Computer Vision
EvoGS: Modeling Deformation Evolution for Dynamic Gaussian Splatting
Wei Dong, Shahram Shirani, Jun Chen et al. (+1 more)
Recent extensions of 3D Gaussian Splatting (3DGS) enable real-time novel view synthesis in dynamic scenes by learning time-conditioned Gaussian deformations. However, existing MLP-based methods typically estimate deformations independently at each timestamp, making them less robust to large or abrupt motions. To address this issue, we propose EvoGS, a 3DGS-based dynamic reconstruction framework that models Gaussian deformation as a temporal
evolution process. EvoGS maintains persistent deformation states for each Gaussian, extrapolates future states from historical deformation states, and corrects the predictions with MLP-derived observations. The correction is adaptively weighted using a temporal residual
memory and
evolution statistics such as deformation velocity and trajectory deviation. To further improve reconstruction quality, EvoGS introduces deformation-aware densification. Clone and split operations are performed along corrected deformation directions, while an uncertainty-aware strategy suppresses densification for Gaussians with unstable deformation histories. Experiments show that EvoGS improves dynamic novel view synthesis quality and achieves competitive performance across
benchmarks.
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All authors Wei Dong, Shahram Shirani, Jun Chen, Han Zhou
Distributed Computing
AInfer-PD: Communication-Safe In-Place Prefill-Decode Multiplexing for Distributed MoE Rollouts
Guowei Wang, Chaokun Yang, Zhenxuan Pan et al. (+6 more)
Rollout inference often dominates the wall-clock time of large-scale reinforcement learning (RL). In agentic RL, each trajectory alternates between model generation and environment interaction over multiple turns. Asynchronous trajectories consequently introduce new prefill (P) work while other trajectories remain in decode (D), making P/D coexistence a persistent property of the rollout rather than a one-time
prompt-ingestion event. On shared accelerators, persistent P/D coexistence can make prefill interfere with latency-sensitive decode and prolong rollout completion. P/D disaggregation avoids this co-location but requires separate device pools and KV-cache transfers. In-place multiplexing retains shared devices and KV state, but existing designs lack the communication isolation needed for large MoE deployments that combine
attention TP/DP with distributed expert execution. In practical implementations, P and D can issue intersecting collectives in inconsistent cross-rank orders; DeepEP's P and D paths also share mutable protocol state. We present AInfer-PD, which extends in-place P/D multiplexing to distributed MoE rollouts. AInfer-PD coordinates P/D collective order across ranks and gives the two DeepEP paths independent communication state, making crossed ADP/ATP and DeepEP paths safe for concurrent P/D execution. The design retains shared model weights and KV storage while coordinating P and D on the same devices. Across repeated single-node prefill-intensive workloads, AInfer-PD reduces fixed-workload rollout completion time by 7.1-22.5% relative to the same AInfer engine with P/D multiplexing disabled and by 24.8-32.9% relative to SGLang. On two nodes, the reductions are 18.0-35.3% and 18.3-31.8%, respectively. In a same-engine ablation, fine-grained boundaries reduce completion time by a further 8.6-19.8% over whole-epoch asynchronous enqueue.
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All authors Guowei Wang, Chaokun Yang, Zhenxuan Pan, Yipeng Wei, Yuhong Guo, Minghua Zhu, Zhechuan Zhang, Shuo Wan, Xiaowei Zhu
Sound
On the Human and Computer Alignment of Attribute-Based Music Matches
Roser Batlle-Roca, Woosung Choi, Joan Serrà et al. (+5 more)
Recent advances in generative AI are raising ethical concerns regarding the originality of generated content and the potential replication of training data, with further implications for transparency, attribution, and intellectual property. In music, several computational approaches have been proposed to identify potential replication, using audio-based similarity metrics. Yet, their
alignment with human judgments across distinct musical attributes remains underexplored. To address this gap, we conduct a perceptual experiment on music matches, defined as strongly similar musical excerpts. We focus on five musical attributes: melody, harmony, rhythm, voice, and timbre. We design a triplet-based forced-choice task comprising 300 cases, including plagiarism examples, cover songs, and AI-generated music. From this experiment, we introduce the MATCHA (Musical Attribute-based Triplet Comparison with Human Annotations)
dataset: a collection of 1105 perceptual assessments of attribute-based music matches from 83 expert participants. Our findings reveal measurable agreement among participants in identifying matches across attributes. We further observe partial
alignment between human judgments and computational similarity measures. Overall, this work underscores the importance of domain-specific and perceptually grounded evaluation frameworks for generative AI in creative practice.
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All authors Roser Batlle-Roca, Woosung Choi, Joan Serrà, Fabio Morreale, Wei-Hsiang Liao, Xavier Serra, Emilia Gómez, Yuki Mitsufuji
Information Retrieval
TGR: Advancing Industrial Recommendation from Generative-Paradigm Ranking toward Unified Generation and Reasoning
TGR Team: Lei Cheng, Haonan Hu, Beibei Kong et al. (+10 more)
Industrial recommender systems typically rely on cascaded
retrieval, pre-ranking, ranking, and reranking stages, whose separately optimized models limit scaling, fragment decision making, and lack semantic knowledge and
reasoning. We present TGR (Tencent Generative Recommendation), an industrial framework that advances recommendation toward the generative paradigm along three coupled directions. TGR-GenRank upgrades ranking through CCFormer, which combines unified feature tokenization, a scalable
Transformer backbone, feature-field separated cross
attention, subspace
token mixing, and hierarchical sequence compression while retaining per-item multi-task outputs. TGR-GenRec explores end-to-end generation under two paradigms: BARGE bridges item-boundary loss and semantic drift in hierarchical semantic-ID generation through item context-aware
attention, hierarchical path reranking, and orthogonal dual-path decoding; HiGR performs whole-slate generation with prefix-structured semantic IDs, coarse-to-fine decoding, and listwise multi-objective
alignment. TGR-Reason injects offline-generated semantic-ID reason
tokens into online decoding, providing
reasoning without request-time rollout. TGR is deployed across Tencent production surfaces serving hundreds of millions of users. CCFormer delivers significant gains in five A/B-tested scenarios and is fully launched in two, including +3.57% CTR and +1.71% advertising revenue. BARGE improves Hit@5 by 10.2-16.9% and yields +0.60% CTR and +1.70% reading time after full rollout. HiGR improves offline slate quality by 15.9-21.3% with a 5x inference speedup and achieves up to +1.22% watch time and +1.73%
video views. TGR-Reason raises cold-start new-user Hit@1 by 477.8% and delivers +1.75% effective consumption and +13.09% new-user exposure-to-conversion online.
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All authors TGR Team: Lei Cheng, Haonan Hu, Beibei Kong, Yudong Li, Zang Li, Yunsheng Pang, Hongyang Su, Jianchao Tu, Yunlong Wang, Bing Wen, Junzhang Zhu, Shaojie Zhu, Chengxiang Zhuo
Computer Vision
Semi-Supervised Virtual Staining via Morphology Preservation and Histopathological Realism Constraints
Baoshun Wang, Weiping Lin, Linwu Wang et al. (+3 more)
Virtual staining aims to computationally generate target-stained histopathological images while reducing the cost and time associated with conventional staining procedures. However, existing methods rely predominantly on strictly paired and accurately registered training data, which are difficult and expensive to obtain in routine practice. To reduce this dependence, we propose a stable semi-supervised virtual staining framework that jointly exploits both limited paired data and abundant unpaired source images. Directly incorporating unpaired images is challenging because their generated results lack corresponding targets for supervision, potentially leading to unrealistic staining, morphological degradation, or even training collapse. To obtain reliable supervision from these images, Hessian-derived morphology preservation extracts structural cues from each source image and constrains the generated output to retain tissue morphology. Histopathological realism constraints further guide the output toward plausible target-stain characteristics, preventing the source-derived structural supervision from degenerating into contour enhancement or simple color transformation. Together, the two components suppress structural and appearance drift, stabilize semi-supervised stain translation, and promote the preservation of diagnostically relevant information. Extensive experiments on H&E-to-IHC translation for Ki67 and HER2, as well as FFPE-to-H&E translation, demonstrate consistent improvements in image quality, morphology preservation, robustness, and downstream diagnostic performance. Code will be available.
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All authors Baoshun Wang, Weiping Lin, Linwu Wang, Yihuang Hu, Baptiste Magnier, Liansheng Wang
Computation and Language
Disclosure-Gated User Simulation for Companion-Agent Evaluation
Yao Liu, Yu He, Jihao Huang et al. (+2 more)
Playing the user with a large
language model is standard in scalable evaluation, and has a repeatedly diagnosed failure: the simulated user is excessively cooperative, so a system under test can score by the number of questions it asks rather than by making the user willing to speak. We answer with a disclosure gate conditioning information release on the companion
agent's behaviour: its state is a ladder of five ordered gates, merged onto three observable depth layers. We specify, ablate, and audit it. The gate is a load-bearing component of the environment: on a published companion-
agent benchmark's English corpus, withdrawing per-example gate statements from training displaces the ranking of the 12 systems beyond the noise band of a seed re-run, while per-system scores show no detectable change. We state two acceptance criteria: a ranking must be order-preserving, and absolute scores must be scale-stable. Only one candidate passes both – the simulator we release – correlating at 0.993 with the published leaderboard. Prompting a frontier model instead barely moves the ranking while shifting every score up, invisible to ranking-only checks; we supply the specification, ablations, and downstream sensitivity analysis the
benchmark's environment lacked.
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All authors Yao Liu, Yu He, Jihao Huang, Xinyang Liu, Feng Zhou
Computer Vision · Cross-listed from eess.IV
Prior-Guided Implicit Neural Representations for Single-Subject Diffusion MRI Super-Resolution
Abdulkader Ghandoura, Marsil Zakour, William Consagra et al. (+1 more)
Resolving complex fiber geometries in
brain white matter requires high-resolution
diffusion MRI at the cost of long acquisition times. This leads many clinical protocols to opt for low-resolution scans, making downstream microstructure estimation and tractography challenging. Implicit neural representations (INRs) can model the
diffusion signal continuously, enabling native single-subject super-resolution by querying the network at arbitrary spatial coordinates, yet existing methods often suffer from long training times and lack a mechanism to incorporate anatomical priors to regularize super-resolution by constraining the space of plausible reconstructions. To address these limitations, we propose a novel transfer-learning framework that pre-trains an INR on a high-resolution template and then adapts it to subject-specific scans via registration and fine-tuning. For $4\times$ through-plane super-resolution from 5 mm to 1.25 mm on Human Connectome Project (HCP) data, our method reduces NRMSE by 36-49% and increases FSIM by 24-43% over a recent baseline with $6\times$ faster training, outperforming competing INR-based methods across both image quality and domain-specific metrics. Code is available on the project page at https://abdulkaderghandoura.github.io/research/msc-thesis/ .
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All authors Abdulkader Ghandoura, Marsil Zakour, William Consagra, Yogesh Rathi
Digital Libraries
The zbMATH Open Knowledge Graph: Tracing Centuries of Mathematical Research
Yuni Susanti and Moritz Schubotz
We present the zbMATH Open Knowledge Graph, a large-scale RDF knowledge graph (KG) covering more than 250 years of mathematical scholarship. Unlike existing scholarly knowledge graphs that primarily capture bibliographic metadata and citation structures, the zbMATH Open KG integrates expert-curated semantic content, including reviews, keywords, subject classifications, software references, and disambiguated authorship. This combination of domain-specific representation of mathematical knowledge and extensive temporal coverage supports analyses that require fine-grained exploration of mathematical concepts, research fields, and scholarly relationships over time. The resulting graph comprises 34 million entities and 168 million RDF triples represented using established Semantic Web vocabularies, supporting interoperability and FAIR data principles. We further demonstrate its capabilities through query-driven historically grounded scholarly exploration use cases, illustrating how the knowledge graph can surface relationships and patterns that may be difficult to identify from bibliographic and citation information alone. The zbMATH Open KG provides an open semantic infrastructure for studying the development of mathematical knowledge and tracing scholarly connections across centuries of scholarship.
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Computer Vision
ReFlowSET: Representation-Aligned Latent Flow Matching for SAR-to-EO Image Translation
Jeonghyeok Do, Seungchul Lee and Munchurl Kim
SAR-to-EO image translation aims to generate electro-optical (EO) imagery from synthetic aperture radar (SAR) observations. Existing latent
diffusion approaches typically inherit a predetermined autoencoder, although reconstruction fidelity can vary substantially across codecs and modalities. Because the latent codec affects the round-trip preservation of both SAR conditions and EO targets, codec selection constitutes a fundamental design choice; nevertheless, existing methods largely rely on codecs pretrained on natural images. To remedy this, we introduce ReFlowSET, a conditional latent flow-matching framework that selects its codec through a joint SAR–EO reconstruction audit. Rather than inheriting a heavyweight pretrained generator, ReFlowSET trains a substantially smaller conditional DiT from scratch in the selected latent space, using dual-stream SAR conditioning followed by joint feature refinement. To provide semantic guidance for this from-scratch training, intermediate noisy-EO features are aligned with clean target-EO representations extracted by a frozen
vision foundation model. This
alignment is used only during training and introduces no additional inference cost. Experiments on QXS-SAROPT and SAR2Opt demonstrate state-of-the-art performance across diverse perceptual fidelity and distributional metrics. Code and pretrained weights are publicly available at https://github.com/KAIST-VICLab/ReFlowSET.
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Artificial Intelligence
CoBRA: Learning Tool-Use Boundaries via Counterfactual Margins
Wenhao Zou, Xianglong Liu, Wendong Bi et al. (+3 more)
As large
language models increasingly act through external tools, deciding when to call a tool has become a central problem alongside deciding how to use it. Unnecessary tool calls introduce latency, cost,
retrieval noise, and error propagation, while missed calls hurt knowledge-intensive queries or questions requiring up-to-date evidence. Existing methods typically trigger tools from absolute query or generation signals, such as difficulty, confidence, or final task reward, and therefore lack an explicit estimate of the instance-level marginal benefit of tool use. We propose CoBRA, a counterfactual boundary-learning framework for tool-augmented
language models. CoBRA first constructs internal and external experts from the same base model, collects paired trajectories, and estimates the reward margin between answering with and without tools. This margin partitions data into internal-favored, external-favored, and ambiguous cases. CoBRA then uses clear-margin samples for Boundary-Aware Cold-Start SFT, followed by MARS-RL with reference-split rollouts and counterfactual marginal advantages to optimize boundary decisions. Experiments with
retrieval as the main tool on Qwen3-4B show that CoBRA improves tool-use efficiency and boundary-sensitive answer accuracy while maintaining strong performance on tool-dependent out-of-distribution questions.
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All authors Wenhao Zou, Xianglong Liu, Wendong Bi, Hanjie Wang, Simin Zhao, Gong Zhi
Sound
ABSE-NET: A Lightweight Neural Model for Active Binaural Speech Enhancement in Open-Fit Hearing Aids
De Hu, Xue Du, Qingying Zhao et al. (+1 more)
Open-fit hearing aids have attracted growing
attention due to their superior wearing comfort. However, the open-fit design inevitably causes acoustic leakage into the ear canal, degrading the performance of existing binaural
speech enhancement (BSE). To this end, we propose ABSE-NET, an active BSE framework integrating active noise control (ANC) with BSE to jointly enhance target
speech and suppress acoustic leakage. The ABSE-NET pipeline cascades a binaural MVDR (BMVDR) with a lightweight neural network (LNN). The former achieves a coarse BSE, whereas the latter simultaneously cancels acoustic leakage and compensates for BMVDR-induced distortion. The LNN uses an encoder-decoder with a feature fusion module, which includes frequency-time dependency learning and convolutional
attention blocks. Unlike traditional BSE+ANC solutions via adaptive filtering, ABSE-NET needs no in-ear microphone in practical deployment. Experiments validate its superiority over state-of-the-art methods. Code repository: https://github.com/Bream101/ABSE-NET.
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All authors De Hu, Xue Du, Qingying Zhao, Qintuya Si
Artificial Intelligence
Few-Shot Out of Domain Intent Detection with Covariance Corrected Mahalanobis Distance
Jayasimha Talur, Oleg Smirnov and Paul Missault
Conversational
agents like chatbots and voice assistants are trained to understand and respond to user intents. On encountering an utterance with an intent different from the ones they have been trained on, these
agents are expected to classify the intent as `unknown' or `out of domain'. This problem is known as out of domain (OOD) intent detection. Podolskiy et al. (2021), showed that Mahalanobis distance can be used effectively for identifying OOD intents, outperforming competing approaches. However, their method fails to outperform the baselines in the practically important few-shot setting. In this paper we analyze the reason for low performance and propose a covariance corrected Mahalanobis distance for detecting out-of-domain intents.
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Computer Vision
Conditional Flow Matching for Cross-Field MRI Harmonisation
Baris Imre, Aram Salehi, Levente Baljer et al. (+3 more)
Magnetic resonance images of the same subject look markedly different across field strengths, which complicates the comparison and pooling of data across sites. We address cross-field
brain-MRI translation for the MRIxFields2026 challenge, and in particular its Task~3: a single model that translates between any directed pair of the five field strengths and across three contrasts. We phrase the problem as a conditional flow matching path: because the source and target volumes are spatially registered, we learn a velocity field that carries the source slice directly to the target slice, rather than starting from noise. To learn this mapping from only three paired subjects, the unified model is trained in three stages: a degradation-bridge pretraining that distills a restoration prior from the abundant unpaired retrospective cohort, a cross-field finetuning over all directed pairs on the paired cohort, and an
adversarial refinement that sharpens the output. At inference, we integrate the learned velocity with a second-order Heun solver in a handful of steps. A restoration prior learned without any paired data already reaches a mean SSIM of 0.837, and each subsequent training stage improves on it. A single 6.3M-parameter model thereby covers all 60 field-pair and contrast combinations, with inference in five solver steps per slice. On the challenge evaluation set the model reaches a mean SSIM of 0.909, averaged over the three contrasts, outperforming regression and
diffusion baselines built on the identical network on all three challenge metrics.
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All authors Baris Imre, Aram Salehi, Levente Baljer, Andrew Webb, Marius Staring, Efe Ilicak
Computer Vision
Candidate-Expanding Routing with Permutation-Stabilized Experts for Mixed-Format Medical VQA
Hai-Dang Nguyen and Huy-Hieu Pham
Mixed-format medical visual question answering (VQA) requires stable option selection and machine-readable free-text output. The two formats fail differently: multiple-choice predictions can change with option symbols or positions, while clinically plausible open answers can fail automated evaluation when serialization is malformed. We address both challenges with an answer-text
memory, a permutation-stabilized
vision–
language expert, and a sparse candidate- expanding router. The cyclic schedule follows prior work; our contribution is to make expert top-2 a routable candidate alongside
memory and expert top-1. On a 1,403-case retrospective internal analysis, this expansion improves a matched binary router from 88.95% to 91.73% (+2.78 percentage points; 95% CI 1.57–3.99), with 56 rescued errors and 17 regressions. Oracle coverage rises from 90.31% to 96.15%, and the final submitted configuration reaches 92.23% on the same retrospective split. For open questions, strict generation and deterministic guards produce 475/475 schema- valid participant-facing outputs without repair, retry, or hard-gate failure. Visual ablations reveal substantial textual dependence. Candidate expansion supplies the principal controlled routing gain; open-path evidence establishes output-contract validity rather than clinical correctness in medical use or deployment.
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Computation and Language
PersianAnonymizer: Evaluating LLM-Labeled Training for Efficient NER-based Anonymization in Persian
Mohammad Hossein Shalchian, Mostafa Amiri and Amir Mahdi Sadeghzadeh
We target practical anonymization of Persian customer chats by training a compact NER model from LLM-labeled supervision and selecting the best labeler for deployment. We compare three instruction-tuned LLMs: DeepSeek-V3-0324, GPT-OSS-120B, and Qwen3-235B-A22B-Instruct-2507, to produce span annotations under a shared JSON protocol, yielding four corpora (OSS_ZeroShot, Qwen_ZeroShot, Qwen_FewShot, DeepSeek_FewShot). A MatinaRoberta-based
token-classifier is trained per corpus and evaluated with
token-level Precision/Recall/F1 (overall and per-class). We also report Label Coverage Recall (LCR), the proportion of gold non-O
tokens predicted as non-O, and quantify cross-labeler behavior via a
token-level Venn on test annotations. Finally, we contrast test-set annotation latency of the LLMs on H200 nodes with the trained NER's test-time labeling on a single RTX 3090. Results show that supervision from OSS_ZeroShot yields the strongest macro-F1 and LCR, while the resulting NER labels an entire 40K-message test set in approximately 2 minutes on one consumer GPU. This establishes a practical path to high-quality, low-cost anonymization for Persian industrial data.
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Computer Vision
PredErase: Training-Free Object-and-Effect Removal with Predictive Latent Guidance
Waikit Xiu, Qiang Lu, Junbiao Chen et al. (+1 more)
Removing an object is not the same as filling its mask. Cast shadows and contact shading usually lie outside the user-provided instance mask M_obj, so a frozen Fill model that edits only that mask leaves the object's photometric footprint on nearby surfaces. Supervised removers learn this joint erasure from paired clean plates. Training-free editors freeze pretrained weights, yet most still treat M_obj as the entire editable support and steer sampling with CLIP or DINO energies that do not predict the occluded scene. We present PredErase, a training-free inference procedure on frozen FLUX.2 and I-JEPA. The method separates where Fill may rewrite pixels from what structure should occupy the hole. A contact-band expansion M_flux of M_obj exposes local residuals on the supporting plane. I-JEPA, pretrained for masked
token prediction, supplies a context-conditioned hole target in representation space; sparse projected gradients align decoded Fill completions with that target inside the instance, while coordinates outside the packed support stay locked. Under instance-only masks on RemovalBench, RORD-Val, and DEFACTO-Val, PredErase improves the native FLUX.2 backbone. Supervised removers remain stronger on several full-image appearance metrics; the supported claim is training-free object-and-effect editing of frozen Fill, not replacement of paired-data erasers.
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All authors Waikit Xiu, Qiang Lu, Junbiao Chen, Xiying Li
Computer Vision
ASSERT: Adaptive Stochastic Sampling for Robust Diffusion Models on Analog Compute-in-Memory Hardware
Yuannuo Feng, Yizhe Chen, Wenshuai Yao et al. (+4 more)
Diffusion models achieve strong image generation quality but incur high iterative denoising costs. Analog compute-in-
memory (CIM) can accelerate
matrix-vector multiplications, yet spatial
memory variations perturb weights and accumulate during sampling. Unlike conventional neural networks,
diffusion models' temporal sensitivity to hardware noise remains underexplored. We investigate
diffusion inference using a noise model calibrated and validated against measurements collected from multiple physical CIM chips. Our results show that the early, high-noise denoising stage is substantially more vulnerable than the final refinement stage. A first-order trajectory analysis attributes this behavior to the repeated propagation of correlated prediction errors induced by a fixed hardware mapping. Based on this observation, we propose ASSERT, a training-free sampler that uses higher stochasticity early and smoothly transitions to deterministic denoising. The injected stochasticity changes subsequent activation trajectories and thereby reduces their
alignment with persistent spatial errors. Across the evaluated settings, ASSERT achieves up to 2.58$\times$ lower FID than deterministic DDIM on high-resolution
datasets and 7.68$\times$ lower FID in the CIFAR-10 step-count study, without changing model parameters or the number of network evaluations.
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All authors Yuannuo Feng, Yizhe Chen, Wenshuai Yao, Yuxin Xie, Ngai Wong, Wenyong Zhou, Wang Kang
Cryptography and Security
Influence of Logging Frameworks on Bind9
Max Schrötter, Hannes Signer and Bettina Schnor
Host-based Intrusion Prevention Systems (IPS) rely on application logs to detect and block malicious activity. However, on modern high-speed networks the logging subsystem itself becomes a bottleneck: an attacker can hide traces simply by generating enough traffic to overwhelm the application's log pipeline, dropping crucial traces. In this work, we show that widely deployed setups such as Fail2Ban monitoring BIND9 can be defeated with less than 65 Mbps of DNS traffic. Further, we show that when replacing core components of the IPS architecture with their higher-performance equivalent, iptables with eBPF and regex matching with Hyperscan, the logging backends themselves become the bottleneck. Therefore, we present FIPS, a new IPC designed for high-performance logging that bypasses the kernel and reduces copying of the log messages to a minimum. FIPS uses per-thread lock free shared
memory ring buffers, supporting multiple independent consumers reading the same log stream at their own pace. FIPS offers both a native API and a drop-in replacement for the syslog interface. Our evaluation with BIND 9 shows that FIPS introduces almost no overhead compared to disabled logging, logs more requests than any other evaluated framework, and enables the IPS to ban malicious clients $2.5\times$ faster than with file logging while sustaining $2^{16}$ attacking clients at one million requests per second.
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Computer Vision
CERF: Communication-Efficient and Retraining-Free Collaborative Perception
Jiuwu Hao, Ziyi Ni, Liguo Sun et al. (+5 more)
Collaborative perception shares information among multiple
agents to obtain a comprehensive scene representation, enhancing the perceptual capability of individual
agents. However, most existing methods rely on transmitting and fusing dense feature maps for collaboration, which incurs inevitable communication overhead and heterogeneity challenges, limiting their practicality for real-world deployment. To address these challenges, we propose CERF, a novel Communication-Efficient and Retraining-Free framework for open heterogeneous collaborative perception. In CERF, we introduce a new virtual modality (termed Poture), which is generated from the perception outputs of other
agents, to augment the extracted Bird's Eye View (BEV) features of the ego
agent. To mitigate transmission delays, we employ a Kalman-filter based tracker and a motion
forecasting model to derive the current predictions from historical perception results. Extensive experiments demonstrate that CERF achieves performance comparable to mainstream intermediate-collaboration methods while reducing communication overhead by 95% across various downstream tasks. Furthermore, CERF enables seamless integration of unknown heterogeneous
agents into the existing collaborative framework without additional retraining costs. Code is available at https://github.com/uestchjw/CERF.
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All authors Jiuwu Hao, Ziyi Ni, Liguo Sun, Yuting Wan, Yueyang Wu, Ti Xiang, Haolin Song, Pin Lv
Robotics
HitMem: Hierarchical Temporal 3D Memory with Multi-Modal Context-Aware Retrieval for Dynamic Environments
Ruijie Tang, Chenye Zou, Guoquan Wu et al. (+3 more)
Executing long-term tasks in dynamic environments requires
embodied agents to maintain robust and adaptive 3D scene representations. However, most existing 3D
memory frameworks rely on static world assumptions. When objects are displaced by human activities or unobserved events,
agents encounter
memory-observation conflicts and often require costly
geometric recomputations or inefficient global re-exploration. To address this, we propose HitMem, a hierarchical temporal 3D
memory framework with a multi-modal context-aware
retrieval mechanism. Through continuous perception, HitMem unifies semantic and spatial information into a lightweight topological graph that captures support relationships, while a temporal decay mechanism dynamically regulates
memory activeness to mitigate the impact of stale representations. In addition, the multi-modal context-aware
retrieval mechanism defaults to filtering candidates using integrated semantic, spatial, and temporal
memory features, and activates a specialized two-stage
retrieval process when object displacement is detected. This process combines spatial constraints inferred from external
agent trajectories with semantic common sense grounded in class affinities, efficiently identifying high-probability candidate regions. Extensive evaluations on our constructed Dyna-THOR
benchmark demonstrate that HitMem significantly improves object relocation accuracy, reduces exploration costs, and enhances task execution performance in dynamic environments.
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All authors Ruijie Tang, Chenye Zou, Guoquan Wu, Jun Wei, Wei Chen, Jiaxin Zhu
Computation and Language
Calibration is the Bottleneck: An Action-Class Diagnostic of Multi-Turn Tool-Calling
Kangjia Zhao, Jiajun Li, Haozhan Shen et al. (+8 more)
Multi-turn tool calling is a core evaluation scenario for large
language model (LLM)
agents. On public tool-calling
benchmarks, open-weight models now approach or even surpass closed-source frontier models in aggregate accuracy. However, this metric averages over many different multi-turn situations and obscures whether progress is balanced across them. We propose an action-class-oriented diagnostic framework that decomposes multi-turn failures into two orthogonal modes: action-class miscalibration and action-execution failure. The framework operates over a four-class action space (TOOL_CALL/ASK/REFUSE/CONFIRM) and introduces a self-revealing upper bound Acc <= GAR (Gold Action Recall); the two modes show up as bound violation (Acc > GAR, exposing state-grader masking of miscalibration) and large bound slack (GAR >> Acc, localizing execution failure within TOOL_CALL). We validate it on a panel of tool-calling models across multiple multi-turn
benchmarks. Across our panel, the diagnostic reveals action-class miscalibration as a substantial failure mode the state grader cannot see. This gap inflates standing for heavily tool-trained families, which our diagnostic separates from families with context-appropriate action choice. Calibration is reshapable through context-only perturbations, but the reshape is heterogeneous: a single perturbation moves accuracy in opposite directions across families (up to +11.5 vs -21.0 pp on the same scenario), and its effect further depends on the perturbation mechanism. We argue that multi-turn tool-calling evaluations should supplement aggregate accuracy with action-class diagnostics that expose what the model actually does in each scenario.
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All authors Kangjia Zhao, Jiajun Li, Haozhan Shen, Wei Chow, Linfeng Li, Hang Song, Lingdong Kong, Chen Zhi, Tiancheng Zhao, Songhua Liu, Jianwei Yin
Computer Vision
From Terminology to Diagrams: Visual-Instruction Generation for Scientific Diagram Understanding
Raul Ortega and José Manuel Gómez-Pérez
Vision-
language models (VLMs) have demonstrated strong performance in visual question answering with natural images. However, they continue to struggle with scientific diagrams, which are designed to convey functional or relational meaning rather than literal scenes. We therefore introduce a framework for generating large-scale diagram-grounded instruction data by leveraging terminology derived from scientific curricula. Our approach systematically extracts domain concepts, synthesizes atomic facts, retrieves relevant diagrams from the web, and generates
multimodal supervision in the form of diagram captions and multiple-choice questions. Using this pipeline, we construct SciGram, a
dataset of over 194K diagrams and 1.4M visual instructions across life, earth, and physical sciences. Despite relying on noisy web data and synthetic annotations, models fine-tuned on SciGram achieve substantial improvements on diagram-centric
benchmarks, including TQA, ScienceQA, and AI2D, outperforming or matching state-of-the-art VLMs while using fewer training instances. Furthermore, augmenting existing models such as LLaVA OneVision with SciGram establishes new state-of-the-art performance on diagram question answering. Our results highlight the effectiveness of terminology-grounded instruction generation as a general strategy for improving
vision-
language reasoning in scientific domains. To support future research in scientific diagram understanding, we release both the SciGram
dataset and models.
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Artificial Intelligence · Cross-listed from stat.ML
Embedded Conditional Independence Tests for Large Language Model Generated Text with an Application to German Parliament Speeches
Marco Simnacher, Georg Keilbar, Benjamin König et al. (+2 more)
Conditional independence tests (CITs) test for conditional dependence between two random objects $X$ and $Y$ given a third random object $Z$. Existing CITs have limited applicability to high-dimensional data, especially
multimodal data like text. However, we show that such tests are of interest for large
language model (LLM) outputs, where we test whether an output $X$ generated from a source text $Z$ carries information about an attribute $Y$ beyond $Z$ itself. For this purpose, we propose embedded CITs (eCITs), which embed $X$ and $Z$ and apply an existing CIT to the resulting representations and to $Y$. We show that, provided the
embedding of $Z$ is sufficient, i.e. retains the information $Z$ carries about either $Y$ or the representation of $X$, the null hypothesis transfers from $X$ and $Z$ to their representations, so that a CIT valid for the embedded hypothesis is valid for the original one. We further give conditions for equivalence of the two hypotheses, and show that sufficiency weakens to mean sufficiency when the embedded test targets conditional mean independence. We propose a semi-synthetic simulation design to assess type I error (T1E) control and power of the eCITs for given
embedding maps on a specific
dataset and task, and use it to evaluate them on our application. Applying the eCITs to German Parliament
speeches, we find for all combinations of
embedding maps considered that the summaries of two LLMs contain information about the speaker's faction and gender beyond the
speech they were generated from.
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All authors Marco Simnacher, Georg Keilbar, Benjamin König, Christoph Lippert, Sonja Greven
Robotics
ProxPI: Proximal Prior Injection for Sampling-Based MPC under Learned-Prior Mismatch
Euncheol Im, Myotaeg Lim and Yisoo Lee
Combining learned policies with model predictive control can leverage learned task priors while retaining online adaptation to new objectives and constraints, but performance degrades when the policy is out of distribution. In policy-guided model predictive path integral (MPPI) control, a policy-centered warm-start approach centers the sampling distribution on the policy output. When the prior is mismatched, centering the sampling distribution on the policy output restricts exploration around an unsuitable solution and prevents recovery toward the task optimum. We propose Proximal Prior Injection (ProxPI), which retains nominal-centered MPPI sampling and incorporates the policy through a soft proximity cost. This matches the in-distribution performance of existing prior-injection schemes while enabling the optimizer to escape an inaccurate policy and recover vanilla MPPI-level performance. We theoretically show that re-centering on the prior discards the optimizer's correction at every update, whereas nominal-centered sampling retains it and converges to a solution set by both the task cost and the prior, and that this failure is not removed by a larger rollout budget. Simulations and real-
robot experiments demonstrate robust performance under both in-distribution and out-of-distribution tasks.
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Computation and Language
A Dataset for Modeling Iterative Problem-Solving
Fagun Patel, Sang T. Truong, Duc Q. Nguyen et al. (+4 more)
Solving problems through repeated attempts is a sequential modeling task: at each step, the solver receives feedback and decides how to revise their solutions. Predicting whether performance improves, plateaus, or regresses across attempts is central to understanding any iterative problem-solving process in both human learners and autonomous
agents. Beyond outcomes, modeling what errors persist and how strategies shift across attempts provides deeper insight into the mechanics of sequential learning. Studying these dynamics requires observing many solvers as they attempt, receive feedback, and revise. Programming courses with automated grading provide this setting, as students iteratively submit code to test suites and receive feedback on every attempt. We therefore curate CodeInsight, a large-scale
dataset of over 3 million submissions from 3,286 undergraduates across 2 introductory C++ courses in 2 academic years, with test-case-level outcomes, timestamps, and source code. On this
dataset, we build a
benchmark that evaluates models spanning parametric, sequential, and generative traditions under a shared calibration-and-scoring protocol, including a Recurrent State Space Model (RSSM) adapted to track solver characteristics through discrete latent variables and an LLM-based predictor that generates explicit solutions. The adapted RSSM achieves the strongest predictive accuracy on three of the four courses. The LLM predictor is less accurate but produces full submissions at each attempt, enabling direct analysis of failure modes. We find that the model's coding proficiency is inversely related to predictive performance in this setting, with the LLM better understood as a generative solver conditioned on context rather than a faithful predictor of solver behavior. We publicly release our code and the
dataset on request to facilitate future research.
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All authors Fagun Patel, Sang T. Truong, Duc Q. Nguyen, Kazunori Fukuhara, Benjamin W. Domingue, Sanmi Koyejo, Nick Haber
Information Theory
Exact Payload-Decoupling Conditions for Pilot-Only BEM Channel Estimation With Application to OTFS
Gianmarco Romano, Francesco A.N. Palmieri, Stefano Buzzi et al. (+2 more)
In high-mobility doubly dispersive links, basis expansion models (BEMs) reduce channel dimensionality, yet unknown payload symbols generally contaminate conventional matched-pilot channel estimates. This paper establishes the exact conditions under which such estimates become payload-independent and derives a pilot, guard, and data-placement rule that guarantees these conditions. We prove a necessary-and-sufficient zero pilot–data interference (ZPDI) condition under which the matched-pilot least-squares (LS) solution coincides with the maximum-likelihood (ML)
estimator for the reduced pilot statistic. When ZPDI holds, estimation requires a single precomputed projection. When it does not, the estimate contains a deterministic, channel-scaled payload bias that persists at high signal-to-noise ratio. A disjoint-support rule, independent of the selected basis, realizes ZPDI through pilot, guard, and data placement. We then specialize the framework to orthogonal time–frequency space (OTFS) and examine its structural and performance consequences. With the generalized complex-exponential BEM (GCE-BEM), the ZPDI
estimator remains within about $2$~dB of the perfect channel state information
benchmark in bit error rate at speeds up to 500~km/h.
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All authors Gianmarco Romano, Francesco A.N. Palmieri, Stefano Buzzi, Giovanni Di Gennaro, Amedeo Buonanno
Computation and Language
DualStake: Dual-Path Confidence Calibration in Deep Research Agents
Yinuo Xu, Yuwei Liang, Jianjie Cheng et al. (+4 more)
Deep Research
agents tackle knowledge-intensive tasks through multi-round
retrieval and decision-oriented generation. However, these
agents suffer from severe overconfidence, making their expressed confidence unreliable for user trust and downstream abstention. To address this, we augment the Deep Research pipeline with step confidence elicitation after each
retrieval, building on the commonly used post-answer verbalized confidence. Interestingly, we find that Evidence Confidence (E-Conf), elicited after the final
retrieval step, provides a stronger uncertainty signal than Answer Confidence (A-Conf), elicited after answer generation, and that A-Conf is largely shaped by E-Conf. Based on these findings, we propose DualStake, a dual-path calibration method that applies margin-clipped, confidence-dependent stake rewards to jointly align E-Conf and A-Conf with answer correctness while limiting extreme confidence optimization. Experiments on Qwen2.5-7B, Qwen2.5-7B-Instruct, and Qwen3-4B across 8 QA
benchmarks demonstrate that DualStake consistently improves calibration without sacrificing answer accuracy. The code is available at https://github.com/FloXXXt/DualStake.
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All authors Yinuo Xu, Yuwei Liang, Jianjie Cheng, Meng Wang, Yongcan Yu, Shuo Lu, Jian Liang
Computation and Language
Context-Grounding Gains Are Mediated by Pre-existing Machinery: Auditing GRPO, SFT, and DPO
Prakhar Gupta and Vaibhav Gupta
Language models can ignore
prompt evidence when it conflicts with memorized knowledge. Post-training can make models follow such evidence more reliably, but it is unclear whether these gains require new machinery or strengthen machinery already present. We compare nine post-training arms spanning GRPO, SFT, and DPO from one starting checkpoint, with key comparisons extended across scales and families. We estimate a grounding direction from that checkpoint before training. Across five tested GRPO variants, grounding gains are small. For the two variants replicated across seeds, equivalence tests bound their effects below the conflict-SFT gain even as the rewarded metric improves. Conflict-SFT improves grounding moderately, while DPO drives grounding near ceiling on its matched distribution. Conflict-SFT and DPO largely use the same causal
attention-head set as the starting model. Subtracting the starting-model direction suppresses both gains, while adding it to the starting model recovers 35% of DPO's gain at a dose passing all stated side-effect checks. After a supervised warm start makes the context answer appear in more rollouts, the same GRPO recipe adds essentially no further grounding gain. In our setting, grounding gains largely depend on machinery already present in the starting model.
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Computer Vision
Beyond the Image Plane: World-Grounded Queries for Multi-Object Tracking
Orcun Cetintas, Guillem Brasó, Tim Meinhardt et al. (+1 more)
Monocular videos record 3D scenes as sequences of 2D image-plane projections, obscuring depth and spatial relationships. Multi-object trackers localize and associate objects primarily using appearance and geometry observed only in the image plane, inheriting these ambiguities. To address this limitation, we introduce
PLANET, an end-to-end multi-object tracker designed to move beyond the image plane. As an enabling step, we lift existing 2D tracking
datasets into 3D. We then form world-grounded queries by
embedding reconstructed 3D scene geometry into the features and positional encodings used during query formation. An auxiliary 3D location prediction task further encourages the queries to encode object positions during training. A complementary dual-resolution temporal
memory preserves this evidence across longer temporal gaps. As a result,
PLANET achieves state-of-the-art performance across three diverse
benchmarks.
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All authors Orcun Cetintas, Guillem Brasó, Tim Meinhardt, Laura Leal-Taixé
Computer Vision
On-the-Fly3R: Towards Robust Online 3D Reconstruction with Feed-Forward 3R Models for Large-Scale UAV Scenarios
Zhe Shen, Liyuan Lou, Yifei Yu et al. (+4 more)
While feed-forward 3D reconstruction (3R) offers efficient end-to-end modeling, its application in large-scale
UAV mapping is hindered by the prohibitive
memory cost of
Transformer attention. Current scalable streaming 3R methods assume temporally and spatially continuous inputs, rendering them ineffective for the weakly ordered or unordered image streams common in cross-strip
UAV operations. To address this, we propose On-the-Fly3R, a training-free, progressive online 3D reconstruction framework for large-scale
UAV images that upgrades various 3R backbones for large-scale
UAV scenarios. Our method enables reconstruction from unordered inputs via
retrieval-guided dynamic subset construction, which adaptively selects spatially relevant images. To further improve the robustness, a validation-rejection-retry mechanism is designed to guarantee global consistency, performing a pre-integration consistency check and automatically rejecting misaligned images and retrying with alternative subset. Finally, inspired by VSLAM, pose graph optimization based on the
retrieval loop closure is employed to mitigate camera drift. Evaluations on several
UAV benchmarks show that our On-the-Fly3R successfully scales various 3R models to over 5,000 images across square-kilometer
UAV scenes, delivering substantially superior accuracy compared to several SOTA streaming 3R methods. Code is available at https://github.com/Sh1nZzz/On_the_Fly3R
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All authors Zhe Shen, Liyuan Lou, Yifei Yu, Guanbo Wang, Quanjian Ji, Xin Wang, Zongqian Zhan