Robotics
LieSpline-DP: Lie-Group B-Spline Diffusion Policy for Smooth Robot Manipulation
Erxuan Xie, Bang Liu, Pingyun Nie et al. (+3 more)
Diffusion Policy (DP) is a powerful Learning from Demonstration (LfD) method for robotic manipulation, yet it suffers from discontinuous and non-smooth trajectories. Spline-based action representations promote smooth motion within individual action chunks, but existing spline-based methods neither guarantee cross-chunk $C^2$ continuity nor account for the group structure of $\mathrm{SE}(3)$. We therefore propose LieSpline-DP, a Lie-group B-spline
diffusion policy that generates end-effector trajectories directly on $\mathrm{SE}(3)$ and couples consecutive plans by sharing their boundary control poses, ensuring $C^2$ continuity throughout the entire planned trajectory. Across three real-
robot tasks, LieSpline-DP produces lower trajectory jerk and higher task success rates than the DP baseline. The gains are particularly pronounced in real-world tasks involving
liquids and flexible objects: in our real-
robot experiments, LieSpline-DP achieved a 100% success rate on both pouring and bucket hooking, whereas the DP baseline achieved only 10% and 30%, respectively.
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All authors Erxuan Xie, Bang Liu, Pingyun Nie, Xingkai Liu, Zhuang Fu, Bo Zhang
Computation and Language
EMR: Self-Evolving Medical Multi-Agent System via Experience Mining and Reuse
Dongsheng Shi, Yue Li, Xin Yi et al. (+1 more)
Large
language model (LLM) driven multi-
agent systems have shown promise in complex clinical
reasoning, yet existing approaches rely on static strategies and lack persistent clinical
memory, preventing self-evolving from prior diagnostic successes and failures. We present EMR, a self-evolving medical multi-
agent system via Experience Mining and Reuse. EMR introduces a hierarchical clinical experience library that organizes accumulated knowledge into three levels: clinical principles, diagnostic patterns, and representative cases. During inference, EMR emulates multidisciplinary consultation: a planner
agent coordinates domain-specific department
agents for specialized
reasoning, while a summary
agent synthesizes their analyses into a final decision. Critically, EMR automatically extracts correct diagnostic insights and failure-related warnings from multi-
agent reasoning trajectories, incrementally updating the experience library to guide future cases. Experiments on medical
reasoning benchmarks demonstrate that EMR consistently outperforms state-of-the-art medical multi-
agent baselines. Further analysis reveals that the hierarchical experience enables cross-specialty generalization and transfer across diverse LLM backbones, offering a scalable and in
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All authors Dongsheng Shi, Yue Li, Xin Yi, Linlin Wang
Artificial Intelligence
T-LoopFormer: Token-Level Elastic-Depth Looped Transformers with Dynamic Routing
Mingqian Yu, Wenpeng Zhang, Shaobo Cui et al. (+1 more)
Looped
Transformers have recently demonstrated strong performance in both
reasoning and
language tasks by reusing a shared set of parameters across multiple iterations, achieving parameter efficiency without sacrificing representational power. Besides, looped
Transformers perform inference directly in the latent space (latent
reasoning) to reduce the number of
tokens consumed during inference, thereby achieving improved sample efficiency. However, these models typically apply a fixed recursion depth uniformly to every
token, leading to suboptimal compute allocation and leaving significant efficiency gains on the table. In this work, we propose
token-level elastic-depth looped
transformers: T-LoopFormer. T-LoopFormer uses dynamic
token-choice routing, which enables each
token to adaptively determine its own number of loop iterations based on its hidden state, which could improve the
token generation accuracy. Moreover, we further introduce recursion-wise KV cache, which maintains an independent key-value cache for each recursion loop of active
tokens; this design ensures that
tokens at different depths only attend to their corresponding cached states, effectively enabling faster autoregressive decoding. Extensive experiments show that T-LoopFormer achieves robust performance on a wide range of
language modeling and zero-shot
reasoning tasks, and our model could reach the lowest decoding latency, which validates the effectiveness of
token-choice router and recursion-wise KV cache. Code is available at https://github.com/YuMingQian1234/T-LoopFormer
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All authors Mingqian Yu, Wenpeng Zhang, Shaobo Cui, Peilin Zhao
Computer Vision
PACE: Progressive Angular-to-Norm Contrastive Embedding
Yanping Li, Wei Zhou, Yawen Liu et al. (+7 more)
Multimodal embedding models encode heterogeneous inputs into a shared
embedding space, enabling efficient similarity computation across modalities and tasks. Most existing methods optimize cosine-based contrastive objectives, which promote stable training but restrict semantic compatibility to angular geometry, precluding
embedding norms from serving as an additional semantic signal. However, directly optimizing the more expressive dot-product similarity, which leverages both angular and norm information, underperforms cosine-based training and exhibits unstable training dynamics. We attribute this discrepancy to premature optimization-space expansion, manifested as angular–norm
entanglement and directional anisotropy in the representation space and further compounded by full-parameter fine-tuning. In this paper, we propose PACE, a two-stage framework that progressively expands both the representation and trainable parameter spaces. Stage I combines cosine-based objective with low-rank adaptation to establish a reliable angular geometry within constrained optimization spaces. Stage II switches to dot-product similarity and full-parameter fine-tuning, enabling
embedding directions and norms to jointly encode semantic information. We further introduce Focal
Embedding Loss, a confidence-adaptive objective that downweights queries with high positive
retrieval confidence while emphasizing ambiguous queries with competitive negatives. Experiments across multiple backbone scales and diverse
multimodal embedding tasks consistently validate the effectiveness of PACE.
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All authors Yanping Li, Wei Zhou, Yawen Liu, Yibo Wang, Ke Zhu, Guangda Huzhang, Qing-Guo Chen, Zhao Xu, Jun Zhang, Wei Wei
Computer Vision
Weakly Supervised Spatial Grounding for Discriminative Attention-Based Ultrasound-Histopathology Alignment in Prostate Cancer Grading
Obed Korshie Dzikunu, Emma Willis, Mohammad Mahdi Abootorabi et al. (+7 more)
Unpaired cross-modal
distillation transfers grade structure from histopathology into a micro-ultrasound (micro-US) encoder by aligning a pooled needle-region
embedding to a frozen histopathology teacher under grade-group correspondence alone. A single objective is thereby required to serve two distinct functions: rendering patch features discriminative of tissue state, and selecting which patches enter the pooled representation. We decouple them. Weak spatial supervision derived from percentage involvement, recorded routinely at biopsy, constrains the predicted proportion of malignant tissue within each core, acting on the encoder features independently of the
alignment objective. The
alignment loss then operates on features that differ across a core, and
attention concentrates on a subset of patches rather than remaining near-uniform. On 7,166 biopsy cores from 811 patients across seven centers under patient-level 5-fold cross-validation, the method reaches 67.1 macro AUC and 68.5 csPCa AUC, against 61.2 and 52.8 for the existing unpaired
alignment method and 63.1 and 62.6 for the strongest unimodal baselines. Ablation against existing
attention regularizers designed to prevent
attention-uniformity collapse shows that such regularizers do not substitute for label-derived supervision: they constrain the
attention distribution, whereas the signal required acts on the features that
attention reads.
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All authors Obed Korshie Dzikunu, Emma Willis, Mohammad Mahdi Abootorabi, Mohamed Harmanani, Zhuoxin Guo, Ferdinand Luger, Adam Kinnaird, Brian Wodlinger, Parvin Mousavi, Purang Abolmaesumi
Machine Learning
Multi-source Transfer Learning of Time Series with a Shapelet-based Distance Measure
Jiseok Lee and Brian Kenji Iwana
Transfer learning is an effective technique for addressing data scarcity in deep learning for time series classification, but its success depends on the selection of source
datasets. Conventional transferability estimation methods are often computationally expensive, as they require fully pre-training a model on each potential source
dataset to assess its suitability. This paper introduces a novel, training-free source selection method named Shapelet Matching. Our approach first identifies discriminative shapelets from the target and potential source
datasets. Then, Shapelet Matching quantifies
dataset similarity by comparing the extracted sets of shapelets. To mitigate the risk of negative transfer from selecting an unsuitable single source, we introduce a multi-source transfer learning method. We select several source
datasets based on their shapelet-based similarity scores, combine them into a single multi-source
dataset, and use this aggregated
dataset for pre-training. The model is then fine-tuned on the target task. We evaluated our method on 128
datasets from the UCR Archive using both temporal CNN and
Transformer architectures. The empirical results demonstrate that our multi-source pre-training reduces the risk of negative transfer on average. Shapelet Matching achieves the strongest performance for the CNN backbone and remains competitive for patch-based
Transformer architectures, while avoiding the cost of pre-training a separate model for every candidate source.
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Computation and Language
Improving Mathematical Reasoning Capabilities in Large Language Models via Reasoning Process Error Classification
Runa Yoshida, Kosuke Nishida and Kyosuke Nishida
The
reasoning ability of large
language models (LLMs) is a critical factor for practical LLM-based applications. To investigate the current
reasoning capability of LLMs, we clarify the types of errors that arise in LLMs'
reasoning processes on mathematical
datasets. We focus on problems where LLMs produce an incorrect answer. We define errors in the
reasoning process as
reasoning errors and manually analyze the features of
reasoning errors. We defined and classified 21 error classes and identified the frequently occurring classes among them. Beyond qualitative evaluation, we leverage the evaluation results to improve the
reasoning capability. We designed a
prompt that explicitly focuses on eight error classes. The experiments demonstrate that this
prompt effectively improves
reasoning performance. Furthermore, the results suggest that the frequent
reasoning errors identified in this paper are common across LLMs of comparable scale.
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Computer Vision
Automated Perceptually-Motivated Assessment of Photographic Consistency in Paired Clinical Photographs: Pipeline Development and Internal Evaluation
Derrick Lin, Samantha Rabinovich, Joclin Rabinovich et al. (+5 more)
Purpose: Paired pre- and post-operative photographs are the standard unit of evidence for plastic
surgical outcomes, yet no objective metric verifies whether two images of the same patient were captured under conditions consistent for comparison. Approach: We developed a perceptually motivated pipeline that analyzes pre/post pairs across thirteen calibrated sub-metrics, partitioned by unsupervised correlation-structure analysis into five data-driven clusters (photometric, texture / sharpness, pose, illumination direction, and pitch), averaged within each cluster and combined across clusters by a weighted sum into a single consistency score. Each sub-metric is calibrated so that its median difference across published within-patient pairs scores 0.5, which is a reference point and carries no pass/fail meaning. The pipeline was calibrated on 134 matched within-patient published pre/post pairs and evaluated against identical-image pairs, synthetic-perturbation pairs, and 134 mismatched cross-publication pairs. Results: The master consistency score S separated matched from mismatched pairs (sensitivity index d' = 2.15, 95% confidence interval (CI) [1.83, 2.55]; area under the receiver operating characteristic curve AUC = 0.928, 95% CI [0.896, 0.959]), closely matching Gaussian-equal-variance predictions. The three head-pose angles did not fall in one cluster: yaw and roll grouped together while pitch separated. Identical pairs scored at ceiling (S = 0.99) and the master score fell monotonically with perturbation magnitude on all five perturbation axes. Conclusions: The score quantifies photographic comparability, not aesthetic or
surgical quality, and provides a freely available web tool for
auditing the photographic comparability of pre/post pairs, pending validation against expert judgment.
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All authors Derrick Lin, Samantha Rabinovich, Joclin Rabinovich, Kassra Garoosi, Sumun Khetpal, Evan Delanoy, Neel Bhardwaj, Jason Roostaeian
Information Retrieval
Top-K Is Not a Budget for Hybrid Retrieval
Chunran Zhang
Modern hybrid
retrieval for RAG typically fuses the Top-$L$ results from dense and sparse retrievers, but a fixed truncation depth may not transfer across changing queries and corpora. Exact fusion removes the dependence on a fixed depth, yet completing a specified Top-$K$ still incurs variable access costs. We present DiBud, which takes an access budget directly as input and incrementally certifies and returns an exact prefix of the RRF ranking over the full lists. Selective access increases certified output within the budget, while budgeted stopping bounds accesses per request. Experiments on five query sets reveal long-tailed costs for completing exact Top-20. At a budget of 2048 accesses, DiBud increases mean certified output within the first 100 positions by 7.86% over balanced access. After budget calibration for 95% quality retention, held-out queries retain 95.05%–97.68% of mean nDCG@20 while using 65.92%–99.53% fewer accesses than completing exact Top-20.
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Robotics
C$^2$Nav: Compare Before You Commit for Zero-Shot Vision-and-Language Navigation
Runtian Zheng, Congpeng Zhang and Ying Liu
Zero-shot
vision-and-
language navigation in continuous environments (VLN-CE) increasingly places foundation
vision-
language models (VLMs) inside the
navigation loop. Existing systems commonly request cardinal outputs such as waypoints, pixels, headings, progress values, or absolute arrival decisions, coupling a generative response to
geometric magnitude or an irreversible commitment. We study a complementary model-
robot interface: the VLM compares controller-constructed alternatives, while geometry, thresholds, action magnitude, and execution remain on the physical side. We instantiate this idea in C2Nav, a training-free framework with three coordinated faculties. Seeing performs ordinal Gaze Election over physically vetted candidate views; Remembering maintains a compact route sketch and compares adjacent instruction-leg hypotheses; and Arriving combines a hesitation ladder, look-back comparison, and revocable walk-back for reliable stopping. On the public OpenNav R2R-CE 100 protocol, C2Nav with Qwen3-VL-8B-Instruct obtains 41.0% OSR, 31.0% SR, and 16.7% SPL, while the same interface with the standard GPT-5.5 model reaches 54.0% OSR, 44.0% SR, and 29.0% SPL. Whole-faculty ablations reduce SR to 14.0% without Seeing, 25.0% without Remembering, and 29.0% without Arriving. Matched role inversions that replace only the comparative answer form with cardinal/absolute questions reduce SR to 12.0%, 28.0%, and 21.0% in the spatial, transition, and terminal slots, respectively. The results indicate that a constrained decision interface and stronger VLM
reasoning are complementary rather than interchangeable.
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Computer Vision
SparseTalk - Sparsifying 3D Gaussian Language Fields for Efficient 3D Visual Question Answering
Davit Soselia, Joseph JaJa and Amitabh Varshney
3D Gaussian
language fields provide an explicit, spatially grounded representation for 3D visual question answering (VQA), but their dense semantic features can require tens of thousands of
embeddings per scene, resulting in substantial storage,
memory, and inference costs. We investigate how much of this representation is actually necessary for downstream
reasoning. Starting from a full
embedding representation, we systematically sparsify its semantic
embeddings, including the previously underexplored regime below a single image-equivalent block down to 8 visual
tokens. We compare random,
geometric, semantic, and joint spatial-semantic selection strategies and introduce an object-based sparsification method that distributes the
token budget across detected object instances while retaining background context. Experiments on ScanQA and MV-ScanQA reveal substantial redundancy in dense Gaussian
language fields. Strong VQA performance is retained with only a few hundred semantic
embeddings, corresponding to less than 1% of the original representation. Object-based selection performs well relative to others, with only modest observed changes down to 256
tokens. At this budget, SparseTalk retains 0.80% of SplatTalk's 32,076-
token inference input and 0.332% of the mean 77,207-Gaussian dense field, increasing inference throughput while reducing decoded-feature
memory 125-fold.
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Artificial Intelligence
HazardAuditor: From Executable Threats to Safer Computer-Use Agents
Yunhao Feng, Ruixiao Lin, Ming Wen et al. (+5 more)
Computer-use
agents increasingly interact with browsers, terminals, file systems, and external services, introducing safety risks that emerge through runtime behavior rather than generated content alone. Existing guard models target static
prompts and responses and are poorly suited to
agent execution; existing executable safety platforms produce evaluation verdicts rather than the normalized supervision a guard model needs to learn across heterogeneous
agent frameworks. We introduce HazardAuditor, an execution-grounded framework that closes both gaps. Its infrastructure runs heterogeneous
agents (Claude Code, Codex, Hermes, and OpenClaw) in controlled environments and normalizes their interactions into a canonical event representation for cross-framework supervision. We further observe that
token-level post-training objectives create a structural mismatch for generative guards, causing longer rationales to dominate gradient updates. Guard Policy Optimization (GuardPO) addresses this by converting deterministic safety outcomes into sequence-level advantages and normalizing rationale and verdict regions, making the safety decision the effective unit of optimization. Across multiple
benchmarks and heterogeneous computer-use systems, HazardAuditor improves accuracy by up to 16.5 percentage points over the strongest prior guard. Code, models, and evaluation artifacts will be available at https://yunhao-feng.github.io/HazardAuditor/.
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All authors Yunhao Feng, Ruixiao Lin, Ming Wen, Yanming Guo, Xingjun Ma, Yutao Wu, Xinhao Deng, Shouling Ji
Computer Vision
AdaVSkip: Adaptive Visual Token Skipping Across Layers For Efficient MLLMs Inference
Yuyao Sun, Tao Deng, Shuang Li et al. (+1 more)
Multimodal large
language models (MLLMs) require substantial computation to process numerous visual
tokens across all
transformer layers. Most methods for efficient MLLM inference exploit horizontal redundancy by compressing visual
tokens. Beyond
token reduction, recent studies exploit vertical redundancy through early exit or fixed-layer skipping. However, we find that the extent and distribution of this redundancy vary across inputs and differ between self-
attention and MLP modules. Motivated by these observations, we propose AdaVSkip, which equips each layer with two lightweight routers that independently determine whether visual
tokens pass through by or skip the self-
attention and MLP modules. These decisions collectively define an input-specific visual-computation path, but their discrete and non-differentiable nature makes learning effective paths challenging. To address this challenge, we develop a progressive two-stage training framework that updates only the routers while keeping the backbone frozen. Stage I establishes an initial routing policy through supervised training with input-specific targets derived from module-wise necessity scores. To further align the routing policy with task performance, Stage II uses reinforcement learning to optimize routing decisions with direct feedback from generated answers. It combines an answer correctness reward with a skip-consistency reward that discourages excessive retention of visual-
token computation. Across three MLLM backbones, AdaVSkip maintains strong task performance with substantially less computation. On LLaVA-NeXT-7B, AdaVSkip reduces FLOPs by 53.2% while preserving the original model's average performance. Combining it with visual
token compression increases this reduction to 91.2%, while retaining 97.2% of the original performance on average.
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All authors Yuyao Sun, Tao Deng, Shuang Li, Deqing Wang
Software Engineering
woma: a real-time foundation model and its fine-tuned models for endoscopy
Thang Tran and Lan Dang
woma is a real-time foundation model for gastrointestinal endoscopy: a network trained without labels on about a million endoscopy frames, from which task models are fine-tuned. We contribute a systematic design for production. Requirements and pass marks were fixed before any run, eight candidates screened under pre-registered rules, self-supervised training taken to a stopping rule, then fine-tuning and deployment optimisation, all on one self-contained library, numbat. We also contribute woma itself with two fine-tuned models, every outcome reported met or missed. Our colonoscopy model finds and outlines polyps, names which colon segment is in view, suggests polyp type and grades bowel preparation. Our gastroscopy model names a station out of 22 protocol sites, flags and outlines lesions, and names one of seven findings. Every number was read on data never seen in training, and shipped weights were chosen on that record. In colonoscopy, 96% of polyps in a six-hospital PolypGen set are found at precision >=0.85, and 19 of 19 polyps across fifteen full REAL-Colon
videos at 1.6 false alarms per procedure. In gastroscopy, landmark region is named correctly on 92% of frames from unseen patients, and 37 of 39 held-out neoplasia frames are flagged at specificity 0.91. On one workstation GPU every task runs over 1080p
video at about 100 frames per second, faster than PyTorch, ONNX Runtime and TensorRT in all four precision regimes tested. TensorRT comes closest: one pass of our foundation model takes it 3 to 27% longer than ours, and we deliver 6 to 31% more frames per second from frame to results. A second build links no vendor library at all – our own kernels over Vulkan – so a site deploys two files and needs no toolkit, no cuDNN and no framework; in f32 it beats the CUDA build on the same card.
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Artificial Intelligence
Medical Knowledge Simplification for Patients in the Era of LLMs: A Case Study on Diabetes
Pallika Kafle, Yipeng Zhou, Guanfeng Liu et al. (+2 more)
Complex medical information is often difficult for patients to understand, making effective medical knowledge simplification essential for improving patient comprehension, informed decision-making, and health outcomes. Recent advances in large
language models (LLMs) provide a promising approach for simplifying complex medical information into patient-friendly
language; however, their effectiveness in real-world patient education remains insufficiently explored through human evaluation. To investigate their practical effectiveness, this paper presents a case study on diabetes knowledge simplification through the implementation and evaluation of MediClear, an LLM-based medical knowledge simplification system enhanced with
Retrieval-Augmented Generation (RAG). Public diabetes-related articles from Diabetes Australia, WHO, American Diabetes Association (ADA), NIDDK, and AIHW are indexed in the RAG knowledge base to retrieve clinically grounded information, which is then simplified by the LLM into accessible patient explanations. We evaluate the generated responses using standard readability metrics, including the Flesch-Kincaid Grade Level (FKGL), and conduct a human study involving 10 participants. Results show that MediClear consistently reduces the reading level of generated responses to the recommended patient literacy range while achieving high user satisfaction and willingness for future use. This case study demonstrates the potential of LLMs to improve the accessibility of medical knowledge for patient education.
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All authors Pallika Kafle, Yipeng Zhou, Guanfeng Liu, Quan Z. Sheng, Cheng-Hsin Hsu
Machine Learning
Omni-Streaming Thinking
Enjun Du, Siyi Liu, Ziyu Zheng et al. (+4 more)
Streaming omni-modal models must decide what and when to answer from the
video chunks and synchronized audio observed so far. Visual cues often support an interpretation before an utterance or sound event is complete. If that interpretation enters
memory as a fact, later
reasoning can keep relaying it even after audio contradicts it. We call this failure premature cross-modal commitment. We propose Omni-Streaming Thinking (OST), which generates structured outputs that include evidence observed so far, forecasts of future evidence, and claims based on this evidence. Each claim is initially marked as pending and linked to a future verification interval. Audio and visual evidence are stored separately, and OST checks a claim against the evidence from the specified modality at the end of the verification interval. When contradictory evidence is detected, a refutation process reduces the influence of the claim and its dependent states, and then guides a state update using the new evidence. An answer gate decides whether the answer-critical claims meet the conditions for giving a response. Using a frozen Qwen3-Omni-30B-A3B-Instruct backbone with lightweight adaptation, OST outperforms the strongest open baselines on five streaming and audio-visual
benchmarks by more than 10% relative on average. We also introduce OST-DiagBench, which holds
video fixed and edits audio to test agreement, absence, contradiction, coexistence, and subtitle-
speech conflict. OST reaches d-
prime = 2.95, compared with at most 1.38 for open baselines, while reducing
vision-induced auditory
hallucinations.
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All authors Enjun Du, Siyi Liu, Ziyu Zheng, Jingyu Li, Yiwen Guo, Yongqi Zhang, Difan Zou
Computation and Language
MoME: Mixture-of-Memory Embeddings for Context-Aware Sparse Lookup
Muchen Li, Leonid Sigal and Renjie Liao
Scaling large
language models efficiently has motivated sparse capacity mechanisms such as Mixture-of-Experts and, more recently, conditional
memory:
token-indexed
embedding tables that augment the backbone with cheap parametric lookups. Existing
memory-
embedding methods retrieve via a deterministic function of the surface form, which collapses different contextual senses of the same
token (e.g., python the
language vs. the animal) into a single fixed entry. We introduce Mixture of
Memory Embeddings (MoME), a context-aware
memory mechanism that replaces each
token's single
memory row with a mixture of M slots and uses a learned gate over the hidden state to choose which slots to read at each position. In controlled pretraining experiments across nanochat, Llama-3/MobileLLM, and Qwen3 backbones, MoME improves over Value
Embedding, Bigram, and STEM baselines in iso-parameter and iso-training-FLOP settings, shows a more promising
memory-size scaling trend at sub-billion scale, and remains efficient in training and inference. Qualitative routing analyses on polysemous
tokens further suggest that the learned mixture exhibits a degree of semantic interpretability, dispatching the same surface
token to distinct
memory slots under different senses.
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Machine Learning
Refinement-Based Flow Policy Optimization
Bumgeun Park, Hyukjun Yang and Donghwan Lee
Flow-based policies offer an expressive representation for online reinforcement learning, but conventional flow matching requires samples drawn from the distribution to be modeled. This poses a challenge when the desired action distribution is defined only implicitly by a Q-function, since directly sampling actions from the resulting distribution is generally intractable. We propose Refinement-Based Flow Policy Optimization (RFPO), a novel framework for training a flow policy in online reinforcement learning by alternating between Q-guided sample refinement and self-target flow matching. RFPO first generates actions from Gaussian noise using the current flow policy and then uses a finite-step
stochastic refinement procedure to move them toward an energy-based distribution induced by the Q-function. Each refined action is then paired with its corresponding initial noise sample and used as a fixed target for flow-matching training. By repeatedly refining its own outputs and learning from the resulting targets, RFPO incorporates Q-guidance into the policy without requiring direct samples from the target distribution, while retaining the capacity to represent multiple action modes. We further provide a theoretical analysis of the distributional dynamics induced by RFPO. Across six continuous-control tasks, RFPO matches or outperforms a standard Gaussian-policy baseline on almost every task. Experiments on six synthetic two-dimensional target distributions with diverse geometries demonstrate that RFPO captures complex
multimodal structure without mode collapse.
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Software Engineering
DepthBenchCAD: When Does Deeper Auditing Yield More Reliable Conclusions?
Hongye Yang, Zhihao Xie, Shengjun Xiong et al. (+1 more)
Generative CAD models are expected to remain behaviorally correct after parameter edits, so increasing the number of edit checks is often treated as a direct route to more reliable evaluation. Under a fixed budget, however,
auditing each program more thoroughly reduces the number of tasks and independent generations that can be evaluated, which can ultimately make model-level estimates less accurate. We study this phenomenon and the conditions under which it arises. We decompose behavioral evaluation into three evidence levels: task templates,
stochastic generations, and within-program edits. We define an average failure risk that is invariant to audit depth, and combine three-level variance with measured execution costs to analyze the tradeoff between deeper edit
auditing and broader independent coverage. Experiments across two CAD environments and five generation systems show that the value of deeper
auditing depends on where evaluation uncertainty originates. When template heterogeneity or generation stochasticity dominates, additional edit checks can increase total estimation error; when within-program state variation is large and generation is expensive, deeper
auditing is more valuable. Variance and cost estimates from calibration predict the direction of this change and provide a diagnostic basis for allocating evidence on held-out tasks. These results show that the thoroughness of program inspection can diverge from the reliability of model evaluation, and they help determine whether the next unit of budget should be spent on a new task, a new generation, or additional edit checks.
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All authors Hongye Yang, Zhihao Xie, Shengjun Xiong, Boxiao Huang
Computer Vision
DNF-SR: Dual-Input and Negative-Aware Feature Fine-Tuning for Real-World Image Super-Resolution
Shuhao Han, Wenjie Liao, Hayden Vance et al. (+4 more)
Benefiting from the powerful generative priors of
diffusion models,
diffusion-based real-world image super-resolution (Real-ISR) methods have demonstrated impressive performance.To achieve efficient Real-ISR, several recent works have designed one-step
diffusion-based models.Howerver, unmediatedly feeding LR into a
diffusion model creates a distributional gap with the model's original input.A straightforward approach to reduce the distribution gap is to introduce noise to the LR latents. However, directly adding noise inevitably corrupts the content of the LR images.In this study, we propose DNF-SR, a Dual-input and Negative-aware Feature fine-tuning method for Real-ISR.Specifically, we use a dual-input strategy that concatenates the original LR image with the noisy LR input and feeds them into a
diffusion-based image editing model, ensuring both high-fidelity one-step super-resolution and improved perceptual and content consistency.Additionally, the noise present in the noisy LR input introduces randomness and diversity into the outputs. We exploit this property and propose a post-training optimization method, Negative-aware Feature Fine-Tuning (NF2T), which guides the model toward producing higher-quality results.NF^2T classifies multiple outputs into positive and negative subsets and then defines implicit policy improvement directions in both the image and feature spaces, thereby further enhancing the stability of the optimization.Extensive experiments show that DNF-SR outperforms other methods.Code will be released.
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All authors Shuhao Han, Wenjie Liao, Hayden Vance, Hang Dong, Rui Zhang, Chun-Le Guo, Chongyi Li
Robotics
Legislating World-Model-Based Planning with Legal Reasoning
Dylan Waldner, Yiannis Kantaros, Guido Governatori et al. (+2 more)
As robotic systems grow more general, legal norms are needed to integrate them into society. This paper extends the isomorphism problem of aligning legal source texts with their encodings, and measures two key challenges to
robot normative control: (1) the grounding isomorphism gap, where perception error grounds false atoms for legal
reasoning, and (2) the ontological isomorphism gap, where one legal conclusion admits many faithful translations into planning constraints. The paper introduces a legal planning stack that employs Defeasible Deontic Logic (DDL) to constrain a motion planner. The stack leverages learned world models to plan and to provide legal context, enabling ex ante governance that intervenes before an illegal action is executed. It was deployed on a simulated
robot arm pushing a cube across a 3x3 grid. The findings were (1) the legislated
agent abided substantially more often than the non-legislated one, and modeling perception uncertainty lifted abidance even further, (2) the legal
reasoning ran efficiently at runtime and its verdicts were auditable, and (3) the stack adapted to exogenous signals and endogenous rule changes. Both gaps were measured: (4) world model and probe error corrupted the factual input for the DDL reasoner, and (5) a single law admitted several faithful metric interpretations yielding drastically different abidance. Thus, ex ante legislation functions as intended, and closing these gaps with a standardized mapping from the law to runtime constraints and improved fact grounding from perception will yield robust laws that align
robot behavior with society's norms. Project page: https://dylanwaldner-cail.github.io/Legislated-Planner/.
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All authors Dylan Waldner, Yiannis Kantaros, Guido Governatori, Risto Miikkulainen, Amir Banifatemi
Machine Learning · Cross-listed from econ.EM
Low-Rank Denoising for Predict-then-Optimize
Henry Aldridge-Krawciw and Irene Aldridge
A shortest-path decision is often made from edge costs that are only observed through noisy samples, such as the average of $m$ simulation replications. When the mean cost vectors of different instances lie near a $k$-dimensional subspace, projecting the averaged sample onto that subspace should remove noise. We make this precise. The mean squared error of any projection
estimator splits exactly into a variance term and a bias term. The best projector is the positive eigenspace of $\Sigma_c - D/m$, where $\Sigma_c$ is the covariance of the true costs and $D$ is the noise covariance. For a rank-$k$ signal, the projection has the error of a sample mean with a factor $d/(\mu k)$ more replications, where $d$ is the number of edges and $\mu$ is an incoherence constant. For a signal that is only approximately low-rank, every rank-$k$ projection has an error floor, and the plain average wins beyond an explicit replication count. A margin bound shows that the chance of choosing a suboptimal path falls at the same improved rate. A projector learned from $n$ noisy instances loses at most an $O(d/n)$ term.
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Computation and Language
When the Wrong Key Wins: Understanding and Detecting Hallucinations in LLMs
Xuhan Tong, Haoyue Bai, Dawei Zhou et al. (+2 more)
Large
language models can hallucinate even when the knowledge required for a correct answer is already available. We study this failure through a latent-key view of inference, where answer selection depends on competition among associations acquired during pretraining. We show that model predictions can be highly sensitive to individual query keywords, that these influential keywords exhibit entity-specific binding, and that their effects are systematically shaped by pretraining frequency. Multiple bindings can also compete and exhibit higher-order interactions within the same query. Based on this mechanism, we introduce a two-stage keyword-perturbation method for
hallucination detection. By removing influential keywords and measuring how the model reorganizes its prediction, the method distinguishes errors caused by misleading key associations from correct decisions supported by diagnostic evidence. Across multiple models and
benchmarks, perturbation provides a strong and transferable detection signal, reaching $0.910$ AUROC on probe-known ScientistQA. Finally, we extend the same probabilistic framework to four
hallucination regimes: knowledge deficit, wrong knowledge, context distraction, and unstable inference. Their operational distributions across
benchmarks provide diagnostic context for why different detector families succeed in different settings.
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All authors Xuhan Tong, Haoyue Bai, Dawei Zhou, Naichen Shi, Jiawei Zhang
Artificial Intelligence · Cross-listed from physics.flu-dyn
Physics Informed Neural Network model for the dynamical study of Abdominal Aortic Aneurysm
Adrián Robles Arques, Martín Ruiz Fernandez, Javier Sanchis et al. (+2 more)
We present the development and application of a three-dimensional Physics-Informed Neural Network (PINN) framework for the investigation of haemodynamic behaviour in the human aorta. The model incorporates a time-resolved simulation of pulsatile blood flow over a two-minute interval, enabling the extraction of pressure and velocity fields with high temporal fidelity. The mechanical stress exerted on the aortic wall was quantified through Laplace's law, with temporal averaging applied to derive representative stress distributions. This approach circumvents the computational overhead associated with conventional computational fluid dynamics (CFD) methods by eliminating mesh generation and exploiting the automatic differentiation capabilities inherent to neural networks. The proposed methodology demonstrates that PINNs can serve as an efficient and accurate alternative for modelling complex vascular flow phenomena, offering significant advantages in scalability and computational cost reduction while maintaining physical consistency.
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All authors Adrián Robles Arques, Martín Ruiz Fernandez, Javier Sanchis, Miguel A. Teruel, Juan Trujillo
Computer Vision
ChatGPT Images 2.5 in the Wild: A Launch-Period Dataset and Detector Evaluation
Dennis Ng, Xingyu Shen, Ankit Raj et al. (+6 more)
An image tool can change its underlying generator while retaining its public name, making version attribution from online posts ambiguous. We study this problem after the ChatGPT Images 2.5 launch. Our frozen collection contains 3,478 images from 2,440 posts across 8 sources. Recorded posting times fall within the first 51.1 hours after the announcement. It records three attribution tiers and retains standalone images after image-form filtering and targeted review. Caption claims and host records provide admission evidence, not independently verified generator identity. The observed content profile depends on the source mixture: NightCafe supplies 39.0% of images but 77.0% of CLIP-assigned fantasy scenes. We then evaluate six frozen detectors at thresholds calibrated to a 5% flag rate on reference photographs. Collection flag rates range from 3.7 to 56.4%, falling 42-81 percentage points below GenImage recall. Held-out artwork false-positive rates range from 1.5 to 96.5%, so a higher collection flag rate does not by itself establish better detection. An exploratory X-only comparison with our April collection finds a higher September flag rate for Effort, and a suggestive difference for DoU, under fixed-threshold post-clustered bootstrap intervals. Attribution, content and processing differences prevent a causal interpretation of these contrasts. The collection supports analysis of reported model use during a product transition, with source and attribution evidence retained for interpretation. The collection is released at https://scam.ai/research.
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All authors Dennis Ng, Xingyu Shen, Ankit Raj, Kidus Zewde, Tommy Duong, Yuchen Zhou, Yuxin Zhang, Neo Tiangratanakul, Simiao Ren
Computer Vision
LG-VLN: A Zero-Shot Vision-and-Language Navigation Framework with LangGraph State Orchestration
Jianhe Zhao, Yanhua Qiu, Zhiyu Zhang et al. (+2 more)
Continuous-environment
vision-and-
language navigation (VLN-CE) requires interpreting natural-
language instructions in unseen 3D environments and executing continuous low-level actions. Existing methods often depend on
LiDAR, panoramic cameras, or extra sensors; separate
geometric-mapping and semantic-
navigation visual representations can cause long-trajectory spatial-semantic inconsistencies. We propose LG-VLN, a
monocular zero-shot framework with shared visual features and LangGraph-based state orchestration. An online feed-forward 3D reconstruction network predicts depth, camera poses, and dense point clouds for
agent-pose estimation and global map fusion. Geometry and
navigation share dense CleanDIFT features: semantic consistency rejects incorrect inter-frame correspondences, while target-instance constraints define visual references whose similarity combines with local BLIP-2 image-text relevance to form a semantic value map. LangGraph represents instruction parsing,
geometric perception, semantic value updates, path planning, action execution, and failure recovery as a directed state graph with conditional transitions, persistent state, and modular recovery mechanisms. On a fixed 550-episode subset of the R2R-CE val-unseen split, LG-VLN achieves 21.3% success and 12.1% success weighted by path length. Ablations show shared semantic features improve
navigation, further boosted by combining visual similarity and image-text relevance. Results establish shared visual representations and explicit state orchestration as effective for zero-shot VLN-CE using
monocular RGB alone. Code will be publicly released for reproducibility.
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All authors Jianhe Zhao, Yanhua Qiu, Zhiyu Zhang, Zibo Zhao, Jinhua Xie
Cryptography and Security
CounterPersona: Append-Only Defense Against Unauthorized Persona Skill Distillation
Pengwei Wang, Zihan Wang, Hangcheng Cao et al. (+3 more)
Persona skill
distillation can extract recurring patterns from personal information and encode them into reusable skills, enabling AI systems to closely replicate an individual's behavior. However, such replication also raises serious concerns regarding personal
privacy and labor autonomy. Unlike existing perturbation-based defenses that require individuals to modify their data before collection, once historical records are collected by an attacker, they can no longer be altered, sanitized, or revoked. Therefore, such defenses are difficult to adapt to this append-only setting. To solve this challenge, we introduce CounterPersona, which constructs targeted counter-persona evidence, packs compatible behavioral states into compact realization units, and strengthens them through rationale-guided consistency rewriting. We conduct extensive experiments showing that CounterPersona achieves strong and consistent effectiveness across lexical, semantic, and LLM-based measures, while remaining robust across distillers. Our work establishes a skill anti-
distillation paradigm for protecting personal
privacy and labor autonomy against unauthorized skill
distillation.
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All authors Pengwei Wang, Zihan Wang, Hangcheng Cao, Qingchuan Zhao, Hongwei Li, Guowen Xu
Artificial Intelligence
OpenAI4S: Code as Action, Science as Sessions
Gongbo Zhang, Hao Li, Yu Wang et al. (+15 more)
AI co-scientists could accelerate computational research, but over a long-running study the workflow also has to stay inspectable, resumable and reproducible, which requires persistent computational state and provenance. Here we present OpenAI4S, an open-source scientific research
agent built around the principle of Code as Action, Science as Sessions. OpenAI4S combines a persistent computing runtime with research-session management: orchestration is handled through structured tool calls, while scientific actions are represented as complete code
cells executed in persistent Python and R kernels. An append-only Action Ledger, per-
cell execution records, versioned artifacts, environment records, and workspace checkpoints preserve how results were produced and support session recovery, branching, and extension. Configurable sandboxing, permission controls, and code and trajectory screening provide complementary safeguards. We evaluate OpenAI4S on 36 research scenarios spanning retrosynthesis, molecular dynamics,
protein binder design,
protein mutation, catalyst screening, and mineral spectroscopy, measuring scientific task accuracy, workflow completeness, and reproducibility of the resulting repositories. OpenAI4S achieves an overall score of 7.83, compared with 5.7–6.4 for a general-purpose coding harness evaluated with three frontier models, with the largest gains on long-horizon and computation-intensive workflows. These results suggest that integrating persistent execution with session-level provenance can improve the reliability of AI-assisted scientific workflows. Environment specification and full rerunnability remain weak for every evaluated system, ours included, so reproducibility is still an open problem for scientific
agents. The system is available under the MIT license at \href{https://github.com/PKU-YuanGroup/OpenAI4S}{github.com/PKU-YuanGroup/OpenAI4S}.
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All authors Gongbo Zhang, Hao Li, Yu Wang, Mujie Lin, Liuzhenghao Lv, Yicheng Mao, Yimi Wang, Jun Zhu, Minhan Tang, Zhengxiang Jiang, Yusong Wang, Jiayu Yao, Kunpeng Ning, Dawei Pang, Yonghong Tian, OpenAI4S Community, Yuyang Liu, Li Yuan
Information Retrieval
Generate to Explore, Select to Exploit: Aligning LLM-based Headline Generation with Personalized Recommendation
Yi Chen, Rufeng Cheng, Qiang Xie et al. (+1 more)
In industrial recommendation feeds, presenting a static headline for an item often fails to satisfy the diverse,
multimodal interests of the user population, particularly suppressing the needs of long-tail audiences. While Large
Language Models (LLMs) have been integrated into recommendation for content understanding or ranking, directly optimizing them to output a single best headline typically leads to mode collapse—converging to generic patterns that satisfy average tastes but miss specific latent intents. To bridge this gap, we introduce GESE (Generate to Explore, Select to Exploit), a framework operating at the system's presentation layer that decouples personalization into generative exploration and selective exploitation. First, we treat the LLM as a probabilistic explorer, utilizing Group Sequence Policy Optimization (GSPO) with a hierarchical reward mechanism to generate a candidate set that maximizes the semantic coverage of potential user interests. Subsequently, a lightweight, real-time feedback-aware selector acts as the exploiter, identifying the optimal realization from the candidate pool based on instant contextual signals. Extensive deployment on a commercial platform with over 100 million daily active users demonstrates that GESE significantly outperforms state-of-the-art baselines, achieving a 2.57% lift in CTR and 0.87% in dwell time. These results validate that decoupling diversity-oriented generation from precision-oriented selection offers a robust blueprint for aligning generative AI with dynamic user utility.
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All authors Yi Chen, Rufeng Cheng, Qiang Xie, Tao Li
Artificial Intelligence
ER-EDF: A Psychology-Grounded Emotion Regulation Framework for Speech Empathetic Dialogue Generation in Large Audio-Language Models
Hongyu Jin, Wenda Zhang, Runqiu Fei et al. (+3 more)
In human conversation, empathy involves not only recognizing a partner's
emotion but also regulating it, listeners identify the emotional state, select a suitable regulation strategy, and implement it in their reply. Current large audio-
language models (LALMs) largely map perceived
emotion directly onto a response, skipping this regulation process. As a result, their responses tend to mirror the user's affect or offer generic sympathy. We propose ER-EDF, a psychology-grounded framework for empathetic dialogue that follows the three stages of Gross's Extended Process Model of
emotion regulation, identifying the user's
emotion from
speech, selecting a regulation strategy matched to that
emotion, and implementing the strategy through a LALM. The framework is model-agnostic and integrates seamlessly into existing LALMs. We further construct a spoken empathetic dialogue
dataset and introduce empathy-aware evaluation metrics beyond lexical matching. Experiments across five LALMs and two
datasets show that ER-EDF consistently improves empathetic response quality in both automatic and human evaluations. These results suggest that modeling how humans regulate others'
emotions, rather than only how they perceive them, is key to empathetic spoken dialogue, paving the way for psychologically grounded empathetic AI.
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All authors Hongyu Jin, Wenda Zhang, Runqiu Fei, Gongping Huang, Mike Conway, Ting Dang