Machine Learning
RLVR$^{2}$: Reinforcement Learning with Verifiable Rubric-based Ranking
Hao Li, Zhengkun Zhang, Gangqiang Hu et al. (+4 more)
Reinforcement Learning with Verifiable Rewards (RLVR) is expanding from tasks with well-defined correctness signals, such as mathematics and code, toward multifaceted quality requirements specified by multi-dimensional rubrics. Since policy optimization consumes one scalar per rollout, rubric-based pipelines must map multiple criterion scores into a scalar reward. This aggregation is often treated as score scaling, but it implicitly determines how quality dimensions trade off during training. The prevailing practice, normalizing each criterion and taking a linear combination, assumes that cardinal score differences are comparable across criteria and that gains on one criterion compensate for failures on another; both assumptions are unreliable when criteria are semantically heterogeneous. We propose Reinforcement Learning with Verifiable Rubric-based Ranking (RLVR$^2$), a verifiable ranking paradigm for rubric-based RLVR. For each criterion, RLVR$^2$ converts rubric scores into criterion-specific within-group ordinal outcomes, recovers a latent utility from the resulting comparison
matrix, and merges these utilities into one training signal. By retaining only within-group ordering and discarding raw score magnitudes, RLVR$^2$ avoids calibrating heterogeneous rubric scales. It further supports objective-preserving attribute adjustment: auxiliary attributes that correlate with observed rankings but are not training objectives can enter the estimation without expanding the rubric or rewarding them directly. Across three model scales and 16
benchmarks, RLVR$^2$ consistently outperforms representative rubric-based baselines, achieving the best overall performance on most
benchmarks at every scale. Analysis shows it controls systematic effects tied to
reasoning efficiency and response formatting while preserving the quality objective.
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All authors Hao Li, Zhengkun Zhang, Gangqiang Hu, Zhen Zhang, Yude Gao, Dai Dai, Jing Liu
Robotics
Smartphone GNSS Booster: Centimeter-Level Pedestrian Positioning Using a Portable Signal Re-Radiator
Taro Suzuki
High-precision positioning using the global
navigation satellite system (GNSS) embedded in smartphones is demanded for sidewalk-level pedestrian
navigation and pinpoint location-based applications. However, current smartphone positioning accuracy for pedestrians remains at the meter level. This is mainly because limitations of the compact linearly polarized (LP)
antenna in smartphones increase GNSS observation noise and hinder stable carrier phase tracking. In this paper, we propose an external GNSS signal re-radiation system that boosts smartphone GNSS observation quality while still using the smartphone's built-in GNSS receiver and
antenna. The system directly connects a compact active helical
antenna and a thin passive patch
antenna for re-radiation, and is designed to be attached directly to the smartphone. The proposed GNSS booster achieves (1) reduced thermal noise and stable signal tracking via increased received signal strength, (2) improved multipath robustness compared with a LP
antenna, and (3) stabilization of
antenna phase center variation. Static experiments confirm a substantial improvement in the observation quality of GNSS carrier phase measurements compared with standard smartphone positioning. Furthermore, in pedestrian experiments, the proposed booster enabled stable carrier phase integer ambiguity resolutions, achieving centimeter-level positioning using a smartphone.
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Distributed Computing
Conflicting Pattern Formation by Teams of Anonymous, Fully Disoriented Robots
Animesh Maiti, Prakhar Shukla and Subhash Bhagat
Two groups of autonomous, anonymous, and oblivious mobile
robots are deployed in the two-dimensional Euclidean plane, each assigned a distinct task. We study a setting where the two groups must simultaneously solve two conflicting pattern formation problems: the gathering problem, where
robots gather at a point not known to them a priori, and the circle formation problem, where
robots occupy distinct positions on the boundary of a circle. Although each
robot knows its own task, it cannot identify other members of its group. A prior solution~\cite{Conflict-1} addressed this problem for asynchronous
robots having direction-only axis agreement and global weak multiplicity detection capability available to all
robots in both groups. In contrast, in this work, we consider fully disoriented
robots without any axis agreement or common chirality. We study the feasibility of a solution to this problem for disoriented
robots. We propose a distributed
algorithm that solves the problem for semi-synchronous disoriented
robots with non-rigid movements. Our proposed
algorithm assumes global weak multiplicity detection only for the gathering group, while for the circle formation group, it requires local weak multiplicity detection.
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Sound
LiteCASS: A Lightweight End-to-End Network for Real-Time Stereo Cinematic Audio Source Separation
Yuanxin Guo, Qiang Ji, Mengmei Liu et al. (+3 more)
Cinematic audio source separation (CASS) decomposes a soundtrack into dialogue, music, and sound-effects (SFX) stems. Existing CASS methods, however, suffer from two critical limitations: they rely on heavily parameterized network architectures and GPU-class hardware, limiting their use in real-time and resource-constrained scenarios, and they are overwhelmingly designed for monaural signals, leaving the stereo scenario largely unexplored. We present LiteCASS, to our knowledge the first lightweight end-to-end network for real-time stereo CASS. LiteCASS combines deterministic STFT subband rearrangement with two jointly trained compact U-Nets: the first extracts dialogue, and the second separates music and SFX from the predicted non-
speech component. A multi-task waveform-domain L1 loss supervises all stems. On a spatialized stereo extension of DnR v3, LiteCASS-K8 uses only 1.06M parameters and 0.72G MACs per second, while achieving the highest averaged SI-SDR among the compared CASS baselines.
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All authors Yuanxin Guo, Qiang Ji, Mengmei Liu, Yuhan Lv, Ningning Pan, Gongping Huang
Computer Vision
Rule-Constrained Assignment for Cue-Ball Identification in Broadcast Snooker
Yuxin Cao, Wei Song, Yuezhong Wu et al. (+1 more)
Accurate cue-ball identification is essential for metric analysis of broadcast snooker. Existing systems evaluate each candidate independently against a fixed white prototype and reject candidates above an appearance threshold. Under broadcast conditions, illumination changes can make colored balls appear white, while intrusions from players and equipment can obscure the cue ball or introduce competing candidates. We formulate cue-ball identification as a rule-constrained assignment problem that jointly assigns detected candidates to the bounded snooker inventory: one cue ball, up to 15 reds, and six colors with known spots. The cue ball is selected by the incremental cost of assigning each candidate to the white slot, and the conventional appearance test follows as the one-slot case. On 419 hand-annotated shots, our method improves identity accuracy from 88.1% to 95.5%, and from 80.5% to 95.2% on held-out venues. Within CueLift, our metric state-recovery system, the assignment expands coverage from 36.2% to 55.4% over 6,241 scorable shots. Assigning an estimate to every shot in a separate evaluation on 2,529 shots preserves this advantage.
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All authors Yuxin Cao, Wei Song, Yuezhong Wu, Jin Song Dong
Information Retrieval
PSD: Pseudo Self-Distillation of Memory Representation Capabilities for LLM Agents
Pirzada Suhail, Menglin Xia, Xuchao Zhang et al. (+3 more)
Memory systems are becoming a core component of LLM
agents, but constructing and maintaining
memory remains expensive because it relies on repeated calls to large proprietary
language models. This cost creates a major barrier to deploying
memory-enhanced
agents at scale. In this paper, we present Pseudo Self-
Distillation (PSD), a framework that enables small
language models (SLMs) to construct hierarchical
memory representations by distilling behavior from a strong black-box oracle through a multi-stage training pipeline. Standard
distillation methods require access to teacher logits or hidden states, which closed models do not expose. Unlike conventional self-
distillation settings, where supervision is derived from a model's own predictions, sampled rollouts, or aggregated outputs, PSD enables a single-model
distillation setup while channeling external oracle knowledge through the
prompt. PSD uses a single small model in two roles: a teacher that sees a privileged
prompt containing the oracle's answer as reference context, and a student that sees only the task
prompt. The student learns to reproduce the teacher's output distribution, absorbing oracle-guided behavior into its own weights without accessing the oracle's internals. On LoCoMo, PSD-trained Qwen3-0.6B, 1.7B, and 4B match or exceed GPT-4.1-mini on downstream
retrieval at a fraction of the deployment cost, with off-policy PSD achieving the strongest results across most conditions. We further show that this
memory-construction capability transfers out-of-distribution to LongMemEval, despite the students being trained exclusively on LoCoMo with no exposure to LongMemEval data.
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All authors Pirzada Suhail, Menglin Xia, Xuchao Zhang, Mayukh Das, Chetan Bansal, Saravan Rajmohan
Robotics
BiRoAD: Learning Shared and Role-Adaptive Representations for Bimanual Manipulation
Yan Shen, Yuchen Liu, Feng Jiang et al. (+5 more)
Bimanual manipulation requires policies that coordinate two arms while adapting their functional roles to scene geometry, object configuration, and task context. Learning such scene-conditioned role adaptation remains challenging, as demonstrations may contain uneven role distributions that limit generalization to underrepresented arm–role configurations. In addition, many bimanual policies predict actions in fixed left- and right-arm action spaces. While this provides a natural parameterization for
robot control, it does not explicitly specify how behaviors should transform when functional roles are exchanged across arms. Across different scene initializations, the two arms may follow a similar coordination pattern, but the role-specific behavior assigned to each arm should change with the scene. Therefore, we propose BiRoAD, a Bimanual Role-Adaptive Decomposition framework for learning shared and role-adaptive representations in bimanual policies. Given bimanual trajectory or action-
token features, BiRoAD decomposes these features into swap–symmetric and swap–antisymmetric components: the former captures coordination structure invariant to arm exchange, and the latter captures role-specific distinctions that vary consistently with functional role assignment. The two components are then recomposed as residual updates to the original paired arm representations, allowing BiRoAD to serve as a modular feature transformation without changing the policy inputs, imitation-learning objective, or requiring manually defined role labels. Across multiple bimanual manipulation tasks with balanced and imbalanced role distributions, BiRoAD improves robustness across role configurations over corresponding base policies, with notable gains on underrepresented role configurations.
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All authors Yan Shen, Yuchen Liu, Feng Jiang, Hangtian Hu, Xiaoqi Li, Shu Chen, Ruihai Wu, Hao Dong
Hardware Architecture
WaveletECO: A Closed-Loop Physical ECO Platform and a Specialized Local Language Model
Guoxiang Xu, Guozhen Ji, Zijian Luo et al. (+3 more)
Engineering change order (ECO) is an important step in repairing timing and electrical violations during the late stages of chip design. Existing Agentic EDA methods primarily focus on tool invocation, with less
attention to model decision quality and targeted training. A central challenge in ECO is multi-round decision-making: the model must use the results of each round to determine the next repair action. We propose WaveletECO, which integrates a closed-loop execution platform with large
language models to enable
agents to execute ECO decisions effectively. We also train a local 9B model through supervised fine-tuning and CPO-SimPO using execution demonstrations and decision-preference data, enabling ECO decision-making with a locally deployed model. Across 594 evaluation runs on 22 designs, WaveletECO-Policy (BF16) and (INT8) score 79.63 and 79.65, respectively, compared with GPT-6 Astra's 77.44. The estimated inference cost of INT8 is about 1/147 of GPT-6 Astra's. These results show that specialized model training supports effective, low-cost multi-round ECO repair, with repair quality retained under INT8 quantization.
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All authors Guoxiang Xu, Guozhen Ji, Zijian Luo, Zhengrui Chen, Qi Sun, Cheng Zhuo
Computer Vision
PhysReflect: Geometry and Perception Guided Diffusion for Physically-Plausible Mirror Reflections
Shuheng Ge, Hongwei Ren, Li Zhang et al. (+1 more)
Diffusion models generate high-quality images, yet often violate the physical laws governing mirror reflections. Reflections often suffer from
geometric aberrations, including positional offsets, directional misalignment, proportional imbalance, and structural distortion. These failures remain evident even in contemporary state-of-the-art generative systems. Existing methods itigate this problem through synthetic data scaling or auxiliary depth conditioning, yet their merely reliance on latent-space noise reconstruction losses as implicit supervision prevents direct enforcement of reflection-specific
geometric and perceptual constraints. To bridge this gap, we present PhysReflect, a geometry and perception guided
diffusion framework that decodes the predicted clean latent into pixel space at each training step and applies annealed supervision through two complementary differentiable objectives. The
Geometric Loss enforces mirror-induced spatial consistency through sparse epipolar correspondence and dense boundary projection
alignment, where a SAM2-based TwinTrack mechanism provides stable in-mirror localization for boundary-aware supervision. The Perceptual Loss preserves reflected appearance by combining Semantic Consistency Loss, which maintains reflected identity and appearance via DINOv2 features, and Lighting Consistency Loss, which regularizes depth, surface-normal, and illumination coherence under
monocular geometry priors. Experiments on synthetic and real-world
benchmarks show that PhysReflect outperforms prior mirror-reflection methods in
geometric, perceptual, and physical-plausibility metrics, as well as qualitative visual results.
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All authors Shuheng Ge, Hongwei Ren, Li Zhang, Xiangqian Wu
Robotics
Receding-Horizon Pushing with Composable Object-Centric Policies
Zhiyi Yuan, Tianrun Hu, Anxing Xiao et al. (+3 more)
Non-prehensile manipulation is practical for relocating large, heavy, or geometrically ungraspable objects. Yet, long-horizon pushing of arbitrarily-shaped 3D objects couples three problems: 1) where to push the object so as to approach the target pose, 2) whether each push is stable and reachable, 3) whether subsequent actions remain feasible. We present an object-centric pushing policy within a feedback-guided hierarchical framework. At the low level, a learning-based policy predicts contact actions from a pose- and scale-normalized point cloud, conditioned on a near single-step subgoal. A stability score is applied to evaluate the predicted contacts by a quasi-static sliding-versus-tipping analysis. At the high level, BIT$^*$ first searches for an object path, and the next several subgoals are checked by contact prediction and
robot motion planning for future feasibility. Failed motion plans, as feedback, change the local path costs and trigger re-planning. During execution, only the first feasible action is executed. In simulation, we evaluate 22 objects in six different scenes, upon which we also conduct comprehensive ablation studies. Results demonstrate that our method outperforms baselines with a clear margin and can reliably achieve long-horizon object pushing tasks under different situations. We also report quantitative real-
robot experiments with a Franka arm and qualitative demonstrations with a mobile manipulator for large and heavy objects, with directly zero-shot sim-to-real transfer.
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All authors Zhiyi Yuan, Tianrun Hu, Anxing Xiao, Yuhong Deng, David Hsu, Hanbo Zhang
Distributed Computing
Economical and efficient big data sharing with i-Cloud
Thepparit Banditwattanawong, Masawee Masdisornchote and Putchong Uthayopas
Big data can be hosted on cloud and being shared distributedly through cloud services in an unprecedented volume, variety and velocity. This causes not only cloud network congestions and delayed cloud services but also increases in public cloud data-out charges. Client-side cloud cache alleviates these problems. Furthermore, cloud cache must be aware of nonuniform data-out costs when big data is stored in hybrid clouds built with different public cloud providers. Deploying i-Cloud approach as the core mechanism of cloud cache could save data-out cost up to 14.78% or 4,425 USD saved per annum based on our representative scenario, and delivered 17.24% byte-hit, 17.96% delay-saving and 29.33% cache hit outperforming LRU, GDSF and LFU-DA approaches. A main finding is that i-Cloud, learning uniform cost patterns, could perform well against nonuniform cost environment.
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Computer Vision
GAPS: Generative Active Pseudo-view Selection for Sparse-View 3D Gaussian Splatting
Hongfei Zhu, Haochen Deng, Sitao Zhang et al. (+1 more)
Novel view synthesis from sparse observations is severely under-constrained. Although 3D Gaussian Splatting (3DGS) enables real-time rendering, it produces floaters, broken geometry, and washed-out backgrounds when trained with few views. We propose an alternating optimization framework that uses a pre-trained image
diffusion model to generate geometrically consistent pseudo-views for additional 3DGS supervision. Generation is constrained by depth-conditioned ControlNet, IP-Adapter style transfer, LoRA scene adaptation, and img2img structural anchoring. We introduce Generative Active Pseudo-view Selection (GAPS) to balance reconstruction informativeness and generative reliability when choosing target views. Its annealing schedule shifts from conservative interpolation early in training to exploratory extrapolation later, gradually covering unobserved regions. A dual-criterion admission gate and uncertainty-weighted losses reject unreliable generations, while density-adaptive DropGaussian reduces overfitting in complex scenes. On LLFF with 3/6/9 views, our method improves average PSNR over vanilla 3DGS by 0.40/0.89/0.70 dB. On Mip-NeRF 360 with 12/24 views, the gains are 1.18/0.80 dB. SSIM improves and LPIPS decreases in every setting. Ablations show that active selection and density-adaptive regularization are both necessary; only the full method reduces LPIPS below the no-pseudo-view baseline on unbounded 360-degree scenes.
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All authors Hongfei Zhu, Haochen Deng, Sitao Zhang, Ling Zhou
Machine Learning
Tool-Augmented On-Policy Distillation for LLM Domain Adaptation in Sequence-Based Omics Tasks
Jie Ying, Zhefan Wang, Zihong Chen et al. (+11 more)
Multi-omics sequences contain complex biological patterns, yet deciphering their mechanisms for automated scientific discovery remains challenging. As large
language models (LLMs) interpret these sequences, evaluating both predictions and scientific
reasoning is critical. However, existing
benchmarks for multi-omics sequence tasks rely on classification and regression metrics, neglecting whether models grasp the underlying biological evidence. We introduce OmicsBench, the first
reasoning benchmark for multi-omics sequences, comprising 1,160 expert-validated questions across six tasks spanning DNA regulation, RNA processing, and
protein function. OmicsBench requires traceable evidence chains, evaluated using instance-specific rubrics developed with domain experts. Evaluating 17 LLMs reveals an inverse relationship: while scientific LLMs outperform general-purpose LLMs in sequence classification accuracy, they fail to provide valid evidence to support their predictions. One plausible interpretation is shortcut learning: specialized models may rely on statistical patterns rather than the biological mechanisms needed for scientific discovery. Motivated by this finding, we introduce tool-augmented on-policy
distillation (TA-OPD), a post-training method to align sequence prediction with evidence-grounded biological
reasoning. Across five Qwen3.5 models spanning 0.8B to 27B parameters, TA-OPD consistently strengthens biological evidence grounding while improving predictive performance on most tasks. These gains persist across model scales, indicating that stronger sequence
reasoning does not arise solely from increased model capacity, but can be improved through evidence-aware training. Together, OmicsBench and TA-OPD provide a framework for diagnosing
reasoning failures in multi-omics LLMs and a path toward models whose predictions are better grounded in biologically meaningful evidence.
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All authors Jie Ying, Zhefan Wang, Zihong Chen, Zhengqing Li, Jinzhe Li, Gang Li, Jian Liu, Fang Hu, Tao Luo, Zhonghang Yuan, Wanli Ouyang, Stan Z. Li, Fan Yang, Nanqing Dong
Robotics
RopeFormer: Cross-Trial Adaptation from Interaction History for Dynamic Rope Manipulation
Menglin Wu, Kaixiang Yao, Shangbo Luan et al. (+2 more)
Dynamic rope manipulation is highly sensitive to unknown object dynamics: the same
robot motion can produce substantially different responses across ropes, while explicitly identifying the relevant physical properties is difficult. We present RopeFormer, a history-conditioned framework that uses prior task interaction as context for subsequent control. The policy retains cross-trial action-response history while keeping its weights fixed and requires no explicit online rope-parameter estimation. In matched simulation evaluations across sustained single-arm rotation, bimanual rotation, and transient whipping, retaining context improves subsequent control relative to resetting the same checkpoint, with the benefit varying across rope dynamics and observation settings. We further deploy the frozen policies on a Unitree H1-2 with previously unseen physical ropes. From T1 to T3, target-acquisition time decreases by 30.9% for Rope Swing and 33.9% for Rope Twirl, while mean Rope Whip target hits increase from 0.2 to 2.3 out of three. These results show that prior interaction can provide effective control context for dynamic deformable-object manipulation.
Robot videos, code, and data are available at https://ropeformer.github.io/.
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All authors Menglin Wu, Kaixiang Yao, Shangbo Luan, Masayoshi Tomizuka, Yuxin Chen
Computer Vision
HOIBlender: Blending Lightweight Detection with Vision-Language Priors for Efficient Human-Object Interaction Detection
Junwen Chen and Keiji Yanai
Human-object interaction (HOI) detection requires grounding an interacting human-object pair and recognizing the verb that links them, often under severe long-tail supervision. Recent methods improve accuracy with stronger detectors and
vision-
language priors, but many still stack heavy
transformer encoders, intricate denoising schedules, or post-hoc semantic calibration on top of the detector. We present HOIBlender, an efficient HOI detector named after its core design principle: blending detector-grounded visual
tokens, spatial subject-object
reasoning, and BLIP-2 semantic priors inside one lightweight decoding pipeline. HOIBlender builds on an RF-DETR/LW-DETR-style foundation with a DINOv2 backbone and selects top-$K$ image-conditioned
tokens directly from the multi-scale projector as subject and object candidates, removing the dedicated encoder stage retained by prior HOI methods. A dual-stage decoder first stabilizes human-object geometry and then performs verb and HOI classification through progressive BLIP-2 prior fusion, with classifier weights initialized from BLIP-2 text
embeddings for long-tail categories. Grouped-query training further enriches optimization without increasing inference cost. Across three model scales (Nano, Small, 2XL), HOIBlender consistently outperforms SOV-STG-VLA and Hybrid-SOV-VLA on HICO-DET, reaching $44.49$ Default Full mAP in only $9$ training epochs while maintaining competitive latency and parameter budgets. These results show that lightweight detection, structured spatial-semantic decoding, and deeply integrated
vision-
language priors can be blended into a single efficient HOI pipeline.
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Computer Vision
RSPDBench: Benchmarking Vision Foundation Models on Earth Observation Tasks Under Physically Grounded Remote-Sensing Product Degradations
Tanjim Bin Faruk, Khondaker Masfiq Reza, Shrideep Pallickara et al. (+1 more)
Vision foundation models for Earth observation (EO) are commonly evaluated on clean
benchmarks, but operational EO products can already contain spatial, radiometric,
alignment, noise, and harmonization defects before reaching the model. Generic image corruptions and broad domain shifts do not isolate these product-level failures. We introduce RSPDBench, a physically grounded remote-sensing-product degradation
benchmark that evaluates seven foundation-model entries and two supervised baselines on five EO
datasets under primitive degradations and compound product chains. Its degradation parameters follow published Sentinel-2 and Landsat~8 specifications and are checked against Sentinel-2's paired $10\,\mathrm{m}$ and $20\,\mathrm{m}$ near-infrared bands. We evaluate each model under its own native protocol and measure robustness as the drop from its own clean baseline. Our analysis reveals that degradation sensitivity is strongly structured: (i) the resolution-conditioned encoder is the most robust to resolution loss yet the most sensitive to radiometric gain on satellite products, (ii) the same physical defect can hurt one model while helping another, and (iii) model rankings under resolution loss and radiometric gain are largely reversed. Compound chains add up to $38$ percentage points of drop beyond their strongest component. On m-BigEarthNet, retraining the probe on degraded views removes about 75% of the noise and harmonization losses, but resolution loss still costs about 20% of the clean score. EO robustness must therefore be measured against the structured defects of remote-sensing products, not by clean accuracy or generic perturbations alone. Code: https://github.com/Tanjim131/RSPDBench
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All authors Tanjim Bin Faruk, Khondaker Masfiq Reza, Shrideep Pallickara, Sangmi Lee Pallickara
Computer Vision
Semi-automated reconstruction of indoor geometry from 360-degree video for CFD-based airflow analysis in classrooms
Dhruv Gamdha, James Afful, Shambhavi Joshi et al. (+3 more)
Computational Fluid Dynamics (CFD) is widely used to evaluate ventilation and contaminant transport in occupied buildings, but deployment at scale is limited by three bottlenecks: acquiring room geometry without costly scanning hardware or manual CAD modeling, decomposing the scene into individually manipulable objects, and reconfiguring those objects for alternative layouts without re-capturing the room. We present a semi-automated workflow that converts a single 360-degree
video of a room into individually editable, simulation-ready geometry assets. A dense point cloud is reconstructed using Neural Radiance Fields (NeRF), and 2D instance masks from text-prompted SAM 3
segmentation are lifted to 3D using multi-view consensus and depth-band filtering. Points are separated into object instances with an octree, and occlusion gaps are healed with a connectivity graph. Chair templates are fitted by Iterative Closest Point (ICP)
alignment, and table geometry is generated procedurally. A browser-based editor supports quality assurance and rapid construction of alternative layout configurations. A steady Reynolds-averaged OpenFOAM solution then drives transient passive-scalar transport; the setup is verified using a mesh-sensitivity study and validated against an IEA Annex 20
benchmark. We apply the workflow to two university classrooms and a tiered lecture-
hall auditorium. The capture-to-geometry pass takes two to five hours per room on a consumer workstation. In a controlled obstruction sequence in one classroom, the modeled half-clearance time varies non-monotonically as furniture is added, and a cross-room comparison indicates that clearance behavior cannot be reliably extrapolated between rooms, motivating per-room geometry acquisition. By making that acquisition low-cost, the workflow makes geometry-resolved comparative ventilation studies practical for spaces such as classrooms.
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All authors Dhruv Gamdha, James Afful, Shambhavi Joshi, Ulrike Passe, Adarsh Krishnamurthy, Baskar Ganapathysubramanian
Robotics
RiverVLN: Phase-Grounded Temporal Vision–Language Navigation for Unmanned Surface Vehicles
Jieling Wu, Yuehao Huang, Jiajun Lv et al. (+3 more)
Vision-
language navigation (VLN) has largely been developed for indoor and terrestrial
robots, where
language can often be treated as a static goal and motion is approximated by discrete or near-instantaneous actions. These assumptions break down for unmanned surface vehicles (USVs): river
navigation requires continuous motion under inertia and limited maneuverability, while long-horizon instructions must be executed through sparse and visually ambiguous maritime landmarks. We introduce RiverVLN, to our knowledge the first
benchmark designed for long-horizon USV VLN under continuous riverine motion, and PGT-NAV, a phase-grounded temporal
navigation framework for USVs. Rather than directly mapping an entire instruction to motion, PGT-NAV converts it into an ordered sequence of visually verifiable semantic phases and maintains the active phase online through grounded visual and motion evidence. This explicit semantic progress state is fused with visual-motion history and phase-specific grounding to predict six local SE(2) pose increments. The resulting trajectory is executed in a predict-execute-re-observe loop, where the vessel executes toward W3, updates phase and grounding, and replans through a map-based safety layer. Experiments show that PGT-NAV substantially reduces recursive position and heading drift relative to GNM-style and ViNT-style baselines and achieves an average success rate of 0.79 in Unity-ROS closed-loop
navigation. Unseen bridge-opening trials and real-world USV experiments further demonstrate that the phase-grounded representation transfers from controlled evaluation to physical USV deployment.
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All authors Jieling Wu, Yuehao Huang, Jiajun Lv, Tao Huang, Yong Liu, Weiwei Liu
Computers and Society
Beyond PUE: A Local Impact Audit Framework for Data Center Environmental Accountability
Sharifa Sultana and Syed Ishtiaque Ahmed
Standard data center sustainability metrics, including Power Usage Effectiveness (PUE), Water Usage Effectiveness (WUE), and Carbon Usage Effectiveness (CUE), measure a facility's resource use and emissions intensity, normalized to IT energy use, without directly representing local resource scarcity, infrastructure capacity, or social footprint. This gap has become politically consequential. In the first quarter of 2026 alone, local opposition delayed or canceled roughly $130 billion in projects across the United States, driven overwhelmingly by recurring concerns over water use, power demand, infrastructure capacity, and transparency rather than internal efficiency, matching the total for all of 2025 [11]. We propose a five-category local impact audit framework covering efficiency, water stewardship, carbon and renewables, regulatory compliance, and local disclosure. The framework is designed for recurring quarterly assessment and independent verification against public records. We illustrate its application using publicly available data from three Illinois facilities that are currently at the center of local policy disputes, and we examine the data-access barriers that constrain independent verification. We position this framework as both a research contribution and a practical instrument for county-level policymakers evaluating data center permitting and moratorium decisions.
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Robotics
HEARTH: An Object-Centric RGB-Thermal-3D Dataset for Temperature-Aware Robot Manipulation
Yuning Su, Borui Li, Yonghao Shi et al. (+2 more)
Language-guided manipulation can depend on physical properties that visible appearance does not reveal. Temperature is one such property, but object
datasets for
robot learning rarely associate measured temperatures with object appearance and geometry. We present HEARTH, an object-centric RGB-thermal-3D
dataset of 90 physical objects from 18 everyday categories, comprising 145 captured object states. Our pipeline maps apparent surface temperatures onto reconstructed meshes through camera calibration and pose transfer. The
dataset includes raw temperature measurements, camera parameters, RGB-textured meshes, and thermal textures for simulation. We use these assets to construct three LIBERO-derived tasks and collect 1,200 demonstrations for fine-tuning a pretrained
vision-
language-action (VLA) model, $\pi_{0.5}$. In an ablation study, adding thermal observations to the VLA increases success on temperature-dependent object-selection tasks from 35.0% for the RGB-only baseline to 75.0%. These results demonstrate the utility of HEARTH for training
robot policies to follow temperature-related instructions.
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All authors Yuning Su, Borui Li, Yonghao Shi, Bofei Liu, Xing-Dong Yang
Software Engineering
Omni2Web: Benchmarking Audiovisual Website Development
Minghao Han, Zhenghao Xing, Xize Cheng et al. (+7 more)
Screen-recorded web editing requests contain weak deictic expressions such as “this” and “there,” whose referents depend on
speech, cursor trajectories, page state, and edit history. Such requests require intent recovery beyond the explicit specifications assumed by many existing web-editing
benchmarks. We introduce Omni2Web, a bilingual
benchmark of 918 instances spanning 13,907 edit steps. It defines three complementary tracks: Direct Editing evaluates webpage editing from recordings, Instruction Recovery measures explicit intent recovery, and Instruction Utility tests whether recovered instructions can drive a fixed code executor. We evaluate 17 open- and closed-source models. The best models attain 51.17 on the Edit Fidelity Score (EFS) for Direct Editing and 49.14 on the Instruction Recovery Score (IRS); under the fixed executor, the strongest recovered instructions reach 51.08 EFS, still far below the 89.69 EFS obtained with oracle instructions. Step-level analyses show that correct grounding does not guarantee successful edits, while some Omni models recover instructions that the fixed coding model executes substantially better than their direct edits. Controlled ablations further demonstrate the value of temporally aligned audiovisual evidence, while alternative judges preserve the leader and broad ordering. Together, these findings reveal substantial headroom in
multimodal intent recovery and code execution and highlight the promise of pairing Omni rewriters with coding models.
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All authors Minghao Han, Zhenghao Xing, Xize Cheng, Yuxuan Wang, Junming Lin, Ling Wang, Yinsong Yan, Yunfei Chu, Qize Yang, Jin Xu
Sound
MuLA-Bench: A Multilingual Long-Form Audio Understanding Benchmark via Multi-Tier Auditing
Zeyu Yang, Xinyu Zhang, Zibo Bi et al. (+5 more)
Long-form audio performance is often summarized by context length and aggregate accuracy, obscuring how
language, evidence, and task jointly shape difficulty. We introduce MuLA-Bench: 5,038 open-ended questions over 1,769 in-the-wild recordings totaling 1,377.9 hours, covering 16
languages and eight domains. A balanced
Language x Domain semantic track supports controlled comparisons, while a complementary acoustic track preserves naturally occurring non-
speech evidence. Evidence-grounded generation, shortcut checks, and
language-expert review provide auditable questions without translating a shared source set or injecting target sounds. We evaluate ten audio-
language models and conduct pooled diagnostics on a fixed eight-model cohort.
Language rankings change across domains and tasks; acoustic-semantic performance gaps vary with the requested operation; and temporal errors can persist after the correct event is identified. Long-range
retrieval is comparatively strong, while precise clock
alignment and factual grounding of natural acoustic events remain fragile. MuLA-Bench thus exposes conditional failure patterns that a single long-context score does not capture.
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All authors Zeyu Yang, Xinyu Zhang, Zibo Bi, Pei Zhang, Xize Cheng, Jin Xu, Baosong Yang, Satoshi Nakamura
Robotics
Daojie Peng, Bingtao Wang, Fulong Ma et al. (+3 more)
Socially competent
humanoid robots must communicate affect and intent through gesture as well as
speech, yet open-ended interaction must become motion that is both expressive and executable on a specific body. This demands semantic flexibility for contextual social intent while preserving deterministic, embodiment-aware
robot control. We present EmoPose, a
vision-
language model (VLM)-guided framework that bridges this gap through an executable semantic interface. Given
language, dialogue history, and optional visual context, the VLM selects an ordered gesture plan containing a communicative class, library variant, intensity, and
speech anchor. A scalable
robot-owned motion library defines the available expressive vocabulary and the source of 14-DoF joint targets. Pose Studio supports automatic trajectory generation, MuJoCo preview, and automatic synchronization of new library entries with the VLM guide; deterministic
robot-side modules validate plans, construct trajectories, schedule gestures, and manage queueing and interruption. This division lets the interaction repertoire grow for new social contexts without changing the control interface or delegating raw joint commands to the foundation model. On the EmoPose-Bench, structured GPT-5.5 planning reaches $98.25\pm0.52\%$ on the Easy tier and $76.50\pm0.54\%$ overall, exceeding same-model direct-label prompting. Further tests validate dialogue-context use and ordered multi-action composition. The system completes the nominal MuJoCo suite and realizes all 29 authored variants on the physical Unitree G1. A four-stop laboratory tour demonstrates expressive narration with interruption, camera-grounded dialogue, and
navigation.
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All authors Daojie Peng, Bingtao Wang, Fulong Ma, Wenjun Yue, Liang Zhang, Jun Ma
Sound
Misrecognition or Abstraction? Rethinking Outputs of Sound Event Recognition
Naoya Tomida, Yuki Okamoto and Keisuke Imoto
Conventional general sound recognition systems typically output deterministic sound event labels, implicitly assuming that the target sound class can be correctly identified from the input audio. However, in real listening situations, the sound event class is not always clearly identifiable. Human listeners may nevertheless understand their surroundings from an ambiguous sound without identifying its exact sound event class. This motivates a discussion of how the outputs of sound recognition systems should be redesigned under such uncertainty. As a basis for this discussion, this paper proposes an output representation for sound event recognition that combines a sound event class, its confidence score, and an onomatopoeic description of the sound. The proposed representation preserves conventional class-based recognition while providing an additional onomatopoeic description of acoustic characteristics that can remain informative even when the class prediction is uncertain. Experiments using ESC-50 and ESC-50-Onomatopoeia show that the proposed method achieves sound recognition performance comparable to that of a conventional recognition-only system. In addition, an LLM-as-a-judge evaluation and subjective listening experiments indicate that the proposed output is preferred over conventional deterministic outputs based on the sound event label, particularly when used to support understanding of the surrounding environment. These results suggest that such output representations can make sound event recognition more informative and communicative under uncertainty.
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Computer Vision
Retrieval Geometry Shapes Cache-Based Clip Adaptation
Mahir Shahriar Tamim, Md. Samiul Alim, Azmine Toushik Wasi et al. (+5 more)
Cache-based test-time adaptation improves CLIP predictions by storing and retrieving examples from the target stream while keeping the model frozen. However, existing methods largely treat the feature space used for image-image
retrieval as fixed, leaving open how much adaptation depends on the
retrieval space itself. We study this question by fixing the
memory and changing only the
retrieval encoder, finding that the same
memory can yield very different gains: across sixteen
retrieval spaces, ImageNet-A cache gain ranges from at most +0.44 points for CLIP and MAE to +19.7 +/- 0.4 for DINOv2-L, while label-free
retrieval-space selection retains 98% of oracle gain on ImageNet-V2. These results show that
memory quality depends not only on which examples are stored, but also on how they are retrieved. Motivated by this finding, we propose MARC (
Memory Augmented
Retrieval for CLIP), a training-free system that uses frozen CLIP for prediction and DINOv2-B for
retrieval with a single fusion weight. A single-view cache repairs 1074 +/- 21 baseline errors, compared with 878 +/- 4 for a 64-view ensemble, at roughly one seventh of the cost. Across four ImageNet distribution shifts, MARC reaches a 67.91% OOD average and, at matched DINOv2-B scale and eight views, achieves 64.17 +/- 0.31% versus 62.75 +/- 0.15% for a graph-based cache system while running 2.6 times faster. Overall, our results establish
retrieval space as a first-order design choice for robust cache-based adaptation in remote sensing, scientific imaging, and changing visual environments.
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All authors Mahir Shahriar Tamim, Md. Samiul Alim, Azmine Toushik Wasi, Shahriyar Zaman Ridoy, Meharun Nesa, Mohammad Abu Yousuf, Alex Lamb, Mohammad Ali Moni
Computer Vision
Accurate Motion Estimation with Bézier Control Point for Efficient Frame Interpolation
Shuhao Han, Chenyang Wu, Chun-Le Guo et al. (+4 more)
In frame interpolation tasks, motion ambiguity in the training set causes models to generate blurry intermediate frames. Moreover, the assumption of uniform motion between frames during inference further leads to inaccuracies in the generated intermediate frames. To tackle these challenges, we propose an Accurate motion estimation
algorithm with Bézier Control point, ABC-Inter, for efficient frame Interpolation. Specifically, ABC-Inter designs an Accurate Flow estimation Module (AFM) by decoupling two-frame features and mapping to corresponding coordinates to better estimate the optical flow between the two frames. Furthermore, ABC-Inter eliminates motion ambiguity in the training set by introducing Bézier control points that are computed using the input frames and the intermediate ground-truth (gt) frames. This allows the model to estimate accurate optical flow between two frames during the training process, thereby solving the blurriness problem in the generated intermediate frames during inference. Benefiting from the more accurate flow estimation between two frames, we can introduce additional frames and directly use multiple flows to calculate Bézier control points for modeling non-uniform motion without retraining the model. Simultaneously, to realize the estimation of non-linear motion using only two frames, we also introduce a new Bézier control point estimation module which achieves better motion estimation between the two frames by performing fine-tuning on the model in the second stage. Experimental results demonstrate that our ABC-Inter achieves state-of-the-art performance on multiple
benchmark datasets and exhibits excellent visual perception.
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All authors Shuhao Han, Chenyang Wu, Chun-Le Guo, Zheng-Peng Duan, Zhen Li, Ming-Ming Cheng, Chongyi Li
Sound
OmniEcho: Audio-Visual Spatial Understanding for Omni-Modal Embodied Agents
Ruixun Liu, Yuxuan Wang, Jiacheng Xie et al. (+10 more)
Humans can effortlessly localize the direction of a sound source and integrate it with visual cues for
reasoning, yet this remains challenging for
embodied agents. In particular, it is still unclear how to effectively evaluate and model spatial audio understanding in
embodied settings. To address this gap, we introduce OmniEchoBench, a unified
benchmark for spatial audio-visual perception and audio-
vision-
language navigation. OmniEchoBench comprises six tasks over 197 real-world spatial audio-visual scenes, 2,972 question-answer pairs, and 900
navigation samples with first-order ambisonics (FOA) audio collected from 30 real-world environments. To enable scalable training supervision, we develop a controllable rendering pipeline for spatial audio. It preserves
geometric consistency among sound sources, visual observations, and
agent trajectories. Building on this, we propose OmniEcho, a spatially aware omni-modal model. It introduces an FOA spatial encoder alongside a pretrained semantic audio pathway. Extensive experiments show that OmniEcho achieves state-of-the-art performance on spatial audio-visual perception. For our sound-guided
navigation, OmniEcho reaches a performance level close to that of traditional
vision-
language navigation. These results demonstrate that spatial audio can serve as a valuable signal for
embodied scene
reasoning and
navigation, while also highlighting fine-grained spatial localization and distance estimation as important open challenges. Our code and data will be available in https://github.com/PKU-VaLuE-Lab/OmniEcho/tree/main
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All authors Ruixun Liu, Yuxuan Wang, Jiacheng Xie, Yuhuan You, Donghua Cai, Junming Lin, Xiong-Hui Chen, Zhifang Guo, Yunfei Chu, Qize Yang, Xize Cheng, Jin Xu, Yiwu Zhong
Cryptography and Security
The Anatomy of Address Poisoning on Ethereum: Funding Mechanisms, Scam Signatures, and Laundering via Tornado Cash
Son Hoang Dau, Thanh Nguyen, Phuong Duy Huynh et al. (+5 more)
Address-Poisoning Transfer (APT) is a prevalent blockchain phishing scam in which a scammer poisons a victim's address book by generating a transfer with a phishing address that looks similar to a benign address that the victim has previously interacted with. Although simple, APT phishing attacks have cost users millions of dollars in recent years, which has captured the
attention of the research community (Ye et al. WWW'24, Guan-Li CCS'24, Chen et al. NDSS'25, Tsuchiya et al. USENIX'25). In this work, we go beyond detection and investigate three important and underexplored aspects of APT: scam funding mechanisms, scam signatures, and scam proceeds laundering via public services. In particular, we propose five families of scam signatures that capture key aspects of APT operations, which are useful for address clustering. We also conduct the first investigation into usage of Tornado Cash for funding APTs and laundering scam proceeds.
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All authors Son Hoang Dau, Thanh Nguyen, Phuong Duy Huynh, Hong Yen Tran, Nicholas Huppert, Jeff Nijsse, Huong Ha, Xun Yi
Computer Vision
ScaleBlind: Point Cloud Completion under Unknown Scale
Shenghui Wu, Chen Wang, Yuan Feng et al. (+3 more)
Point cloud completion aims to infer a complete 3D shape from a partial point cloud and serves as a fundamental building block for downstream tasks such as reconstruction, editing, and simulation. Despite the recent progress, existing learning-based methods often implicitly rely on access to the ground-truth shape scale (GT-scale) during both training- and testing-time normalization, assuming privileged information that is unavailable in real-world inference. This hidden assumption limits practical deployment and can lead to severe completion artifacts, e.g., over- or under-completion and nested shells, once the oracle GT-scale cue is removed. We observe that the recent foundation image generation models exhibit a strong capability of understanding objects and geometries, and producing multi-view consistent renderings, making them promising priors for GT-scale-free 3D completion. Motivated by this insight, we propose ScaleBlind, a novel framework that leverages foundation-model-based image completion to recover global scale directly from partial inputs and then faithfully produces the 3D completion. Specifically, ScaleBlind dreams out complete multi-view appearances from rendered partial views, lifts the inferred missing regions back into 3D to obtain a geometry-aware coarse completion, and further refines it via a powerful cross-modal fusion network with the original partial point cloud. By harnessing 2D foundation priors, our method eliminates the need for accessing GT-scale information at inference. Moreover, it provides a principled bridge between 2D generative priors and 3D point cloud completion. Extensive experiments demonstrate the superiority of our framework, making ScaleBlind the new state-of-the-art for the point cloud completion task.
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All authors Shenghui Wu, Chen Wang, Yuan Feng, Guangshun Wei, Yuanfeng Zhou, Changjian Li
Human-Computer Interaction
Alignment and Divergence between Humans and AI in Interpersonal Privacy Decisions
Hanxiang Zeng, Shuning Zhang, Xinyuan Zhou et al. (+5 more)
AI assistants increasingly mediate interpersonal communication on behalf of their primary user, but they risk violating the
privacy expectations of third-party information owners. Resolving these tensions requires understanding how humans anticipate interpersonal
privacy boundaries. Therefore, we conducted a dyadic study (N=76) and a matched evaluation of AI models across 18 information types and 3 recipient relationships. We found that data owners'
privacy judgments are highly contextual and relationship dependent. While familiar data co-owners show meaningful
alignment with owners' expectations, they significantly overestimate the need for permission. Interestingly, greater familiarity within the owner-co-owner dyad was associated with both higher disclosure acceptability and lower co-owner misalignment, whereas our exploratory four-item empathy measure was not. In contrast, AI models significantly underperform human co-owners in anticipating the data acceptability, even when provided with within-dyad examples. These findings underscore a core HCI design challenge to develop
privacy-aware AI that respects multi-stakeholder information boundaries.
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All authors Hanxiang Zeng, Shuning Zhang, Xinyuan Zhou, Tianqi Song, Yuhan Yuan, Yuting Yang, Shuai Ma, Xin Yi