Artificial Intelligence · Cross-listed from cs.CL
LCoT-GV: Graph Attention Networks for Verifying Long Reasoning Chains in Large Language Models
Bérénice Jaulmes and Mehwish Alam
Large
Reasoning Models produce Long Chains-of-Thought (LCoTs) which involve breaking down the problem into smaller
reasoning steps before reaching the conclusion. However, these steps often contain contradictions, unsupported inferences, or irrelevant steps, even when the final answer is correct. We propose Long Chain-of-Thought Graph Verifier (LCoT-GV), a graph-based framework that represents LCoTs as
reasoning graphs. Each node in the graph represents a
reasoning step and the edges encode semantic and logical relations. A Graph
Attention Network is then trained to predict chain-of-thought correctness from the
reasoning graph. We construct a new graph-oriented verification
dataset from multiple
reasoning benchmarks for question answering in various domains. The results show that our method is competitive with the most similar approaches.
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Artificial Intelligence
MedAgent-R1: Faithfulness-Aware Reinforcement Learning for Evidence-Grounded Medical Reasoning
Jiangwang Chen, Chenghao Zhang and Hengxing Cai
When medical AI systems hallucinate clinical
reasoning, the consequences extend beyond incorrect answers: fabricated justifications that superficially reference retrieved evidence can mislead clinicians into unsafe treatment decisions. Medical
reasoning agents must therefore produce not only correct answers but also faithful justifications that clinicians can verify against cited evidence. We identify a systematic failure mode in RL-trained
retrieval agents: outcome-only rewards improve accuracy while degrading faithfulness, a phenomenon we term confident
hallucination. The
agent learns to answer from parametric
memory and backfill plausible but unsupported justifications; citation fabrication rates rise from 16.5% to 31.8% even as accuracy improves by 5 points over the supervised baseline. We address this with a faithfulness-gated reward design: accuracy credit is conditioned on evidence grounding via a hard gate, complemented by
retrieval validity and conciseness signals that close exploitation paths unique to agentic
retrieval. The resulting system, MedAgent-R1, reduces citation fabrication from 31.8% to 4.7% and raises evidence completeness from 58.7 to 82.6 while maintaining 75.1% accuracy, with 13.2-point gains on HealthBench Safety. Under the same agentic
retrieval setup, MedAgent-R1 outscores GPT-4o on faithfulness-specific dimensions (Factual Support 4.55 vs. 4.25; Overclaiming 4.40 vs. 4.15) while remaining below GPT-4o in overall accuracy, suggesting that explicit faithfulness training yields evidence-grounding gains not achieved by scaling alone.
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Artificial Intelligence · Cross-listed from cs.LG
CoMPASS: Collaborative Molecular Property Prediction via Adaptive Small-Large Model Synergy
Wentao Li, Jiangjie Qiu, Yijun Li et al. (+2 more)
Accurate molecular property prediction requires both statistical reliability and chemical
reasoning. Graph neural networks can be calibrated directly on labeled assays but remain limited by the coverage of their training data. Large
language models (LLMs) can compare molecular evidence and articulate chemical rationales, yet are unreliable as standalone quantitative predictors. The central challenge is therefore to determine when an LLM should influence a calibrated model and by how much. Here we present CoMPASS, a
retrieval-calibrated framework for small-large model collaboration. CoMPASS retains a graph
attention network (GAT) as the predictive anchor, retrieves locally relevant training molecules, provides
attention-grounded evidence to an LLM, and converts its proposal into a bounded correction through an agreement-aware gate. Across six classification and two regression
benchmarks, CoMPASS improves the GAT anchor in regions of correctable uncertainty while limiting LLM intervention in high-confidence regimes. Ablations show that the gains arise from validation-calibrated
retrieval and bounded fusion rather than prompting alone. These results suggest that generative
reasoning should augment calibrated prediction through evidence-grounded, controlled corrections rather than direct output replacement. Code is available at https://github.com/littlepeachs/CoMPASS.
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All authors Wentao Li, Jiangjie Qiu, Yijun Li, Leyi Zhao, Xiaonan Wang
Artificial Intelligence
HiRS-Agent: A Hierarchical Multi-Agent System for Reliable Long-Horizon Remote Sensing Task Solving
Boyang Mu, Zhiwei Wei, Mugen Peng et al. (+1 more)
Recent advances in large
language models and
multimodal models have pushed remote sensing (RS) processing from simple perception models to agentic systems designed to tackle complex, long-horizon RS tasks. However, existing systems often rely on monolithic decision-making frameworks, which fail to accommodate the multi-stage, interdependent nature of RS tasks. This centralized approach leads to challenges such as unstable task execution, incorrect tool usage, and error propagation across stages. To address these issues, we propose HiRS-
Agent, a hierarchical multi-
agent system for long-horizon RS task solving. HiRS-
Agent adopts a two-level collaborative architecture: the Manager Layer handles dynamic routing, step-level verification, replanning, and termination control, while the Specialist Layer organizes domain-specific tools according to the RS workflow and is responsible for subtask
reasoning and tool execution. To further enhance the system's capability, we introduce a two-stage supervised tuning strategy and a verification-guided hierarchical reinforcement learning stage to jointly optimize coordination and tool-use policies. Experiments on Earth-
Agent Benchmark and ThinkGeo show that HiRS-
Agent substantially improves long-horizon tool-use capability and final-task correctness, demonstrating the effectiveness of structured multi-
agent collaboration for reliable RS
agents. The code is publicly available at https://github.com/IntelliSensing/HiRS-
Agent.
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All authors Boyang Mu, Zhiwei Wei, Mugen Peng, Wenjia Xu
Artificial Intelligence · Cross-listed from cs.CV
Joonki Min, Chaeyun Kim, Hyungwook Choi et al. (+4 more)
Multimodal Large
Language Models (MLLMs) are increasingly deployed in multi-image scenarios requiring complex
reasoning across visual contexts. However, current MLLMs remain fundamentally limited by object
hallucination-generating plausible yet factually inconsistent descriptions about objects. Existing
benchmarks, designed primarily for single-image settings or providing only high-level multi-image assessments, cannot systematically diagnose how visual complexity and
reasoning demands trigger
hallucination. To address this gap, we introduce MIOH, a fine-grained multi-image object
hallucination benchmark that systematically evaluates object
hallucination across four foundational tasks (existence, counting, attribute, position) through three multi-image
reasoning patterns (comprehensive, comparative, selective) under three controlled
adversarial pressures (visual context scale, perceptual difficulty, contextual bias). Through evaluation of 29 models, we reveal that even state-of-the-art systems like GPT-5 and Gemini-2.5-Pro exhibit distinct failure patterns across different
reasoning patterns and tasks. Our evaluation reveals that
hallucination stems not merely from perceptual failures but from integration-stage limitations when maintaining object representations across multiple images. MIOH provides a controlled framework for analyzing multi-image object
hallucination and serves as a critical evaluation tool for developing more reliable
multimodal AI systems.
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All authors Joonki Min, Chaeyun Kim, Hyungwook Choi, Yejin Kim, Kihyun Kim, Yohan Jo, Joonseok Lee
Artificial Intelligence
PyKEEN-NSX: A Modular Framework for Static, Dynamic and Schema-Aware Negative Sampling in PyKEEN
Ivan Diliso, Nicola Fanizzi and Claudia d'Amato
Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs (KGs).
Embedding models are trained relying on both positive and negative samples of triples. However, since KGs generally contain only positive assertions, negative samples are artificially generated through negative sampling strategies, ranging from simple random corruption to more sophisticated approaches that exploit structural, semantic, or
embedding information. The design and implementation of advanced negative samplers remains challenging, as most popular Knowledge Graph
Embedding (KGE) libraries provide support only for basic strategies and lack a unified framework for developing more advanced and customized solutions. To address this gap, we introduce PyKEEN-NSX, an extension of PyKEEN, the popular KGE framework, that provides a modular engineered abstraction for negative sampling. The proposed architecture separates the generation of candidate negative pools, conditioned on an explicit context, from the selection strategy, enabling the development and integration of static, schema-aware and dynamic approaches within a consistent framework. Based on this abstraction, we implement six negative samplers, while remaining fully compatible with existing PyKEEN workflows and pipelines. As a proof of concept, we study negative availability across four
datasets, showing that constrained pools frequently fall below the requested number of negatives, so that the encoded criterion is to a large extent replaced by the random fallback that supplements them.
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Artificial Intelligence
Geometry of Divergence: Tracking Hidden-State Trajectories for Adaptive Multi-Turn Reasoning
Jie Liang, Zhengxin Yu, Hamid Nasiri et al. (+1 more)
LLM
agents need to sustain goal-consistent
reasoning across long multi-turn interactions under strict resource constraints. However, as the multi-turn context accumulates, it can destabilize the underlying LLM's internal representation of task-relevant information from earlier turns, blurring the boundary between constructive
reasoning and representation drift. We formulate multi-turn
reasoning as a hidden-state trajectory of the underlying LLM that is characterized via two complementary signals: temporal
curvature that captures the directional consistency of turn-to-turn updates, and variance slope which measures the expansion or contraction of the exploration space. Across four tasks and three underlying LLMs, we observed that these
geometric signals distinguish between correct and incorrect episodes prior to completion. We further decompose each episode into three-action chains formed from four actions (Read, Write, Respond, Transfer) and show that separability is action-dependent, with different signals distinguishing various chain patterns. Our experiments demonstrate that trajectory geometry can identify critical turns in the
reasoning process, increasing task success rates on $\tau$-Bench from 24.1% to 39.6% while reducing
token cost by 11.2%.
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All authors Jie Liang, Zhengxin Yu, Hamid Nasiri, Peter Garraghan
Artificial Intelligence · Cross-listed from cs.CL
BiG-SURE - Bipartite Graph for Semantic Uncertainty and Reliability Estimation of LLMs
Debarpan Bhattacharya, Malay Phadke and Sriram Ganapathy
Reliable uncertainty estimation is a crucial requirement for deploying large
language models (LLMs) and
vision-
language models (VLMs) in safety-critical settings, especially when the model parameters are not accessible (black-box). We propose BiG-SURE, an uncertainty
estimator based on cross-temperature semantic agreement. The method samples low-temperature responses as stable semantic anchors and high-temperature responses as probes under meaning-preserving input transformations. It then constructs an anchor-probe Bipartite Graph (BiG) using NLI-based entailment scores and defines confidence through the normalized squared spectral energy of this
matrix, with uncertainty given by its complement. This bipartite graph-based Semantic Uncertainty and Reliability Estimation (SURE) score measures whether high-temperature probes remain semantically aligned with the model's stable low-temperature belief or not. We evaluate BiG-SURE on text QA,
multilingual QA, and
multimodal QA tasks across multiple model families. In these experiments, BiG-SURE improves average abstention AUROC over prior black-box uncertainty
estimators, while remaining simple, unsupervised, and applicable to black-box model settings.
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Artificial Intelligence · Cross-listed from cs.CL
GMTS: Gradient Magnitude-based Token Selection Improves RLVR Training for LLM Reasoning
Outongyi Lv, Yuanwei Zhang and Xiaoqun Zhang
Reinforcement learning (RL), particularly RL with Verifiable Rewards (RLVR), has recently emerged as a central paradigm for enhancing large
language models' (LLMs)
reasoning abilities, demonstrating remarkable effectiveness across
reasoning tasks. Recent studies suggest that high-
entropy tokens play an exceptionally important role in model training, since training with only the highest 20%
entropy tokens yields significant performance gains. However, why such high-
entropy tokens are beneficial remains insufficiently understood. In this work, we find that although high-
entropy tokens within one answer tend to correlate with large gradient magnitude,
entropy alone fails to consistently reflect
token importance across different answers, considering the variations in the answer-level reward signals. Based on this observation, we introduce the Gradient Magnitude-based
Token Selection (GMTS) method to quantify
token importance, which leverages the
entropy-gradient connection to approximate gradient-magnitude rankings for
token selection. We find that training on the top 20%
tokens ranked by GMTS consistently outperforms
entropy-based
token selection across three
reasoning domains and various model sizes, suggesting that GMTS provides a more fine-grained estimate of
token contribution for RLVR training.
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Artificial Intelligence · Cross-listed from cs.CV
Cost-efficient Active Learning for Referring Image Segmentation and Grounding
Junbeom Hong, Seonghoon Yu, Hyung Rok Jung et al. (+2 more)
Collecting natural-
language referring expressions along with region annotations, such as masks or boxes, is a major bottleneck in visual grounding (VG), as annotators must write descriptions that distinguish target regions from visually similar ones. We tackle this by formulating active learning (AL) for VG under the realistic setting where only raw images are available without accompanying text. Since ground-truth text is unavailable, sample selection must estimate which images contain ambiguous regions that would require discriminative referring expressions. To address this, we generate auxiliary region-text pairs using foundation models, and introduce Referred Region Ambiguity, a new acquisition function that measures whether the model's confidence collapses onto a single region or disperses across multiple candidates. It allows our method to prioritize images with strong cross-region competition, which are more informative due to their visual ambiguity. We also design a referring-expression annotation interface that helps annotators quickly focus on writing discriminative
language with a few clicks. Experiments on RIS and REC
benchmarks show that our AL framework consistently outperforms several AL baselines, while a user study shows up to 1.6X faster description labeling of ours.
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All authors Junbeom Hong, Seonghoon Yu, Hyung Rok Jung, Sundong Kim, Jeany Son
Artificial Intelligence · Cross-listed from cs.CL
Hidden Threat in Synthetic Data: Covert Targeted Bias Injection through Benign Text
Minkyung Cho, Jihyo Kim, SeungWoo Song et al. (+4 more)
Synthetic data is increasingly used to train large
language models (LLMs), yet its security implications remain poorly understood. Prior work on subliminal learning suggests that models can inherit behavioral traits from seemingly unrelated training data. In this work, we investigate whether such mechanisms can be exploited to inject targeted social biases into aligned models through semantically benign synthetic data. We construct a pipeline in which a misaligned teacher model generates filtered synthetic
datasets across domains such as creative writing and code generation, which are then used to fine-tune aligned student models. Our experiments show that benign-looking synthetic data can act as a covert channel for transmitting targeted biases while largely preserving the student model's general task capabilities. These results reveal a previously underexplored security risk in synthetic data-driven LLM training pipelines and highlight the need for improved safeguards. As one possible step toward this goal, we suggest that log-linearity-based scoring may provide a useful signal for screening seemingly benign synthetic data.
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All authors Minkyung Cho, Jihyo Kim, SeungWoo Song, Junghun Yuk, Minjoon Kee, Hoyun Song, KyungTae Lim
Artificial Intelligence · Cross-listed from cs.CL
Reading the News: Adapting Large Language Models to Swedish Journalism Through Continued Pre-Training
Lukas Borggren, Jenny Kunz and Marco Kuhlmann
Large
language models are increasingly capable in general, but their utility can remain modest in niche or understudied areas. One approach to address this limitation is to specialise existing models through additional training on target-domain corpora. In this work, we investigate such continued pre-training for adapting large
language models to Swedish journalism, using a high-quality
dataset that we curate from millions of news articles. To evaluate the adaptation efficacy, we also construct a novel domain-specific
benchmark that covers six editorial tasks. Through full and parameter-efficient fine-tuning across two model sizes, we find that continued pre-training yields benefits in the target domain, but only when paired with experience replay to mitigate forgetting. We observe consistent enhancements in the models' generation quality and factual knowledge, but not their proficiency in discriminative tasks. Exploring a training-free method to facilitate instruction following, we see further improvements, but exclusively for models trained with low-rank adaptation. Crucially, we demonstrate the importance of targeted evaluation in the adaptation process, as an existing Swedish
benchmark largely fails to capture the models' in-domain performance gains.
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Artificial Intelligence · Cross-listed from cs.IR
Generative Retrieval for E-commerce: Jointly Learning Embedding and Codebook with Same Product Cluster
Songtao Fang, Zihao Xu, Shaowei Wei et al. (+2 more)
With the development of large
language models (LLMs), generative
retrieval is becoming increasingly important in e-commerce scenarios. Current mainstream approaches typically use a two-stage training strategy: first train a product
embedding model, and then learn a codebook that maps
embeddings to product IDs. This cascaded approach suffers from two major issues: (1) error accumulation-if the
embedding model in the first stage produces biased representations, the codebook in the second stage cannot correct these errors, degrading final
retrieval performance; and (2) codebook learning relies solely on product
embeddings and lacks modeling of query-to-product and product-to-product interactions. As a result, products belonging to the same cluster may be assigned inconsistent IDs by the codebook, further hurting
retrieval accuracy. To address these problems, we propose a novel method that jointly trains the
embedding model and the codebook, and incorporates same product cluster information as an additional supervision signal. Experimental results demonstrate that our method significantly improves e-commerce
retrieval performance while simultaneously enhancing both
embedding and codebook learning.
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All authors Songtao Fang, Zihao Xu, Shaowei Wei, Jin Zhang, Zhuojun Wang
Artificial Intelligence · Cross-listed from cs.CV
DiffSAC: Diffusion-guided Sampling for Consensus-based Robust Estimation
Chang Nie, Guangming Wang, Zhe Liu et al. (+1 more)
Robust estimation is a core computer
vision task frequently tackled using sample consensus. However, traditional methods suffer from inefficient sampling as they struggle to identify effective minimum sets before hypothesis evaluation. To address these challenges, we propose a novel
Diffusion-guided Sampling for Consensus-based Robust Estimation (DiffSAC) framework. DiffSAC introduces a
diffusion model to learn the distribution of effective minimum sets. It refines the confidence for each data point, indicating whether it belongs to a good minimum set, rather than ranking the data points as in previous work. This significantly reduces the need to process numerous bad sets. To constrain the refinement direction,
geometric features are incorporated as conditions within our
diffusion model. Consequently, DiffSAC outputs a small number of high-quality minimum sets, enabling identification of the best hypothesis via consensus evaluation. Notably, compared to previous works requiring evaluating over ten thousand hypotheses, DiffSAC achieves state-of-the-art performance with only dozens, significantly boosting efficiency. Extensive experiments across five classic computer
vision tasks demonstrate the superiority of DiffSAC. The
diffusion model's sampling accelerators enable real-time operation, and DiffSAC can be used as a plug-and-play module to improve existing sample consensus methods.
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All authors Chang Nie, Guangming Wang, Zhe Liu, Hesheng Wang
Artificial Intelligence
Automated Testing of LLM-Based Post Hoc Explainers Using Model Checking as an Oracle
Dennis Gross and Helge Spieker
Large
language models (LLMs) are used as post hoc explainers of sequential decision-making policies, producing natural-
language explanations of why an action was chosen. However, LLMs often generate plausible but incorrect statements, and no existing approach systematically tests whether such explanations are faithful to the underlying environment. Two classic software testing challenges stand in the way: there is no oracle for the correctness of an explanation, and the test inputs, natural
language queries about a policy's behavior, lack the structure needed for systematic test case generation. We address both. Probabilistic model checking provides the test oracle, computing exact reference results against which LLM answers are graded automatically. A taxonomy of post hoc query categories structures the input space around the environment-level facts from which policy explanations are composed; test cases generated from it are prioritized by question-specific diagnostic difficulty scores. Across seven MDP environments, the testing separates three open-weight LLMs: a
reasoning model passes 85% of test cases, a mid-size model 70%, and a 1B model falls below the random baseline, while prioritization surfaces significantly harder cases than random selection. Our results indicate how trustworthy LLM-generated explanations are in model-free settings, where the same LLMs are used but no oracle exists to verify them.
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Artificial Intelligence · Cross-listed from cs.LG
Collapsibility of Performance Metrics in Clinical Predictive AI
João Matos, Ben Van Calster, Richard D. Riley et al. (+2 more)
Background: Population level assessments of predictive artificial intelligence (AI) can conceal performance disparities across subgroups.
Fairness evaluations commonly rely on performance analyses across subgroups. However, some performance metrics are non-collapsible, meaning that the overall population performance value does not equal the weighted average of subgroup specific values. Objective: To examine the collapsibility properties of commonly reported performance metrics in predictive AI, with a focus on the area under the receiver operating characteristic curve (AUC, also known as c-statistic). Methods: We investigate the collapsibility of 15 performance metrics, either by expressing each metric as a linear combination of its stratum specific values or, where non-collapsible, by providing a counterexample inspired by Simpson's paradox as a formal disproof. Results: Five performance metrics (AUC, calibration intercept, calibration slope, expected calibration error, and Nagelkerke R^2) are shown to be non-collapsible, and ten (O:E ratio, logloss, Brier score, accuracy, F1-score, true positive rate, true negative rate, positive predictive value, negative predictive value, and net benefit) are shown to be collapsible. The AUC is shown to be non-collapsible because it decomposes into within- and cross-group AUC terms when subpopulations coexist, such that its overall value may fall outside the range of subgroup specific AUCs. Conclusions: Non-collapsibility of performance metrics has important consequences for reporting, model appraisal, and
fairness evaluation. It can generate spurious differences between subgroup and overall performance, which may mislead
fairness evaluations. Explicitly acknowledging and reporting the collapsibility properties of performance metrics improves both the interpretability and transparency of
fairness assessments.
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All authors João Matos, Ben Van Calster, Richard D. Riley, Paula Dhiman, Gary S. Collins
Artificial Intelligence
TuringLLM: Efficiently Scaling Foundation Models Toward Physical AI
Yuheng Zhang, Yizhao Wang, Da Zhu et al. (+19 more)
We present Turing-20B-A2B, a 20B-parameter Mixture-of-Experts
language model that activates approximately 2B parameters per
token, designed for long-context and latency-sensitive physical AI applications. The model adopts
Quantile Routing in a dynamic top-k configuration, enabling
token-adaptive expert allocation while maintaining balanced expert utilization and a controlled average compute budget. During deployment, we further apply capacity-constrained routing to
prompt prefill for more regular and efficient expert execution, while retaining dropless routing during pretraining. Turing-20B-A2B also employs a hybrid
attention architecture that combines Lightning
Attention with a small number of full-
attention layers for efficient long-context modeling. The model is pretrained with a progressive three-stage curriculum and extended to a native context length of 128K through continued pretraining, with further inference-time extension to 512K using YaRN. Despite its compact active-parameter budget, Turing-20B-A2B achieves, at the base-model stage, overall general capability exceeding Qwen3-8B Base and approaching Qwen3.5-9B Base, while maintaining strong long-context performance and favorable prefill-latency scaling. These results demonstrate an effective balance among model capability, long-context scalability, and practical inference efficiency.
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All authors Yuheng Zhang, Yizhao Wang, Da Zhu, Hua Zhou, Yue He, Jiahui Hu, Shaman Tang, Hanlin Chen, Yuhua Wei, Anhua Liu, Shuang Su, Rui Xin, MingYuan Wang, MingHao Li, HaoJie Yang, Siqi Liu, Jianlei Zheng, WeiChao Huang, Qiman Wu, Hang Zhang, HongGou Yang, Xianming Liu
Artificial Intelligence · Cross-listed from cs.LG
Q-Strata: Hierarchical Bit Allocation for Mixed-Precision Quantization of Mixture-of-Experts LLMs
Deokjae Lee, Sihun Chu and Hyun Oh Song
Mixed-precision quantization (MPQ) assigns a different bitwidth to each linear layer of a large
language model (LLM) to minimize the quantization-induced quality loss under a fixed budget, but Mixture-of-Experts (MoE) models contain these layers in every expert of every MoE block, so the allocation space grows far larger than in a dense model. Existing methods either allocate within each block under a uniform per-block budget, or allocate across blocks through an additive proxy, and neither directly optimizes a model-level objective over the choices that couple the blocks. We propose Q-Strata, a bi-level allocator that ranks within-block assignments with a cheap proxy and allocates across blocks with a model-level objective evaluated on the assembled quantized model. Its inner stage caches a Pareto frontier of candidates per block over finely spaced budgets, leaving the outer stage to set one budget per block instead of a bitwidth for every linear layer. With the search reduced to one budget per block, the outer stage optimizes this model-level objective directly, capturing the inter-block coupling that additive proxies miss. On Mixtral-8x7B-Instruct, Qwen1.5-MoE-A2.7B, and DeepSeek-V2-Lite, Q-Strata consistently achieves lower WikiText2 perplexity than uniform-bitwidth GPTQ and the state-of-the-art MoE MPQ methods MxMoE and GEMQ in the low-bit regime. The code is available at https://github.com/snu-mllab/Q-Strata/tree/main.
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Artificial Intelligence
AdaPath: Query-Adaptive Path-Finding via Path-Bank for Multi-Hop Implicit Biomedical KGQA
Jun Hyeong Kim, Dongki Kim, Yinhua Piao et al. (+1 more)
Path-finding over knowledge graphs has become an effective way to ground LLM
reasoning on multi-hop questions. However, biomedical QA introduces two distinct challenges that general-domain methods are not designed for: (i) queries do not expose intermediate
reasoning and can be answered through multiple valid pathways, and (ii) biomedical knowledge graphs are densely connected, so path-finding methods easily take wrong turns. To address these challenges, we propose AdaPath, a path-finding framework that retrieves query-adaptive meta-paths from Path-Bank, which captures both query semantics and biomedical knowledge graph structure. AdaPath provides the missing cues in biomedical queries while effectively
pruning dense knowledge graph neighborhoods during multi-hop
reasoning. We further release BioStrat-QA, a biomedical KGQA
benchmark that stratifies multi-hop queries by how much intermediate
reasoning they expose. Across biomedical KGQA
benchmarks, AdaPath consistently outperforms baselines, sustaining meaningful path-finding even when multi-hop queries expose less surface information. The source code is available at https://github.com/Jun-Hyeong-Kim/AdaPath.
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All authors Jun Hyeong Kim, Dongki Kim, Yinhua Piao, Sung Ju Hwang
Artificial Intelligence · Cross-listed from cs.IR
Preference Shapes Relevance: Cross-component Hierarchical Semantic Alignment for Personalized Generative Retrieval
Gaoming Zhang, Angqing Jiang, Jianchun Song et al. (+4 more)
Generative
Retrieval (GR) has emerged as a promising paradigm by mapping queries directly to Semantic IDs (SIDs) with powerful representation capabilities for candidate items. However, existing SIDs derived solely from item content create a semantic gap, failing to align dynamic query intents with static item representations. Furthermore, current generative paradigms rarely model user behavior sequences and are always bottlenecked by the high inference latency of beam-search autoregressive decoding. To address these challenges, we propose $\textbf{C}$ross-component $\textbf{H}$ierarchical semantic $\textbf{A}$lignment for $\textbf{P}$ersonalized generative
retrieval ($\textbf{CHAP}$), a novel personalized GR framework from a hierarchical perspective. First, we design a Hierarchical Semantic
Alignment module to align query's latent space with item's quantization path and synchronize multi-granular semantics. Second, we construct a personalized GR framework that models user behavior by synergizing discrete SIDs for structural guidance and continuous representations for fine-grained semantic refinement. Notably, we introduce a Residual Cascading Generation mechanism to restrict the costly multi-step
Transformer Decoder to a single-pass inference, boosting inference throughput while mitigating information loss. Extensive experiments on three public
datasets, one proprietary industrial
dataset, and online A/B tests demonstrate CHAP's superiority, validating the effectiveness and practical value of our approach. The code is publicly available at https://github.com/zzzgm/CHAP.
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All authors Gaoming Zhang, Angqing Jiang, Jianchun Song, Kena Qi, Dayao Chen, Wei Lin, Defu Lian
Artificial Intelligence
GarmentWeaver: Schema-Aware Structured Synthesis for Multimodal Sewing Patterns
Yinwen Lu, Weihao Luo and Yueqi Zhong
Multimodal Sewing pattern generation aims to infer executable sewing patterns from design cues such as sketches and textual descriptions. As an interpretable and simulation-compatible representation, sewing patterns are particularly valuable for digital garment creation. However, existing methods often model garment specifications as flat long sequences, which entangles garment structure with detailed parameters and leads to redundant components, inaccurate local details, and poor simulation compatibility. In this paper, we present GarmentWeaver, a schema-aware framework for
multimodal Sewing pattern generation. GarmentWeaver constructs compact hierarchical targets by activating garment-relevant structural branches and predicts executable Sewing patterns in a structured manner. Specifically, we introduce a schema-aware target construction strategy, build the generator on top of a pretrained
vision-
language model for
multimodal garment understanding, and impose feasibility-aware regularization to encourage structurally valid and simulation-compatible outputs. Extensive experiments show that GarmentWeaver produces more accurate and more executable sewing patterns than strong baselines, while also yielding better simulation results. These findings demonstrate the effectiveness of schema-aware structured generation for reliable
multimodal Sewing pattern prediction.
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Artificial Intelligence
Designing an Auditable LLM-Supported Workflow for Qualitative Thematic Analysis
Nadia Jul Jeldtoft and Tariq Yousef
Large
Language Models (LLMs) offer new possibilities for scaling qualitative analysis, but existing applications often provide limited methodological transparency regarding how qualitative methods are translated into computational procedures. This paper presents an auditable and
privacy-preserving computational operationalization of inductive and latent Thematic Analysis (TA). This paper first derives five design principles from the methodological requirements of TA and the conditions introduced by LLM-based inference: preserving interpretative context, maintaining traceable relationships between empirical material and analytical outputs, representing analytical constructs and
reasoning explicitly, constraining LLM inference to interpretative tasks, and enabling
privacy-preserving local deployment. Second, it presents a proof-of-concept for a two-phase workflow that operationalizes these principles by combining interpretative LLM inference with deterministic procedural control to generate codes, analytical justifications, themes, and theme descriptions while preserving explicit links to the source material. Third, it proposes an evaluation framework combining structural comparison with human-led TA and independent expert assessment of analytical quality. The evaluation is conducted on semi-structured Danish interview transcripts. and the results shows that the workflow produces code-level outputs with coverage broadly comparable to human annotations and highly rated analytical justifications, while generating a more compressed thematic structure characterized by fewer and broader themes. The findings demonstrate the feasibility of auditable LLM-supported TA through a modular workflow designed to scale to larger
datasets, accommodate different LLMs, and support transfer across research domains, with domain adaptation primarily requiring adjustments to the prompting strategy.
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Artificial Intelligence
DiffPDE: Masked Diffusion Language Models as PDE Solver
Wenxuan Guo, Yuyang Hong, Lubin Fan et al. (+4 more)
Existing approaches for synthesizing Partial Differential Equation (PDE) solvers predominantly rely on autoregressive models, yet their global left-to-right decoding incurs substantial redundancy when addressing inherently localized bugs. In this work, we challenge this inefficient paradigm and propose DiffPDE, a framework leveraging discrete
diffusion language models for targeted code repair. By introducing a localized re-masking and infilling strategy, DiffPDE regenerates only erroneous regions while preserving correct context, naturally aligning generation with the sparse nature of PDE errors. Furthermore, to handle coupled bugs requiring sequential interventions, we present Iterative Debugging GRPO (ID-GRPO), a reinforcement learning scheme that enables multi-round debugging within single trajectories via intermediate rewards. Experiments on PDEBench show that DiffPDE achieves competitive accuracy, outperforms same-scale AR models, and significantly accelerates repair.
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All authors Wenxuan Guo, Yuyang Hong, Lubin Fan, Zhaojin Fu, Lin Chen, Kun Ding, Shiming Xiang
Artificial Intelligence
Learning-Assisted Congestion-Aware Route Scheduling for Semiconductor Fab Material Control Systems
Hao Yin, Meiqi Tu, Anbang Liu et al. (+2 more)
Automated material handling systems in
semiconductor fabs are operated by a material control system (MCS) that must schedule a relay route for every transport command online, before execution. This is a data-driven scheduling problem in which route cost is dominated in the upper tail by queueing at heterogeneous, partially observable relay equipment, so route selection requires estimating both delivery time and congestion risk at the decision moment. This paper proposes a transport-network-aware dynamic congestion representation (TN-DCR). Built on a static directed transport graph induced by historically observed relay segments, TN-DCR combines structural route priors, multi-window network-wide congestion context, route-level bottleneck exposure, and an inductive graph-aware route
embedding, all constructed under a prediction-time-safety invariant that admits only information observed strictly before the prediction moment. The representation feeds separate queue- and transfer-time regressors and an ordinal multi-label classifier producing calibrated multi-threshold exceedance scores, with an empirical-Bayes stock-key residual correction reducing systematic queue-time underprediction. The predictions serve as costs in a risk-constrained route-scheduling rule that minimizes predicted delivery time subject to a bound on extreme-congestion probability,
embedding the learned predictors within a lightweight operations-research decision model. In a controlled closed-loop evaluation, mean delivery time falls by 16.4% and internal resource waiting time by 22.6% while throughput remains essentially unchanged.
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All authors Hao Yin, Meiqi Tu, Anbang Liu, Shaochong Lin, Max Z.J. Shen
Artificial Intelligence
ScienceArena: Benchmarking LLMs on Latest Scientific Olympiad Competitions
Guangxiang Zhao, Qilong Shi, Xusen Xiao et al. (+13 more)
Benchmark saturation and data contamination increasingly obscure genuine scientific
reasoning in frontier LLMs. We introduce ScienceArena, an olympiad-style
benchmark from thirteen public science competitions in physics, chemistry, and biology, including IPhO and IChO 2025–2026, IBO 2023, USAPhO 2026, and USNCO 2025. Its open-ended, multi-step problems use process-credit rubrics, making faithful scoring difficult. We build ScienceArena through an expert-audited digitization pipeline that converts official exams, figures, solutions, and rubrics into structured items verified by olympiad medalists. To scale evaluation beyond costly human grading, we calibrate LLM-as-judge against medalist ground truth on archived answers from five models across IPhO and IChO; two strong judges stay within one point of expert total scores. Medalist notes show that failures often stem from visual grounding, structure fidelity, and global problem control rather than missing terminology. Evaluating fourteen recent LLMs with interleaved solving, we find that top models obtain medal-equivalent rubric scores on several public international exams, while chemistry and long-horizon consistency remain key bottlenecks. We provide an interactive \href{https://science-arena.onrender.com/}{demo}.
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All authors Guangxiang Zhao, Qilong Shi, Xusen Xiao, Wenpu Liu, Yaoming Li, Linfeng Hao, Shuyang Hou, Zijian Guo, Xinrui Zhang, Yuntian Zhao, Zhengyang Wang, Wenrui Liu, Yuhan Wu, Tong Yang, Lin Sun, Xiangzheng Zhang
Artificial Intelligence · Cross-listed from cs.HC
TSExplorer: An interactive data annotation and exploration tool for time-series data
Einari Vaaras, Manu Airaksinen and Okko Räsänen
We present TSExplorer, a cross-platform tool for interactive annotation and exploration of time-series data. The tool enables users to inspect high-dimensional
datasets through multiple complementary 2D visualizations derived from high-dimensional feature representations. TSExplorer is designed as a general-purpose research tool supporting a wide range of workflows, including exploratory data analysis, annotation of unlabeled or partially-labeled
datasets, comparison of feature representations, and post-hoc inspection and refinement of existing labels with interactive visual feedback.
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Artificial Intelligence · Cross-listed from cs.LG
Trajectory-Initialized Neural Double Q-Routing for Large-Scale Overhead Hoist Transport Systems
Cheng Gu, Qiusheng Zhao, Anbang Liu et al. (+2 more)
Large-scale industrial
robot fleets share constrained physical infrastructure, making vehicle travel times dependent on safety separation, intersection access, downstream blocking, and station contention. We study this problem in overhead hoist transport (OHT) systems, a representative ceiling-mounted material-handling system used in
semiconductor fabs. Static shortest-path routing cannot account for these time-varying traffic costs, whereas tabular Q-routing adapts online but learns each destination–node–action value independently, limiting information sharing across sparsely visited routing contexts and making startup behavior sensitive to inaccurate value estimates. We propose Neural Double Q-routing, which replaces destination-indexed tables with a shared state–action value network. The network is warm-started through return-to-go regression on mixed simulator-generated routing trajectories and then refined online using Double-Q updates, local congestion correction, and event-stratified structured replay. Across nine matched fleet-size–arrival-rate settings with 100, 150, and 200 OHTs, the proposed framework reduces mean completion time relative to tabular Double Q-routing by $0.8\%$–$8.8\%$. It achieves the lowest mean completion time among all compared methods in the six 150- and 200-OHT settings, whereas Dijkstra remains best in the three 100-OHT settings. Completed-task counts remain within $1\%$ of tabular Double Q-routing in eight of nine settings, and 95th-percentile completion time decreases in eight settings. In two matched startup scenarios, offline initialization increases the number of completed tasks by up to $23\%$ and reduces tail completion time by up to $15\%$.
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All authors Cheng Gu, Qiusheng Zhao, Anbang Liu, Shaochong Lin, Max Z.J. Shen
Artificial Intelligence · Cross-listed from cs.CV
Lot Machine: Multimodal Lot Extraction from Auction Catalogs
Mathias Zinnen, Alisha Mund, Sabine Lang et al. (+3 more)
For provenance research and art
market studies, auction catalogs are an essential resource to trace specific objects over time and space. While historical auction catalogs follow established domain conventions, their internal formatting remains highly variable, and their large-scale analysis is currently restricted by the lack of machine-readable representations of the auction lots. We propose a pipeline to automatically extract structured lot-level metadata from German Sales, a large database of historical auction and sales catalogs from the 19th and 20th centuries. Using a manually annotated test set of representative catalog pages, we evaluate
Vision-
Language Models (VLMs) under varying
prompt strategies and constrained decoding frameworks. To reflect the practical constraints faced by cultural heritage institutions, including budget, compute resources, and data
privacy requirements, we
benchmark the methods across different deployment modes ranging from commercial providers to locally hosted, quantized models. We find that commercial endpoints establish the performance ceiling, while institutional gateways offer a viable,
privacy-preserving alternative. Local deployments remain feasible, but strictly require enforcing the output structure during generation to guarantee a valid JSON format. While varying degrees of human-in-the-loop correction are still necessary, this work demonstrates that a VLM-based pipeline can successfully unlock historical auction catalogs for large-scale automated analysis.
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All authors Mathias Zinnen, Alisha Mund, Sabine Lang, Lukas Hüttner, Thomas Gorges, Vincent Christlein
Artificial Intelligence · Cross-listed from cs.LG
Tensor Methods for Language Models: From Token Representation to Training, Adaptation, Inference, Compression, and Interpretability
Matvei Tarasov, Salman Ahmadi-Asl, Andre L. F. de Almeida et al. (+1 more)
Large
language models (LLMs) are built from structured high-dimensional objects such as
token representations, weights, adaptation updates, caches, and activations, whose multilinear structure is underexploited by the conventional
matrix-centric view. Tensor decompositions and tensor networks provide a principled algebraic
language for this structure, yet the literature often treats them as isolated compression mechanisms. This survey organizes tensor methods for LLMs through two complementary views: a seven-stage lifecycle taxonomy covering tokenization,
embeddings, pre-training, adaptation, compression, inference, and interpretability, and a component view covering
embeddings,
attention, and feed-forward networks. We provide unified notation and theoretical foundations, analyze tensorization strategies for individual
Transformer components, and compare methods at each lifecycle stage while making differences in evaluation protocols and model scales explicit. We further connect tensor methods to neighboring efficiency techniques and probabilistic tensor networks. Finally, we synthesize open challenges and introduce $\rho_{\rm gap}$, a metric for the compression-realization gap between theoretical
memory reduction and measured system-level speedup. By treating tensorization as a common structural principle, the survey provides a structured entry point to tensorized
language models and clarifies when parameter savings can plausibly translate into
memory efficiency, computational efficiency, or interpretability. The GitHub page dedicated to this paper is accessible at \href{https://github.com/ma-tt-a/awesome-tensor-methods-for-llms}{this https URL}.
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All authors Matvei Tarasov, Salman Ahmadi-Asl, Andre L. F. de Almeida, Andrzej Cichocki
Artificial Intelligence
CM2: Multimodal Cultural Reasoning via an Integrated Multi-Agent Framework
Qi Li, Zhaojie Kang, Yingjie He et al. (+6 more)
Multimodal Large
Language Models (MLLMs) have shown remarkable success in STEM domains, where progress is often driven by vertical, step-by-step deduction under relatively stable symbol systems. Their horizontal, interdisciplinary cultural
reasoning, however, remains underexplored.We propose CM2, a multi-
agent framework grounded in the cognitive pathway of human cultural interpretation. CM2 integrates
multimodal perception,
retrieval-augmented generation, networked
reasoning, gated fusion, and reward-driven feedback.Experiments on CM2D across multiple MLLM backbones show consistent gains over CoT and typical
reasoning paradigms; ablations validate each module's contribution, and conflict analyses confirm genuine cross-modal arbitration.
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All authors Qi Li, Zhaojie Kang, Yingjie He, Zheng Lin, Hao Zhang, Guangxin Wu, Yan Gong, Rong Fu, Jianyuan Ni