shipment-level counterfactual nearest-neighbor matching. Across all shipping corridors, the XB FC Selection Model achieves 8.5-51.1% reductions in delivery time and 4.4-48.3% reductions in logistics cost relative to human expert-defined FC selection policies, with up to 6.5 percentage-point improvements in 5/5 target compliance (5 days, $5 USD). Pareto analysis shows the model consistently dominates single-objective ranking strategies and unconstrained LP on the efficiency-reallocation frontier, with all results statistically significant.
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URL
https://arxiv.org/abs/2611.14174
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Sim-TurboReg: Robust and Efficient Scale-Aware Registration via Tri-Scale Consistency
2026-11-10 14:23:34Zijun Zhou, Shaocheng Yan, Zhenjun Zhao, Yutong Hu, Ke Yang, Yingdong Gu, Pengcheng Shi, Jiayuan LiAbstract
Feed-forward 3D foundation models recover geometry at high fidelity, yet inference memory constraints keep them from processing long sequences natively. Current streaming systems work around this limit with sliding-window chunking, which introduces severe monocular scale ambiguity. Resolving this ambiguity requires Sim(3) registration that stays accurate and efficient under outlier contamination. We introduce Sim(3)-TurboReg, which reformulates the compatibility graph construction of TurboReg by replacing absolute distance constraints with a strict Tri-Scale Consistency (TSC) criterion. Our analysis shows that TSC suppresses both mismatching and coordinate-distortion outliers. We integrate the resulting solver into existing streaming systems and obtain high-fidelity, kilometer-scale reconstruction for feed-forward models. On controlled unknown-scale registration at 90% outliers, ours matches RANSAC accuracy with 92.5% lower median latency. In DA3-Streaming on KITTI, ours improves mean ATE RMSE by 22.2% and reduces mean end-to-end time by 16.6%. Code: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2611.14135
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Few-Step Streaming Video Generation via Condition Shifting
2026-11-10 13:01:20Deyuan Liu, Jungang Li, Kaiwen Zheng, Kai Jiang, Jintao Chen, Yaole Wang, Ming Lin, Tao Lin, Jianfei Chen, Jun Zhu, Jintao ZhangAbstract
Few-step sampling reduces the cost of streaming video generation. Distribution Matching Distillation (DMD) supervises few-step generators with a separate fake-score network, which adds training memory and computation and must track the evolving generator. We extend APEX to streaming video by using condition shifting to estimate the velocity field of the generated distribution within the generator. The original text condition selects generation, while a shifted condition selects fake estimation using the same parameters. This removes the independent fake network while retaining S2-Avatar's frozen reference teacher, Self-Replay Forcing, and perceptual regularization. We evaluate APEX on 180-second videos using three backbone evaluations per block and a one-step spatial Refiner, with a 70-case human study and a 320-case StreamAV-Bench comparison. Compared with S2-Avatar, APEX achieves higher video instruction fulfillment on the Progressive track (3.856 versus 3.831) and higher visual aesthetics across both tracks (0.641898 versus 0.632606). Human raters favor APEX most strongly on instruction following, with +30.0% net preference, followed by consistency and video quality at +4.3% each. Motion quality favors S2-Avatar, audiovisual synchronization is predominantly tied, and usability is unchanged. Separate measurements under matched training settings show 4.4% lower peak allocated GPU memory per GPU and 9.2% shorter mean iteration time than DMD.
Abstract (translated)
URL
https://arxiv.org/abs/2611.14033
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Lipschitz-Controlled Flow Matching for Robust Image Restoration
2026-11-10 12:40:17Sedjro Salomon Hotegni, Konstantin Sonntag, Paul-Christian B\"urkner, Sebastian PeitzAbstract
Flow-matching models can produce high-quality image restorations, but their learned dynamics may amplify small input perturbations and make reconstructions unstable. We introduce LipsFlower to reduce this amplification. We derive stability bounds for Flow-from-$Y$, which initializes the ordinary differential equation from a noise-perturbed observation, as well as for observation-conditioned posterior sampling, which conditions the velocity field on the observation throughout integration. Based on this analysis, we develop LipHDiT, a Lipschitz-aware hourglass diffusion transformer that serves as the vector-field architecture of LipsFlower. LipHDiT incorporates stabilized approximate spectral normalization, bounded modulation, and convex residual mixing. We also use adversarial finite-radius gain regularization at sampled interpolation states to penalize excessive sensitivity to the state and, for conditional sampling, the observation. We test both formulations on blind face restoration, measuring adversarial robustness and the perceptual quality of the reconstructions. LipsFlower reduces perturbation amplification while keeping restoration quality competitive with the evaluated baselines. These results suggest that Lipschitz-aware flow matching can improve the stability of generative image restoration.
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URL
https://arxiv.org/abs/2611.13995
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TreeAgent: Structure-Aware Memory Agent for Processing Long Hierarchical Data
2026-11-10 11:33:05Wei Gu, Weiwei Qi, Jiajun Tang, Tianhang Zheng, Bo Chen, Zhibo Zhang, Jinfei Liu, Kui RenAbstract
Practical Web applications increasingly require agents to interpret long conversations or documents. For inputs exceeding the context windows of large language models (LLMs), memory agents process segments sequentially and retain past information in bounded memory. However, they typically divide inputs into flat token segments without preserving structure or hierarchy, causing three limitations for structured data such as API responses or HTML documents. First, fixed-length segmentation can split fields of the same record, breaking their relationships. Second, segments lack hierarchical information, obscuring their structural context and positions. Third, successive memory updates may discard details of earlier sibling records needed for reasoning on later segments. To address these limitations, we propose TreeAgent, a structure-aware agent that uses the input hierarchy to guide both segment-wise context processing and access to past information. Specifically, TreeAgent constructs segments from complete subtrees and groups of adjacent sibling subtrees to preserve local structural relationships. We provide theoretical guarantees on segment counts to guide token budget selection toward matching the segment count of fixed-length segmentation. We further attach structural paths to the segments for the agent to identify their ancestors and hierarchical positions. We also introduce residual memory links from the segments of low-level children to the segments of high-level parents, enabling direct information flows beyond successive memory updates. We evaluate TreeAgent with four representative memory agents on eight retrieval and aggregation tasks. The inputs are derived from real-world application data in JSON, HTML, YAML, and JavaScript and range from 8K to 1M tokens. TreeAgent improves the average score across the four datasets for all four agents, with 3.61% performance gain on average.
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URL
https://arxiv.org/abs/2611.13946
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A 10K drone image dataset with component-level annotations for rooftop photovoltaic inspection
2026-11-10 10:52:27Penglei Zhang, Wan Zhang, Ruizhe Deng, Bo Chen, Wenhao Duan, Jinhe Zhang, Yanzhen Zhang, Qi Chen, Zhiling Guo, Haoran Zhang, Yuntian ChenAbstract
Component-level information supports rooftop photovoltaic (PV) inspection, but recognizing individual components across complex scenes remains challenging. We present PVIS-10K, a dataset of 10,065 drone images with component-level annotations covering 63,632 physical PV modules across 22 rooftop installations. The high-resolution red-green-blue (RGB) images were acquired by unmanned aerial vehicles (UAVs, also known as drones) in Ningbo, Shaoxing and Shenzhen, China. Multi-angle oblique observations capture installation-height and scale variation, dense arrangements, occlusion, shadows and reflections. Three-dimensional delineation, reprojection and image-by-image review produced 3,516,675 image-level polygon annotations, stored in Common Objects in Context (COCO) format. Station-disjoint partitions support evaluation on unseen stations within and across cities. Four supervised models and text-prompted zero-shot Segment Anything Model 3 (SAM 3) provide full-resolution benchmarks of instance matching and foreground coverage. PVIS-10K supports component localization, delineation and image-based counting for drone-based rooftop PV inspection research.
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URL
https://arxiv.org/abs/2611.13892
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Spatial-Spectral Coupling as Attention: Scaling Shape Correspondence to High-Resolution Surfaces
2026-11-10 10:31:28Bence Demeter, Kilian Ehrenreich, Amelie Kleber, Ioana Oprisiu, Philipp Spohn, Christian Wachinger, Fabian Bongratz, Lennart BastianAbstract
Establishing dense correspondence between high-resolution 3D shapes remains challenging due to the spatial--spectral coupling at the core of many functional-map methods, which grows quadratically with mesh resolution. We observe that this coupling is equivalent to a scaled dot-product attention formulation, with learned descriptors serving as queries and keys and spectral basis functions as values. This connection makes efficient attention implementations directly applicable to shape matching and an effective mechanism for scaling spatial-spectral coupling. We show that optimized quadratic attention implementations can scale modern deep functional map pipelines to large mesh sizes, improving both runtime and memory consumption, while sub-quadratic methods introduce errors, particularly for non-isometric shape pairs, where an exact coupling proves most necessary. These insights motivate \textit{Geo-FlashAttention}, which restricts the exact softmax to geometry-selected surface patches and achieves near-linear empirical scaling while preserving correspondence accuracy of exact implementations. Across six standard benchmarks and three functional-map pipelines, rigorous analysis shows that efficient couplings preserve correspondence accuracy. At $60k$ vertices, these insights reduce peak training memory from 106.08 to 5.67\,GiB, with Geo-FlashAttention being $1.5\times$ faster per training step than FlashAttention. This enables end-to-end learning for correspondence problems on large meshes such as cortical surfaces, where high resolution is required to preserve disease-relevant intricacies of the surface geometry. Project page available this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2611.13870
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STABLE: A Continual Learning Optimizer with Adaptive Drift Control
2026-11-10 10:00:14Xiaoyan Li, Lanpei Li, Massimo Coppola, Vincenzo Lomonaco, Antonio CartaAbstract
Continual learning suffers from catastrophic forgetting, leading to the loss of past knowledge and unstable adaptation across task streams. Existing approaches typically address this issue through replay buffers, architectural changes, or additional regularization terms. We argue that forgetting is a consequence of unstable learning dynamics, which may benefit from an optimizer-level solution. The main challenge is that learning dynamics are time-dependent, as demonstrated by empirical phenomena such as the stability gap. First, we formally prove that, for distance-based classifiers, bounded parameter drift guarantees bounded margin erosion under a local score-stability condition. Based on this insight, we propose STABLE, a primal-dual optimizer with adaptive drift control. STABLE enforces a drift budget by increasing the dual variable when the budget is violated and decreasing it otherwise. This results in high stability without penalizing plasticity. Experiments on long-horizon unimodal streams and CLIP-based multimodal streams show that vanilla PEFT baselines trained with STABLE match or outperform specialized continual learning methods. In the multimodal setting, STABLE further preserves zero-shot transfer, matching or exceeding dedicated multimodal continual learning methods. Code is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2611.10655
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MusicFlux: Flexible Conditioning for Text-Guided Music Generation and Editing
2026-11-10 10:00:03Teysir Baoueb (IP Paris, LTCI, IDS, S2A), Leonardo Boulitreau de Menezes Martins Marques (IP Paris, LTCI, IDS, S2A), Paraskevas Stamatiadis (S2A, LTCI, IDS), Ga{\"e}l Richard (S2A, IDS, IP Paris, LTCI)Abstract
Music editing transforms existing recordings while preserving their original attributes. Recent approaches adapt pretrained text-to-music models for tasks like inpainting, event insertion, or restoration by conditioning them with context audio. It was recently proposed for image editing to unify generation and editing by treating the source image as in-context information. However, applying this directly to music is computationally expensive due to long audio sequences. In this work, we develop MusicFlux, a text-guided music editor built on an efficient pretrained flow-matching text-to-audio model. We introduce two conditioning approaches that trade off editing performance and computational efficiency. Experiments show that both are competitive with strong baselines, with one achieving better editing quality and the other preserving the original sequence length and substantially improving computational efficiency.
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URL
https://arxiv.org/abs/2611.13834
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picoEmergencyNet : Attention-Enhanced Ultra-Small Deep Learning Model for Aerial Image Classification in Emergency Monitoring
2026-11-10 06:28:44Christos KyrkouAbstract
Real-time disaster recognition from aerial imagery requires deep learning models that operate under strict memory, compute, and latency constraints. While recent approaches achieve high accuracy, their computational cost limits deployment on resource-constrained UAV platforms. This work introduces picoEmergencyNet, an ultra-lightweight convolutional neural network designed for aerial image disaster classification. The proposed architecture combines depthwise dilated convolutions with efficient channel attention and channel shuffling to enable multi-scale feature extraction at minimal computational cost. picoEmergencyNet has 23k parameters and 7M FLOPs, requiring only 0.1 MB of model storage. Evaluated on the AIDER and AIDERv2 disaster datasets, it achieves up to 96.1% accuracy, matching or exceeding state-of-the-art lightweight models that are orders of magnitude larger. Deployment experiments on the NVIDIA Jetson AGX Orin show that, without hardware-specific optimizations, picoEmergencyNet achieves 258 FPS on GPU and 46 FPS on CPU with a 7x lower activation memory footprint compared to competing models. These results demonstrate that careful architectural specialization, rather than model scaling, enables highly efficient and accurate TinyML-ready disaster recognition for real-time UAV-based emergency monitoring.
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URL
https://arxiv.org/abs/2611.13638
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PUMA: Probability-Constrained Sequential Interaction for Forensic Localization
Abstract
Image manipulation localization must jointly exploit semantic structure and noise-sensitive forensic evidence. However, the effects of spatial correspondence, matching quality, and interaction order are often conflated. Existing semantic-forensic fusion methods provide complementary representations, but do not explicitly preserve quality-mismatch information during spatial correspondence or adaptively select the order in which information is propagated across branches. We propose PUMA (Probability-constrained Unbalanced Matching and Aggregation), which combines spatially constrained unbalanced optimal transport with position-wise convex selection between two sequential operators through a shared positional interface. Before normalization, PUMA retains signed transport-mass residuals, constructs row-stochastic cross-branch and within-noise-branch operators, and updates noise features through a gated residual path. PUMA achieves strong localization results on four test sets, with scores on COVERAGE and NIST16 exceeding the published results of the representative methods listed in our comparison. The experiments support the utility of local matching states and adaptive sequential interaction for cross-source localization and several post-processing conditions.
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URL
https://arxiv.org/abs/2611.13621
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PhyRubric: Rubric-Grounded Diagnostic Evaluation of Physical Commonsense in Text-to-Video Generation
2026-11-10 04:50:38Huiyi Chen, Weitai Kang, Shicheng Fan, Xinshuo Wang, Zhixiang Shen, Yan Yan, Philip S. Yu, Baoxin LiAbstract
As text-to-video (T2V) models are increasingly positioned as world simulators, it is essential to evaluate whether their videos conform to physical commonsense. However, existing benchmarks rely on annotators who rate each video with a single score based on intuition, yielding ratings that are subjective and do not tell which physical principle is violated. We introduce PhyRubric, a rubric-grounded diagnostic framework for evaluating physical commonsense in T2V generation. For each caption, PhyRubric constructs a caption-specific rubric before any video is seen, decomposing the caption into semantic and physical items, and derives scores from item-level verdicts grounded in timestamped evidence. We instantiate this framework as PhyRubric-Bench, which spans 34 categories across five physical domains, and scale it up to PhyRubric-Train with 3,788 videos, on which we fine-tune the open-source PhyRubric-Judge. Empirical results across ten T2V models show that physical commonsense remains far from solved, and that PhyRubric-Judge correlates more closely with human judgments than existing open-source judges while matching the frontier proprietary Gemini 4 Argon. Further analysis provides actionable insights for T2V models.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13576
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An evidence-traceable benchmark of clinical reasoning in large language models
2026-11-10 04:28:57Zhiqun Zhao (Advanced Medical Research Institute and Key Laboratory for Experimental Teratology of the Ministry of Education, Cheeloo College of Medicine, Shandong University, Jinan, China), Xiaodong Yang (Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China), Bruce Gu (Shandong Computer Science Center, National Supercomputer Center in Jinan, Jinan, China, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China), Ming Li (Department of Rheumatology and Immunology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China), Yi Fu (Information Network Management Office, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China), Song Li (Department of Medical Oncology, Qilu Hospital, Shandong University, Jinan, China), Qi Mei (Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China), Zhi Liu (Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China), Kai Zhang (Department of Breast Surgery, General Surgery, Qilu Hospital, Shandong University, Jinan, China), Kexue Fu (Shandong Computer Science Center, National Supercomputer Center in Jinan, Jinan, China, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China), Jing Wu (Department of Central Laboratory, Public Health Clinical Center Affiliated to Shandong University, Shandong University, Jinan, China), Xiaofei Lu (Department of Breast Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, China), Yule Li (Advanced Medical Research Institute and Key Laboratory for Experimental Teratology of the Ministry of Education, Cheeloo College of Medicine, Shandong University, Jinan, China), Zheng Wang (Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China), Haiquan Lu (Advanced Medical Research Institute and Key Laboratory for Experimental Teratology of the Ministry of Education, Cheeloo College of Medicine, Shandong University, Jinan, China), Xiaoming Wu (Shandong Computer Science Center, National Supercomputer Center in Jinan, Jinan, China, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China)Abstract
Benchmark scores for medical large language models (LLMs) are difficult to interpret when scoring criteria cannot be traced to clinical evidence. Here we developed Med Evidence Bench (MEB; this https URL), comprising 1,536 physician-constructed items across three levels: guideline knowledge, standardized clinical scenarios and real-world laboratory scenarios. Each criterion stating a medical claim is linked to a versioned source-text span. Across ten LLMs, the Evidence Trace Score (ETS) was 0.975-0.997 for guideline knowledge, 0.292-0.658 for standardized scenarios and 0.182-0.447 for real-world scenarios, and rankings varied across levels. For real-world scenarios, scores for identifying clinical information (mean, 0.770) exceeded those for matching evidence (0.165) or formulating recommendations (0.160). On 1,296 criterion-level judgments, automated grading agreed with the majority vote of three physicians (macro-F1, 0.930; 95% confidence interval, 0.915-0.945), although agreement was lower for evidence application. These findings from Chinese-language materials indicate that guideline recall does not ensure patient-specific evidence use and do not establish clinical effectiveness.
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URL
https://arxiv.org/abs/2611.13562
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Are Dense Rewards Worth Their Tokens in GRPO?
2026-11-10 04:25:22Vikash Singh, Weicong Chen, Debargha Ganguly, Yanyan Zhang, Mohsen Hariri, Sreehari Sankar, Wang Yang, Chaoda Song, Alexander Nemecek, Osama Zafar, Yuting Shao, Kai Ye, Biyao Zhang, Xinpeng Li, Shouren Wang, Erman Ayday, Jing Ma, Sanmukh Rao Kuppannagari, Vipin ChaudharyAbstract
RLVR trains language models to reason by rewarding correct final answers. A widely used algorithm, group-relative policy optimization (GRPO), gives every token of a solution the same credit, so dense process rewards have been proposed to credit intermediate steps, for example with a Monte-Carlo reward that scores each step by sampling continuations from it. Comparing such a reward with GRPO per training update, or vetting it with a gradient test on a single batch, leaves out what it costs: in our setting, the continuations alone take 91% of all generated tokens. We introduce a matched-compute protocol that gives every training intervention the same budget, measured in generated tokens and training GPU-hours, and tests it in preregistered studies with controls for side effects. Across 1,668 runs on six models, matching compute reverses the ranking. On Qwen2.5-1.5B, the Monte-Carlo reward leads standard GRPO per update, yet ends 14.3 percentage points lower at equal tokens and 7.8 points lower at equal training time, and none of four dense-reward variants outperforms GRPO even with as much training time. A per-token gradient test orders these rewards as training does but overestimates each by one to two points, as much as the gains it is meant to detect. In contrast, training on the prompts whose predicted success is nearest 1/2 improves accuracy at matched compute, by 1.7 points on Qwen3-0.6B and 0.8 on Qwen2.5-1.5B. These gains replicate on fresh seeds and unseen problems, and a simple rule, that the candidate prompts are predicted to fail more often than not, predicted where they appear in five of six preregistered tests. Both gains come early in training, and standard GRPO with twice the initial learning rate comes within half a point of them. We argue that training interventions should be validated at matched compute, which overturns some apparent gains and confirms others.
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URL
https://arxiv.org/abs/2611.13560
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DeRep: Learning Depth Representations for End-to-End Navigation in Cluttered Environments
2026-11-10 03:11:47Hanyu Jin, Kenji ShimadaAbstract
End-to-end navigation in cluttered environments relies on compact visual representations to support collision avoidance and goal-directed motion. Existing methods typically learn these representations jointly with the policy or through observation reconstruction, which does not explicitly specify what information the representation should preserve. Moreover, they are usually evaluated with a single downstream policy learner, although the benefit of a representation may depend on how the policy is learned. It therefore remains unclear how different representation-learning objectives affect downstream navigation performance and robustness. This work proposes DeRep, a navigation-oriented representation-learning method that combines local bird's-eye-view reachability supervision with depth reconstruction. To compare representation objectives under controlled conditions, we pretrain encoders with a common architecture and freeze them during downstream learning. We evaluate the representations using a linear policy probe, flow-matching imitation learning, and reinforcement learning with PPO. DeRep achieves the lowest linear policy probe prediction error and outperforms reconstruction alone across all six tested PPO conditions. In imitation learning, both reachability-based objectives achieve higher mean success rates than reconstruction alone, with similar mean success rates between the reachability-only and joint objectives. These results demonstrate consistent benefits of incorporating reachability supervision into depth representation pretraining across different downstream learning methods and evaluation conditions. We further deploy the PPO policy with the frozen DeRep encoder in Gazebo and on a physical quadrotor without fine-tuning, demonstrating its capability for zero-shot sim-to-sim and sim-to-real transfer.
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URL
https://arxiv.org/abs/2611.13499
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PneuGen: Task-Driven Agentic Co-Design of Soft Pneumatic Robots
2026-11-09 23:13:59Shaohong Zhong, Marco Pontin, Perla MaiolinoAbstract
Task-driven design of soft robots requires the consideration of factors such as geometry, morphology, material properties, and actuation, resulting in a large and complex design space that is challenging to explore. Computational optimisation methods leverage signals from simulation to iteratively improve designs to achieve task objectives. However, these methods typically demand high computational load for both optimisation and simulation. We present PneuGen, an agentic co-design framework for soft robots that leverages Large Language Model (LLM) agents to search over the soft robot design space efficiently. A designer agent proposes design programs from a parametric component library, and a separate critic agent analyses simulation diagnostics and recommends targeted revisions. Fitted module response models provide reusable physical priors relating geometry and pressure to deformation. Design programs are automatically validated and converted into finite-element simulations, enabling iterative evaluation. Across grasping tasks on primitive shapes and challenging objects, as well as shape-matching tasks that require multi-directional planar and 3D deformation, the proposed framework significantly outperforms prior computational optimisation baselines. Ablation experiments also highlight the utility of the critic agent and the module response model in facilitating efficient design. We additionally demonstrate the agent-generated soft robot designs in real-world shape-matching and grasping tasks.
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URL
https://arxiv.org/abs/2611.13344
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