Computation and Language
Revisiting the Estimation of Unigram Surprisal in Computational Psycholinguistics
Zachary Hopton, Francesco Ignazio Re, Samuel Kiegeland et al. (+3 more)
Contextual and unigram surprisal are standard predictors of reading time in computational psycholinguistics. While contextual surprisal is typically derived from neural
language models, unigram surprisal is usually obtained from independent corpus counts. Although both quantities are deterministic functionals of a single underlying distribution—capturing conditional and marginal structure respectively—this practice breaks their probabilistic link and may introduce estimation error from the mismatch. We propose three principles for estimating unigram surprisal in psycholinguistic research, including the requirement that both quantities be derived from the same
language model. We operationalize this principle by introducing a Monte Carlo
estimator of unigram surprisal from a
language model, along with a marginalized variant that has lower variance empirically. When unigram surprisal is re-estimated under this unified framework, we find evidence that predictors change in their predictive contribution to reading time. We also confirm prior findings that frequency can be a stronger predictor of processing effort than contextual surprisal under this unified framework.
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All authors Zachary Hopton, Francesco Ignazio Re, Samuel Kiegeland, Andreas Opedal, Eleanor Chodroff, Ryan Cotterell
Machine Learning
Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs
Mikhail Aleksandrov, Nail Bashirov, Maksim Ivanov et al. (+6 more)
Prompt-based adaptation offers several ways to specialize a frozen
language model, ranging from natural-
language prompt optimization to learned continuous
prompts. These methods are usually compared through downstream performance, although similar outputs need not arise from similar internal computations. We study how discrete
prompt optimization,
prompt tuning, and prefix tuning alter the processing of dense and mixture-of-experts
language models. Our analysis shows that the location of the adaptation predicts how task information is carried to the output: optimized text primarily changes response construction, input-level
prompts write their influence into content representations, and layer-wise prefixes maintain direct control through
attention. In MoE models, these differences further induce distinct expert-routing patterns. At the same time, common representation-level measurements respond strongly to generic context changes and do not reliably distinguish useful adaptations from matched placebos. We argue that understanding
prompt adaptation requires separating representation drift from the part of that drift that is aligned with, and causally used by, the model's task computation. This perspective provides practical guidance for selecting, evaluating, and interpreting
prompt adaptation methods.
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All authors Mikhail Aleksandrov, Nail Bashirov, Maksim Ivanov, Andrew Tsukanov, Timofey Karyagin, Alexander Miasnikov, Gleb Molodtsov, Valentin Samokhin, Aleksandr Beznosikov
Information Retrieval
Federica Santi, Vasilije Marković, Daniel Scott et al. (+1 more)
Dense vector
retrieval can miss evidence connected through intermediate entities, while direct graph
retrieval requires access to the graph at query time. We compare five offline graph-based
embedding transformations and strategies for using the resulting graph-modified
embeddings alongside the originals, without query-time graph access. On a synthetic professional-network
benchmark with complete supporting-evidence sets, direct replacement of the original
embeddings reduces evidence recovery. Multi-stage strategies improve recovery by using graph-modified
embeddings to guide the search while retaining the originals for semantic relevance. On questions requiring linked facts or comparisons among candidates, the strongest configuration improves recovery by up to 25.9% relative to plain vector
retrieval. It also matches baseline recovery with 40% fewer returned nodes. Controls confirm that graph information contributes beyond multi-stage
retrieval alone. Across the tested methods,
retrieval performance depends more on how graph-modified
embeddings are used than on how they are produced.
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All authors Federica Santi, Vasilije Marković, Daniel Scott, Lazar Obradović
Information Theory
Full classification of $\mathbb{Z}_{p^s}$-linear generalized Hadamard codes
Dipak K. Bhunia
The $\mathbb{Z}_{p^s}$-additive codes are subgroups of $\mathbb{Z}_{p^s}^n$. A $\mathbb{Z}_{p^s}$-linear generalized Hadamard (GH) code is a GH code over $\mathbb{Z}_p$ which is the Gray map image of a $\mathbb{Z}_{p^s}$-additive code. A recursive construction gives such codes $H_p^{t_1,\dots,t_s}$, of type $(n;t_1,\dots,t_s)$ and length $p^t$, and a partial classification of them is known. In this paper, we complete the classification of these codes for every
prime $p$. The main tool is the derived code, obtained by puncturing the code outside a block of coordinates determined by its kernel. For a nonlinear code $H_p^{t_1,\dots,t_s}$ with $t_1\geq2$, the derived code is a $\mathbb{Z}_{p^{s-1}}$-linear GH code of the same family, although the mechanism for odd $p$ differs from the binary one. As a consequence, for $t\geq3$, there are $\mathcal P(t+1)-\mathcal P(t)$ nonequivalent such codes of length $p^t$ if $p$ is odd, and $\mathcal P(t+1)-\mathcal P(t)-1$ if $p=2$, where $\mathcal P(n)$ is the number of partitions of $n$. In particular, this completes the classification of the $\mathbb{Z}_{2^s}$-linear Hadamard codes. We also prove a
conjecture on the codes with $t_1\geq2$ and $t_s=0$.
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Computation and Language
Prompt-Injection Probes Learn the Conversation, Not Just the Injection
Kaan Kale and Sriram Vishwanath
Linear probes on a model's hidden states are used as cheap monitors for indirect
prompt injection: they screen the tool outputs an
agent reads and send suspicious ones to a slower second check, so every false alarm has a cost. But a probe learns whatever separates its training examples. The standard evaluation of injection probes can be passed without the model: conversation position and text lengths alone reach 0.91-0.98 AUROC. When each attack is compared with the same conversation without the injection, or with harmless text formatted like an injection, standard probes flag most of these harmless cases. We add such cases to the training data as matched negatives. Across six open models, in applications seen during training and at the default threshold, changing only the training data cuts false alarms on injection-free conversations from 41-80% to 4-27%, and on harmless look-alikes from 92-98% to 3-16%. With a threshold set on normal traffic, our probe still catches a median 95% of the attacks that fooled the
agent (91% without our data). In new applications, false alarms stay low, but detection depends on the model and on how the threshold is set. The same data also helps a text-only filter.
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Machine Learning
Multi-Stream Residual Computation in Diffusion Language Models
Ekaterina Alimaskina, Suren Kazarian, Gleb Molodtsov et al. (+1 more)
Diffusion language models reconstruct masked
tokens in parallel, offering an alternative to autoregressive generation. Yet their
Transformer backbones typically carry visible evidence and evolving masked predictions through a single residual stream. We equip masked
diffusion models with multiple residual streams that persist through depth and adaptive routing between them, then investigate how they support these complementary roles. On the pretraining task, two streams reduce reconstruction perplexity by 5.71% on validation data and 5.13% on average across external corpora compared with standard residual connections. We observe that, without predefined roles, visible and masked positions learn distinct routing patterns. Causal interventions show that this distinction organizes read and write routing, while
token-specific write strength remains important within each role. Guided by these findings, a model trained with role-conditioned routing recovers half the validation gain from adding a second stream. State interventions explain the benefit of the additional stream: it preserves early
token representations for later reuse while evidence accumulates at masked positions. This mechanism recurs at a larger model scale, showing how persistent streams support context preservation alongside prediction refinement.
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All authors Ekaterina Alimaskina, Suren Kazarian, Gleb Molodtsov, Aleksandr Beznosikov
Networking
Tanchun: Robust Flow-Based HTTP(S) DDoS Detection via Behavioral Attribute Guidance
Qingyang Li, Yunpeng Tan, Mingxin Yang et al. (+5 more)
HTTP(S) DDoS attacks have surged in frequency and scale, posing a significant threat to network security. Flow-based detection is a promising solution for large-scale HTTP(S) DDoS detection due to its decryption-free operation and scalability. However, the coarse-grained numerical statistics in flow data limit accurate attack detection and lack robustness when benign and attack traffic patterns drift over time. In this paper, we present Tanchun, a drift-aware flow-based HTTP(S) DDoS detection system that relies solely on decryption-free flow data. Specifically, Tanchun groups flows by IP-pair and aggregates them over consecutive time slots into behavior sequences, capturing temporal access patterns to compensate for the limited information in individual flows. It further abstracts these sequences into human-understandable behavioral attributes that capture stable behavioral semantics. Tanchun then aligns behavior sequences with their corresponding behavioral attributes to guide representation learning, enabling robust detection under traffic drift. We evaluate Tanchun across diverse HTTP(S) DDoS scenarios. Tanchun achieves the best overall performance, with an average F1-score of 0.988, and outperforms the second-best method by 5.3% and 9.6% under benign-traffic and attack-traffic drift, respectively. Experiments on real-world traffic traces further demonstrate its practical effectiveness.
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All authors Qingyang Li, Yunpeng Tan, Mingxin Yang, Yannan Hu, Lei Zhang, Yong Cui, Zongming Guo, Xinggong Zhang
Systems and Control
Impact of Security Modeling Fidelity on Hyperscale Data Center Hosting Capacity Assessment
Haoxiang Wan, Linhan Fang, Jonghwan Kwon et al. (+5 more)
This paper assesses hyperscale data center hosting capacity in transmission grids. Hosting capacity (HC) analysis quantifies the maximum additional load a bus can accommodate without violating operational constraints. This paper defines data center HC as the maximum time-invariant active power demand at a single target bus over a 24-hour peak-day horizon, and evaluates it under four models of increasing fidelity (i.e., the extent to which the model enforces real-world operational security requirements) on the modified IEEE 24-bus system: (i) DC optimal power flow (DC-OPF) without unit commitment (UC), (ii) basic SCUC (security-constrained unit commitment) without reliability constraints, (iii) SCUC with spinning reserve, and (iv) N-SCUC with both reserve and branch-contingency security. Comparing these models quantifies the impact of unit commitment, reserve requirements, and N-1 security on bus-level HC estimates. The N-SCUC yields an average HC of 444 MW; omitting N-1 security raises this to 532 MW, a 19.8% overestimation. The DC-OPF and basic SCUC models further overestimate HC, indicating a trade-off between model complexity and estimation accuracy.
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All authors Haoxiang Wan, Linhan Fang, Jonghwan Kwon, Jesus Silva-Rodriguez, Jianyi Yang, Jaya Yellajosula, Fan Jiang, Xingpeng Li
Machine Learning · Cross-listed from eess.SP
Overcoming Catastrophic Forgetting: Continual Learning for Radar Waveform Classification
Mohamed Rabie, Chinthana J. Panagamuwa and Konstantinos G. Kyriakopoulos
Automated radar waveform recognition classifies the waveform and modulation type of a received signal. As new waveform variants appear over time, a deployed classifier must learn them without retraining from scratch. Catastrophic Forgetting occurs when learning new waveforms overwrites prior knowledge. Continual Learning (CL) methods tackle this challenge, but prior radar studies only evaluate a few methods on narrow
benchmarks. Practitioners lack evidence on which method to deploy or which mechanisms are effective. This work presents the first large-scale
benchmark of continual learning for radar waveform recognition. Eighteen CL methods are evaluated on a sequential ten-task problem, drawn from four public
datasets spanning 54 waveforms, under task- and class-incremental (TIL, CIL) settings and single- and multi-epoch regimes. Methods that retain data from old tasks lead both TIL and CIL settings. Plain replay alone closes 77-88% of the gap between the lower and upper practical limits under TIL, but only 46-55% under CIL. The internal mechanisms of the leading methods are ablated and statistically tested to identify effective mechanisms. Each ablation is paired against its full method over n=6 training seeds, topped up to n=12 when inconclusive, and p-values are Holm-Bonferroni corrected within each method's family. Ablation results show that several mechanisms confer smaller but statistically significant gains. Combining these mechanisms improves the leading F1 score by 3.4-5.5% and 2.7-4.9% under TIL and CIL, respectively, and the combined models exceed every benchmarked method in both settings for all SNRs. These results give researchers of radar waveform classification systems a mechanism-level basis for design.
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Computers and Society
Institutionalizing Trust in Data Exchange: Trust-Anchor Model of the Data Credit Guarantee Organization and Its Simulation-Based Comparison
Hiroshi Mano, Chie Sugiyama and Teruaki Hayashi
Data trading
markets that span industries and regions are expanding, but the implicit trust that supports data use within an organization does not carry over to cross-domain exchange. This paper examines the Data Credit Guarantee Organization (DCGO), a proposal to institutionalize trust through third-party verification and certification, and asks what it means to trust data, what such certification can support, and under what conditions DCGO outperforms existing trading models. We decompose trust in data into four layers: authenticity of entity, authenticity and integrity of data, rights and conditions of use, and history and auditability. A third party can certify authenticity but not the correctness of content, and DCGO is positioned as a trust anchor spanning existing marketplaces and data spaces. We then extend the Law Merchant model with three data-specific properties, namely imperfect ex-post detection, cheap pseudonyms, and non-rivalry of harm, and compare bilateral trading with DCGO by
agent-based simulation. DCGO keeps the buyer's payoff near its theoretical upper bound regardless of the
market environment, whereas bilateral trading loses even when fraud is always detected. Identity verification proves to be a prerequisite for record sharing, and pre-verification is the only function that limits the spread of harm. On the seller side, honest sellers join voluntarily whenever the price premium exceeds the fee. Lock-in and a critical mass for
diffusion, however, call for fee governance and support for entry.
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Computer Vision
Bifocal: Training-Free Mixed Granularity Visual Token Pruning for Multimodal LLMs
Peiran Yin, Hao Wu, Zihao Yang et al. (+6 more)
Visual
token pruning for
multimodal large
language models (MLLMs) appears highly effective, with existing methods retaining over 95% of unpruned performance on common VQA
benchmarks using a quarter of the visual
tokens or fewer. However, our extensive evaluation of ten methods across three MLLM families and five ability groups reveals a critical gap: strong general performance often comes at the cost of severe degradation on fine-grained tasks such as OCR and visual grounding. We trace these failures to two limitations of the prevailing paradigm of retaining the highest-ranked native patches. A single importance ranking overlooks the complementary evidence required by different tasks, while sparse patch selection disrupts both image content and spatial integrity. To address these limitations, we propose Bifocal, a training-free, two-stage
pruning method built on a mixed-granularity framework. It allocates separate budgets to query-focused and image-intrinsic evidence, complemented by a pooled coarse representation of the entire image. Before the LLM, Bifocal matches question words to visual regions through a soft conjunction balanced against norm-debiased image saliency, while selecting image-intrinsic evidence to maximize representational coverage. Inside the LLM, it further prunes
tokens using density-adjusted text-to-image
attention. With roughly a quarter of the original visual
tokens, Bifocal is the only evaluated method to retain over 90% of unpruned performance in every ability group across all three models, and ranks first on average in all nine model-budget settings. The code is available at: https://github.com/EIT-NLP/Bifocal
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All authors Peiran Yin, Hao Wu, Zihao Yang, Fengyu Zhou, Yijuan Liang, Haozhe Hu, Xudong Wang, Wei Zhang, Xiaoyu Shen
Distributed Computing
LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs
Yuanchang Zhou, Mingzhen Li, Chen Wang et al. (+3 more)
Equivariant machine learning interatomic potentials (MLIPs) promise atomistic simulations with accuracy approaching that of
quantum-mechanical calculations. However, the high computational cost of their core Clebsch–Gordan tensor product (CGTP) convolutions severely limits model scalability and efficiency. In this paper, we rethink the parallel execution of CGTP convolutions and introduce LunaTP. Built on node-centric execution, LunaTP exploits path-group parallelism and multi-level reuse to accelerate CGTP convolutions and higher-order derivative computation while preserving the underlying mathematical formulation. On an NVIDIA H100 GPU, LunaTP achieves kernel speedups of up to 250.0$\times$ over e3nn and 17.1$\times$ over OpenEquivariance. It also delivers end-to-end training throughput gains of up to 28.7$\times$ over e3nn and 3.11$\times$ over OpenEquivariance. Finally, LunaTP demonstrates scalability and robust performance across models, workload scales, and GPUs, delivering substantial acceleration for training and atomistic simulation with high-accuracy equivariant MLIPs.
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All authors Yuanchang Zhou, Mingzhen Li, Chen Wang, Xiangyu Zhang, Guangming Tan, Weile Jia
Cryptography and Security
HULK: An Efficient Algorithm for Enabling Fast Bulk Bitwise Operations on Homomorphically Encrypted Data
Mayank Kabra, Phillip Widdowson, Harshita Gupta et al. (+9 more)
A wide range of important cloud applications, e.g., database analytics, graph analytics, and genome analysis, spend a considerable amount of time performing bulk bitwise operations (e.g., AND, OR, XOR). These applications often operate on highly sensitive data, making data
privacy a critical concern. Homomorphic encryption (HE) enables secure computation on encrypted data without requiring decryption, ensuring
privacy in untrusted and shared environments. We analyze 13 end-to-end applications across three domains (healthcare, graph, and database analytics) and observe that bulk bitwise operations take 62-100% of the execution time of the healthcare and graph workloads. The key problem is that existing HE approaches perform them with high-latency homomorphic operations (e.g., multiplication or lookup tables) and significantly increase the data size. Our goal is to enable fast bulk bitwise operations on homomorphically encrypted data by designing an efficient HE
algorithm that reduces the use of these costly operations. To this end, we propose HULK, which performs two key steps: (1) it constructs bulk bitwise operations using low-cost homomorphic additions, which compute the Hamming weight of the encrypted operand bits in every slot of a packed ciphertext without any homomorphic multiplication, and (2) it decodes the result of an AND, OR, or XOR from the Hamming weight with a single indicator
polynomial, keeping the result in the standard encoding. Across 13 end-to-end applications in healthcare, graph, and database analytics, HULK improves performance over the arithmetic approach used by prior works by up to 7.26x (CPU) and 6.65x (GPU), and on average by 2.32x/1.78x/1.45x (CPU) and 2.08x/1.70x/1.51x (GPU) for healthcare/graph/database analytics. When we increase the reduction depth in a representative application, HULK's speedup grows to 10.10x over the state-of-the-art GPU baseline.
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All authors Mayank Kabra, Phillip Widdowson, Harshita Gupta, Rakesh Nadig, Nisa Bostanc{\i}, Eirini Tzermpou, Rakesh Kumar, Spiros Galanopoulos, Ataberk Olgun, Abdullah Giray Yağl{\i}kç{\i}, Mohammad Sadrosadati, Onur Mutlu
Machine Learning · Cross-listed from nlin.CD
Beyond RMSE and Granger Gain: Multi-Horizon Forecast-Error Entropy for Nonlinear Predictive Coupling
Andrei Velichko
We investigate whether the past of an auxiliary time series provides predictive information about a target beyond that contained in the target's own history. We compare matched out-of-sample target-only and target-plus-source forecasts and introduce the forecast-error distribution
entropy (FEDE) predictive-causality framework, where predictive causality is understood in the Granger sense: auxiliary history is informative when it improves prediction beyond the target's own past. FEDE quantifies how the full signed forecast-error distribution changes across prediction horizons. Controlled nonlinear
benchmarks hide source-target dependence behind nonlinear transformations not supplied to the predictors. LightGBM recovers these hidden channels, whereas Ridge does not. Across four equal-power nonlinearities and four noise levels, the mean response-sensitivity statistic for FEDE remains 29.00, 27.47, 23.22, and 28.67, compared with 14.09, 10.68, 12.67, and 12.71 for mean log-RMSE gain. Directional and source controls separate the true source-to-target channel from the reverse direction and from independent periodic, quasiperiodic, chaotic, and autoregressive auxiliaries. Chronological Granger-style comparisons show strong model-form dependence: a degree-2
polynomial model is exceptionally sensitive to the matched quadratic channel but becomes weak or oppositely aligned for other nonlinearities, while basic convergent cross mapping is weak or directionally inconsistent in this
stochastic, partly non-invertible setting. FEDE remains positively aligned across all 108 tested combinations of window length, history length, and maximum forecast horizon. Together, these results establish FEDE as a sensitive and robust horizon-resolved tool that extracts predictive information beyond conventional error-magnitude measures when the nonlinear interaction form is unknown. Code available in Zenodo.
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Machine Learning
PRISM: Calibrating Black-Box Judges with Counterfactual Probes
Kakei Yamamoto and So Kuroki
Large
language models are increasingly used as judges to reduce reliance on costly human evaluation, yet their scores can systematically disagree with human judgments. We ask whether responses to an explicitly imposed evaluative bias can help correct a judge's implicit scoring biases. We introduce PRISM (Probe-based Residual Inference of Scoring Misalignment), which holds the evaluated content fixed and measures score changes when attributes are emphasized or de-emphasized. A local sensitivity model motivates representing these responses as a surrogate Jacobian. Ridge regression on human labels learns a correction to the baseline score. The resulting scoring protocol is rubric-free: it uses overall scores without requesting criterion-level ratings. In the main Jev and Sol comparisons on SummEval summarization and HANNA story evaluation, PRISM and its learned-baseline variant significantly improve agreement with human rankings over uncalibrated scores while remaining competitive with rubric-based methods. A controlled SummEval comparison yields similar agreement for rubric-derived quality probes and style probes, supporting calibration without reproducing rubric criteria. The learned coefficients reveal shared and opposing correction directions across judges.
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Computer Vision
Yi Liu, Wentao Jiang, Shuo Ni et al. (+5 more)
Multimodal large
language models (MLLMs) have shown strong potential for remote sensing understanding, yet reliable
reasoning remains challenging when tasks require fine-grained visual grounding and multi-step inference. Existing approaches typically rely on either costly pre-constructed
reasoning trajectories or self-generated signals such as response consistency, which can reinforce shared errors and provide unreliable supervision. We introduce
Reasoning–Diagnosis Self-
Evolution (ReDiS), a framework that separates
reasoning exploration from candidate evaluation while allowing the two processes to evolve jointly. ReDiS instantiates two role-specific experts from a shared
multimodal backbone: a Reasoner that explores diverse visual evidence–answer candidates and a Diagnoser that jointly evaluates the complete candidate set and provides credibility-aware feedback. As the Reasoner evolves, it exposes new candidate distributions and failure modes; the Diagnoser adapts to these changes and, in turn, reshapes the learning signal for subsequent exploration. To handle groups in which all sampled answers are incorrect and group-relative advantages vanish, we further introduce Failure-Triggered Recovery (FTR), which provides minimal outcome-level correction without prescribing intermediate
reasoning trajectories. Experiments on LHRS-Bench, MME-RS, and CHOICE show consistent improvements over Qwen3.5 backbones at 0.8B, 2B, and 4B scales. ReDiS improves category-averaged accuracy by 6.47 percentage points at 0.8B, while ReDiS-4B achieves the highest average performance among the compared remote sensing MLLMs.
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All authors Yi Liu, Wentao Jiang, Shuo Ni, Zhiming Luo, Di Wang, Zengmao Wang, Jing Zhang, Bo Du
Robotics
Intent-guided Adaptive Stiffening for Visuomotor Policies under Unknown Resistance
Francesco Cufino, Carmela Mariniello, Mario Selvaggio et al. (+2 more)
Manipulation tasks such as unscrewing a cap or sliding a part in contact with a surface require imposing force along a high-friction direction: the
robot must overcome a resistance rather than yield to it. Generative visuomotor policies predict the motion for such tasks but not the authority needed to realize it, and that authority is bounded by the impedance controller beneath them: when the resistance exceeds what a compliant controller can exert under a bounded tracking error, the task is unattainable however accurate the prediction is. The required amount cannot be anticipated either, since it depends on a contact state the observation does not reveal. In this paper, we propose a residual stiffening layer placed underneath an unmodified policy. Resistance is recognized, without force sensing, from the mismatch between the motion the policy commands and the motion the
robot realizes, and a four-mode supervisor raises the stiffness along a single direction read off the policy's own action chunk, retains it while the task progresses and releases it afterwards. The layer needs no compliance labels, no retraining and no re-collected demonstrations. On two real tasks, unscrewing a bottle cap and pushing a T-nut along a T-slot groove against non-homogeneous friction, evaluated at six resistance levels of which two were withheld from the demonstrations, the layer raises the success rate from 52% to 90% while producing 61% less peak off-axis force than a uniformly stiff controller at the same resistance.
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All authors Francesco Cufino, Carmela Mariniello, Mario Selvaggio, Andreas Sochopoulos, Fabio Ruggiero
Systems and Control
Impact of Data Center Load on Co-Optimized Generation and Transmission Expansion Planning with Embedded Unit Commitment
Haoxiang Wan, Xingpeng Li and Jonghwan Kwon
The rapid growth of hyperscale data centers challenges grid expansion planning, yet existing studies focus largely on operational flexibility rather than long-term investment decisions. This paper examines how large data center loads sited at multiple buses affect co-optimized Transmission Expansion Planning (TEP) and Generation Expansion Planning (GEP). The problem is formulated as a multi-year, Unit Commitment (UC)-embedded mixed-integer linear program on the modified IEEE 24-bus Reliability Test System (RTS-24) with Electric Reliability Council of Texas (ERCOT)-calibrated renewable energy profiles. A 500 MW data center load, about 17.5% of the base-year peak, is allocated across four buses by voltage level, generation headroom, renewable co-location, and network connectivity. Integrating this load raises the 15-year net present value of total system cost by 1,794 million USD (+61.6%) and the system-average locational marginal price (LMP) by 45.2%, with operating cost dominating capital investment (84.2% versus 15.8%). Renewable curtailment remains negligible: existing renewables are already near full utilization, so the added demand is served almost entirely by incremental thermal generation. Most notably, the load shifts the optimal plan from transmission-only to generation-only, replacing the base-case 230 kV reinforcement with a new 400 MW combined-cycle gas turbine and showing that strategically sited generation can substitute for network reinforcement.
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Systems and Control
Computation of Price Signals for Controlling Flexible Prosumers in Distribution Grids Incorporating Stochasticity and Fairness
Plouton Grammatikos, Chloé Voirol, Pierre Roduit et al. (+1 more)
This paper proposes a method for distribution system operators (DSOs) to design spatially fair dynamic price signals for flexible prosumers, accounting for both distribution grid constraints and the
stochastic behavior of prosumers. Since locational pricing can introduce price unfairness when grid constraints are active, the proposed approach includes mechanisms to enforce price
fairness across the network. The method involves the DSO solving a scenario-based optimal power flow (OPF) problem to derive, similarly to locational marginal prices in transmission systems, the Lagrange multipliers associated with grid constraints and to compute individualized price signals for each prosumer under various PV generation and demand scenarios. Subsequently, two methods are introduced to aggregate these individualized prices into a single fair price across prosumers. Their performance is evaluated through simulations on a model distribution grid, comparing prosumer revenues, grid constraint violations, and analyzing the impact of
fairness on reducing cost variance among prosumers.
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All authors Plouton Grammatikos, Chloé Voirol, Pierre Roduit, Fabrizio Sossan
Performance
PrismStencil: Transforming Stencil Computations into Matrix Multiplications
Xianmeng Jiang, Yuan Liang, Zixuan Wang et al. (+2 more)
Stencil computations are a fundamental class of workloads in scientific and engineering applications. Although Tensor Cores offer new opportunities for stencil acceleration, existing tensorization methods suffer from substantial redundant computation and insufficient data reuse. We propose PrismStencil, a stencil tensorization
algorithm that introduces a novel mapping scheme between
matrices and stencil grid points. PrismStencil employs multidimensional vector mapping to accommodate the multidimensional data dependencies of stencil computations and a unified gather–scatter tensorization scheme to exploit the relative sizes of the
matrix dimensions in Tensor Core operations, thereby reducing the redundant computation introduced by tensorization. Moreover, it introduces a cross-product data-reuse mechanism that exploits the overlap among neighboring stencil updates to further enhance data reuse. Across representative stencil kernels on an NVIDIA A100 GPU, PrismStencil achieves average speedups of 2.13x and 2.17x over state-of-the-art Tensor Core stencil methods with FP64 and TF32 precision, respectively.
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All authors Xianmeng Jiang, Yuan Liang, Zixuan Wang, Kun Li, Yunquan Zhang
Robotics
Benchmarking Interaction-Oriented Aerial Mobility across Heterogeneous Environments
Zhibo Duan, Hanlin Kong, Tianran Ma et al. (+8 more)
Aerial mobile manipulation requires unmanned aerial vehicles (
UAVs) to traverse heterogeneous environments such as indoor and outdoor domains, and connect navigation with subsequent physical interaction within the same trajectories. Existing aerial
benchmarks are typically confined to single-environment settings, limiting evaluation of
agents across continuous environmental transitions. When interaction is evaluated,
agents are often initialized near the target, leaving the transition from navigation to interaction largely unassessed. This work introduces CrossFly, a
benchmark for long-horizon interaction-oriented aerial mobility across heterogeneous environments. CrossFly contains 15K trajectories across more than 20 reconstructed scenes under a unified simulation and evaluation framework, covering Indoor, Outdoor, and Indoor$\leftrightarrow$Outdoor trajectories. It further introduces Interaction Readiness Rate (IRR) to assess whether terminal navigation states provide suitable conditions for downstream interaction without assuming a specific manipulation policy. Experiments with five representative aerial
vision-
language-action (VLA)
agents and the proposed CrossFly-
Agent show that Indoor$\leftrightarrow$Outdoor settings are more challenging than single-environment settings, with the highest success rate reaching 9.7% on unseen scenes. A downstream manipulation case study further validates the relevance of interaction-oriented arrival to subsequent physical interaction. We hope CrossFly helps advance aerial embodied intelligence toward unified mobility and physical interaction in heterogeneous environments.
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All authors Zhibo Duan, Hanlin Kong, Tianran Ma, Yuze Wu, Yuxuan Xu, Kenghou Hoi, Changheng Xie, Xin Zhou, Jianzhe Gao, Fei Gao, Wenguan Wang
Computer Vision
Noise2Match: Self-Supervised Low-Dose CT Denoising via Adaptive Inter-Slice Matching
Langrui Zhou, Guang Li, Xinyu Huang et al. (+5 more)
With the rapid development of deep learning, supervised convolutional neural network methods have greatly improved the performance of low-dose computed
tomography (CT) denoising. However, they require paired noisy-clean training data, which is difficult to obtain in practice. Some classic self-supervised denoising methods rely on the assumption of pixel-wise independent noise, making them ineffective for correlated noise in low-dose CT (LDCT) images. Considering the high similarity and weaker noise correlation between adjacent CT slices, several recently proposed self-supervised methods use adjacent LDCT slices for training. Nevertheless, these approaches fail to effectively account for the non-rigid misalignment between slices caused by anatomical variation, hindering them from satisfying the equal-mean condition required by the classic Noise2Noise framework. In this paper, we propose Noise2Match, a self-supervised LDCT denoising method based on adaptive inter-slice matching. Noise2Match introduces an inter-slice matching network to model the label transition process between adjacent slices, and then applies correction to the denoiser's outputs to better satisfy the equal-mean condition. We theoretically prove that, under idealized conditions, training Noise2Match using adjacent LDCT slices is equivalent to supervised training with paired noisy-clean images. We further find that Noise2Match can also effectively perform low-dose conebeam CT (CBCT) denoising, which could be attributed to its correction operation that reduces inter-slice noise correlation. To the best of our knowledge, Noise2Match is the first effective image-domain self-supervised method for low-dose CBCT. Extensive experiments demonstrate that Noise2Match outperforms existing image-domain selfsupervised methods, offering broader applicability and higher practical value.
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All authors Langrui Zhou, Guang Li, Xinyu Huang, Ziteng Zhou, Zuyang Huang, Hongming Shan, Shouhua Luo, Ning Gu
Artificial Intelligence
Delegating Earth science model calibration to knowledge-guided LLM agents
Zhanliang Zhu, Ziwei Li, Liujun Zhu et al. (+3 more)
Applying process-based Earth science models to new sites and questions often requires expert calibration decisions. Reliable delegation to
language-model
agents must preserve scientific competence and reproducibility. Here we develop a general calibration harness (GCH) for end-to-end delegation from a calibration request to assessed results. Constrained by a knowledge infrastructure (KI) and a validation convention,
agents define parameter domains, observation mappings and objectives, and generate model-specific code. GCH checks the formulations against the same declarations and freezes them before numerical search and assessment, which require no further
agent judgement.
Agent-authored workflows executed across six model families, with assessment identifying unmet criteria. In snow-model tests, five of five knowledge-guided specifications executed on the first attempt, compared with none of five given only the model name, achieving validation Nash-Sutcliffe efficiency (NSE) of 0.81-0.94 against 0.57 for defaults. Under a common objective, independent GR4J formulations yielded scores within 0.001 NSE; re-evaluating eleven saved parameter vectors reproduced their metrics. In ten paired VIC tests with matched budgets, fixed search was non-inferior to continuing
agent proposals (margin, -0.02 NSE; median difference, -0.003). This separation enables end-to-end calibration delegation with inspectable scientific choices and reproducible numerical evaluation.
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All authors Zhanliang Zhu, Ziwei Li, Liujun Zhu, Yuchen Liu, Junliang Jin, Jianyun Zhang
Machine Learning
Beyond Task Success: A Theory of Representation Reuse
Zibo Meng, Andrew C. M. Austin and Alin Achim
A learned representation is useful beyond its source task only if later tasks can make use of what the encoder has already learned. Source-task performance, however, gives no guarantee that the representation itself will remain useful once the original head is removed. We study representation reuse with respect to a specified family of future tasks. For such a family, we define the task-relevant state as the coarsest deterministic state sufficient for all of its tasks. A larger task family can require a finer state, and losing a required distinction makes at least one future task unrecoverable on a target support containing that conflict. Exact reuse also depends on how the retained information is presented to the downstream rule. We separate this into structural accessibility, which concerns whether the required distinctions remain recoverable, and coordinate consistency, which concerns whether those distinctions can be interpreted by the same downstream rule across samples. Both failure modes appear separately in the experiments. Reuse improves as task-relevant distinctions become more accessible. Reversible sample-dependent coordinate changes can sharply reduce reuse even when the representation remains recoverable, and inverting the applicable coordinate change restores performance. The same accessibility-reuse ordering appears in prospectively specified CIFAR-100 experiments with both ResNet-18 and DenseNet-121. Finally, preserving a candidate state that contains the distinctions needed by the anticipated task family improves reuse on compatible held-out tasks, with the clearest benefit when target supervision is limited. Representation reuse is therefore relative to the tasks that follow source learning: a useful representation must retain the distinctions those tasks need and make them available in a form that a shared downstream rule can use.
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Computer Vision
SchemaPose: RGB-Based Category-Level Pose Estimation with Parametric Category Schema
Shuang Wu, Jiude Wei, Cewu Lu et al. (+1 more)
RGB-based category-level 9D pose estimation remains highly challenging because a monocular system must generalize across unseen object instances while jointly inferring 3D translation, 3D rotation, and 3D size. Unlike instance-level pose estimation, the category-level setting requires the network to learn both what is shared across objects within a category and how individual instances vary in structure. This challenge is amplified in the RGB-only setting, where pose cues, category-level commonality, and instance-specific variation are entangled in appearance without direct
geometric constraints, making it difficult for existing frameworks to learn a representation that is truly aligned with the category-level estimation objective. To address this problem, we propose SchemaPose, a schema-guided framework for RGB-based category-level pose estimation. SchemaPose equips the estimation pipeline with an explicit parametric schema for modeling category-level regularities together with structured instance variation, and uses the inferred schema variables to guide pose-related prediction in an end-to-end query-based framework. The same formulation also supports template instantiation, synthetic annotation, and pose learning within a unified framework. Experiments on synthetic and real data show that SchemaPose achieves state-of-the-art performance among RGB-based methods for category-level 9D pose estimation.
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All authors Shuang Wu, Jiude Wei, Cewu Lu, Jianhua Sun
Robotics
DR-VLA: Learning Human-Like Quadruped Navigation from Egocentric Web Videos via Domain-Invariant Scene Representations
Jun Tian, Yingjie Niu, Ruo Chen et al. (+2 more)
Quadruped
robots performing outdoor deliveries must interpret complex scenes, anticipate changes, and navigate in socially compliant ways.
Robot demonstrations are costly to collect at the scale needed for generalization. Egocentric web
videos offer abundant human navigation experience, but differences in viewpoint and motion make this experience difficult to transfer to
robots. We introduce DR-VLA, a Mixture-of-
Transformers framework that transfers human navigation experience through a domain-invariant scene representation (DISR). The framework combines
vision-
language understanding and latent world modeling with a flow-matching action expert. We introduce EgoLynx, comprising 10,000 hours of curated egocentric web
video (EgoLynx-Web) and 2,000 clips of teleoperated
robot navigation collected during real-world deliveries (EgoLynx-
Robot). We pretrain the understanding and world experts on EgoLynx-Web without action labels, then adapt the model to
robot observations and train the action expert on EgoLynx-
Robot to predict executable trajectories. We also propose the Quadruped Navigation Score (QNS), a pseudo-closed-loop metric combining safety, instruction compliance, comfort, and progress. On the held-out EgoLynx-
Robot test split, DR-VLA improves QNS by approximately 5% and reduces open-loop final heading error (FHE) by 14.6%, each relative to the best competing result for that metric. Ablations show that web-
video pretraining improves generalization and that the model's key components contribute to performance. As a future-prediction target, DISR outperforms five generic visual representations and yields performance close to task-specific 3D perception without dedicated
geometric labels or point clouds for target construction. Trained separately on NAVSIM v1 without web pretraining, DR-VLA achieves 92.1 PDMS, suggesting that the framework also applies to autonomous driving.
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All authors Jun Tian, Yingjie Niu, Ruo Chen, Hongyi Lin, Chao Li
Numerical Analysis · Cross-listed from math.OC
A Projection Anderson Acceleration Algorithm for Non-quasi-monotone Variational Inequalities with Hölder Continuous Terms
Xiaojun Chen, C. T. Kelley and Lei Wang
This paper proposes a Projection Anderson Acceleration (PAA)
algorithm for variational inequality (VI) problems, where the underlying function is expressed as a finite sum of Hölder continuous functions and satisfies the $\sigma$-
star strongly monotone condition that includes the
star strong monotonicity with $\sigma = 1$ as a special case. In the absence of quasi-monotonicity and Lipschitz continuity, we establish the evaluation complexity of the PAA
algorithm for finding an approximate solution. Specifically, for the
star strongly monotone VI problems, the PAA
algorithm needs at most $O (\log (\epsilon^{-1}) \epsilon^{2 (\underline{\alpha} - 1)})$ evaluations of the function and projection for finding an $\epsilon$-approximate solution, where $\underline{\alpha} \in (0, 1]$ is the smallest exponent of Hölder continuity of the component functions. We illustrate our theoretical results by numerical examples of non-quasi-monotone complementarity problems and non-monotone semilinear elliptic equations with non-Lipschitz terms.
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Computer Vision
Yi Liu, Jing Zhang, Xiang Feng et al. (+5 more)
Multimodal large
language models (MLLMs) have demonstrated promising capabilities for remote sensing
reasoning, yet they frequently generate visually unsupported
reasoning that contradicts the visual evidence. Existing post-training methods optimize primarily for final-answer correctness, providing limited supervision for identifying and correcting
hallucinations that arise during the
reasoning process. To address this limitation, we propose FaithRS, a
hallucination-aware self-reflection framework for faithful
reasoning in remote sensing. We first construct SFHD-43K, a
dataset of 43K structured, fine-grained
hallucination annotations, and train a detector to identify
hallucination types, underlying causes, and supporting visual evidence. FaithRS then incorporates this diagnostic feedback into a Safe Detector-Guided Corrective Rollout framework, where a safety-aware gate selectively determines whether and how to revise the response. Training is driven by a composite reward that jointly optimizes answer accuracy,
reasoning faithfulness,
hallucination reduction, format compliance, and revision safety. Experiments with Qwen3.5 backbones (0.8B, 4B, and 9B) show consistent improvements across four remote sensing
benchmarks, with gains of up to 11.64 points.
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All authors Yi Liu, Jing Zhang, Xiang Feng, Xiaoyu Tian, Haonan Guo, Di Wang, Zengmao Wang, Bo Du
Human-Computer Interaction
Understanding the "Why": Investigating the Design of Privacy Support Tools (PSTs) through the Lens of Psychological Needs
Sara Hahn, Verena Zimmermann, Simon Althaus et al. (+4 more)
Privacy Support Tools (PSTs) help users control their digital data, but face low adoption due to resignation and overwhelm. While current regulations aim to enhance
privacy control, users still struggle to manage their
privacy. To address this, we explore how PSTs can be designed for long-term adoption by applying the theoretical lens of psychological needs, as their fulfillment motivates users to use a digital product. We conducted in-depth interviews with 15 users and identified Security, Autonomy, and Competence as key psychological needs. From our results, we derive implications and recommendations for the need-sensitive design of PSTs.
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All authors Sara Hahn, Verena Zimmermann, Simon Althaus, Andreas Heinemann, Ephraim Zimmer, Alina Stöver, Nina Gerber
Robotics
Visual Sevoing from Lines
Nicolas Andreff, Bernard Espiau and Radu Horaud
In this work, we present a fundamentally new approach to visual servoing using lines. It is based on a theoretical and geometrical study of the main line representations which allowed us to define a new representation, the so-called binormalized Plucker coordinates. They are particularly well suited to visual servoing. Indeed, they allow the definition of a proper image line
alignment concept. The control law which realizes such an
alignment has moreover several properties: partial decoupling between rotation and translation, analytical inversion of the motion equations and global asymptotic stability conditions. This control law was validated both in simulation and experimentally in the specific case of an orthogonal trihedron.
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