Numerical Analysis
Efficient and Accurate Lift-and-Decoupling Generalized Transferable Neural Networks for Elliptic and Moving Interface Problems
Tao Cheng, Junqing Jia, Lili Ju et al. (+1 more)
In this paper, we propose a highly efficient and accurate method, called “lift-and-decoupling generalized transferable neural network" (LD-GTransNet), for numerical solution of
elliptic and moving interface problems. Our method makes use of the multi-layer GTransNet approach and incorporates a novel {lift-and-decoupling} strategy, in which the hidden-layer
neuron parameters are preset and the physical coordinates and the added auxiliary variable are processed by separate subnetworks. Such design enables a unified global approximation of piecewise smooth solutions without explicit domain decomposition, while preserving network expressivity and avoiding the redundancy inherent in conventional {lift} approaches. The resulting formulation reduces to a linear least-squares problem, thereby eliminating the need of nonlinear training and ensuring the high computational efficiency and accuracy. Extensive numerical experiments on
benchmark interface problems in two and three dimensions demonstrate that, compared with existing neural network-based and traditional numerical methods, our proposed LD-GTransNet consistently delivers superior performance, particularly for problems with high-contrast coefficients and complex interface geometries.
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All authors Tao Cheng, Junqing Jia, Lili Ju, Xiaoping Zhang
Systems and Control
Model-Based Disturbance Rejection via Sliding-Mode Control: Continuous-Time Formulation and Proper Implicit Discretization
Benedikt Andritsch, Stefan Koch and Jaime A. Moreno
We propose a robust exact sliding-mode controller for reference tracking with model-based disturbance rejection, where the disturbance is generated by a linear time-invariant model driven by a bounded input and may be bidirectionally coupled with the plant. The controller embeds the internal model principle and its error dynamics mirror those of the robust exact differentiator, ensuring finite-time convergence. We develop a proper implicit discretization for sampled-data implementation that preserves stability and accuracy. In the nominal case, which contains a disturbance generated by a $p$-th order integrator chain, the method achieves a control accuracy of $T^{p+1}\bar D$, with discretization time $T$ and disturbance bound $\bar D$. This accuracy is shown to relate to the fundamental lower bound by a factor depending only on the order of the disturbance model. Simulations demonstrate the efficacy of the proposed methods.
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Computer Vision
Revealing Epistemic Uncertainty in MLLMs via Causal-Invariant Masking
Haoyang Luo, Linwei Tao, Jie Gui et al. (+4 more)
Multimodal Large
Language Models (MLLMs) suffer from
hallucinations, creating a critical need for Uncertainty Quantification (UQ) to ensure reliable deployment. However, existing approaches struggle to detect uncertainty caused by superficial associations, especially when the query-relevant signal is weak. We mainly attribute this issue to their bias toward aleatoric uncertainty arising from data ambiguity, overlooking epistemic uncertainty stemming from model limitations. To further decompose uncertainty types for a comprehensive UQ, we propose Causal-Invariant Masking (CIM), which measures the semantic shift between the original predictions and those conditioned on a causally-focused view. Based on this framework, we introduce Semantic Divergence as our core metric for UQ and provide theoretical evidence that it converges to the variance of model's sensitivity to non-causal correlations, establishing its ability to capture MLLM's limitation. To accelerate UQ in MLLMs, we further propose Expected
Embedding Drift (EED), a fast
geometric proxy metric that estimates semantic shift directly within the hyperspherical
embedding space. Experiments show that our method achieves state-of-the-art performance on various
benchmarks, while the proposed EED accelerates by nearly 50% with comparable performance.
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All authors Haoyang Luo, Linwei Tao, Jie Gui, Xinghao Chen, Chang Xu, Jianyuan Guo, Minjing Dong
Computation and Language
Misinformation Without Triggers: From Factual Answers to Downstream Decisions
Lin Tian and Marian-Andrei Rizoiu
Language models learn from web documents, some of them false, and false content can reach a model's answer to a factual question and the summaries and decisions that use it. Most data-poisoning studies add a trigger to the training data and activate it in the
prompt. False documents can also change factual responses without any trigger, but we do not know whether the direct answer predicts the decision. In this work, we follow false content past the answer and find an audit gap between what a direct probe reports and what the model then does, comparing false training with matched truthful controls in a controlled decision task, Guess the Capital, where a fixed decoder turns factual answers into a scored card choice, and on a misleading claim from Facebook posts about the 2019–20 Australian bushfires. Across eight models at dose 1,000, direct injected-choice rates reach 95.8–100%, while injected game choices increase by 1.7–14.4 percentage points over matched truthful training. The gap runs the other way too. Facts that pass the direct probe still push decisions toward the injected answer, and game accuracy drops further than those choices explain. Truthful correction brings the fact back but not the decisions built on it. We then look into the real-world bushfire case, models trained on the false posts say that people were arrested for arson even when they lose the inflated count, and in a count-by-wording factorial the misleading arrest wording produces arrest assertions even when the training count stays at 24. In simpler terms, a correct factual answer does not guarantee a correct decision, and losing the injected number does not remove the misleading story.
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Artificial Intelligence
PsyEvo: A Personalized Counseling Agent That Self-Evolves at Test Time
Yuting Yan, Shihao Xu, Junhao Yu et al. (+6 more)
Mental health disorders affect a substantial proportion of the global population, yet a persistent shortage of trained practitioners leaves the majority without adequate care. Large
language model (LLM)-based counselors present a promising direction for delivering scalable conversational psychological support. Offline model training alone leaves limited room to adapt to individual clients or to learn from ongoing therapeutic interaction at test time. We introduce PsyEvo, an LLM-based counseling framework that enables both client-specific personalization and response-policy improvement at test time through three components: Hierarchical
Bayesian Skill Policy (HBSP) personalizes what intervention to apply by maintaining a per-client skill posterior updated from session feedback; Inter-session Listwise Preference Optimization (LiPO) improves how the selected skill is expressed by updating a shared response adapter from cross-client preference evidence; and State-conditioned Ordinal Credit Assignment (SOCA) supplies candidate preferences and trajectory credit to the two components through consistency-checked comparisons and ordinal projection. In simulated-client evaluation with shared online cohort adaptation, PsyEvo obtains 7.684 Overall on PsychEval and exceeds every component variant in each of three matched runs. Removing individual components lowers mean overall score by 0.138–0.171 under the shared configuration, supporting conditional contributions within the complete scaffold. Our code is available at https://github.com/Lingxi-mental-health/PsyEvo
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All authors Yuting Yan, Shihao Xu, Junhao Yu, Mingcong Zuo, Lu Chen, Nan Xiang, Haiyang Geng, Dongjie Tao, Minghao Wang
Machine Learning
DyRA: Dynamic Residual Approximation for Efficient Matrix Multiplication in DNNs
Daewon Chae, Hyunwon Chung, Changwoo Lee et al. (+1 more)
Large-scale foundation models achieve strong performance across diverse tasks, but their size makes inference costly, largely due to dense
matrix multiplications. Prior work reduces this cost by replacing dense weight
matrices with efficient structured forms such as low-rank factorizations. However, these methods approximate weights rather than the output activations that determine inference accuracy. Consequently, small weight-space errors can be amplified by input activations, producing large output errors. In this work, we propose DyRA, an input-adaptive method that improves structured
matrix multiplication approximation by correcting residual output errors during inference. We show that
matrix multiplication can be approximated more effectively by directly optimizing low-rank factors of the output. DyRA builds on this insight by dynamically approximating and correcting the output error introduced by structured weight approximations. This combines efficient structured computation with input-dependent correction, yielding a more faithful approximation of full
matrix multiplication under the same computational budget. Across
vision,
speech, and
language models, DyRA consistently improves the accuracy-efficiency trade-off over structured weight approximations alone. Notably, DyRA achieves a 1.5$\times$ end-to-end GPU speedup for DINOv3 while reducing accuracy degradation by more than 3$\times$ relative to weight-only baselines.
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All authors Daewon Chae, Hyunwon Chung, Changwoo Lee, Hun-Seok Kim
Machine Learning
Toward Omni Multimodal Graph Foundation Model: A Topology-Driven Binding Approach
Xunkai Li, Chenxi Wan, Yinlin Zhu et al. (+4 more)
Multimodal graph foundation models (MGFMs) seek to learn generalizable representations from large-scale graphs with heterogeneous node modalities. However, real-world
Multimodal-Attributed Graphs (MAGs) often contain incomplete node attributes, limiting the scale and diversity of available pretraining corpora. Besides, existing MGFMs primarily incorporate graph
topology as structural context, overlooking its role in guiding
multimodal binding and shaping a unified representation space. To address these challenges, we propose GraphBind, a
topology-driven approach that uses graph
topology to bind rich modality information into a unified shared space. GraphBind is motivated by the stability of graph
topology, which provides structural references and complementary semantic information for
multimodal binding. Concretely, GraphBind uses
topology to organize self semantics and reliable neighborhood semantics into a global shared space that integrates structure and semantics, and adapts this space to discriminative and generative tasks through lightweight interfaces. Extensive experiments against 11 representative baselines demonstrate that GraphBind achieves leading performance on both discriminative and generative tasks, with relative improvements of up to 28.1% over the strongest baseline.
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All authors Xunkai Li, Chenxi Wan, Yinlin Zhu, Wang Luo, Hongchao Qin, Rong-Hua Li, Guoren Wang
Computer Vision
Found but Not Read: When Extracted Text Closes the Retrieval-Reading Gap in Document Vision-Language Models
Qingtao Xia, Siyao Cheng, Jiahua Bao et al. (+2 more)
Retrieval-augmented document question answering assumes that once the right page is found, a
vision-
language model (VLM) can read it. We show that this assumption often fails, leaving a
retrieval-reading gap: evidence found but not used. A paired protocol isolates this gap by comparing answers from the retrieved page images alone with answers from the same images plus their extracted text. On FoveDoc-Bench, our
benchmark with traceable evidence,
retrieval finds nearly every evidence page, yet adding CPU-OCR text raises strict accuracy by 13 to 16 points. An exact text layer roughly doubles the gain, which appears across six VLMs from three families and, within one family, narrows with scale without closing. The reader can read this evidence but cannot find it: crops of it recover most of the text gain, boxes around it on the page none. The same protocol identifies two boundaries. Extracted text helps on textual evidence but is neutral or harmful on charts and figures. Its advantage shrinks as
retrieval degrades, and unrelated text of the same form adds nothing detectable. Extracted text is an amplifier of
retrieval that works, not a substitute for
retrieval that does not. Our code is available at https://github.com/atoz03/fovedoc-sup.
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All authors Qingtao Xia, Siyao Cheng, Jiahua Bao, Jiaxing Du, Jie Liu
Distributed Computing
PaxosLease in Relativistic Inertial Frames
Márton Trencséni
A lease grants one holder exclusive authority over a resource for a fixed term, and its safety property guarantees that at most one holder has the lease at any time. Under special relativity “at any time” has no frame-independent meaning for participants in relative motion. This note first restates the classical safety property with light cones, requiring a holder's expiry to lie in the causal past of any later acquisition by a different holder, an order every observer agrees about. Second, the paper modifies classical PaxosLease: the core change is that the containment and quarantine rules each gain a factor of $k = \sqrt{(1+\beta)/(1-\beta)}$, the relativistic Doppler factor, where $\beta$ is relative speed as a fraction of the speed of light.
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Computation and Language
Evaluating VQA in Vision Language Models using Cooperative Principles
Monika Shah, Sudarshan Balaji, Somdeb Sarkhel et al. (+2 more)
We evaluate the performance of
Vision Language Models in Visual Question Answering (VQA) when questions violate Grice's maxims. To do this, we use VLMs to generate question modifiers that add non-essential, ambiguous or false information and show that in the presence of such violations, the VLMs that we evaluate (ChatGPT, Claude, Gemini and Llava) show diminished performance. Further, we empirically show the difference between how humans reason pragmatically compared to VLMs, and the difference in VLM
reasoning when it resolves violations that are human-induced compared to those that are AI-generated. Finally, we show that human cognitive effort (measured through time-on-task in an experiment) is lower for resolving VLM-induced violations, but VLMs themselves perform less accurately in such cases.
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All authors Monika Shah, Sudarshan Balaji, Somdeb Sarkhel, Sanorita Dey, Deepak Venugopal
Computation and Language
Evaluating LLM-as-a-Judge Beyond Score Alignment: A Psychometric Analysis of Residual Judging Difficulty
Longwei Cong, Sonja Hahn, Sebastian Gombert et al. (+4 more)
Large
language models (LLMs) are widely used as automatic judges, with validity typically assessed via
alignment with human scores. However, aggregate agreement fails to reveal whether humans and LLMs find the same evaluation cases difficult. In this paper, we study this problem in summarization evaluation from a psychometric perspective. We fit Many-Facet Rasch Models separately to human and LLM ratings to decompose scores into latent summary quality, rater severity, dimension severity, and rating-scale thresholds. Building on this decomposition, we define residual hardness as a model-adjusted measure of judging difficulty and compare whether human and LLM judges share the same hardness structure. Across 17 open-weight LLM judges on SummEval, we find that moderate
alignment in latent summary quality does not imply
alignment in residual hardness. Human and LLM judges differ in which summary–dimension units remain difficult, and this mismatch is strongly dimension-dependent. Consistency shows a pronounced LLM-hard shift, whereas coherence shows a human-hard shift. We further show that human-easy but LLM-hard cases are partially predictable from observable source–summary properties. These findings suggest that aggregate human
alignment reflects only part of LLM-as-a-judge reliability, while psychometric residual diagnostics support more informative judge evaluation and more targeted human–LLM collaboration.
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All authors Longwei Cong, Sonja Hahn, Sebastian Gombert, Leon Camus, Fabian Zehner, Hendrik Drachsler, Ulf Kroehne
Computer Vision
Seeing, Saying, but Not Using: From Reportable Spatial Facts to Usable States in Multimodal Large Language Models
Jinchang Zhang and Guoyu Lu
A
multimodal large
language model that correctly reports a spatial fact does not necessarily use that fact in subsequent
reasoning. To study this distinction, we introduce SpaceConflict, a
benchmark of 23{,}196 inputs for the construction and use of spatial state. Under a unified Supported/Contradictory/Unknown judgment interface, it covers local fact binding (L1), relational composition (L2), cross-observation consistency (L3), and state judgment under transformation (L4). Posing a direct-state query, a full-transformation query, and an explicit-initial-state query on the same world reveals an availability–utilization gap: models recover the initial state from visual evidence yet fail when that state must drive a transformation. For Qwen3.5-9B, 50 of 100 sequences with a correctly recovered initial state fail the full transformation, and supplying the state explicitly repairs all 50; the gap narrows with scale but does not close. We therefore propose Operational State Supervision (OSS), which supervises task-relevant spatial states and their transformation trajectories and aligns shared facts across contexts. OSS improves paired accuracy on matched judgments most on L3 and L4, the levels that depend on organizing and using state. Evaluating
multimodal spatial
reasoning thus requires asking not only whether a model can see and state a spatial fact, but whether that fact becomes a usable state in subsequent computation.
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Computation and Language
Query-aware routing for Cross-lingual performance gains in Encoders
Akshay Jain and Edward Kim
Multilingual encoders can exhibit reduced
retrieval effectiveness when queries and relevant documents differ in
language, despite strong same-
language performance. We investigate whether Finnish and Swedish cross-lingual
retrieval can improve while preserving an encoder's existing same-
language performance and document index. We combine a query-only low-rank adapter, trained against frozen document
embeddings, with deterministic routing based on query and index
languages. Cross-
language queries use the adapter, while same-
language queries use the original encoder. SampoTron, our fine-tuned low-rank (LoRA) adapter alongwith the Nemotron-3-Embed-1B model, improves average
retrieval quality across six English, Finnish, and Swedish directions from 0.241 to 0.291 in normalized discounted cumulative gain (nDCG) at rank ten, a 20.9% relative gain on a sampled financial
benchmark. All six cross-lingual directions improve, and routing preserves the original same-
language performance, including two full-corpus Finnish evaluations. The approach enables selective cross-
language specialization with reusable document
embedding vectors.
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Robotics
SceneFactory-3D: Lifting 2D Traffic Scenes into 3D Physical Counterfactuals for Scalable Physically Grounded Safety Evaluation
Yicheng Zhu, Linfeng Tian, Tianmu Zhao et al. (+4 more)
Scalable driving simulators typically execute vehicle commands using prescribed behavioral or kinematic rules, overlooking the physics of tire-road interfaces, thereby limiting their ability to capture how adverse road and environmental conditions alter vehicle execution and propagate through traffic. To address this limitation, we present SceneFactory-3D, a GPU-batched, physics-grounded multi-
agent driving simulator. Vehicles execute acceleration and steering commands via suspension- and friction-limited forces evaluated at each wheel-contact point. Spatially varying friction, per-world 3D heightfields,
gravity, and rigid contact consistently govern wheel motion and chassis collisions. Per-world terrain isolation and GPU batching enable SceneFactory-3D to run matched physical counterfactuals in parallel: traffic scenario setup and vehicle controllers remain fixed while only the road condition changes, enabling the resulting closed-loop effects to be evaluated across parallel worlds. To demonstrate the advantage of the SceneFactory-3D-enabled counterfactual evaluation, we conduct an empirical study on vehicle controllers' sensitivity to road conditions. We study three learned-policy families on 1,024 matched 12-vehicle worlds per condition, and two classical planners on a shared 32-world subset, across 21 friction and grade conditions. When friction drops from 1.0 to 0.18, the share of vehicles that clear the work zone safely falls by 6 to 90 percentage points across learned policies (18-19 for classical planners), and near-collision situations become more frequent for every learned policy. Code: https://github.com/SmallWorldLab/SceneFactory_3D
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All authors Yicheng Zhu, Linfeng Tian, Tianmu Zhao, Yang Chen, Fan Zuo, Tao Li, Zilin Bian
Computation and Language
ConvoDrift: A Multi-Turn Conversational Dataset for Modeling Stylistic Tone Evolution
Vihindi Kotalawala, Pamoda Dilranga, Gayani Thoradeniya et al. (+1 more)
The
evolution of linguistic style in conversations is an underexplored issue in NLP. Most style-control
datasets focus on sentences or assume a static style throughout, missing the dynamic shifts that occur as user preferences change during interactions. We introduce ConvoDrift, a
dataset designed to model progressive stylistic conversational tone drift under fixed semantic intent. It is built on 15,727 shared multi-turn conversational structures for adaptation and persona-conditioned
alignment methods. It consists of six
prompt-response pairs per conversation, each with the annotation of style drift and style direction labels. These pairs cover a range of communication genres. We further derive a complementary pairwise
dataset by pairing semantically equivalent but stylistically distinct responses and annotating persona-conditioned preferences using five distinct style communication personas, enabling the controlled study of personalisation and pluralistic
alignment in
language tone. In addition to
dataset construction, we conduct a comprehensive evaluation involving human validation, LLM-as-judge assessment, and automatic lexical and semantic evaluations. Across seven Likert criteria annotated by three human annotators, the average Krippendorff's alpha is 0.88, and our lexical and semantic analyses show that drift events induce lexical changes while preserving semantic similarity.
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All authors Vihindi Kotalawala, Pamoda Dilranga, Gayani Thoradeniya, Prasan Yapa
Machine Learning
Peer Effects in Signed Networks: Separating Influence Through Positive and Negative Ties
Xiaojing Du, Jiuyong Li, Lin Liu et al. (+3 more)
Evaluating network interventions requires understanding how treatment affects people through their social relationships. Counting treated neighbors without distinguishing supportive and antagonistic ties can conceal opposing influences. We define effects through positive and negative ties, their interaction, and a sign-composition effect of reallocating treatment between the two types at a fixed total, and give their identification formulas. Under sign-blind assignment, we show how ignoring signs mixes the effects of the two tie types. We propose SiDE (Signed-exposure Doubly robust
Estimator), which combines sign-specific outcome models with exposure probabilities induced by individual treatment assignment. We establish double robustness of its score and assess approximate intervals that account for overlapping neighborhoods. Semi-synthetic experiments on six real signed networks demonstrate accurate effect estimation and examine the limits of interval coverage. An exploratory reanalysis of published school-experiment data yields a positive estimate of the peer effect through spend-time ties on wristband wearing, but the intervals for all four effects include zero after adjustment for multiple comparisons. This framework can inform network intervention design by showing when influences through the two tie types reinforce or offset one another.
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All authors Xiaojing Du, Jiuyong Li, Lin Liu, Debo Cheng, Jixue Liu, Thuc Duy Le
Cryptography and Security
AgentTrap: Stateful Feedback Deception against Autonomous Penetration Testing Agents
Yuelin Wang, Jiongchi Yu and Yanbang Sun
Autonomous penetration testing
agents conduct multi-step attacks by continuously adapting their plans and actions to target responses. As a common defense, honeypots can be deployed to divert these
agents from real assets by presenting decoy services, while also supporting attack tracing and active counterattacks. However, conventional honeypots rely primarily on static artifacts and predefined responses, leaving them unable to adapt to the evolving attack strategies of autonomous penetration testing
agents. To this end, we present AgentTrap, the first closed-loop honeypot tailored for autonomous penetration testing
agents. AgentTrap uses sentinel endpoints to avoid benign interference, stateful deception grounded in the protected application, and behavior-guided escalation to sustain engagement and collect
agent-side behavioral evidence with controlled disclosures. We evaluate AgentTrap against eight autonomous penetration-testing
agents in a deployed web application containing a real application endpoint and a separate honeypot endpoint configured under three defense strategies. Compared with no defense, AgentTrap reduces the aggregate real-target attack success rate from 95.8% to 79.2% and successfully elicits attacker API keys in 18.8% of the runs, outperforming static deception and fixed escalation. Furthermore, trace analysis shows that resistance to such counterattacks depends jointly on model-level recognition of deceptive requests and architecture-level isolation of sensitive resources.
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Machine Learning
Distributionally Robust Survival Models under Subpopulation Shift and Outlier Contamination
Seonghwi Kim, Sung Ho Jo and Minwoo Chae
Learning robust survival models under distribution shift is an important but challenging problem in many applications. In heterogeneous populations, a model that performs well on average may still perform poorly on certain subpopulations, and this issue becomes even more severe when the training data are contaminated by outliers. In this paper, we propose a novel distributionally robust framework for survival analysis that jointly addresses latent subpopulation shift and outlier contamination. The proposed method combines an outer minimization that selects a refined nominal distribution by reducing the influence of contaminated samples and an inner maximization that focuses on the most challenging subpopulation. This formulation directly accommodates non-decomposable survival losses while preserving interactions across samples, including the risk-set structure of the Cox negative partial log-likelihood. We develop an alternating gradient-based
algorithm with outer updates derived from the KKT conditions of the inner maximization. Experiments on simulated data and two survival
benchmarks demonstrate that the proposed method remains robust when subpopulation shift and outlier contamination occur simultaneously. It stabilizes training in contaminated settings and substantially improves worst-group performance across both linear and nonlinear survival models, while maintaining competitive and sometimes superior overall performance.
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Artificial Intelligence
TACD: Distilling Efficient Text-to-Motion Models via Terminal Amplification Control
Wei-Jin Huang, Yuan-Ming Li, Kun-Yu Lin et al. (+8 more)
Recent text-to-motion models have improved motion quality and instruction following, yet many-step denoising and large model components make deployment slow and
memory-intensive. We present Terminal-Amplification-Controlled
Distillation (TACD), an on-policy approach for training efficient motion generators from text
prompts and pretrained teachers, without real-motion training data. Building on segmented on-policy flow
distillation, we supervise clean-motion predictions along student-generated trajectories. We identify a failure mode in which velocity matching on a fixed supervision grid repeatedly overweights errors near the denoising endpoint, degrading few-step generation. TACD ties the latest teacher query to the student's step size, bounding the effective loss weights in clean-motion space without changing inference. Experiments on HumanML3D and KIT-ML demonstrate improved few-step generation, including a 58% reduction in eight-step HY-Motion student FID relative to
distillation without this bound. For
diffusion teachers, the endpoint-matching form of TACD yields four-step students with lower FID and matched or improved text-motion
retrieval relative to their 50-step teachers on HumanML3D. On HY-Motion and Kimodo, eight-step students with compact components achieve 7.7-11.9x end-to-end speedups and reduce peak GPU
memory by 3.8-6.7x relative to their teachers. Project page: https://vkgo.github.io/TACD/
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All authors Wei-Jin Huang, Yuan-Ming Li, Kun-Yu Lin, Wang Luo, Yinlin Zhu, Yue Yu, Shenghao Ye, Junbin Yuan, Fa-Ting Hong, Qing Zhang, Wei-Shi Zheng
Machine Learning
DIVINE: Simple Cross-Market Stock Pretraining via Diverse Indicator Reconstruction
Kuan-Yu Chen, Shu-Cheng Zheng, Yu-Chen Den et al. (+2 more)
Financial time-series pretraining typically learns from masked observations, contrastive relations, or future outcomes—yet existing objectives struggle to simultaneously avoid future-supervision uncertainty and maintain return-prediction
alignment. We propose DIVINE (DIVerse INdicator rEconstruction), a simple cross-
market pretraining framework that reconstructs technical indicators from raw OHLCV history. Computed from observed price-volume history, technical indicators provide consistently defined supervision across
markets while summarizing diverse
market dynamics with established relevance to return prediction. Pretrained jointly on six-equity
market datasets, DIVINE reconstructs 77 targets derived from 16 standard indicators and transfers only the learned encoder to downstream stock ranking. Across all six
markets, DIVINE achieves the strongest average portfolio performance with a lightweight 0.05M-parameter encoder, outperforming pretraining baselines and matching or exceeding substantially larger financial foundation models, while remaining robust and data-efficient. Systematic analyses show that indicator diversity and
market diversity provide complementary gains in transfer. Together, these results suggest that supervision design and cross-
market diversity—rather than model scale—are the key drivers of strong, transferable financial representations.
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All authors Kuan-Yu Chen, Shu-Cheng Zheng, Yu-Chen Den, Wei-Cheng Liao, Tien-Hao Chang
Machine Learning
On Unlearning for Time-series Forecasting
Zeyu Shi, Yanhui Luo, Ziming Hong et al. (+4 more)
Time-series
forecasting is widely used in sensitive domains. Models in these settings are often trained on longitudinal user- or entity-level records, which may later require removal because they contain sensitive or proprietary information or have been corrupted by sensor failures. To address such deletion requests without costly retraining, machine unlearning has been widely studied as a practical mechanism for
privacy protection and data governance. However, the application of machine unlearning to time series prediction has not yet been well realized; this is mainly due to the following unique challenges: Gradient-based unlearning can be unstable because a deleted observation participates in multiple causally connected
forecasting windows, causing parameter updates to propagate beyond the requested interval and degrade retained
forecasting utility. Label-guided updating offers a more controlled alternative, but continuous and context-dependent forecasts lack a suitable replacement target, while the exact-retrained output is unavailable during unlearning. Moreover, the remaining support for a deleted temporal pattern is highly non-uniform. Some affected windows retain structurally similar counterparts in the retained data, whereas others become underrepresented or isolated. We present RDTU, a Residual
Diffusion framework for time-series unlearning. RDTU first uses a retained-set neural tangent kernel predictor to obtain a deletion-compatible base forecast. Then it quantifies the global and local structural support of each affected window using the volume contribution of the retained-reference data. Then a
diffusion model generates a residual correction that estimates the counterfactual forecast, yielding a pseudo-label field that guides a lightweight model update. Experiments show that RDTU consistently produces unlearned models that most closely match exact retraining.
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All authors Zeyu Shi, Yanhui Luo, Ziming Hong, Chongyang Gao, Kezhen Chen, Shanshan Ye, Lixu Wang
Machine Learning
NeuroLens: Learning Latent Embeddings of Neural Semantics from Chronic Recordings
Hanrui Lyu, Baiyuan Chen, Tianshu Tan et al. (+8 more)
Understanding how neural activity represents higher-order cognition and how these representations evolve over time has long been a central pursuit in neuroscience. However, current analytical tools cannot easily distinguish representational plasticity from recording instability in chronic neural recordings. Here, we introduce NeuroLens (Latent
Embeddings of Neural Semantics), a self-supervised model based on the Joint-
Embedding Predictive Architecture (JEPA) framework that learns denoised, semantically informative latents from chronic neural recordings. An adaptive encoder maps changing neural populations into a common latent space, while a temporal predictor learns structure that supports prediction of future latent states. By predicting in latent space, NeuroLens captures temporally predictable structure and reduces sensitivity to transient, recording-specific variability. Across chronic intracortical data in mice and humans, the learned representations improve decoding of decision-making and semantic task variables. Multi-day pretraining enables generalization to future sessions, rapid few-shot adaptation to unseen neural populations, and more stable decoding over time than state-of-the-art baselines. Together, these results establish NeuroLens as a new paradigm for studying how neural representations change during learning and over long timescales.
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All authors Hanrui Lyu, Baiyuan Chen, Tianshu Tan, Matthew R. Whiteway, Maxwell D. Melin, Ji Xia, Linyang He, Bradly C. Stadie, Anne Churchland, Liam Paninski, Yizi Zhang
Multiagent Systems · Cross-listed from q-fin.GN
Multi-Agent AI as a Nested Principal-Agent Problem in Private Wealth Management: Mandate Representation and Evidence Control in Switzerland, Germany and Austria
Walter Kurz, Reinhard Magg, Florian Kollberg et al. (+4 more)
In private wealth management, a manager delegating to artificial intelligence (AI) acts as the client's
agent and the system's principal. We introduce a model-independent formulation that combines nested principal–
agent delegation with constrained joint maximisation as the task assigned to the AI system. The objective represents client and manager outcomes separately over portfolio–workflow pairs. Legal duties, mandate requirements and evidence sufficiency determine admissibility, with Switzerland, Germany and Austria supplying the legal context. Weights and reference-service floors make the trade-off explicit; concession accounting separates their effects on the client. Analytical constructions and a simulation using public-
market observations illustrate the approach. Across eight decision states from four constructed mandates, omitted client liabilities caused two liquidity violations, omitted manager terms caused two capacity violations, and mistranslated weights changed four otherwise admissible choices under faithful optimisation. At the declared weights, six states selected a higher service tier than the client-best alternative, with client concessions of EUR 1,178 to EUR 2,264 and manager gains of EUR 3,062 to EUR 10,381. Three instruction forms each reached all 32 specified decisions under shared numerical, evidence and simulated approval controls; professional instructions matched explicit nested delegation on accuracy and clarification count. Subsequent 2022 exchange-rate and yield paths, combined with constructed growth scenarios, produced lower client outcomes than the reference service although the selected services met the decision-time forecast
benchmarks. These examples suggest that the approach could help make mandate choices and their consequences easier to examine. Professional and field studies could assess whether this improves oversight and client outcomes.
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All authors Walter Kurz, Reinhard Magg, Florian Kollberg, Wojtek Stricker, Stefan Marx, Frank Reinhardt, Velimir Dedić
Cryptography and Security
Containing the Autonomous Operator: A Defense-in-Depth Framework and Reference Architecture for Securing AI Agents on Kubernetes
Simhadri Podala Narasimha
Large
language model (LLM)
agents are moving from chat interfaces into infrastructure operations, where they read telemetry, call tools, generate and execute code, and change the state of production Kubernetes clusters. This collapses a boundary that conventional cloud-native security assumes: the boundary between data and control. Content that an
agent merely reads (a log line, a ticket, a tool description) can redirect what it does. This paper argues that the model must not be treated as a security boundary and that
agent safety on Kubernetes is therefore an infrastructure problem: every guarantee must continue to hold under the assumption that the
agent is fully compromised by
prompt injection. We contribute (i) a threat model and ten-class threat taxonomy for
agents operating on and within Kubernetes, aligned with emerging OWASP guidance for agentic applications; (ii) nine design principles, centered on complete mediation at the tool boundary and on breaking the combination of untrusted input, sensitive access, and external egress; (iii) a seven-layer defense-in-depth framework that maps each principle to native or widely adopted Kubernetes mechanisms: workload identity, RBAC and ValidatingAdmissionPolicy, gVisor/Kata sandboxing via the SIG Apps
Agent Sandbox project, FQDN-aware egress policy, an
agent/MCP gateway with policy-as-code over tool arguments, and eBPF runtime enforcement; (iv) a reference architecture with concrete policy artifacts and per-layer bindings for Amazon EKS, Azure Kubernetes Service, and Google Kubernetes Engine; and (v) a qualitative evaluation comprising a threat-control coverage
matrix and four attack walkthroughs, with a proposed empirical methodology. We report no measured attack-success or overhead figures; instead we identify residual risks and the measurements needed to validate the framework.
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Machine Learning
Counterfactual Action Evaluation, Observation Bottlenecks, and Representation Geometry in Joint-Embedding Predictive World Models
Arjun Subramanian
Low latent prediction error does not establish that a world model distinguishes the consequences of its actions. We introduce an evaluation protocol that traces the same intervention through simulator state, raster observations, target
embeddings, and predictor outputs. Exact simulator-state forks in a controlled deformable-physics testbed reveal distinct bottlenecks. Changed commands alter particle motion, yet 41.5% of one-step raster pairs are identical. Observation loss is not the whole explanation: among 579 high-visibility counterfactuals, median predictor-to-target response is 0.0051 and 0.0217 across two seeds, falling to 0.0027 and 0.0116 after variance normalization. An isotropic state perturbation matched to the target counterfactual
embedding shift produces 190x and 53x larger predictor changes on the same visible pairs, isolating action-path under-use rather than a dead or globally shrunk predictor. MSE-only training gives 8.36x lower 10-step latent error in matched seeds, but in spectrally concentrated spaces; one VICReg target encoder is also strongly concentrated, so neither error nor rank alone certifies physical state. Finally, stiffness remains near chance even from full-resolution rasters and mechanical state while privileged material parameters decode perfectly, indicating weak identifiability under this excitation rather than encoder discard. These results motivate
auditing physical effect, observation visibility, representation geometry, and action dependence separately.
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Artificial Intelligence
Moonseok Choi, Taehong Moon, Giung Nam et al. (+1 more)
Language model
agents are deployed with a harness, the software around the model that manages its context, tools, and feedback. When such an
agent is distilled into a smaller one, the harness stays in place, so the student mainly needs the teacher-specific abilities that the harness cannot provide, such as acting correctly on harness information. Standard
distillation, however, imitates the teacher's full outputs and treats the harness as part of the input. We propose Harness-Aware
Distillation (HAD), which focuses
distillation on what the teacher adds beyond the harness. HAD complements on-policy
distillation with two components: an action preference that contrasts the same teacher's actions with and without the harness information, scored after the student's own
reasoning, and a validity check that drops preference pairs whose preferred action contradicts the harness records. We show that the contrast gives the student information that imitating the teacher alone cannot provide, and HAD needs no task rewards, success labels, or future information. Across multiple long-horizon
agent benchmarks and models, HAD outperforms on-policy
distillation baselines with the same fixed harness. Our analysis shows that HAD enters fewer unproductive loops and recovers from errors more often than the baselines, and suggests that it adaptively keeps learnable feedback in its weights while reading state information from the harness.
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All authors Moonseok Choi, Taehong Moon, Giung Nam, Juho Lee
Computation and Language
Adaptive Mutual Distillation for Balanced Multi-Task Post-Training of Large Language Models
Baohang Li, Xiaocheng Feng, Yichong Huang et al. (+6 more)
Multi-task post-training of large
language models (LLMs) aims to improve performance across tasks with unequal amounts of training data. Existing methods focus primarily on balancing task contributions during single-model training. Different task-balancing strategies can produce models with complementary strengths, creating opportunities for mutual
distillation. However, the usefulness of cross-model supervision can vary across tasks, transfer directions, and stages of training. We propose Adaptive Mutual
Distillation (AMD), a collaborative post-training framework that jointly trains two models with different task-balancing strategies. AMD evaluates candidate adjustments to
distillation weights through short training probes shared across tasks, then uses task-wise validation scores to select an adjustment for each task and transfer direction. Across six
benchmarks and three LLM backbones, both AMD models achieve higher average
benchmark scores than supervised fine-tuning (SFT) baselines trained with the same sampling strategies. They also outperform the task-balancing methods evaluated in our experiments. Merging the two trained models can further improve their average
benchmark score while yielding a single model for inference. The merged models outperform multi-task SFT by an average of 2.91 points across the three backbones.
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All authors Baohang Li, Xiaocheng Feng, Yichong Huang, Chengpeng Fu, Wenshuai Huo, Zekun Zhou, Zekun Yuan, Tingjia Zhang, Bing Qin
Artificial Intelligence
Bounded Reachability & Jailbreak Detection via Contraction-Constrained State Space Models
Omanshu Thapliyal
Safety heads are lightweight classifiers attached to pretrained
language models for flagging harmful inputs before generation. Their empirical detection performance has been studied, but their formal robustness properties remain largely unexplored. We ask when a State Space Model (SSM)-based safety head can be certified to produce the same prediction for all inputs within a bounded
embedding-space perturbation. We prove that the answer turns on a single condition: the $l_\infty$ norm of the state transition
matrix must satisfy $\norm{A}_\infty<1$ (the contraction condition), which enables exact interval bound propagation (IBP) certification for linear time-invariant classifiers. When the contraction condition holds, the reachable output interval has bounded steady-state width and examples can be certified as robustly classified. When it fails, the interval grows exponentially with sequence length and certification is impossible at any practical perturbation radius. We enforce contraction with a hinge penalty and show on toxic comment data that certified fraction improves from 41% to 59%, with a sharp empirical phase transition at $\norm{A}_\infty=1$ matching the theory. Applying a contraction-regularized S4 head to jailbreak detection on JailbreakBench, we achieve a zero-shot transfer to AdvBench (DR=0.994) and HarmBench (DR=0.988). A logistic regression on mean-pooled Mamba-130M
embeddings matches or exceeds the S4 head on every detection metric, confirming that harmful intent is already linearly separable in the
embedding space. The S4 safety head's contribution is not superior discrimination but the formal certification that no probe-based approach provides.
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Distributed Computing
ByteSplat: Efficient Distributed 3D Gaussian Splatting Training via Intra- and Inter-GPU communication reduction
Shuo Wu, He Zhu, Han Zhao et al. (+9 more)
3D Gaussian Splatting (3DGS) enables photorealistic scene reconstruction, but training large-scale scenes requires substantial
memory and computation. Distributing training across multiple GPUs increases available
memory capacity, yet its performance is strictly constrained by data movement. We identify two dominant bottlenecks: repeated off-chip
memory accesses during forward and backward rasterization, and inter-GPU communication of partial Gaussian gradients. To address these bottlenecks, we present ByteSplat, a distributed 3DGS training framework that jointly reduces intra- and inter-GPU data movement. First, ByteSplat fuses forward rasterization and backward rasterization into a single GPU kernel, retaining intermediate results on-chip to eliminate redundant off-chip transfers. Second, to alleviate the increased on-chip storage by fused rasterization, ByteSplat introduces hardware-aware
pruning that considers both rendering quality and per-tile shared-
memory constraints, increasing the overall performance of fused execution. Lastly, ByteSplat exploits gradient sparsity to eliminate zero partial gradients from inter-GPU communication. GPU-efficient encoder and decoder kernels compact the remaining records and directly aggregate the received gradients on their owner GPUs, reducing both communication volume and local aggregation overhead. We evaluate ByteSplat across six
datasets. Compared with the baseline, ByteSplat reduces intra-GPU off-chip traffic and backward inter-GPU communication volume by 63.4% and 65.8%, respectively. On eight GPUs, ByteSplat achieves up to 6.1$\times$ training speedup while preserving reconstruction quality.
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All authors Shuo Wu, He Zhu, Han Zhao, Xiaohui Zhang, Yaqian Zhao, Hui Wei, Ruyang Li, Hongzhi Shi, Lihua Lu, Jingwen Leng, Yu Feng, Minyi Guo
Robotics
Permutation Robustness Is Not Enough: Action Collapse in Multi-Agent Transformer Policies
Amit Thakur and Mukesh Singhal
Transformer policies are attractive for multi-
agent robot learning because self-
attention can model interactions among
agents. However, multi-
agent teams are unordered, while
transformers typically process
agents as ordered
token sequences. We study how this mismatch affects cooperative
navigation policies under
agent-order permutations. Our results show that low permutation error alone can be misleading: policies may appear robust simply because all
agents choose the same action. We therefore evaluate policies using both permutation-consistency metrics and action-collapse diagnostics, including action diversity, same-action fraction, and maximum action frequency. A PPO-ID baseline yields non-collapsed behavior but remains order-sensitive, while strong equivariance regularization can still induce homogeneous behavior. A weak equivariance penalty improves the robustness while preserving more diverse actions for teams with \(N=3\)
agents, whereas teams with \(N=4\)
agents require substantially smaller regularization weights. These findings suggest that multi-
agent transformer policies should be evaluated not only by return and permutation robustness, but also by whether they maintain non-collapsed, differentiated multi-
agent behavior.
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