Relation
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On Maximum Clique and Maximum Consensus Formulations in Point Cloud Registration
Abstract
Maximum clique and maximum consensus are widely used formulations for robust point cloud registration in robotics. Maximum consensus seeks one transformation that aligns as many matches as possible, while maximum clique selects matches through pairwise distance tests. Maximum clique is often used as a substitute for or an approximation to maximum consensus, with empirical evidence suggesting a close relationship. However, how closely the two formulations agree is not fully understood. We study this relationship theoretically. We show that a maximum clique can have arbitrarily large alignment error and be arbitrarily larger than the maximum consensus set at any fixed positive tolerance, and derive geometric conditions that bound this error. When rotation is fixed, we derive sharp error bounds and tests that exactly characterize maximum consensus for the translation subproblem. Finally, we study group-$k$ consistency, which tests groups of $k$ matches, and determine the smallest group sizes that guarantee alignment error bounds independent of point cloud scale and geometry. These are $d+1$ for rotations and translations and $d+2$ when reflections are allowed, in dimensions $d\ge 2$. Interactive examples and proof verification using the Lean theorem prover are available at this https URL.
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URL
https://arxiv.org/abs/2610.27275
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When Models Know but Can't Linearly Say: Relational Encoding Limits Linear Vulnerability Classification in Code LLMs
2026-10-19 20:26:32Rui Melo, Andre Catarino, Claudia Mamede, Corina Pasareanu, Rui AbreuAbstract
Large language models can detect vulnerable code, yet the form of vulnerability encoding in their representations remains unclear. We analyze 8 code models spanning 6.7B-15B parameters on C programs using three datasets (DELTASECOMMITS, SVEN, and PRECISE-BUGS), comparing two diagnostic tasks on the same representations: identifying what patterns the code instantiates, and determining whether it is vulnerable. Models encode the first with high fidelity. Under the studied paired C-code datasets and linear-probing setup, vulnerability information is more accessible as a paired relational signal-whether code A is riskier than code B-than as a globally separable categorical label. This structural difference has implications for classification: the model reliably ranks which code is riskier but standard classifiers on isolated samples achieve limited accuracy because vulnerable and secure samples form overlapping distributions in representation space.
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https://arxiv.org/abs/2610.27257
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Target-Aware Curation of Hallucination Stress Tests for Large Language Models
2026-10-19 20:20:00Jiho Lee, Bingquan Zhang, Lin Ting Liang, Soroush Vosoughi, Weicheng MaAbstract
Hallucination stress tests can yield similar average scores across language models even when different prompts elicit hallucinations in each model. We develop a procedure that iteratively edits source-grounded seed prompts to expose hallucinations in a target model. An LLM editor revises each prompt in response to the target's answer and a judge's category-specific score and explanation. In the main setting, the target also receives feedback on its previous response. Experiments cover 500 seeds from seven datasets and ten topics, with intrinsic and extrinsic hallucinations evaluated across open-weight and closed-source models. Editing substantially increases judge-rated severity over the seeds. In the baseline configuration, retained responses fall in the judge's strong-hallucination bin in 77.2% of runs targeting extrinsic hallucination and 74.2% targeting intrinsic hallucination. Later editing rounds account for more than half of the mean score increase. Among 219 prompts that pass model-assisted source-validity checks, 89 elicit responses containing unsupported claims. Across five models, 200 curated prompts yield similar average severity scores, although pairwise Spearman correlations between prompt-level scores are only $\rho=0.0387$ to $0.1897$. Curated prompts can be reused across models, but their diagnostic value depends on source validity and the failures they expose in each model.
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URL
https://arxiv.org/abs/2610.27247
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Predicting Linear Relational Decodability Without the Relation
2026-10-19 20:07:21Jonas Rohweder, Adrian M\"ulthaler, Farid Mehri, Sebastian Gerstner, Hinrich Sch\"utzeAbstract
Linear relational embeddings (LREs) approximate a language model's subject-to-object mapping with an affine function. Their faithfulness varies enormously across relations, from near zero for person_father to the ceiling for country_capital_city. We investigate the factors that contribute to this variation and demonstrate that it can be significantly predicted from a readout that is not aware of the underlying relation. To do so, we propose a novel form of the Jacobian lens, the entity lens. Whereas the Jacobian lens is estimated across all tokens and concepts, the entity lens EL_t is trained to predict what the model will output for an entity of type t under the neutral prompt "{} is known for"; e.g., we train ELs for countries and persons. After estimating an entity lens, we measure how highly it ranks the model's answer for a relation without using any relation-specific information in estimation and prediction. Across 28 relations of the LRE dataset on Olmo-3-7B this relation-agnostic score correlates with LRE faithfulness at \rho = 0.7355 (p < .01). This suggests that linear decodability is largely a property of what a subject's representation already carries, rather than of the relation being decoded. Moving the unit of analysis from the relation to the individual fact, we conclude that new relation datasets and analysis methods are needed to address several problems in existing datasets, most prominently semantically weak first tokens (e.g., most predicted universities' first token is "University") and reference competition (e.g., the LLM confuses obscure actor names with competing non-actor entities that have the same last name). Our full pipeline is available at this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2610.27241
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Language Guidance for Supervised Vision Training: An Empirical Study of Generalization
2026-10-19 19:43:04Shruthi Gowda, Nikita Jain, Elahe Arani, Bahram ZonoozAbstract
Deep neural networks have achieved remarkable success on vision benchmarks, yet they continue to struggle with many generalization challenges. Supervised vision training relies on one-hot labels, which provide limited information about semantic structure and shared attributes between classes. This limited supervision can leave visual representations vulnerable to distribution shifts, spurious correlations, texture bias, adversarial perturbations, and forgetting in sequential learning settings. We study whether pretrained language models can serve as lightweight auxiliary supervision for vision training without requiring paired image-text data, prompt engineering, or contrastive objectives. Specifically, we evaluate two forms of language guidance, Explicit Language Guidance (ExLG) and Implicit Language Guidance (ImLG). We conduct a comprehensive evaluation across six generalization regimes, including in-distribution, out-of-distribution generalization, shortcut and spurious correlation resistance, texture and shape bias, adversarial robustness, and continual learning. Our analyses show that the two mechanisms have complementary strengths, with explicit guidance consistently benefiting in-distribution, low-data performance, and continual learning retention, while implicit guidance is often more useful in shortcut-sensitive settings and under stronger adversarial perturbations. Importantly, both are lightweight and add minimal parameters and training overhead. The analyses characterize when language-derived structure helps supervised vision training and provides a practical roadmap for using off-the-shelf pretrained models from another modality as auxiliary supervision.
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URL
https://arxiv.org/abs/2610.27208
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Learning to Be Different: Component Specialization for Compositional Diffusion Policies
2026-10-19 19:29:58Alec Laprevotte, Haonan Chen, Xiaoshen Han, Yilun DuAbstract
Diffusion policies with linearly composed scores have emerged as a promising approach to multitask robotic learning by combining separate factors to enable modular, task-wise adaptation, compositional generalization, and interpretable functional decomposition. However, current training objectives do not encourage factor specialization, often resulting in uninterpretable denoisers whose composed prediction is an average over identical copies, undermining their architectural premise. To address this, we propose a drop-in score-decorrelation auxiliary loss, motivated by projective composition theory. Our approach encourages specialization by penalizing overlap directly between component noise predictions, with little architectural change or computational overhead. We demonstrate across multiple benchmarks that our decorrelation loss consistently reduces measurable double-counting between components, improves task decomposition, and produces behaviorally meaningful specialization without compromising multitask performance. Website: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.27194
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MoSAIK: Motion Search with Artificial Intelligence in K-space
2026-10-19 18:49:59Oscar Dabrowski, Abijith Jagannath, Fr\'ed\'eric Grouiller, S\'ebastien CourvoisierAbstract
Object: Motion artefacts remain a major limitation of routine two-dimensional T2-weighted turbo spin-echo (TSE) brain MRI, reducing diagnostic confidence and often necessitating repeated acquisitions. MoSAIK (Motion Search with AI in K-space) retrospectively estimates in-plane rigid-body motion directly from standard multi-coil TSE k-space. Without a motion-free reference, external navigators, or additional acquisitions, MoSAIK infers motion from k-space self-consistency violations and incorporates predicted parameters into model-based, motion-aware reconstruction rather than synthesizing corrected images. Materials and Methods: Selected complex coil data were represented as log-magnitude and phase channels. A decoupled convolutional embedding and Transformer encoder estimated phase-encoding-line-indexed rotation and two-dimensional translations. Training used motion-free clinical acquisitions with simulated motion; four held-out subjects were evaluated. Component-wise errors assessed estimation; SSIM and PSNR in silico and entropy focus criterion (EFC) in vivo assessed reconstruction. Results: Motion estimation errors were low for both rotation (MSE $<$ 0.2 deg$^2$) and translation (MSE $<$ 0.2 pixels$^2$), with Pearson correlations of approximately 0.96 and 0.94, respectively. In every in-vivo acquisition, more than 75% of slices showed information gain relative to uncorrected reconstructions. MoSAIK reduced visible artefacts while preserving anatomical detail. Discussion: Transformer-based k-space motion-trajectory estimation enables fast retrospective correction of standard clinical TSE brain MRI.
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URL
https://arxiv.org/abs/2610.27156
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Ontic state recovery through prediction
2026-10-19 18:34:43Ping Zhang, Quang-Huy Nguyen, Yingtie Lei, Wei-Lun ChaoAbstract
We encounter a system through its responses, yet its state concerns how the system is. Predicting further responses promises knowledge beyond what is immediately observed. What that knowledge tells us about the system's state depends on what we mean by a state in the first place. We give this notion a model-relative meaning: a state is the complete response profile over a specified query universe, and configurations agreeing on every query are regarded as the same state. From this perspective, a learned state is understood through the distinctions between these profiles that it retains. Prediction concerns selected observations; recovery concerns the profile as a whole. We examine their relationship through query ambiguity, state-space geometry, and the choice of prediction targets. Synthetic systems with known response laws and neural recordings provide complementary settings for this investigation. The aim is to clarify how prediction can become a way of knowing a system, and what it would mean for a learned state to preserve the state being studied.
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URL
https://arxiv.org/abs/2610.27139
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SciFigBench: A Benchmark for Scientific Method Figure Understanding
2026-10-19 18:00:07Lijia Wu, Zheng Li, Yuxuan Liu, Jingwen Zhang, Penghai Zhao, Xiang LiAbstract
Scientific method figures often provide researchers with a clear visual entry point into a paper's methodology and core contributions, yet their dense layouts and complex information flows are difficult for Vision Language Models (VLMs) to interpret. Existing benchmarks predominantly focus on coarse-grained tasks, such as context-based retrieval or holistic comprehension, overlooking the fine-grained content and inter-module relations within complex methodological diagrams. To address these overlooked aspects, we introduce Scientific Figure Benchmark (SciFigBench), a benchmark for evaluating core capabilities required for scientific method figure understanding. Specifically, it targets three capabilities: dense content perception, visual relation comprehension, and information-flow tracing. For this purpose, we curate 4,352 scientific method figures from real-world research papers and construct 40,670 QA pairs to operationalize these capabilities as measurable question-answering tasks. All annotations are generated by dedicated pipelines and then manually verified. We further propose SciFigCue, a training-free visual prompting module that externalizes arrow-induced dependencies as visual relational evidence for method-figure reasoning. Experiments across open-source and proprietary VLMs show that even strong models remain substantially limited in visual relation comprehension, while SciFigCue improves performance across several tasks without parameter updates. The code and related resources are available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.27087
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BUMP: Body-driven Unified Multi-object Physics for HOI Synthesis
Abstract
Generating human-object interactions (HOIs) with multiple objects is challenging because the resulting motions arise from different underlying causes. Human motion reflects semantic intent, intentionally manipulated objects must remain coordinated with the human, while surrounding objects move primarily as physical consequences of contact and collision. We present BUMP, a text-driven framework for multi-object HOI generation based on a simple principle: learn intentional motion and simulate physical consequences. Our method first generates full-body human motion from text and scene context, then predicts trajectories for a small number of intentionally manipulated driver objects conditioned on the human motion. We then obtain responder object motions through physics simulation, allowing the framework to handle varying numbers and configurations of surrounding objects without expanding the learned trajectory output space. To address the scarcity of paired full-body multi-object HOI data, we combine heterogeneous supervision from human-only motion, full-body HOI, and hand-centric manipulation data augmented with plausible body motion. We further introduce outcome-conditioned interaction editing, which infers a local modification of the causal driver object trajectory from a desired responder object behavior without requiring an explicit target driver object trajectory. Experiments demonstrate strong performance on both single-object and multi-object HOI benchmarks. Qualitative results showcase diverse scenarios involving inter-object relationships and edits, highlighting the advantage of our unified model formulation.
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URL
https://arxiv.org/abs/2610.27066
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Discrete Neural Interlingua for Ontology-Agnostic EHR Modeling
2026-10-19 17:58:11Tong Ding, Caiwei Tian, Dongmin Bang, Ming Y. Lu, Sophia J. Wagner, Andrew Zhang, Rowland Pettit, Long Phi Le, Faisal MahmoodAbstract
EHR foundation models promise scalable clinical prediction across health systems, yet their deployment is constrained by a tokenization bottleneck. The same clinical concept can appear as different ontology codes, local identifiers, synonyms, or free-text descriptions, which standard EHR tokenizers treat as unrelated symbols. A model trained on MIMIC-III with a closed-vocabulary tokenizer can use only 13% of eICU events. We introduce MEDIATOR, a discrete ontology-agnostic tokenizer that maps any clinical concept expressed as text into a shared vector-quantized codebook, turning EHR harmonization into open-vocabulary tokenization without manual mappings or a fixed vocabulary. Trained with synonym supervision and guided by enriched concept descriptions, code co-occurrence, and relation graphs, MEDIATOR collapses equivalent expressions while organizing distinct concepts in a medically meaningful discrete space. Used as the token interface of an EHR Transformer, MEDIATOR improves mean AUPRC over the strongest prior tokenizer by 4.0 points (8% relative) on 20 MIMIC-III tasks and by 8.6 points (29% relative) in zero-shot transfer to 14 eICU tasks, where it uses all events. The transfer gain combines coverage and representation, as MEDIATOR remains the best tokenizer under identical inputs (+1.0 and +1.9 points on operational and subcohort-mortality tasks). Same-encoder controls show that the text encoder drives in-domain accuracy, while the learned objectives of MEDIATOR drive transfer robustness and entity-linking quality. MEDIATOR also enables transferable attribution across hospitals.
Abstract (translated)
URL
https://arxiv.org/abs/2610.27047
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Decomposed Relation Extraction from Aviation Accident Narratives: Evaluating Transformer-Based Safety Knowledge Discovery under Relational Sparsity
2026-10-19 12:25:20Aziida Nanyonga, Mustafa Tahsin Yilmaz, Saad Ezzini, Ugur Turhan, Graham WildAbstract
Relation extraction from aviation accident narratives is challenging because genuine safety relations are sparse among possible concept pairs. This study evaluates a decomposed framework separating relation classification from concept identification. A benchmark of 300 National Transportation Safety Board (NTSB) narratives containing 2,524 unique concept-text nodes was expanded into 21,038 ordered candidate pairs, with only 248 (1.18%) representing RESULTS_IN or CONTRIBUTES_TO relations. A TF-IDF Linear Support Vector Machine (SVM) and weighted RoBERTa classifier were developed using an independent graph-annotated resource with hard-negative sampling and frozen before sealed evaluation. Development Macro-F1 scores of 0.4738 (SVM) and 0.4809 (RoBERTa) declined to 0.2741 and 0.2420 under exhaustive evaluation, with Matthews correlation coefficients of 0.0128 and 0.0197. False positives dominated: the SVM recovered 96 reference relations but generated 7,018 false positives, while RoBERTa recovered 125 but generated 9,824. Post-test diagnostics revealed overlapping RoBERTa confidence distributions and false positives even within valid relation-category directions. Complementary end-to-end SmolLM2 evaluation identified missed and unsupported relations alongside malformed outputs. These findings demonstrate that known concept endpoints do not guarantee reliable relation extraction and that sampled-negative development performance may poorly reflect exhaustive evaluation. Controlling false positives and managing candidate-space sparsity remain essential for aviation safety knowledge extraction.
Abstract (translated)
URL
https://arxiv.org/abs/2610.27013
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Evaluating Tibetan Speech Recognition Across Dialects and Adaptation Regimes: A Multi-Granularity Analysis
2026-10-19 09:44:36Jacob Moore, Sheng LiAbstract
Tibetan speech recognition is usually evaluated with character or syllable error rates because the writing system does not mark word boundaries. These metrics can rank systems differently and respond differently to adaptation failures. We combine character error rate, syllable error rate, and segmented word error rate in one evaluation framework. We use dictionary-based, neural, and language-model-based segmentation and compare their boundaries with expert annotations. We evaluate five ASR architectures on Lhasa Tibetan, then test zero-shot transfer and target-corpus adaptation on five additional corpus subsets. We also vary LoRA rank and training-data size to examine adaptation failure. The results show that a lower character error does not always lead to a lower syllable or segmented-word error. These ranking differences persist across corpora. Metric correlations also weaken in some unsuccessful adaptation settings. Higher LoRA ranks improve syllable and segmented-word scores but remain worse than full fine-tuning in the tested range. A standardized phonological representation provides another view of spelling errors. These findings support evaluation at several linguistic levels and motivate better segmentation and adaptation methods for low-resource ASR.
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URL
https://arxiv.org/abs/2610.27002
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HierDTA: Hierarchical Structure-Aware Representation Learning for Drug--Target Affinity Prediction
2026-10-19 09:17:26Huangchao Xu, Baohua Zhang, Qian Liu, Zhong Jin, Yi DuAbstract
Drug--target affinity (DTA) prediction plays an important role in computer-aided drug discovery, where reliable computational models can help prioritize compound--protein pairs before experimental validation. A key challenge in DTA prediction is to effectively mine multi-level molecular information from drug structures while accurately modeling the binding-site environment of target proteins. To address these issues, we introduce HierDTA, a hierarchical structure-aware framework for DTA prediction. \textcolor{black}{HierDTA represents each drug as an atom--motif heterogeneous graph and introduces an augmentation module that propagates functional-motif semantics to the corresponding atoms. For proteins, kinase-domain structures are converted into residue interaction graphs. The resulting atom and residue representations are integrated using cross-attention to capture pair-specific relationships, while molecule fingerprints and pretrained ESM-2 embeddings provide complementary global information. Compared with state-of-the-art methods, HierDTA achieves superior performance on the Davis dataset across all metrics and attains the highest \(r_m^2\) of 0.791 on KIBA. It also exhibits strong generalization under cold-start settings, particularly on KIBA, where MSE is reduced by 21.7\% in the unseen-protein scenario. Ablation study and interpretability analysis supported the contributions of the hierarchical augmentation and interaction components.} Case studies further showed that HierDTA provided early enrichment of candidate bioactive compounds. These results indicate that HierDTA is an effective framework for affinity prediction and target-specific virtual screening.
Abstract (translated)
URL
https://arxiv.org/abs/2610.27001
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Curvature as a Baseline: Monocular Pose Measurement of Cylindrical Objects with Dual Colour Stripe Markers
Abstract
Thin cylindrical objects offer little space for planar markers. We propose a dual-colour-stripe marker that uses the known curvature of an object to improve pose measurement with a single camera. The boundary between two colours provides a long centre line, while dots and printed edges supply features at different depths. We analyse how this depth separation improves the estimation of rotation about the cylinder axis, or roll. For a camera-facing marker at 15 cm, the Cramér-Rao lower bound on roll standard deviation is 2.00°for a flat marker, 0.38°for a single curved marker and 0.21°for a wrap-around marker. Monte Carlo roll RMS errors are within about 10% of the corresponding standard-deviation bounds. A first-order relation connects the sagitta of the curved marker to the roll sensitivity provided by a stereo baseline. The analytical and simulation results explain how curvature improves monocular measurement and provide a basis for designing marker geometry.
Abstract (translated)
URL
https://arxiv.org/abs/2610.26003
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REST: Reliability-Guided Spatial Transcriptomics Supervision for Gene Expression Prediction from Histopathological HE-Stained Images
2026-10-19 06:07:51Lanfeng Zhong, Xiangyu Sun, Guixuan Xu, Rongzhao Zhang, Jie Xu, Shaoting Zhang, Guotai WangAbstract
Histopathology is central to cancer diagnosis, but morphology in Hematoxylin and Eosin (HE)-stained images does not directly reveal the molecular states that characterize disease heterogeneity and inform tumor characterization, prognostic assessment, and biomarker discovery. Spatial Transcriptomics (ST) measures spatially resolved gene expression, providing molecular information within tissue architecture, yet differences in assay coverage and spot informativeness make paired HE--ST observations unequally reliable for representation learning. We propose Reliability-Guided Spatial Transcriptomics (REST) supervision with Biological Program Bank (BPB), a program-structured and reliability-guided ST-supervised training framework for predicting molecular information from H\&E images. REST constructs BPB from curated signature, pathway, and cell-type gene sets to provide a shared structure for ST encoding, reliability estimation, and molecular reconstruction. Guided by BPB, Spot-Reliability Contrastive Alignment (SRCA) measures the reliability of each spot from assay-level program coverage and spot-level activity dispersion, and reweights HE--ST alignment accordingly. Biology-Informed Program Reconstruction (BIPR) further uses learnable program queries to reconstruct member-gene expression from histological morphology. REST's training corpus comprised 209 samples (407,475 spots across 16 organs); evaluation used 70 disjoint samples (230,056 spots across eight organs) for gene-expression, pathway-activity, and cell-signature prediction. REST achieved the highest Macro correlation among state-of-the-art ST-supervised training methods and outperformed existing pathology foundation models. Code is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.26988
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DexGraspFlow: Topology-Aware SE Flow Matching for Cross-Embodiment Dexterous Grasp Generation
2026-10-18 13:09:00Bin Zhou, Boyu Ma, Xinyu Zhou, Jiahua Dong, Chuhao Zhou, Yizhou Liu, Hao Wan, Jianfei YangAbstract
Differences in hand geometry, joint parameterization, and kinematic topology pose challenges for cross-embodiment dexterous grasp generation. However, spatial relations among links alone are insufficient to fully capture the hand's kinematic topology. We propose DexGraspFlow, a conditional link-level $\mathrm{SE}(3)$ flow-matching framework integrating geometric relations and kinematic topology. The framework encodes local object geometry, link geometry, and current poses. A dual-stream DiT models link--link coordination and link--object interaction, while attention biases derived from kinematic-tree distances provide explicit structural information for modeling evolving geometric interactions. The model jointly learns translational and rotational velocity fields, integrates them to generate link poses, and recovers joint configurations through inverse kinematics. Additionally, inference-time palm-orientation guidance enables controllable grasp generation without retraining. Across three robotic hands and 30 object--hand pairs, DexGraspFlow achieves a 95.33\% average simulation success rate, with an average generation latency, including inverse kinematics, approximately 49\% lower than that of a state-of-the-art baseline. Further experiments demonstrate zero-shot adaptability to seven finger-removal variants and palm-orientation control. A model trained on demonstrations for the deployed hand achieves a 91\% success rate over 100 real-robot grasping trials, supporting the framework's applicability to physical grasp execution.
Abstract (translated)
URL
https://arxiv.org/abs/2610.26962
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A Multi-granularity Interaction Network for Relational Triple Extraction
Abstract
Relational triple extraction identifies entity relations from raw sentences without depending on entity type information. The performance of traditional methods is seriously degenerated by ``unseen'' entities, because these methods can't build contextual dependencies relevant ``unseen'' entities. This paper addresses this challenge by proposing a multi-granularity interaction network that leverages word-word, entity-relation, and head-tail interactions to capture deep semantic dependencies. By incorporating multiple iterations, the multi-granularity interaction network is effective to refine the representations corresponding to head and tail entities. It enhances the global semantic features associated with relational triples, aiding in the remediation of triples that may have been overlooked or omitted in earlier steps. Strong and consistent performance is achieved by our method on two rearranged datasets, with a significant improvement in identifying relational triples, including those involving ``unseen'' named entities. For example, our model improves F1-score by 3.1%/20.5% over previous approaches on the NYT-R/WebNLG-R datasets in the Unseen metric. Code: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.26952
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Multilingual Brain Decoding Reveals Shared Cortical Semantic Geometry
Abstract
Human languages differ markedly in lexical and syntactic form yet can convey similar semantic content. The neural basis of this cross-language semantic commonality remains unclear. Here we developed a multilingual brain decoding framework and analysed brain recordings from 227 participants spanning Chinese, English and French. We found that cortical semantic representations exhibited consistent semantic geometry relationships across languages and individuals, revealing a shared cortical semantic geometry. Importantly, the cross-language consistency of cortical semantic geometry increased from sensorimotor to higher-order association cortex, forming a shared semantic geometry gradient closely aligned with the principal cortical functional gradient. Our work establishes generalizable multilingual brain decoding and identifies stable cortical semantic geometry as a neural basis for semantic commonality across languages.
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URL
https://arxiv.org/abs/2610.26927
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MOSS: A Production Framework for Monitoring and Diagnosing Semantic Failures in Search and Recommendation Systems
2026-10-16 21:26:06Alina Nesen, Prathyusha Kanmanth Reddy, David Relyea, Rohit Avinash Mujumdar, Samaneh Karimi, Bo Shen, Jordan HashemiAbstract
Detecting semantic failures in production recommendation and search systems is hard: behavioral metrics lag by days, manual review does not scale, and most LLM evaluation frameworks are designed for offline, fixed datasets rather than continuous operational monitoring. We present Model Sanity Service (MOSS), a production framework that addresses this operational gap by embedding LLM-based relevance scoring into a recurring monitoring loop with targeted sampling, strategy-aware prompting, structured alerting, and diagnostic follow-up prompting that categorizes failures into actionable root causes. MOSS has been running in production at a large U.S. retailer for over six months across recommendation, search, and offer surfaces, and has surfaced two confirmed serving defects. For one of these, a backfill blending defect, MOSS quantified the impact of the proposed fix offline as a 76.4\% relative reduction in alert rate. On a 498-pair human-annotated recommendation benchmark, frontier LLM judges achieve higher F1 and AUC point estimates than non-LLM baselines (internal production embeddings and SBERT), with the largest gains on strategy-dependent tasks requiring intent-level relational reasoning. The same judge, score scale, and threshold transfer to a 466-pair search benchmark spanning 21 queries with only a surface-specific prompt adaptation, achieving strong F1 and AUC without retuning. These results show that a single LLM monitoring framework can provide useful, actionable quality signals across multiple product discovery surfaces in a large-scale e-commerce setting.
Abstract (translated)
URL
https://arxiv.org/abs/2610.26902
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