Databases
Transformations for Evolving Property Graph Schemas
Gauvain Devillez, Stefania Dumbrava and Angela Bonifati
Property graph databases are widely used to represent complex and evolving data, yet systematic support for property graph schema
evolution remains limited. In practice, schema transformations are typically defined manually, coupled to specific application contexts, and difficult to reuse across schemas or
evolution scenarios. We present GRAFT, a logic-based framework that models property graph schema
evolution as reusable, order-constrained meta-transformations derived from atomic edits. Schema
evolution is formulated as the exploration of a finite meta-graph with schemas as nodes and grounded meta-transformations as edges. To ensure tractability, GRAFT combines similarity-guided search and
pruning, guaranteeing duplication-freeness, termination, and correctness. An experimental evaluation on four
benchmark and real-world property graph schema
evolution scenarios shows that GRAFT efficiently computes high-quality schema transformation sequences. Using greedy exploration, GRAFT reaches the exact target schema on most
datasets, producing stable transformation sequences while keeping runtimes low. A qualitative study on both real-world and synthetic large-scale
datasets further shows the quality and robustness of the obtained reusable meta-transformations.
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Machine Learning
Instance-Adaptive Prompts as Context for Time-Series Foundation Models
Zehao Xiao, Shifeng Xie, Lei Zan et al. (+5 more)
Longer histories can improve time-series foundation models (TSFMs), but require substantially higher inference cost. We therefore ask whether contextual information can be provided more efficiently through a compact set of learned
token embeddings. We introduce PaCTS, which generates a small set of instance-adaptive latent
prompts in the form of continuous
embedding tokens conditioned on the visible context. These
prompts serve as compact context surrogates for frozen TSFMs. PaCTS constructs them from instance-specific global statistics and further refines them with segment-level temporal information, capturing both global characteristics and local temporal variations. The
prompt module is jointly trained and deployed across heterogeneous time series with the frozen backbone. Extensive experiments demonstrate the effectiveness of
prompts as context, consistently improving
forecasting across context lengths and model architectures. With a shorter input context, PaCTS can outperform the same frozen backbone using double context while requiring substantially less inference computation. Compared with weight-space adaptation methods, PaCTS achieves stronger improvements and better out-of-distribution generalization.
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All authors Zehao Xiao, Shifeng Xie, Lei Zan, Jianfeng Zhang, Lujia Pan, Ievgen Redko, Malik Tiomoko, Keli Zhang
Artificial Intelligence
BEHAVE: Functional Behavior Modeling Enables Self-Improving Agents for Hardware Design and Verification
Yuheng Wu, Berk Gokmen, Sujeeth Jinesh et al. (+5 more)
Developing
agents for hardware design and verification requires reliable correctness feedback. As a hardware specification may permit correct implementations with different latencies, matching design and reference outputs cycle by cycle can reject valid designs. To address this, we introduce BEHAVE, an agentic framework for multi-turn joint hardware design and verification through functional behavior modeling. We define Behavior IR to express task functionality as executable behavior models without prescribing implementation timing beyond the specification. The
agent iteratively develops a register-transfer-level (RTL) design and a behavior model as the design's verification reference. Our evaluator, BEHAVE-Sim, checks both artifacts separately against a hidden golden behavior model using input stimuli generated by random sampling and solver-guided search. BEHAVE thus supports power, performance, and area (PPA) exploration across task-permitted latencies and microarchitectures. During training, the same evaluator provides verifiable reinforcement learning (RL) rewards from specification-behavior pairs without reference RTL. For self-improvement, the
agent continually searches for high-level implementations relevant to its capability gaps, constructs and checks specification-behavior pairs, and trains on the expanded task pool. We release BEHAVE-Train and BEHAVE-Eval with 600 human-reviewed specification-behavior pairs for realistic hardware workloads. Starting from 60 seed tasks and acquiring 100 new tasks, self-improvement raises Qwen3.8-27B's RTL pass@1 on BEHAVE-Eval from 55.0% to 75.0%, reaching performance comparable to RL using a 540-task pool.
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All authors Yuheng Wu, Berk Gokmen, Sujeeth Jinesh, Lauren McLane, Aarav Wattal, Qi Yang Huang, Zhaozhuo Xu, Thierry Tambe
Computer Vision
Projective Normal Fields: A Convex Optimization Method for Constructing Smooth UDFs
Jiayi Kong, Chen Zong, Fei Hou et al. (+3 more)
Constructing a smooth approximation of an unsigned distance field (UDF) from a raw point cloud is challenging because the input provides neither surface connectivity nor consistently oriented normals. Methods that directly learn a scalar UDF must also handle its non-differentiability on the zero level set and weak supervision away from the samples, which can lead to unstable optimization and spatial artifacts. We introduce Projective Normal Fields (PNFs), an orientation-free representation and convex optimization framework for estimating bidirectional normals from point positions alone. Each normal axis is encoded by a rank-one projector, which is invariant to normal reversal. We relax the non-convex set of hard projectors to its convex hull: the symmetric positive-semidefinite
matrices with unit trace. Each soft tensor defines a local quadratic distance model and retains the relative weights of candidate normal axes. We estimate a coherent PNF by combining local tangent-plane fitting, soft-PCA anchoring, and overlap regularization on a fixed neighborhood graph. With positive anchoring weights, the objective is strongly convex and admits a unique global minimizer. Principal eigenvectors provide bidirectional normals, while the corresponding eigengaps provide spectral confidence indicators. We use these indicators to select and weight directional sources for heat
diffusion, followed by Poisson integration to construct a regularized UDF approximation. By separating local geometry estimation from scalar-field construction, PNF avoids directly fitting the non-differentiable UDF. Experiments demonstrate reduced sensitivity to neighborhood size, competitive reconstruction under noise and outliers, and improved accuracy near non-
manifold junctions. The project page is available at https://anonymous17777367.github.io/PNF-page/
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All authors Jiayi Kong, Chen Zong, Fei Hou, Junhui Hou, Wenping Wang, Ying He
Computation and Language
TQTS-Bench: A Multi-Syntax Benchmark for Text-to-Query over Time-Series Databases
Fei Lyu, Zhiyi Peng, Jiaming Liu et al. (+5 more)
Large
language models (LLMs) have significantly advanced natural
language querying over relational databases, yet their ability to query time-series databases (TSDBs) remains largely unassessed. Existing
benchmarks fail to adequately capture the non-unified query syntaxes, diverse application domains, and unique time-specific query intents inherent to TSDBs. To address this gap, we introduce TQTS-BENCH, a multi-syntax
benchmark for evaluating text-to-query capabilities over TSDBs. TQTS-BENCH contains 6,125 high-quality question-answering (QA) pairs spanning 97 TSDBs, 23 distinct query syntaxes, 22 application domains, and 4 types of time-specific query intents. It is constructed through a human-centric AI-assisted workflow, where all QA pairs are carefully reviewed and revised by domain experts to ensure quality and correctness. Extensive evaluations of advanced LLMs and state-of-the-art text-to-query methods reveal challenges in querying TSDBs. Even the best-performing model evaluated, Claude-Opus-5, achieves only 48.98% execution accuracy, while humans reach 87.34%. Error analysis reveals that this performance gap mainly stems from the heterogeneous query syntaxes across different TSDBs, misinterpretation of time-specific intents, and incorrect schema linking. These findings highlight new opportunities to narrow the gap between current LLM capabilities and the requirements of TSDB queries in real-world applications. The
benchmark is available at: https://anonymous.4open.science/r/TQTS-Bench-00CD.
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All authors Fei Lyu, Zhiyi Peng, Jiaming Liu, Yixuan Yang, Changjian Chen, Zhuo Tang, Jiapeng Zhang, Kenli Li
Robotics
CoHuB: A Simulation Benchmark for Multi-Humanoid Collaboration
Hyunjin Park, Jebeom Chae, Minwoo Park et al. (+11 more)
Many physical tasks in human environments require collaboration, from assisting a partner to jointly manipulating an object. Yet, existing
humanoid benchmarks largely focus on single-
humanoid skills and lack evaluation of multi-
humanoid collaboration under
egocentric visual observations. We introduce CoHuB (Collaborative Multi-
Humanoid Benchmark), a simulation
benchmark for multi-
humanoid collaboration under
egocentric visual observations. CoHuB provides 10 tasks, eight with two
humanoids and two with three
humanoids, spanning diverse collaboration patterns. We also provide synchronized demonstrations collected through a multi-operator VR teleoperation pipeline, in which each operator controls one
humanoid from its
egocentric view. Experiments with representative visuomotor policies reveal substantial challenges across different forms of coordinated perception and control. CoHuB provides a foundation for developing and evaluating multi-
humanoid collaboration policies.
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All authors Hyunjin Park, Jebeom Chae, Minwoo Park, Sunghyun Park, Hanjun Yoo, Seoyeon Choi, Soochul Yoo, Joohwan Seo, Sarmad Idrees, Jae-Sang Hyun, Jongmin Lee, Roberto Horowitz, Youngwoon Lee, Jongeun Choi
Computer Vision
When VLMs Trust Context: Evaluating Scene Text Recognition under Misleading Context
Yuxing Cheng, Yuan Wu and Yi Chang
Vision-
language models (VLMs) can read text in natural scenes, but their predictions may be influenced by the surrounding context. When the printed text conflicts with what the scene suggests, a model may return a more plausible word instead of the shown text. We introduce SceneFaith, a
benchmark of 781 generated scene images for studying this behavior. Each output is classified as Literal, Canonical, or Other, separating faithful transcription from context-consistent rewriting and ordinary recognition errors. Across 15 models from seven families, all models show rewriting on clear images, with rates ranging from 8.45% to 58.51%. Controlled experiments further show that surrounding context matters: removing surrounding scene information reduces rewriting and improves literal accuracy, while changing the scene around the same text patch can also change model outputs. Moreover, weakening the target text with blur increases rewriting. These results show that reliable scene-text recognition requires VLMs to balance visual character evidence with contextual information, preserving clear text while using context mainly when the visual evidence is uncertain.
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Artificial Intelligence
Applying Language Models in Clinical Medicine: Recent Trends and Perspectives
Erik Aerts
The use and applicability of artificial intelligence (AI) in medical research and clinical practice has received increasing
attention in the literature over recent years. The emergence of large
language models (LLMs) has expanded discussions in regards to applications of AI within healthcare. While traditional deep learning based AI applications in medicine have often focused on specific and defined tasks, LLMs offer broader capabilities and flexibility in working with available data,. At the same time of writing, the integration of LLMs into medical settings raises important questions regarding their reliability, accuracy, transparency, safety, and appropriate role in a medical setting. This text presents and discusses recent talks and articles concerning the application of LLMs in medicine, with particular emphasis on their potential utility in research and clinical practice. It considers both the opportunities offered by these technologies and the challenges associated with their implementation, aiming to provide a perspective on the current and emerging role of LLMs within the medical field.
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Information Theory
Divergence-Minimizing Distribution Matching for Parallel Channels and Channels with Memory
Francesca Diedolo and Gerhard Kramer
Theory for distribution matchers (DMs) is extended to distributions with
memory. The divergence-minimizing DM is shown to select the sequences with the highest target probability, as in the memoryless case. A scaling law for divergence is extended to distributions driven by innovation processes. The theory is applied to parallel additive white Gaussian noise channels. A modified enumerative sphere-shaping (ESS) method with a weighted energy constraint is used in implementations. An illustrative example with three channels shows that joint ESS across channels reduces the rate loss by a large factor compared to product DMs at short blocklengths. The gains are confirmed by simulations with probabilistic amplitude shaping and a 5G-NR low-density parity-check code.
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Software Engineering
TraceLib: System-Call Bitmap Feedback Mechanism for Language-Agnostic Web Fuzzing
I Putu Arya Dharmaadi, Elias Athanasopoulos and Fatih Turkmen
Coverage feedback is an important source of guidance for fuzzing. However, obtaining such feedback normally requires application-level instrumentation that is specific to the
language and runtime of the application. Given that modern web applications span multiple
languages and runtimes, this application-level instrumentation is costly to implement and maintain. Therefore, we present TraceLib, a system-call feedback mechanism for enabling
language-agnostic web fuzzing. Our proposed approach observes the transitions of system calls, enriches selected transitions with bounded argument hashes, and converts them into a 65,536-position AFL-like bitmap. By using the generated bitmap, any web fuzzer can decide whether to retain requests that add previously unseen bitmap positions without consulting application code coverage. We integrate TraceLib into WebFuzz and evaluate it under five WebFuzz feedback modes: the two proposed TraceLib variants (one over every traced system call and one projected onto monitored file paths and recognized SQL buffers), the N-gram comparator adapted from Xiao et al. representing the most recent work to our knowledge, WebFuzz's Native grey-box feedback, and black-box fuzzing without feedback. We evaluate TraceLib on sixteen web applications under test (WUTs): eight PHP applications and eight further applications spanning Node.js, Ruby, Java, Go, and Python to demonstrate platform portability. The results show that our proposed TraceLib projected exceeds black-box on all eight PHP WUTs while exceeding Native on four: Joomla, Drupal, PrestaShop, and Bagisto. In addition, measured on an identical replayed request workload, the tracer costs approximately one millisecond of server-side latency per request. These results indicate that compact system-call feedback is a useful runtime-independent proxy for coverage guidance.
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Artificial Intelligence
Page-Aware Retrieval-Augmented Generation for EvalLLM 2026: A Five-Variant Study on French PDFs
Abdelhak kelious
We study
retrieval-augmented generation (RAG) for questions about French PDF documents when both the answer and its supporting document pages are evaluated. Five system variants add dense
retrieval, rank fusion, reranking, and query decomposition to a BM25 baseline. On 595 challenge questions, the complete system scores 0.4450 MRR@10 and 0.4013 Recall@10, compared with 0.3430 and 0.2994 for BM25. Dense
retrieval alone and a simple lexical–dense fusion both underperform BM25. Reranking improves the hybrid system, whereas adding query decomposition produces the largest further gain, with higher latency and more detected output artifacts. The complete system slightly exceeds the reported anonymous overall mean on two answer metrics but falls below it on most page-
retrieval metrics. These results identify accurate page selection, rather than semantic
retrieval in isolation, as the main opportunity for improvement in this setting.
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Artificial Intelligence
Before the Token Commits: Trajectory-Level Benchmarking of Visual Hallucinations in Diffusion VLMs
Yadong Wang, Siping Yue, Yu Tian et al. (+2 more)
Multimodal diffusion language models generate responses by iteratively unmasking
tokens, making each answer the endpoint of a multi-step trajectory rather than an immediate commitment.
Hallucination benchmarks built for autoregressive models evaluate only the final output, and therefore cannot determine whether an unsupported claim in
diffusion VLMs appears late or has already stabilized before any answer
token is revealed. We introduce DynaHall, a trajectory-level
benchmark of annotation-backed binary visual propositions covering object existence, counting, attributes, and relations, with controlled hard negatives graded by visual prior. DynaHall is paired with a commitment-aware protocol that records the intermediate answer tendency at every unmasking step alongside the committed output. Across five
diffusion VLMs from three architecture families, visual
hallucination is settled before commitment: an unsupported answer is already the preferred state while the answer position is still masked, and later unmasking steps rarely reverse it, so the failure is not introduced at the write step. This holds across decoding schedules, answer formats, and open-ended generation. DynaHall also exposes failures hidden by final-output metrics, including counting and relation collapse, prior-driven false positives, and attribute errors whose direction changes by type. Guided by this diagnosis, PGS (Pre-commitment Gradient Steering) edits still-masked answer states to reduce false positives, bringing the affirmation rate close to balance, and transfers to another architecture without degrading general ability. DynaHall and PGS suggest that
hallucination should be measured and mitigated along the generation trajectory of
diffusion VLMs, not only at the final answer.
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All authors Yadong Wang, Siping Yue, Yu Tian, Chuanxing Geng, Xiang Chen
Artificial Intelligence
When Do Model Internals Help? Exploring the Role of Representation Engineering in LLM Safety
Tianyi Guan, Jianhui Chen and Liangming Pan
Reliable AI safeguards require both control mechanisms that reduce unsafe behavior and monitoring mechanisms that detect safety risks during model interactions. Established behavioral safeguards include
alignment methods that optimize model outputs and text monitors that assess interaction text. Representation engineering instead reads or modifies internal model states, but the relative strengths of these approaches remain unclear because they are often evaluated under different settings. We present a matched evaluation across two tracks. For safety control, we compare DPO, a behavioral
alignment method, with three representation steering methods across robustness, practicality, and granularity. DPO provides the strongest overall control and generally improves with increasing training data, although its safety can degrade after subsequent benign fine-tuning. Representation steering remains competitive primarily in low-data settings, particularly with high-quality contrastive data. For safety monitoring, we compare representation probes with fine-tuned and open-weight text monitors across full-response detection, early detection, and computational cost. Specialized text monitors achieve the strongest overall detection accuracy, while representation probes remain competitive at substantially lower marginal cost. Finally, monitor-guided interventions recover much of the safety lost by DPO after benign fine-tuning, with little additional over-refusal. Overall, representation engineering does not generally replace behavioral safeguards, but offers practical advantages under specific conditions and can provide complementary safety benefits.
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Computation and Language
Reference-Grounded Data Curation for Instruction-Following Thai-English Machine Translation
Thodsaporn Chay-intr, Krittapad Harnchang, Mahannop Thabua et al. (+2 more)
Instruction-following machine translation (IF-MT) requires respecting
prompt-level rules on terminology, formatting, and register. Rule compliance typically trades off against translation quality, a tension that general-purpose IF data augmentation methods do not address. We propose Reference-Grounded Data Curation, a two-phase pipeline that extracts every supervised constraint from a reference translation that already satisfies it, ensuring feasibility by construction. Phase 1 applies Instruction-Following Difficulty (IFD) scoring to retain the hardest-but-learnable instances from an English-Thai parallel pool. Phase 2 extracts constraints from each reference target and keeps only generations satisfying every constraint, yielding the 1.97M-record Grounded
dataset. We fine-tune open-weight bases on Grounded to produce ChindaMT, a Thai-English translation family at 4B, 2B, and 0.8B parameters. Under length-controlled pairwise judging, ChindaMT outperforms or matches every same-size baseline at every tier on both plain translation and under explicit rules, reaching up to a 68.4% win rate against the strongest baseline. The recipe transfers cleanly across Qwen generations. We release model weights, the Grounded
dataset, and evaluation suites.
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All authors Thodsaporn Chay-intr, Krittapad Harnchang, Mahannop Thabua, Kobkrit Viriyayudhakorn, Thanaruk Theeramunkong
Computation and Language
LongPuzzleBench: Evaluating GUI Agents on Long-Horizon Visual Puzzles
Bingo Zhang, Haochuan Lu, Zongjie Li et al. (+3 more)
GUI
agents need long-horizon visual
reasoning: they must interpret a changing interface while keeping a multi-step plan viable as earlier actions constrain later ones. Existing
benchmarks evaluate grounding, computer use, and game play, but rarely test whether
agents stay coherent across long chains of coupled decisions. Long-horizon visual puzzles expose this capability directly: a legal move that looks like progress can make the puzzle unsolvable, and the loss shows only several moves later. We introduce LongPuzzleBench, 114 levels in six puzzle games played through native GUI actions, where one objective can take a human over a thousand actions on persistent boards and dead ends go unannounced. With Native GUI Actions alone, the strongest
agents solve most objectives, but success falls sharply on harder, longer boards: seven of ten general-purpose
agents solve nothing harder than Medium, and none completes Bolt Unscrew Hard, which a human solves along with every other objective. Code Execution CUA does not close this gap, and its scores mix visual solving with algorithmic search. Controlled diagnostics trace these failures to one limitation that neither rules, state hints, nor failure
memory removes:
agents judge each move by the visible progress it makes, not by the future options it leaves.
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All authors Bingo Zhang, Haochuan Lu, Zongjie Li, Genjian Li, Ari Yu Zhang, Chaozheng Wang
Artificial Intelligence
Privacy-Preserving Full-Body Meshing from mmWave Radar via Mesh Foundation Model Supervision
Shuxing Zhang, Yongquan Ni, Zhenyu Ding et al. (+1 more)
Millimeter-wave (mmWave) radar enables
privacy-preserving human perception, but the extreme sparsity of point clouds from commercial single-chip sensors (mean ~6.5 points/frame; ~28% empty frames) has confined prior art to body-part keypoints or discrete action classification. We present a cross-modal teacher-student framework that lifts commercial radar to full-body, per-frame, metric 3D mesh reconstruction with per-joint uncertainty. Three innovations: (1) a mesh-foundation-model teacher - SAM 3D Body produces whole-body MHR ground truth (70 joints, 18,439 mesh vertices) from a single RGB frame with zero training, slashing annotation cost by orders of magnitude; (2) StudentPoseFormer - set encoding with masked
attention pooling, a temporal
Transformer, and a CVAE multi-hypothesis head that outputs both the pose mean and per-joint variance, honestly reporting where the radar cannot see; and (3) a multi-stage ground-truth quality pipeline (confidence gating, depth validation, temporal smoothing, bone-length consistency, bad-frame rejection) plus systematic information-lever ablations. On the public MM-Fi
benchmark (same TI IWR6843 sensor, cross-subject), our full configuration reaches 7.45 cm 12-joint MPJPE, with ablations proving the causal value of point accumulation (k = 3, -0.34 cm), Doppler (-0.85 cm; -2 cm at the wrist on fast actions), and velocity loss (-0.27 cm). On our own synchronized radar + RGB-D corpus with block-level held-out splits, the pipeline achieves 21.47 cm end-to-end (per-joint hierarchy from 4.8 cm at the hip to 34.7 cm at the wrist - matching physical information limits), could be improved to 15 cm with ~30k diverse samples, and a scaling law shows sample diversity, not volume, is the binding constraint. Deployment inference is radar-only - no camera, no image.
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All authors Shuxing Zhang, Yongquan Ni, Zhenyu Ding, Yawen Lin
Computer Vision
Beyond Reconstruction Loss in Post-Training Quantization: Balanced Fitting for Large Vision-Language Models
Minchan Kang, Kyeonghye Park, Seungyeon Sa et al. (+3 more)
Post-training quantization (PTQ) enables efficient deployment of large
vision-
language models (LVLMs), but is typically calibrated on a small set while expected to generalize across diverse downstream tasks. Although recent PTQ methods for LVLMs incorporate sensitivity signals, they still minimize reconstruction loss with respect to the full-precision model, potentially over-preserving FP behavior and calibration-specific bias. Rather than treating quantization solely as an error to be minimized, we observe that it can also provide beneficial regularization for certain layers and modalities. Motivated by this observation, we propose Balanced Fitting, a quantization effect-based framework that balances precision and regularization beyond reconstruction-based optimization. By measuring layer- and component-wise quantization effects for weights,
vision activations, and text activations, Balanced Fitting combines fine-grained fitting for sensitive components with coarser fitting to exploit potential regularization benefits. Experiments on multiple LVLMs show that our method consistently outperforms prior PTQ approaches under both weight-only and weight-activation quantization, while lower reconstruction loss does not reliably translate into better downstream performance. The source code is publicly available at https://github.com/kmc3661/BFQ
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All authors Minchan Kang, Kyeonghye Park, Seungyeon Sa, Seoyoung Cho, Daeshik Kim, Yucheol Cho
Databases
WeaveData: A Multimodal Data Analysis System with Self-Critiquing and Self-Evolving LLM Plans
Min Jia, Shihao Zhou, Jun-Peng Zhu et al. (+9 more)
Multimodal data analysis, which answers questions over relational tables, text, and images, has attracted growing
attention in the data management community. Large
language models (LLMs) enable such analysis in natural
language by generating analysis plans over relational and semantic operators. However, LLM-generated plans are error-prone: a plan may silently compute something other than what was asked, fail during execution, or return a result that misses the question. This paper presents WeaveData, a
multimodal data analysis system with self-critiquing and self-evolving LLM plans. First, WeaveData generates a typed logical plan for each question and critiques it step by step before execution, and it checks the executed result against the question afterwards. Second, WeaveData evolves a plan that fails or misses the question: it diagnoses the failure with the actual data, reuses the results that remain valid, and accumulates planning experience for later questions. Third, WeaveData grounds planning in a metadata knowledge graph of all modalities, clarifies ambiguous questions with the user, and backs every model judgment with evidence in an interactive notebook. We demonstrate WeaveData on two public
multimodal datasets.
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All authors Min Jia, Shihao Zhou, Jun-Peng Zhu, Peng Cai, Kai Xu, Chao Zhang, Li Li, Aoying Zhou, Heng Long, Qiu Cui, Liu Tang, Qi Liu
Information Theory
GPU-Accelerated Robust Beamforming for OTFS Massive MIMO with CSI Uncertainty
Haonan Zhang and Weihua Wu
This paper focuses on robust beamforming for orthogonal time frequency space (OTFS)-enabled massive multiple-input multiple-output (
MIMO) systems under channel state information (CSI) uncertainty. In high-mobility satellite communications, uncertain CSI severely degrades beamforming accuracy and poses a major challenge to meeting users' quality of service (QoS) requirements. To address this challenge, we first formulate a chance-constrained optimization problem aiming to minimize the total transmit power while guaranteeing a predefined outage probability. Building on a 3D delay-Doppler-angle (DDA) channel representation, we propose a data-driven approach using support vector clustering (SVC) to model the uncertain CSI as an asymmetric uncertainty set. We then derive a robust counterpart that reformulates the intractable chance constraints into a deterministic semidefinite program. Finally, we design a graphics processing unit (GPU)-accelerated parallelizable alternating direction method of multipliers (ADMM)
algorithm to address the computational complexity of large-scale
antenna arrays. Simulation results show that the proposed SVC-based design reduces transmit power compared with conventional beamforming schemes, and that the GPU-accelerated ADMM achieves a speedup. These results confirm that the proposed framework achieves improved robustness, energy efficiency, and efficient computation in dynamic massive
MIMO networks.
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Numerical Analysis
Addressing Data Engineering Challenges in the Cold-Chain Sector to Reduce its Environmental Impact Through Analytics
Mathilde Marcy, Camille Fertel, Thomas Suquet et al. (+1 more)
Data analytics offer a great opportunity for organizations within the cold-chain industry to become more sustainable and reduce their environmental footprint. Yet, despite recent progress in data sciences, they still face challenges to ensure data quality and streamline data processes through adapted full-scale ecosystems, limiting their ability to perform sustainable analytics. Most data techniques and tools stem from the data-science community and are not always suitably adapted to domain specificities. For instance, nearly all data-quality frameworks are designed to individually consider entities, i.e. representations of real-world elements. However, achieving environmental impact reduction in industrial settings, particularly within the cold-chain sector, often requires modelling and studying entire systems comprised of multiple interdependent entities. This publication challenges the application of conventional data science and engineering concepts in data ecosystems within the cold-chain sector and proposes an alternative approach to sustainably address prevalent challenges and thus optimize analytical processes.
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All authors Mathilde Marcy, Camille Fertel, Thomas Suquet, Gérald Cavalier
Computer Vision
Zhen Wang, Changpeng Wang, Zhe Liu et al. (+5 more)
Recent
vision-
language models (VLMs) have advanced
vision-and-
language navigation (VLN), enabling models to predict
navigation actions from visual observations and
language instructions. In this work, we explore VLN with panoramic observations and introduce PanoVLN. The motivation is straightforward: more complete visual context should enable better-informed
navigation decisions. For example, a panorama can reveal a passage outside a perspective camera's field of view, allowing the model to identify the intended route without additional exploration. However, we find that simply replacing perspective images with panoramas yields only limited gains. Our diagnosis suggests that fully exploiting wider visibility requires modifications to action prediction, training supervision, and visual representation. First, wider visibility supports longer-horizon action planning. We make the model predict longer action sequences, enabling larger turns and subsequent movement from a single panorama. Specifically, we introduce a confidence-guided execution (CGE) strategy that dynamically determines how many predicted actions to execute before replanning. Second, wider visibility also brings more complex route choices. We therefore construct training routes with frequent branching points and clear instructions to provide targeted supervision for route selection. Third, panoramic
navigation requires understanding spatial relationships across viewing directions, beyond recognizing individual landmarks. We combine semantic and
geometric features from RGB panoramas to capture both scene content and spatial layout without adding visual
tokens. With a 4B backbone and RGB-only input, PanoVLN surpasses the previous SOTA by 11.9% and 8.7% in success rate on R2R-CE and RxR-CE Val-Unseen. Real-world experiments on a quadruped further demonstrate faster
navigation with fewer pauses than prior VLN methods.
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All authors Zhen Wang, Changpeng Wang, Zhe Liu, Zhangyang Qi, Yuxiang Lu, Zimo Zeng, Donglian Qi, Xi Chen
Machine Learning · Cross-listed from stat.ML
Statistical Benefits of Fine-Tuning from Pretrained Initialization in Diagonal Linear Networks
Alexandre Declèves, Etienne Boursier and Nicolas Flammarion
Adapting pretrained models to downstream tasks with limited data has become a central paradigm in modern deep learning. Yet, despite its widespread practical success, how fine-tuning leverages information from pretraining remains poorly understood theoretically. We study fine-tuning from pretrained weights through the lens of sparse linear regression and two-layer diagonal linear networks. In our setting, pretraining provides information through the support (and signs) of the initialization predictor, which may contain coordinates relevant to the downstream task. We show how pretrained information reshapes the implicit bias and training dynamics, and can thereby reduce the sample complexity of recovering the target parameters and support. In particular, for a clean initialization with correctly inherited signs, we show that the required sample size is comparable to that of a weighted Lasso
estimator that explicitly exploits the pretrained support through a suitably chosen regularizer. Our results thus show how information encoded in pretrained weights can be implicitly exploited by gradient-based fine-tuning, reducing the amount of data needed to recover a downstream task.
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Computation and Language
Draft-KV: Learning Useful Latent Communication Between Language Models
Linquan Wu, Shichang Meng, Tianxiang Jiang et al. (+7 more)
Latent communication passes internal states between
language models instead of decoded text, but higher receiver accuracy does not show that the receiver used the message content. Across five method-
dataset pairs, replacing each message with one from an unrelated question changes accuracy by at most 0.60 points, even when communication adds 15.44 points over the receiver alone. Thus the interface can supply the gain while making the sharer dispensable. Draft-KV instead sends the key-value states formed while the sharer drafts an answer to the current question. Linear projections place these states in a side
memory read through a gated
attention branch, and progressive training moves from message reconstruction to answer supervision under a guard on harm from mismatched messages. Both models remain frozen and the interface trains 1.05M parameters, 348x fewer than C2C. With a Qwen3-8B sharer, a frozen Qwen2.5-0.5B-Instruct receiver reaches 78.04% on MMLU-Redux, versus 37.45% alone and 36.40% with reassigned messages. At fixed interface size, scaling the sharer from 0.6B to 8B raises accuracy from 46.11% to 78.04%; communication also transfers to held-out tasks and can exceed both models when each holds different evidence.
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All authors Linquan Wu, Shichang Meng, Tianxiang Jiang, Haoyu Yang, Peng Zhong, Fengming Zhu, Xi Peng, Linqi Song, Jacky Keung, Jingyu Zhang
Distributed Computing
Distributed Lower Bounds via Automatic Self-Reduction
Alkida Balliu, Francesco d'Amore and Dennis Olivetti
The development of round elimination into a general-purpose technique [PODC 2019] marked a turning point in our understanding of the hardness of many graph problems in the distributed setting and led to several breakthrough results. However, the round elimination technique seems unable to yield randomized lower bounds of $\omega(\log \log n)$ rounds as a function of the number $n$ of nodes. Very recently, Khoury and Schild [FOCS 2025] introduced a new technique called round elimination via self-reduction, which bypasses the limitations of classical round elimination. Using this approach, the authors show that any randomized
algorithm for maximal matching requires $\Omega(\sqrt{\log n})$ rounds in the LOCAL model. Their elegant technique is, in some respects, similar to classical round elimination while being fundamentally different in others. However, it is tailored specifically to maximal matching rather than being applicable to a broad class of problems. In this paper, we show that self-reduction is, in fact, a special case of classical round elimination, thereby turning it into a general-purpose approach. In particular, we introduce a new way to measure the error of an
algorithm and show that, under this new measure, classical round elimination can indeed yield $\omega(\log \log n)$ randomized lower bounds. More specifically, we identify a large class of problems for which this improvement is entirely black-box: once a problem is shown to belong to the class, stronger randomized lower bounds follow automatically from the classical round-elimination framework. As an application, we prove $\Omega(\sqrt{\log n})$ randomized lower bounds for a range of graph problems, namely, maximal matching on regular $2$-colored graphs, $\frac{1}{k}$-integral matching, and maximal $H$-packing.
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Software Engineering
Multi-SWT-Bench: A Multilingual Benchmark for Reproduction Test Generation
Kazuki Kusama, Sota Nakashima, Haruka Tokumasu et al. (+3 more)
Reproduction test generation translates a natural-
language issue description into executable tests that fail on the original code and pass after the issue is resolved, providing executable evidence for verifying candidate patches. Existing
benchmarks are constructed for individual programming
languages, preventing a unified evaluation across diverse programming ecosystems. To address this limitation, we introduce MULTI-SWT-BENCH, a
multilingual benchmark for reproduction test generation consisting of 1,963 instances across eight programming
languages: Python, Java, TypeScript, JavaScript, Go, Rust, C, and C++. Using this
benchmark, we conduct an empirical study of state-of-the-art LLMs with four representative methods (MSWE-
agent, MOpenHands, Codex, and Claude Code) and perform a failure analysis across programming
languages. Our evaluation reveals a systematic
language gap. Across every evaluated method and LLM, the success rate on Python exceeds the aggregate success rate across all
languages, while C++ exhibits particularly low success rates. Our failure analysis identifies both
language-specific challenges arising from repository testing conventions and cross-
language challenges in inferring implicit setup requirements and preserving the target behavior through iterative revisions. These findings demonstrate the importance of
multilingual evaluation and provide actionable directions for developing reproduction test generation methods that generalize across software ecosystems and reliably capture issue-specific behavior.
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All authors Kazuki Kusama, Sota Nakashima, Haruka Tokumasu, Masanari Kondo, Lingming Zhang, Yasutaka Kamei
Machine Learning
A Unifying Framework of Concept-based Explainable AI with Completeness Guarantees
Vojtěch Kůr, Adam Kukučka, Tomáš Brázdil et al. (+1 more)
Concept-based explanations describe neural network predictions through human-understandable properties of inputs called concepts. The field encompasses approaches that differ in how they define and represent concepts and connect them to model predictions. We introduce a theoretical framework that describes these approaches in a common mathematical
language and supports a shared analysis of their properties. For concept discovery, which identifies concepts automatically within a latent space of a trained model, we employ a concept autoencoder view. An encoder extracts concept representations from the model's latent space, and a decoder uses them to reconstruct the original latent representation. The autoencoder's reconstruction error measures how accurately its decoder recovers the original latent representation. We revisit model completeness: how well the concepts can reproduce the model's outputs. We show that model incompleteness of the concepts can be bounded by the autoencoder's reconstruction error. The autoencoder view also provides a common way to define individual concept attributions, which measure each concept's contribution to a prediction. We establish when these attributions sum to the model's prediction, and bound the discrepancy otherwise, thus providing attribution completeness guarantees.
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All authors Vojtěch Kůr, Adam Kukučka, Tomáš Brázdil, Vít Musil
Computer Vision
CoDrive: Cross-Vehicle World-Consistent Video Generation with Precise Trajectory Control for Cooperative Driving
Yu Meng, Baining Zhao, Junta Wu et al. (+10 more)
Real-world driving is inherently multi-
agent, yet most existing driving world models generate observations from a single ego vehicle. Independently extending them to multiple vehicles does not ensure that different
agents observe a consistent shared world. We present CoDrive, a cross-vehicle, multi-view driving
video generation framework that jointly generates observations of vehicles sharing the same dynamic scene with precise camera-trajectory control. CoDrive interleaves local self-
attention, which models spatiotemporal dependencies among the views of each vehicle, with global self-
attention, which enables information exchange and consistency modeling across vehicles. To explicitly encode their spatial relationships, all camera trajectories are represented in a shared world coordinate system and injected into the
attention layers through projective relative positional encoding. We further adopt a progressive mixed-task training strategy that combines large-scale real-world single-
agent data with synthetic cross-
agent interaction data, allowing the model to benefit from real-world appearance distributions while learning cross-
agent consistency from simulation. For systematic evaluation, we introduce CoDrive-Bench, a
benchmark covering real and synthetic multi-vehicle scenarios and evaluating trajectory controllability, scene geometry consistency, and instance-level consistency. Experiments show that CoDrive improves trajectory controllability and cross-
agent geometric and instance consistency while maintaining competitive visual quality.
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All authors Yu Meng, Baining Zhao, Junta Wu, Tengfei Wang, Rongze Tang, Haiyu Zhang, Wenqiang Sun, Chen Gao, Zhibo Chen, Xinlei Chen, Yong Li, Xiao-Ping Zhang, Chunchao Guo
Machine Learning
No Pain, More Gain: Iterative Merging for Effective Multi-Teacher On-Policy Distillation
Seonghyeon Kim, Chaeyun Jang, Noah Lee et al. (+2 more)
Multi-teacher on-policy
distillation (MOPD) combines independently developed domain teachers into a single student by distilling their predictions on student-generated samples. We study a setting where teachers share a reference model but undergo different post-training procedures, and find that MOPD can struggle to recover some teacher capabilities. Because
distillation occurs on student-generated prefixes, the student initialization can strongly affect subsequent recovery. However, initial
benchmark performance is not a reliable predictor of a good MOPD initialization. For example, merge initialization can start below SFT warm-up yet finish higher after MOPD. We further find that effective merging depends on both the relative teacher contributions and the overall merge scale, with some strong configurations lying outside the simplex of convex parameter averaging. Thus, selecting a good merge initialization requires evaluating not only its immediate performance but also the learning it enables under MOPD, making one-shot coefficient search difficult. We propose Iterative Merging for MOPD (IM-MOPD), which starts from a uniform merge and progressively adds task-vector increments for under-recovered domains during
distillation. In a 5-domain setting, IM-MOPD achieves higher average normalized recovery than MOPD with either uniform merge initialization or SFT warm-up, showing that effective teacher contributions can be determined progressively during training.
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All authors Seonghyeon Kim, Chaeyun Jang, Noah Lee, Boseop Kim, Juho Lee
Cryptography and Security
Optimizing and Securing the Modern Watermarking Channel for Images
Enoal Gesny and Eva Giboulot
To comply with recent regulations requiring traceable generated content, modern watermarking has adopted multi-bit post-hoc watermarking schemes. These modern designs rest on an encoder-decoder pair implemented as deep neural networks. These models are usually treated as pure black-boxes trained end-to-end, with the noise of the watermarking channel modeled through a fixed set of
geometric and valuemetric transforms applied to watermarked images. We argue that this purely empirical approach leads to unquestioned design flaws and a lack of theoretical performance guarantees. This work proposes a general theoretical model of modern post-hoc watermarking schemes grounded in a statistical analysis of the outputs of the encoder/decoder pair. We show that these deep neural networks implicitly define a watermarking channel modeled as parallel AWGN channels, with messages transmitted using BPSK modulation. This imposes a binary alphabet, greatly limiting the capacity of these watermarking systems. Another fatal flaw is their lack of a secret key, making them intrinsically insecure. We make this notion of watermarking security precise for post-hoc schemes by linking it to the possibility of estimating the secret key under a given statistical model of the decoder's output. By putting together the results from this theoretical analysis, we introduce SNW: a novel post-hoc watermarking system that significantly outperforms existing state-of-the-art baselines in terms of capacity while also providing strong security guarantees. Notably, it does not depend on a fixed codebook or binary alphabet, allowing it to reach a rate close to Shannon capacity through the use of capacity-achieving error-correcting codes.
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Robotics
Simulation for Planetary Robotic Perception and Autonomy: A Concise Survey of Recent Capabilities and Gaps
Hoyun Kim and Giseop Kim
Planetary robotics is an important enabler of scientific exploration in environments where direct human-in-the-loop operation is costly, hazardous, or infeasible. However, developing and validating planetary robotic systems remains difficult because representative field testing is expensive, limited, and often unrepeatable under mission-relevant conditions. In this setting, simulation serves as a central tool for perception and autonomy research, synthetic data generation, system integration, and pre-deployment evaluation. Despite its importance, the literature on planetary robotics simulation remains dispersed across different simulation engines, implementations, and application settings. This paper surveys simulation works for planetary robotic perception and autonomy across four practical axes: Openness and Availability, Scenario and Platform Coverage, Sensor and Perception Support, and Environmental and Operational Realism. The surveyed simulation works report visual or physical fidelity and support perception-oriented workflows. They also indicate uneven public availability, rover-centered coverage, partial support for specialized sensing modalities, and uneven reporting of operational constraints such as onboard computation, energy, and communication restrictions.
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