Computers and Society
Declared but Unaccountable: Measuring the Seller-Identity Gap in Advertising Supply-Chain Transparency
Tommaso Grotto, Alessandro Cantelli-Forti and Alessio Merlo
Public advertising transparency relies on a minimum accountability condition: an account authorized by a publisher in ads$.$txt or app-ads$.$txt should appear in the applicable sellers$.$json registry. When an available registry omits the declared seller ID, the authorization remains visible and machine-readable, but its public verification path breaks. We call this failure the seller-identity gap. We measure the gap using 233.4 million normalized advertising-transparency records and an absent-only, coverage-aware evidence rule. A declaration enters the numerator only when the applicable registry is available and omits its seller ID; unavailable, malformed, confidential, and other non-evaluable states remain separate. The resulting row-level floor is 24.25% in a high-popularity, low-removal reference population and 30.71% among publishers associated with at least 100 copyright-removal requests, a 6.45 percentage-point increase. The gap then remains near one third at stricter cumulative thresholds. Standardizing the populations to common weights over 1,469 shared ad systems reduces the primary contrast to 4.65 points: about 72% remains within systems, while 28% forms a composition-associated residual. We connect these population estimates to recurring seller-account coordinates, byte-identical declaration configurations, and provenance-bearing case packets. We frame this declaration-centered investigative use of public advertising records as Ad Transparency Intelligence: it reconstructs declared supply-chain relationships, identifies where public verification stops, and prioritizes evidence for corroboration without equating correlation with attribution. Advertising transparency supports accountability and investigation only when visible authorizations remain publicly verifiable.
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Human-Computer Interaction
Impact of Expertise and Trust on AI-assisted Work
Qianou Ma, Pranav Khape, Rock Pang et al. (+4 more)
Artificial intelligence (AI)
agents promise to extend what users can accomplish beyond their existing expertise, but they also raise the risk of producing plausible-looking work that users may struggle to evaluate. We ask how task-relevant expertise and trust in AI shape
agent-assisted work. To isolate these factors, we conduct a think-aloud study in which 20 professionals with varying data-science expertise use the same agentic tool to complete the same data-science hiring assessment, used at a major U.S. company. We find that surface productivity can come at the expense of understanding: participants who delegated more completed more of the task, but were also more likely to produce invalid solutions. Experts more often resisted this tradeoff, sacrificing completion to decompose the task and scrutinize
agent outputs. Our findings call for evaluating agentic assistance not only by how much it accomplishes, but by whether users remain able to evaluate the work they produce.
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All authors Qianou Ma, Pranav Khape, Rock Pang, Ken Koedinger, Tongshuang Wu, Siddarth Suri, Shamsi Iqbal
Artificial Intelligence
RASSG: Repair-Aware Semantic Survival Gating for Streaming Tool Execution
Daijin Hu and Xi Wang
Streaming
speech assistants can launch tools before users finish speaking, overlapping execution with incoming
speech to reduce user waiting time. However, subsequent self-corrections can invalidate apparently stable arguments, causing stale execution whose irreversible effects persist even after the final call is corrected. We propose Repair-Aware Semantic Survival Gating (RASSG) to jointly control tool launch and cancellation during streaming
speech. RASSG launches a tool when three consecutive
speech recognition results arrive with identical nonempty arguments, then combines calibrated argument invalidity risk with execution costs to decide cancellation at the last feasible opportunity before effects become irreversible. Evaluation on ReCAST, FDB-v3, and Audio2Tool uses a controlled execution clock with fixed tool runtimes and cancellation parameters. On ReCAST's prospective-repair subset, RASSG reduces the stale execution rate from LocalAgreement-2's 50.00% to 3.15%, with a mean delay of 3.676s across successful trajectories. The stale reduction also holds against every early-execution baseline on both public
speech datasets. Ablations with identical initial launches support active cancellation, showing the value of controlling ongoing calls before their effects become irreversible. Code and data are available at https://github.com/daijinhu/RASSG.
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Numerical Analysis
Learning Optimal Robust-Pathwise Control of Elliptic Equations with Random Coefficients
Qi Sun, Yanhan Cui, Ling Guo et al. (+1 more)
Conventional control approaches to optimization problems under uncertainty entail a practical compromise: deterministic controls offer robustness at the expense of pathwise accuracy while
stochastic controls can adapt to individual realizations but suffer from the curse of dimensionality. To achieve both robustness and adaptivity, a neural operator-based approach is proposed in this paper for distributed elliptic optimal control problems with high-dimensional random inputs. A data-driven training
algorithm is first established to learn the mapping from coefficient realizations directly to their optimal pathwise controls. To bypass the burden of generating training data pairs, a physics-informed training
algorithm is then proposed and recast as weakly supervised learning using pseudo label targets produced by standard adjoint-based control updates. Numerical experiments with rough coefficients are conducted to validate the effectiveness and efficiency of our robust-pathwise controls, which outperforms the
benchmark robust deterministic control on unseen coefficient realizations owing to the generalization capability and fast inference of trained models.
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All authors Qi Sun, Yanhan Cui, Ling Guo, Lili Ju
Computer Vision
TGuide: Faster Autoregressive Video Diffusion via Historical Trajectory Guidance
Zhuo Chen, Bin Zou, Fanyue Wei et al. (+3 more)
Can autoregressive
video diffusion be accelerated further when only four denoising steps remain? Existing feature caching approaches struggle in this regime, as large feature changes between denoising timesteps undermine both direct reuse and lightweight predictors. We observe that features across
video chunks remain strongly correlated at matching denoising timesteps, offering a complementary source of guidance. Building on this insight, we introduce TGuide, a framework that accelerates autoregressive
video diffusion through a lightweight Anchor-Transport-Correct (ATC) predictor guided by historical sampling trajectories. Our ATC combines two complementary references: the current chunk's preceding-step features preserve its spatial structure, while the previous chunk's same-timestep features provide information at the target denoising stage. By integrating these references through spatial
retrieval and adaptive correction, ATC improves velocity prediction accuracy despite the large gaps between denoising steps. To mitigate error accumulation across both denoising steps and
video chunks, we train the predictor on its own autoregressive rollouts, allowing it to adapt to states shaped by its own earlier predictions. An optional confidence-aware scheduler further adjusts the quality-efficiency trade-off. Experiments on Self Forcing, Causal Forcing, and HY-WorldPlay show that TGuide preserves generation quality while achieving denoising speedups of up to $1.89\times$ over the original 4-step generators. On Self Forcing, TGuide generalizes from 81-frame training
videos to 333-frame generation, approximately four times the training length, while exceeding the original model's VBench score.
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All authors Zhuo Chen, Bin Zou, Fanyue Wei, Lixin Duan, Angela Yao, Wen Li
Robotics
Global Optimality of Pose Graph Optimization
Shuan Ding, Ruishuang Chen, Mengfan Cao et al. (+1 more)
Pose Graph Optimization (PGO) is a classical and widely applied problem in state estimation for robotics, playing a fundamental role in localization, mapping, and navigation. Yet, it is nonconvex in nature and may have many local minimizers, thus difficult to analyze. In this paper, we investigate the optimization landscape and establish a sufficient condition of global optimality for PGO. The ultimate goal of this paper is to theoretically confirm that the optimization landscape is benign and attractive around the global minimizer, if noise is smaller than an explicit and a priori bound. To reveal such an insight, this paper develops three main technical results. First, we identify a proper basin of attraction (BOA) containing global minimizer, and characterize its
geometric property by showing that its radius is controlled by noise in an explicit way. Then, by exploring the first order optimality conditions of PGO, we prove that a local minimizer is globally optimal if a noise-controlled criterion is satisfied. Finally, by combining above two results, we prove that one and only one local minimizer exists in the BOA and this minimizer is globally optimal, when the noise is smaller than an explicit bound that can be verified a priori. Numerical simulations validate the proposed theory under various random cases, showing that the obtained conditions provide an effective criterion for certifying the global optimality of PGO.
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All authors Shuan Ding, Ruishuang Chen, Mengfan Cao, Zaiyue Yang
Computer Vision
Prospective Forcing: Learning to Look Ahead for Autoregressive Long Video Diffusion
Yifan Shen, Yewei Xia, Gongxu Luo et al. (+6 more)
Autoregressive
video generators synthesize long
videos through chunk-wise rollouts, yet they are typically trained to predict only the immediate future. This limited prediction horizon makes it challenging to maintain
video quality over extended rollouts. To address this problem, we introduce Prospective Forcing, a multi-head prediction framework that injects long-range predictive signals into autoregressive
video representations. Built on a rolling-window generator, Prospective Forcing attaches lightweight, offset-specific heads that share the same conditioning information and independently predict chunks at different future temporal offsets. By extending supervision beyond the next chunk, these heads encourage the shared history representation to retain information useful for longer-horizon prediction, supporting temporally consistent generation. At inference time, we accelerate sampling by using accepted future-chunk predictions to skip full-window backbone executions. We equip the head with learnable
embeddings to generate multiple candidates for the next chunk, and a learned verifier either selects a candidate or rejects all and falls back to standard autoregressive generation.
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All authors Yifan Shen, Yewei Xia, Gongxu Luo, Peiyuan Zhu, Boyang Sun, Zhenhao Chen, Guangyi Liu, Guangyi Chen, Kun Zhang
Computer Vision
TAG: Calibrating SAM3 for Universal Image Segmentation without Dense Supervision
Hao Zhu, Heyu Sun, Shuo Jin et al. (+2 more)
The recent Segment Anything Model 3 (SAM3) extends powerful promptable segmentation from spatial cues to concepts, raising the prospect of a single foundation model for universal image segmentation. However, this promise often fails to translate into reliable performance on downstream segmentation tasks. In this work, we empirically identify concept recognition, rather than mask quality, as the primary bottleneck: SAM3 accurately delineates the objects it recognizes yet struggles to robustly determine which concepts are present. Driven by this, we present \textit{\underline{T}ext \underline{A}nchor re-\underline{G}rounding (\TAG{})}, a simple yet effective framework that rectifies the concept recognition of SAM3 on the target domain without dense annotations. Specifically, \TAG{} introduces an Anchor Residual Pathway comprising image-conditioned, low-rank adapters to re-ground text anchors in the target distribution and modulate subsequent text-conditioned interactions. Guided solely by image-level category labels, \TAG{} jointly optimizes binary cross-
entropy with Soft Boundary Ranking objectives to calibrate presence confidence and distinguish present concepts from confusable negatives. Building upon this, we devise Confidence-Weighted
Distillation to couple rich semantic context of a
multimodal large
language model with fine-grained spatial priors of SAM3, thereby achieving effective calibration in the absence of manual annotations. Extensive experiments demonstrate that \TAG{} unlocks SAM3's segmentation potential with only 3.1M trainable parameters, achieving impressive performance across diverse scenarios, and notably, even surpassing leading mask-supervised methods on several metrics. \href{https://github.com/MiSsU-HH/TAG_SAM3}{Our code will be publicly available at GitHub \faGithub}.
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All authors Hao Zhu, Heyu Sun, Shuo Jin, Jiayu Xiao, Feng Dai
Computer Vision
Can Synthetic UAV Benchmarks Be Trusted? Real-Synthetic Validation for UAV Model Selection
Yuyi Zhi, Min Du, Bill Shi et al. (+4 more)
Synthetic
UAV imagery can expand pre-deployment test evidence when task-specific Real data are scarce, but visual plausibility or semantic label
alignment alone does not establish validity for model selection. We study when Synthetic evaluation preserves the model-selection conclusions obtained from Real
UAV data through two complementary cases under fixed evaluation protocols. In a requirement-specific road-debris
benchmark, four
language-conditioned perception systems obtain lower absolute F1 on Synthetic data, yet the point-estimate ordering and top-ranked model are preserved; bootstrap analysis yields no supported pairwise reversal, and size-matched resampling preserves the top-model decision. In contrast, a closed-set stress test on a separately materialized business corpus, using four VisDrone-compatible detectors over eight empirically supported classes, changes the point-estimate top-ranked detector and reverses three of six pairwise point-estimate preference directions. A complementary audit of five
UAV business scenarios further reveals substantial Real-Synthetic mismatch in class composition, object density, and target scale, including systematic under-representation of small targets. These results show that Synthetic
UAV benchmark trust is conditional: semantic coverage is insufficient, and validity must be established empirically for the task, operating regime, and model panel of interest.
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All authors Yuyi Zhi, Min Du, Bill Shi, Guodong Yuan, Junheng Liang, Peifan Liao, Xin Guan
Game Theory
Beyond Independent Values: Optimal Auctions with Correlated Valuations for LLM-Native Advertising
Shiman Sheng and Mingyu Guo
Conversational assistants are becoming the place where people ask what to buy, and sponsored content is moving into their answers. Several advertisers then compete for one sponsored slot, and all of them read the same conversation, so their willingness to pay usually moves together. Existing auctions for LLM advertising treat these values as independent. We therefore present AffMyerson, an auction that captures this correlation and turns it into revenue. We model the advertisers' values given the conversation as a multivariate normal and turn the economic premise of positive dependence (affiliation) into a sign condition on the precision
matrix. We design a neural parameterization whose every output satisfies it. We prove that every prior in this family yields a truthful, ex-post individually rational auction, so estimation error can cost revenue but never truthfulness. We derive the conditional virtual values in closed form. By the design of the prior, pricing an auction needs no
matrix inversion and takes O(n^2) time, fast enough to run at query time. We also work out the log-normal family used in empirical ad-auction work. There we prove that the auction is truthful for every prior and revenue-optimal when the prior is correct, and that the virtual value is regular everywhere whenever the conditional spread is below a loose threshold, which our fitted priors stay well under. With LLM-
agent bidders, AffMyerson earns 6.2% more than a second-price auction with a tuned reserve, 6.1% more than Ronen's lookahead auction with the same prior, and 9.2% more than Myerson's auction run as if values were independent. This gain is still 4.5% on real keyword search queries where willingness to pay is hard to read. The correlation comes from common demand.
Agents that judge the conversation bid together and
agents told to calculate do not. The gain from conditioning moves with the correlation.
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Artificial Intelligence
Shaping Latent Mixture-of-Experts: From Expert Geometry to Layerwise Capacity Allocation
Yaoxiang Wang, Kai Zhang, Ruobing Xie et al. (+4 more)
Fine-grained mixture-of-experts (MoE) models commonly increase expert count by narrowing each expert, treating expert shape as a by-product of routing granularity. LatentMoE moves expert computation into a lower-dimensional space, exposing the input dimension and intermediate width of each expert as explicit design choices. We show that an expert's local Jacobian rank is jointly bounded by these two dimensions, and that balancing them maximizes the rank ceiling at fixed per-expert parameters. Controlled shape sweeps across model scales and routing granularities consistently favor near-square experts. We further use layerwise intrinsic dimension (ID) to guide capacity allocation across depth, co-varying the number and shape of experts while keeping per-layer parameter count, activated FLOPs, and communication volume constant. Compared with layer-homogeneous baselines, our ID-guided allocation achieves lower validation loss and stronger downstream performance. These results establish expert shape and layerwise allocation as practical design axes for MoEs.
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All authors Yaoxiang Wang, Kai Zhang, Ruobing Xie, Lanrui Wang, Guoliang Zhao, Junhui Li, Jinsong Su
Artificial Intelligence
AegisVox: Factorized Privacy and Clinical Preservation for Long-Form Speech
Praphul Singh, Akshat Agarwal, Ratul Ghosh et al. (+5 more)
Clinical
speech reveals identity through both spoken identifiers and voice, yet its pauses, delivery, and speaker roles can also carry useful clinical evidence. Protecting that
speech therefore requires more than preserving its words. We present AegisVox, a factorized framework that assigns consistent surrogate voices and edits identifier-bearing turns using document-consistent replacements. Across 390 public simulated or non-patient conversations totaling 63.6 hours, a matched comparison of two
speech renderers separates replacement realization from retained identifiers, speaker linkage, and clinical preservation. Text-to-
speech rendering realizes 535/601 planned replacements versus 453/601 for the codec-based
speech editor. However, similar aggregate clinical preservation conceals more model-labeled contradictions, and conversation-wide speaker scores conceal greater edited-
speech linkage under a score-direction sensitivity analysis. Fresh transcription of original audio provides a reference for interpreting the clinical measurement chain; an independent judge tests sensitivity to model-based assessment. Better replacement realization can therefore coexist with worse clinical or speaker-linkage measurements. AegisVox supports use-specific evaluation of those tradeoffs, not a guarantee of identifier removal or clinical safety.
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All authors Praphul Singh, Akshat Agarwal, Ratul Ghosh, Aashna Kanuga, Devashish Khatwani, Anurag Dwarakanath, Sri Gadde, Krishnaram Kenthapadi
Information Theory
Optimal Receive Design on the Observation Axis for RSMA Satellite-UAV Downlinks
Xianwei Meng
A receiver can change its observation state while the physical signal remains the same. In rate-splitting multiple access (RSMA) downlinks, this distinction separates receive-beam design and phase-reference transport from the motion of the satellite–
UAV line of sight. We formulate receive optimization within the physical–observation dual-axis (PODA) structure and obtain three complementary results. For a known line-of-sight response and positive-definite receiver-noise covariance, a noise-weighted matched combiner simultaneously maximizes every decoding SINR and the minimum user rate. Under directional uncertainty, the principal eigenvector of the conditional response covariance globally maximizes mean array gain; for a Gaussian directional model, an equal-amplitude receiver is optimized by steering towards the conditional mean. Mean rate requires a different objective. We prove that the RSMA common-rate water level is jointly concave in the users' receive gains and derive a semidefinite relaxation with a computable global upper bound. A feasible rank-one beam can therefore be compared directly with the optimum of the finite-scenario problem. We also derive a precision-weighted linear predictor that suppresses uncertain physical-velocity estimates after observation-frame transport. Paired dynamic simulations reduce direction-cosine RMSE by 51.29% relative to unrestricted nine-point extrapolation. In a separate uncertainty experiment, direct rate optimization increases mean minimum spectral efficiency from 0.32425 to 0.41712 bit/s/Hz, an improvement of 28.64%; the corresponding design objective is within $2.28\times10^{-4}$ bit/s/Hz of a global upper bound. The phase-transport equation fixes a coherent representative of each optimal beam without changing its gain.
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Systems and Control
Defending Consensus-Based Cooperative Sensing Against Conformity-Driven Byzantine Attacks in 6G UAV Swarm Networks
Ruixing Ren, Junhui Zhao, He Fang et al. (+4 more)
In integrated sensing and communication (
ISAC) enabled six-generation (6G) networks, unmanned aerial vehicle (
UAV) swarms increasingly rely on consensus-based cooperative sensing: each node iteratively exchanges and fuses decisions with its neighbors to approach a global picture of the environment, supporting the high-reliability, low-latency inference demanded by mission-critical tasks. Yet this very mechanism of conformity becomes the lever of Byzantine attacks. By exploiting the update rule, malicious nodes manufacture a false consensus that lures honest nodes into drifting away from their true observations round after round, so that a normal node's actual error probability evolves dynamically and rises far above its intrinsic sensing error. Conventional fusion models, which assume node errors are fixed and independent, are therefore severely mismatched. This article argues for a different defensive perspective: instead of hunting for bad nodes, we seek to understand how good nodes turn bad, characterizing the inducement dynamics with evolutionary game theory and handing this law to the fusion center so that it can dynamically correct the true error level of normal nodes. Simulations show that the approach sustains near-perfect situational inference across a wide range of attack intensities and network sizes, while naturally supporting low latency decisions. We further discuss several open challenges on the path toward secure
ISAC-6G swarm sensing.
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All authors Ruixing Ren, Junhui Zhao, He Fang, Xiaoke Sun, Jiamin Li, Pengcheng Zhu, Dongming Wang
Artificial Intelligence
PEARL: Process-Experience Aware Reinforcement Learning for Multi-Turn Agents
Jie Yang, Mianqiu Huang, Yiwei Zhao et al. (+3 more)
Terminal rewards make multi-turn
agents trainable without step-level annotations, but group-relative advantages provide little guidance about which intermediate decisions to adjust. Failed trajectories also contain reasonable actions, while unfavorable decision patterns can recur as training proceeds. We introduce PEARL (Process-Experience Aware Reinforcement Learning), which improves local credit assignment without an additional critic. Given two continuations from the same history with different outcomes, PEARL locates their first divergence and builds a feature representation of each branch's local decision. It clusters these decision features in recent failure and success
memories to identify recurring local patterns. Using this pattern
memory, PEARL selectively strengthens negative advantages at lower-reward decisions, while leaving positive advantages and the base policy optimizer unchanged. On AppWorld, PEARL improves over ARPO on Normal and Challenge tasks at both 4B and 8B scales. On ALFWorld, it achieves 97.9% mean success at 4B, 2.7 points above the strongest baseline, while remaining competitive at 8B. Ablations of shaping location, success-bank filtering, and
memory window support the value of selective, recurring-pattern-based updates.
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All authors Jie Yang, Mianqiu Huang, Yiwei Zhao, Bo Wang, Yang Liu, Yuxin Wang
Computation and Language
PVFH-Bench: Benchmarking Premise-Induced Visual Fixation Hallucinations in Vision-Language Models
Xinhao Wang, Junbo Cui, Zhenghao Liu et al. (+4 more)
Large
Vision-
Language Models (LVLMs) have achieved remarkable progress across a wide range of
multimodal tasks. However, object
hallucination remains a persistent challenge, undermining model reliability in applications such as visual question answering, embodied
agents, and human-AI decision support systems. In this paper, inspired by cognitive science studies on perceptual bias, we investigate a new failure mode termed Premise-Induced Visual Fixation
Hallucination (PVFH). Specifically, PVFH refers to a premise-induced
reasoning error in which models become anchored to visually confusable evidence, accept a false textual premise, and subsequently generate responses about an absent yet visually plausible object. To systematically study this phenomenon, we introduce PVFH-Bench, a
benchmark that automatically constructs visually confusable but absent targets and pairs the core
hallucination setting with diagnostic subsets to trace how models move from object recognition to Premise-Induced Visual Fixation
Hallucination. Experimental results on 10 state-of-the-art LVLMs demonstrate that all evaluated models are highly vulnerable to Premise-Induced Visual Fixation
Hallucination. Moreover, we find that explicit chain-of-thought
reasoning can further exacerbate this failure mode by encouraging models to overinterpret weak visual cues as supporting evidence for hallucinated objects.
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All authors Xinhao Wang, Junbo Cui, Zhenghao Liu, Yuan Yao, Xiaohua Li, Yu Gu, Ge Yu
Robotics
VbotNav-0: Asynchronous Cloud-Edge Dual-Policy Navigation across Indoor and Outdoor Environments
Shiqi Tan, Yujun Zhang, Keyu Li et al. (+4 more)
Continuous indoor-outdoor navigation connects destination-level
reasoning with motion decisions that must respond to current observations. We present VbotNav-0, a cloud-edge dual-policy framework that links these processes through asynchronous policy-to-policy conditioning. The cloud policy produces subgoal guidance from visual history, navigation instructions, and route
tokens; the edge policy combines available guidance with current RGB and
LiDAR observations to generate local trajectories. This formulation allows the local perception-action loop to continue between cloud updates. A common route representation connects indoor paths and outdoor map routes, and the training recipe combines navigation demonstrations, corrective data, and grounded visual question answering (VQA). In the reported closed-loop evaluation of 258 episodes across 145 scenes, VbotNav-0 achieves 55.04% success rate and 55.03% success weighted by path length.
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All authors Shiqi Tan, Yujun Zhang, Keyu Li, Shuaixi Pan, Siyu Zhou, Hailong Qin, Yinan Yu
Computer Vision
Automatic assembly modeling through attention-based assembly feature mate prediction and assembly feature pattern recognition
Ziheng Dai, Wanbin Pan, Zhihong Tang et al. (+1 more)
Assembly modeling plays an important role in constructing product models, yet it is still mainly performed via interactive operations that are experience-dependent and labor-intensive. Data-driven strategies hold promise for automating this process; however, existing approaches either predict poses that cannot directly generate editable B-Rep assembly constraints or require prescribed part pairs or predefined assembly faces. To reduce the large candidate space of constraint prediction without predefined assembly faces, we propose a data-driven framework based on assembly feature mate prediction. The framework performs automatic assembly modeling from unassembled B-Rep parts, using assembly features to group the
geometric entities of local mating interfaces and restrict candidate formation. Given the lack of suitable assembly feature annotations in existing
datasets, we construct a large-scale
dataset through an automatic annotation pipeline. Using the annotated
dataset, we train an
attention-based network to predict assembly feature mates, with Engineering Compatibility Fusion (ECF) incorporating explicit engineering compatibility into learned matching scores. To improve the accuracy of automatic assembly modeling, we use assembly feature patterns (AFPs) to guide the matching process. Finally, a post-processing search selects assembly feature mate and AFP mate candidates, whose associated constraints are generated and solved to complete assembly modeling. Experimental results show that the framework achieves assembly constraint accuracies of 74.79% on the six-assembly comparison set and 64.09% on the extended test set of 16 assemblies, which includes the comparison set. With ground-truth face labels supplied as additional input, the corresponding accuracies increase to 81.78% and 80.93%, respectively.
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All authors Ziheng Dai, Wanbin Pan, Zhihong Tang, Shuming Gao
Information Retrieval
The Company a Question Keeps: Auditing Open Jev-Style Models for Retrieval
Jianbo Chen
Jev's documented interface evaluates typed questions independently against shared evidence. We audit request independence in seven open Jev-style checkpoints on 276 constructed three-passage states from 92 TREC
retrieval queries. Paired interventions separately change requested questions, displayed evidence, and candidate eligibility. Request expansion induces strict binary flips in 9.1%/8.0% of Nimble's judgments and changes 21.4%/18.9% of selection-or-abstention actions in TREC DL2019/2020 under a threshold of 0.5 fixed before inference. Correct-action rates change by 0.0/+8.2 percentage points, respectively, with 95% query-bootstrap intervals of [-6.0,6.0] and [3.8,12.6]. Field masking traces all 70 request-set flips to the schema fields serialized into each scoring
prompt. In Von, fixing a shared separator's position eliminates two eligibility-induced preference flips in selected diagnostic states. Laya-typed's default CUDA path produces five passage swaps without strict binary flips, and both yearly action-change intervals include zero; in an FP32 rerun, its probability movement vanishes at the model's four-decimal reporting resolution.
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Computer Vision
DuSRFlow: Dual-Lens Super-Resolution via Parallax-Aware Flow Estimation and Complementary Reference Warping
Fangpu Zhang, Huanjing Yue, Zifan Cui et al. (+3 more)
Dual-lens super-resolution (DuSR) aims to enhance a wide-angle low-resolution (LR) image by exploiting a telephoto reference (Ref) image. A central challenge in DuSR is reliable reference utilization under large parallax, occlusion, and cross-camera imaging discrepancies. Existing methods often employ off-the-shelf monocular optical flow models to provide
geometric priors. However, their assumptions of photometric consistency and smooth motion are violated in dual-lens scenarios, leading to unreliable
alignment and degraded reference-based reconstruction. To address these issues, we propose DuSRFlow, a dual-lens super-resolution framework that improves reference utilization from both correspondence learning and reference aggregation. First, we introduce DuFlowNet, a dedicated dual-lens flow
estimator for robust cross-view
alignment. To train DuFlowNet without ground-truth flow annotations, we develop a real–synthetic data fusion strategy that composites synthetic foreground objects onto real dual-lens image pairs, generating controllable large-parallax samples while preserving realistic cross-camera imaging characteristics. Second, we observe that reference reliability is highly spatially uneven in DuSR, where unreliable local reference responses may suppress the utilization of reliable cues. We therefore propose a Complementary Reference Warping strategy that coordinates patch-based warping and kernel-free-guided deformable warping according to local reference reliability, producing a more reliable reference representation before LR–Ref fusion. In addition, we construct DuSR-RealV2, a challenging real-world
benchmark with complex depth variations, large parallax, and occlusion. Extensive experiments on multiple DuSR
benchmarks demonstrate that DuSRFlow achieves state-of-the-art performance in reconstruction quality, perceptual quality, and cross-
dataset generalization.
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All authors Fangpu Zhang, Huanjing Yue, Zifan Cui, Shaoxiong Tu, Qirui Yang, Jingyu Yang
Software Engineering
Executable but Unfaithful: Mock Object, Stub, and Assertion Generation by LLMs and Coding Agents
Hao Ning, Jiahui Dong, Zhen Yang et al. (+7 more)
Mocking is a foundational practice in unit testing, enabling developers to isolate the code under test by replacing real dependencies with simulated alternatives. Automating the generation of mock objects, stubs, and mock assertions has been a long-standing research goal. However, capture-and-replay approaches rely on recording the runtime behavior of actual dependencies. Evolutionary approaches such as StubCoder overcome this requirement but constrain generation to predefined symbol pools. Large
language models (LLMs) and coding
agents offer an alternative by generating mocking artifacts from source code without requiring recorded dependency executions or fixed symbol pools. Yet, no systematic study has evaluated their capabilities across all three artifact types. To address this gap, we construct MockBench, a
benchmark comprising 165 mock artifact generation tasks from real-world Java and Python projects, with validated reference mock objects, stubs, and mock assertions. We evaluate six LLMs and three coding
agents in terms of executability, behavior fidelity, and interaction-fault detection capability, complemented by comparisons with existing tools and sensitivity analysis. Experimental results show that LLMs and coding
agents indeed have been the state-of-the-art techniques in mock-based testing. More specifically, compared with LLMs, coding
agents substantially improve executability, while these gains do not consistently translate into higher behavior fidelity or better interaction-fault detection. Furthermore, we analyze 660 failures from LLMs and coding
agents, and identify ten error types across the three artifact categories, revealing behavioral deviations that passing tests can conceal. Our findings highlight the challenges of mock artifact generation and provide practical guidance for both developers and researchers.
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All authors Hao Ning, Jiahui Dong, Zhen Yang, Guanda Yan, Yichen Sun, Xuanyu Zhu, Zheng Shen, Yifei Pei, Jacky Keung, Jie M. Zhang
Machine Learning
TagBO: LLM-Driven Task-Aware Graph Bayesian Optimization for Scientific Discovery
Xinzhe Yuan, Zimu Zeng, Zhuo Chen et al. (+6 more)
Mixed-variable optimization in scientific discovery often requires exploring large combinatorial spaces under costly experimental evaluations, making sample?efficient
Bayesian optimization (BO) essential. However, existing BO methods typ?ically rely on structural descriptors to define similarity among categorical variables, implicitly assuming that structural similarity reflects similarity in task performance. In scientific optimization, this assumption is often violated, leading to surrogate representations that are poorly aligned with the optimization objective. To address this limitation, we propose TagBO (Task-aware Graph
Bayesian Optimization), a method that integrates LLM-derived relational priors with descriptor-based struc?tural information for mixed-variable
Bayesian optimization. Rather than directly using LLM outputs as predictive scores, TagBO treats LLM outputs as a potentially noisy source of task-aware relational priors and extracts coarse ordinal relational signals from LLM
reasoning to construct task-aware similarity structures for scien?tific optimization. TagBO then performs constrained Gaussian process
Bayesian optimization over the resulting joint discrete-continuous representation space. Ex?periments on diverse chemistry and materials formulation tasks demonstrate that TagBO consistently improves sample efficiency under fixed evaluation budgets across LLMs of varying capabilities.
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All authors Xinzhe Yuan, Zimu Zeng, Zhuo Chen, Jianshu Zhang, Jinzong Dong, Jingyi Chen, Nanyang Ye, Huan Xiong, Qinying Gu
Artificial Intelligence
From Existing Methods to New Research Ideas: Two Perspectives on Recursive Self-Improvement
Leijun Zhou, Yanke Yu, Zhihao Liu et al. (+10 more)
Recursive self-improvement (RSI) offers a route to AI systems that participate in their own continued development. Recent methods allow
language agents to retain changes to their parameters,
memory, skills, and code for subsequent execution and improvement. We examine how these methods can inform new research ideas through two complementary perspectives. First, we introduce a unified taxonomy with nine design dimensions organized into four groups: update targets, update organization, update schedule and scope, feedback and selection. This taxonomy places existing methods in a shared design space, where examining individual dimensions and their interactions can inspire new research ideas. Second, we examine how existing RSI methods draw on traditional deep learning, for example through optimization, regularization, data augmentation, continual learning, and
adversarial learning. We develop further ideas by examining more specific learning mechanisms and drawing on evolutionary computation. We also review how existing
benchmarks assess self-improvement under online, offline, and offline-to-online settings. Together, these perspectives help researchers develop new RSI research ideas from existing designs and learning mechanisms. RSI papers and resources are available at https://prism-shadow.github.io/awesome-rsi/.
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All authors Leijun Zhou, Yanke Yu, Zhihao Liu, Qu Xiang, Tiancheng Hu, Yiru Dong, Yihan Wu, Yangxi Xiang, Xuejun Wu, Xi Chen, Buyue Qian, Yaowei Zheng, Junhao Hu
Computer Vision
EarthAlign: Learning Language-Aligned Representations of Earth System Data
Fadillah Adamsyah Maani, Numan Saeed, Jiaqi Ma et al. (+4 more)
Interpreting Earth system conditions is central to understanding environmental risks and informing decisions across weather- and
climate-sensitive sectors. Existing
multimodal weather systems primarily target task-specific prediction, generation, or question answering, while general-purpose
alignment between Earth system data and
language remains underexplored due to the limited availability of diverse
language supervision grounded in meteorological conditions. To fill this gap, we introduce EarthAlign, a large-scale
dataset with broad geographic coverage spanning 2000-2024 that combines 10 expert-curated natural event and disaster data sources with climatology-derived normal-weather samples. Each sample includes temporal and geospatial information and four grounded caption styles aligned with 63-variable ERA5 meteorological data. Building on EarthAlign, we introduce MeteoLIP, a dual-encoder framework for learning aligned meteorology-
language representations. MeteoLIP incorporates MetTok, which uses a geolocation- and time-conditioned query to aggregate variable-specific patch
embeddings into a compact sequence of meteorology
tokens. To comprehensively assess Earth system
multimodal representations, we introduce three downstream tasks: cross-modal
retrieval, weather-condition classification, and physical variable estimation. Our experiments show that MeteoLIP improves upon the corresponding CLIP and SigLIP baselines across these tasks. Further analyses highlight the importance of meteorological input, temporal and geospatial conditioning, and multi-style supervision for meteorology-
language alignment. Thus, EarthAlign and MeteoLIP provide a foundation for learning and evaluating
language-aligned representations of Earth system data. Our code,
dataset, and weights are publicly available at https://github.com/mbzuai-oryx/EarthAlign.
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All authors Fadillah Adamsyah Maani, Numan Saeed, Jiaqi Ma, Dat Tien Nguyen, Youssef Wehbe, Duncan Watson-Parris, Salman Khan
Machine Learning
From Rainfall to Road-Network Functionality: Transferable Graph Forecasting for Flood-Disrupted Transportation Networks
Junyan Wang and Naiyu Wang
Extreme rainfall can rapidly degrade road-network functionality (RNF), disrupting emergency mobility, accessibility, evacuation, logistics, and infrastructure restoration. Existing flood-transportation impact models are often difficult to deploy across regions because they rely on local calibration, intensive simulation workflows or target-region retraining. This study proposes G-R2R, a transferable spatiotemporal graph-learning framework that reframes rainfall-induced road-network disruption as a region-conditioned,
topology-flexible graph forecasting problem for unseen transportation networks. The framework integrates ego-centered subgraph sampling, terrain-conditioned graph representation, region-conditioned dual-level static encoding, and dynamic rainfall-network feature learning. Static geographic and infrastructure attributes are fused with rainfall forcing and recent RNF states to forecast node-level RNF across heterogeneous road networks. G-R2R is evaluated using simulation-derived RNF sequences from ten flood-prone regions in Wenzhou, China, under unseen-region testing protocols. It outperforms representative forecasting baselines, achieving a stabilized MAPE of 2.33% and $R^2$ of 0.855 under the fixed unseen-region
benchmark. Leave-one-area-out evaluation yields stabilized MAPE values of 2.30%-3.54%. Ablation analysis supports the contributions of the terrain-conditioned graph representation and dual-level static encoding. A historical-event replay of Typhoon Fitow further shows that G-R2R reproduces principal temporal trajectories and node-level spatial patterns of the simulation-derived RNF reference under observed rainfall forcing without target-region retraining. These results support rapid and transferable node-level RNF forecasting across the regional road-network domains represented in this study.
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Systems and Control
Shifted-Chart Composition for Horizon-Optimal Time-Varying LQR
Viswas Dinesh and Spandan Roy
Horizon optimization in finite-horizon LQR treats the horizon $T$ as a decision variable and adds a penalty $wT$ to the cost. Recent composition-based methods evaluate all candidate horizons in $O(Nn^3)$, instead of the $O(N^2n^3)$ of one Riccati recursion per horizon, but they track the inverse cost-to-go $P^{-1}$ and therefore require every stage and terminal cost to be positive definite, whereas LQR requires only positive semidefiniteness. This excludes costs that leave some states unpenalized. In this work, we track instead the shifted inverse $\widehat{P}_k=(P_k+cI)^{-1}$, the inverse coordinate after a symplectic shear of the state-costate variables. Its domain contains every positive semidefinite Riccati
matrix, and $P_k=\widehat{P}_k^{-1}-cI$ recovers the Riccati solution. We show that the shift can be absorbed into the stage coefficients of the inverse-coordinate composition, so the $O(Nn^3)$ search carries over, exact in exact arithmetic, subject to invertibility conditions. These hold for all small $c$ when the dynamics contract the unpenalized directions, fail for at most finitely many $c$ outside a degenerate case, and are certified per horizon at run time. In our experiments, diagonal loading $Q_k+\varepsilon I$ either left cost errors several orders of magnitude above those of the shifted chart or, for small fixed $\varepsilon$, selected the wrong horizon. The shifted chart with a fixed default $c$ selected the correct horizon on all tested instances, reporting long horizons as uncertified in one stress case.
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Numerical Analysis
Dimension-independent eigenvalue approximation for sparse matrices
Natalia Hajlasz, Christopher Musco, Jonathan Weare et al. (+1 more)
Many applications in science and engineering require solving sparse Hermitian
eigenvalue problems that are so large that even
algorithms whose runtime scales linearly with the
matrix dimension are unacceptably slow. Responding to this challenge, there is a growing body of work on sparsified iterative methods and submatrix selection methods that often perform well in practice. However, theoretical understanding of these methods is limited. In this paper, we take a step toward better understanding sublinear-time
eigenvalue computation by analyzing the sparsified power method. We prove that the method approximates a leading eigenpair to a prescribed accuracy in a number of iterations comparable to the standard power method. The required number of retained nonzeros depends explicitly on the target accuracy, the initial overlap with the leading eigenvector, the compressibility of the leading eigenvector, and the relative spectral gap. When these quantities and the column sparsity are independent of dimension, the runtime is independent of dimension. In contrast, we show that a comparable worst-case guarantee cannot hold for a prototypical sparsified submatrix selection method. Submatrix selection is the approach of choice in many
quantum chemistry applications. However, we show that, even with substantial initial eigenvector overlap, a single iteration of the method can produce a vector that is orthogonal to the leading eigenvector, after which the method cannot recover. Nevertheless, experiments on
matrices from the SuiteSparse
matrix collection and
quantum chemistry problems illustrate the practical effectiveness of submatrix selection methods in a variety of domains despite these worst-case limitations.
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All authors Natalia Hajlasz, Christopher Musco, Jonathan Weare, Robert J. Webber
Information Theory
Limitations on Autoregressive Token Generation via Sequential Sampling
Gal Arnon, Andrej Bogdanov, Paul Lou et al. (+3 more)
We study autoregressive
token generation through the notion of sequential samplers: A
circuit that samples the next symbol only given the previous ones and possibly a small amount of state. We show that (1) a sequential sampler with $k$ bits of state can be simulated by a stateless one at a roughly $O(2^{k})$ size overhead; (2) efficient insertional sampling (Stern, Chan, Kiros, Uszkoreit, ICML 2019) is more powerful than efficient sequential sampling in any fixed order; (3) sequential samplers with small state cannot establish certain forms of covert cryptographic channels. Result (1), together with the pseudoentropy vs. sequential sampleability duality of Vadhan and Zheng (STOC 2012), characterizes efficient sequential sampleability with small state. Result (3) provides a potential safety mechanism against subliminal coordination among malicious LLM
agents.
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All authors Gal Arnon, Andrej Bogdanov, Paul Lou, Giulio Malavolta, Alon Rosen, Lorenzo Taschin
Machine Learning
LLM-Augmented Bayesian Optimization with Adaptive Policies for Chiplet Design Space Exploration
Hang Liu, Zhengfei Chen, Zhengrui Chen et al. (+3 more)
Chiplet technology is a key approach for extending Moore's Law. However, its design process is constrained by two primary challenges: the scarcity of early-stage platforms for rapidly evaluating chiplet-based heterogeneous systems, and the complex relationships between design parameters and optimization metrics. To address these challenges, we propose a synergistic framework that integrates a Large
Language Model (LLM) with
Bayesian Optimization (BO), enabling architectural-level simulation and automated optimization for the 2.5D/3D CPU-GPU heterogeneous chiplets. We extend the open-source chiplet simulator to evaluate 3D designs by supporting graph
topologies. Further, to enhance the exploration efficiency of BO, we introduce an LLM that generates high-quality policies to guide the optimization process, enabled by our proposed adaptive directional policy
reasoning (ADPR) and an exploration gain-aware fine-tuning method. Finally, we use the policy-guided BO to perform an automated and efficient exploration of the complex chiplet design space. Experiments on 2.5D and 3D chiplet reveal that our framework improves design space exploration efficiency.
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All authors Hang Liu, Zhengfei Chen, Zhengrui Chen, Hao Geng, Yu Li, Qi Sun
Computer Vision
EviScene: Beyond "Is It AI?" for Agentic Image Factuality Assessment
ZiAn Wang, Ruiqi Liu, Zhengbo Zhang et al. (+10 more)
Determining whether an image is AI-generated does not establish whether the situation it depicts is factually supported. We formulate Image Factuality Assessment: given an image and a generic verification instruction, a system returns a supported or refuted verdict with an evidence-grounded explanation. Without a supplied textual claim, the system must progressively refine factual questions during investigation and align external evidence with the specific situation depicted. We introduce EviLens, an expert-reviewed
benchmark of 10,174 supported and refuted images from controlled construction and web collection, covering twelve factual error types and evaluating both verdict accuracy and evidence sufficiency. We develop EviScene, a
multimodal ReAct
agent that interleaves visual inspection, external
retrieval, and evidence analysis, using new findings to refine its questions and revisit relevant image details. We train a compact policy with supervised fine-tuning on reviewed teacher trajectories, followed by privileged on-policy self-
distillation applied to verified decisions from the policy's own investigations. On the 1,527-image test set, the trained Qwen3.5-9B
agent achieves 79.05% balanced accuracy and a Strict Evidence Sufficiency Rate (SESR) of 52.26%, improving over the same-backbone base
agent by 6.71 and 9.37 percentage points, respectively. Its SESR is the highest among the evaluated
agents.Code, models, and data will be released at https://github.com/wangzian828/EVISCENE and https://huggingface.co/ZiAnwang/EviScene.
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All authors ZiAn Wang, Ruiqi Liu, Zhengbo Zhang, Yi Han, Jialiang Shen, Ruoxin Chen, Manni Cui, Ximo Zhu, Jin Jiang, Mingzhe Liu, Linhao Wang, Wenhao Wang, Shu Wu