Computation and Language
Stress-Testing LLM Lie Detectors: Role-Play Failures and Spurious Correlations
Maximilian von Klinski, Sebastian Lapuschkin, Wojciech Samek et al. (+1 more)
Lie detection probes aim to predict from a
language model's internal states whether its output is truthful or dishonest. However, role-play complicates what "truth" means for an LLM:
language models can adopt a wide range of personas that take very different claims to be true, including personas whose beliefs clearly contradict reality, such as a conspiracy theorist. In this work, we investigate whether lie detection probes reliably flag falsehoods generated under such an anti-factual persona or whether they instead follow the persona's beliefs. We introduce a
dataset of 8,916 human-reviewed, on-policy responses from three LLMs adopting anti-factual personas. Evaluating eight probes from prior work, we find that many fail in this setting, particularly when correct and incorrect answers are evaluated under the same persona
prompt. To investigate why, we construct three novel confounder
datasets in which truth is anti-correlated with a potential confounding concept. Our experiments reveal that many existing probes strongly track concepts that are spuriously correlated with truth in their training data, such as instruction compliance or response likelihood. Based on these findings, we introduce a simple linear probe that achieves the strongest overall performance on both the persona and confounder stress tests. Our results suggest that current lie detection probes are far from reliable and highlight the need for training data in which truth is decorrelated from confounding concepts.
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All authors Maximilian von Klinski, Sebastian Lapuschkin, Wojciech Samek, Lennart Bürger
Numerical Analysis
Exact barycentric rational interpolation of analytic functions with an application to the Bessel functions
Brett R. Green
We derive a method of analytic function expansion using barycentric rational interpolation, in which an arbitrary analytic weight function defines the interpolation and nodes. Truncating these expansions yields successive approximations, and the derivation of the expansions gives the approximation error as a byproduct. We demonstrate the method with cosine and the Bessel functions of the first kind for several choices of weight functions, and we examine the accuracy and convergence of the approximate interpolations as a function of the weight and the number of nodes used in the interpolation.
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Distributed Computing
From Pilots to Production: Lessons in Cross-Institutional Federated Training and Artificial Intelligence for Science
Olivera Kotevska, Max Carlson, Yan Gao et al. (+9 more)
Many of the most valuable scientific
datasets cannot be centralized: they are proprietary, export-controlled, classified, or bound by data-sovereignty restrictions. This inverts the usual paradigm: the model must move to the data, making
federated artificial intelligence (AI) core infrastructure for open science. We synthesize lessons from U.S. Department of Energy national laboratories, industry deployments, and the open-source community into a structured account of moving
federated AI from pilot demonstrations to dependable, multi-site production. The lessons come from five concurrent efforts spanning leadership-class supercomputers and cloud infrastructure in regulated settings. We organize them around an adapted five-domain readiness frame and a stratified view of the stack beneath a trained model: data architecture,
privacy and security controls, trust and verification, governance and socio-technical factors, and operations, the layers that decide whether a pilot becomes dependable. The question shifts from “can we train it?” to “can we operate it, audit it, and change it safely?” We report systems lessons in
memory efficiency, reliability, and synchronization; set out what
privacy, security, and decentralized trust require in production, and what is not yet validated there; and identify cross-cutting open problems (asynchronous federation, leakage
auditing, verification standards, and harmonized data contracts) that we argue warrant a dedicated, international, open-science working group.
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All authors Olivera Kotevska, Max Carlson, Yan Gao, Francis Jeanson, Yijiang Li, Mohammad Naseri, Minseok Ryu, Sahil Tyagi, Jerry Watkins, Feiyi Wang, Ravi Madduri, Kibaek Kim
Machine Learning
RATIO: Reasoning Analysis and Token-level Inference Optimization for Quantized Reasoning Models
Chengzhu Bao, Xianglong Yan, Tianao Zhang et al. (+3 more)
Post-training quantization (PTQ) has become a widely adopted technique for reducing the
memory footprint and inference cost of large
language models (LLMs). However, recent studies reveal that when applied to
reasoning models, PTQ not only degrades
reasoning performance but also exacerbates overthinking, leading to longer
reasoning trajectories. These issues may offset the efficiency gains expected from lower-precision inference. Existing approaches mainly rely on complex optimization procedures. More recent lightweight inference strategies instead use predefined overthinking markers, limiting their adaptability across quantized models. To address these issues, we propose
Reasoning Analysis and
Token-level Inference Optimization (RATIO), a framework that identifies model-specific overthinking
tokens and assigns each a tailored penalty. RATIO first introduces Quantization-aware
Reasoning Behavior Analysis (QRBA) to identify overthinking
tokens by analyzing discrepancies between full-precision and quantized models. It then adopts
Token-Specific Penalty Determination (TSPD), which leverages full-precision guidance to derive
token-specific penalties without additional training. Extensive experiments show that RATIO achieves a better accuracy-efficiency trade-off than existing
token-level interventions. Specifically, RATIO achieves up to 9.8 points accuracy improvement and reduces chain-of-thought (CoT) length by up to 51.3% compared with quantized baselines. The code will be available at https://github.com/steven-bao1/RATIO.
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All authors Chengzhu Bao, Xianglong Yan, Tianao Zhang, Jiaqi Chen, Shaoqiu Zhang, Yulun Zhang
Machine Learning
Finite-Horizon Fisher Memory in Two-Sided Power-Bounded Recurrent Systems
Jeonghoon Lee
We analyse allocation, admission and post-write retention in finite-horizon linear-Gaussian noisy recurrent
memories. At every horizon, the directional Fisher
memory $M_n$ satisfies $\operatorname{tr}M_n=N$: non-normality redistributes information but cannot raise its spherical average, while normal carriers satisfy $M_n=I$. For bi-power-bounded carriers, we derive uniform $1/n$ lag bounds, identify the limit of $M_n$ with the inverse of the classical Cesàro asymptotic limit of $W^\top$, and give finite-horizon error bounds. A time-varying coupling defines an end-to-end store operator. The writer-optimal direction need not be store-optimal. After writing ends, an invertible hold preserves the full stored Fisher
matrix. Additive contamination bounded by $\alpha$ times the closure covariance retains at least $1/(1+\alpha)$ of that
matrix; a covariance-aware decoder attains the corresponding accuracy. With recurrent carriers held fixed, training input masks and linear readouts approached the task-specific optimum in 160 runs, with median normalized Rayleigh efficiency above $0.998$. Binary accuracy matched the Gaussian prediction to mean absolute error below $0.002$ over more than four orders of magnitude in $J$. In a separate pre-specified study of 320 runs, trained masks followed the designated input-time objective in both carrier types, in 16 of 16 draws. These studies used development-seen carriers and are pre-specified validations, not blind holdouts. The same fixed design reproduced the objective-specific result in 16 of 16 draws on carriers unused before run commitment. Exact isolation preserved information, while a decoder fixed at its training horizon fell to chance; inverse-adjoint transport restored its sampled decisions to numerical precision.
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Distributed Computing
Darpan: A Digital Twin Framework for the Next-Generation Computing Continuum
Zhiyu Wang and Rajkumar Buyya
Computing-continuum applications distribute work across devices, edge systems, fog resources, and clouds. While a placement, scheduling, or recovery decision is being made, resource availability, network conditions, and application progress may change, so the decision can be invalid by the time it is executed. Existing runtimes enact predetermined decisions, whereas simulation tools compare alternatives in preconfigured environments; neither explores alternative outcomes directly from the current state of a running application. We propose a Digital Twin framework, called Darpan, that supports both physical and digital execution: the physical side runs real applications and continuously observes their runtime state, while the digital side maintains a continuously updated, executable virtual counterpart based on these physical observations. Darpan evaluates candidate decisions independently from the same starting point and returns the selected decision to the physical side, where its feasibility is validated against the latest physical state before actual execution. Across real Directed Acyclic Graph (DAG) workloads, Darpan predicts physical response time with a mean absolute error of 0.485 s and retains 93.1% scale-out efficiency at 40 nodes. In the state-change trials, Darpan takes about 2 ms on average from state capture to rejection of an invalidated decision, stopping the request before data transfer and avoiding unnecessary physical execution overhead. Under the same physical-DAG budget, Darpan-generated experience enables a weaker Proximal Policy Optimization (PPO) scheduler to outperform two state-of-the-art Deep Reinforcement Learning (DRL) schedulers by 29.1% and 20.2%.
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Machine Learning
Probabilistic Adversarial Training
Andi Zhang, Xingyu Zhao and Siddartha Khastgir
Building on a probabilistic perspective in which
adversarial examples arise from the overlap between a distance-based distribution $p_{\mathrm{dis}}$ and a victim-classifier-induced distribution $p_{\mathrm{vic}}$, we start from a simple intuition:
adversarial examples become harder to generate when these two distributions are pushed apart, as their overlap becomes smaller, thereby increasing robustness. This intuition naturally motivates a KL-based robustness objective. We then prove that $\mathrm{KL}(p_{\mathrm{dis}}\|p_{\mathrm{vic}})-\log Z_{\mathrm{vic}}$ is a lower bound on probabilistic robustness (PR), where $Z_{\mathrm{vic}}$ denotes the normalizing constant of $p_{\mathrm{vic}}$. Since PR is generally intractable to compute directly, maximizing this KL-based lower bound provides a tractable surrogate objective for improving PR. We further show that this objective recovers a scaled form of
adversarial training, offering a probabilistic interpretation of
adversarial training and a principled route to robustness improvement. We call the resulting method probabilistic
adversarial training. Experiments show that it consistently improves PR, and ablation studies demonstrate that the induced scaling factor can even enhance the PR of non-probabilistic
adversarial training methods.
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Robotics
Tool-Policy Co-Design for Powder Weighing in Laboratory Automation
Nikola Radulov, Xin Yang, Kevin S. Luck et al. (+1 more)
Autonomous powder weighing is one of many bottlenecks in laboratory automation due to the complex, non-linear dynamics of heterogeneous materials.
Robot chemists performing this task utilise standard tools shaped for the dexterity of human hands, whose fixed geometry sets the dynamics that the control policy needs to regulate. This work introduces a tool-policy co-design framework that concurrently optimises the morphology of a dispensing tool and its control policy for use by
robots in chemistry laboratories, formulated as a bi-level optimisation that minimises dispensing error over a target distribution of powder flowabilities. The outer loop varies tool-design parameters such as tool depth, width and rim spike
topology using
Bayesian optimisation and hyperband, while an inner loop optimises a control policy for each candidate morphology. We also introduce a
geometric similarity metric that warm-starts policy training from cached policies of structurally similar designs, exploring 28% more configurations under the same compute budget. The proposed framework is evaluated on a robotic powder weighing task across seven materials with distinct physical dynamics in a flowability-informed
robot-material simulation framework. Experimental results demonstrate that our co-designed tool morphology reduces real-world weighing errors by 45% relative to a standard tool, including on previously unseen materials. These results demonstrate our method can adapt both the control policy and the physical tool to the dynamics of the target material, bringing a new paradigm for material manipulation to the field of laboratory automation.
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All authors Nikola Radulov, Xin Yang, Kevin S. Luck, Gabriella Pizzuto
Computer Vision
Inline Memory Meets Reusable Skills: Memory-centric Framework for Vision-Language-Action Model
Zaijing Li, Rui Shao, Bing Hu et al. (+3 more)
Vision-
Language-Action (VLA) models have shown strong promise for general-purpose robotic manipulation, yet adapting them to new tasks and domains remains inefficient: existing methods often rely on parameter tuning, incurring substantial costs and risking catastrophic forgetting of previously learned tasks. To address this, we propose Optimus-R, a
memory-centric VLA framework that formulates robotic adaptation as explicit query-skill
memory tuning. Optimus-R introduces: (i) An Inline
Memory Interface for skill extraction. It inserts learnable
memory tokens into the VLA prefix stream, allowing the backbone to derive control-aware query and skill representations within the native action-conditioning pathway. (ii) A Query-Skill
Memory Bank for skill learning. It externalizes skills into query prototypes for deciding what to retrieve and skill values for specifying how to act, supporting skill reuse and expansion with limited parameter updates. (iii) A lightweight Bridge-and-Adapt mechanism for skill updating. It aligns target-domain queries and skills with the existing
memory space through a lightweight adapter and residual
memory updates. Experiments on in-domain adaptation, cross-domain adaptation, and lifelong learning show that Optimus-R enables data-efficient skill learning while mitigating catastrophic forgetting.
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All authors Zaijing Li, Rui Shao, Bing Hu, Haoyu Zhang, Dongmei Jiang, Liqiang Nie
Machine Learning
TopTimeNet: Topologically-assisted time-series classification model
Sharareh Sayyad and Sophia Bazzi
Distinguishing periodic from chaotic dynamics in a time series is a fundamental challenge in both physics and engineering. Yet, end-to-end learned architectures must discover both a representation and a decision boundary from data, at substantial cost. We introduce TopTimeNet, which decouples these tasks: a fixed, non-learned stage extracts a $42$-dimensional
geometric and topological descriptor from Takens delay
embeddings and persistent homology, and a lightweight learnable stage performs classification. On a
benchmark of $49$ nonlinear dynamical systems, a $1{,}638$-parameter configuration matches the mean accuracy of one with $33\times$ more trainable parameters. Additionally, this approach delivers mean accuracy comparable to convolutional neural networks and surpasses the average performance of converged
Transformer models, while requiring three to four orders of magnitude fewer trainable parameters. Robustness also depends sharply on where noise is introduced: TopTimeNet degrades gracefully under perturbations to its precomputed features, but degrades sharply when noise is introduced into the raw signal and the full feature-extraction pipeline is recomputed, showing that robustness to perturbations of the precomputed features does not imply robustness of the complete raw-signal-to-prediction pipeline. These results show that decoupling fixed
geometric and topological feature construction from a lightweight discriminative stage can achieve comparable classification accuracy with substantially fewer trainable parameters.
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Machine Learning
Pseudo-Label-Triggered Retraining from Forecast Errors for Online Time Series Forecasting
Yeryeong Kwak, Yoo-Min Jung and Jonghun Park
Real-world time series
forecasting systems operate under non-stationary data streams, where
forecasting performance may degrade over time. Although retraining can recover the performance, it incurs non-trivial computational and operational costs. Under limited deployment resources, the key challenge is therefore not only how to retrain but also when to retrain. While existing retraining policies often rely on indirect indicators such as drift alarms or model staleness, we instead use realized forecast errors as direct deployment feedback. In this paper, we propose PILOT (Pseudo-label-Informed Learned Online Trigger), an online retraining framework that learns when to retrain from forecast-error dynamics. Since ground-truth retraining labels are unavailable, PILOT constructs a pseudo-label from future increases in forecast error and trains a lightweight scorer to predict it from observed error states. At deployment, PILOT uses only completed forecast errors and serves as a plug-in module for arbitrary
forecasting backbones without architectural modification. We evaluate PILOT under standard multivariate
forecasting settings across eight
benchmarks with three representative backbones—DLinear, iTransformer, and TimesNet. Across all three backbones, PILOT achieves state-of-the-art average-rank performance among retraining policies while maintaining a favorable performance–efficiency trade-off.
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Artificial Intelligence
Safety of Latent Communication in Multi-Agent Systems
Muhammad Huzaifa, Sina Mavali and Thorsten Eisenhofer
Latent communication enables multi-
agent systems to exchange information directly in internal representation space, reducing the
token, computation, and latency overhead of text-based communication. To this end, lightweight trainable links are introduced to map the sender's representations into the receiver's input space. In this work, we show that even benign link training can increase harmful compliance relative to text-based communication while the underlying safety-aligned
agents remain unchanged. An attacker can amplify this effect by optimizing the links on harmful query–response pairs or poisoning otherwise benign training data. We further develop a reinforcement-learning attack that rewards harmful compliance alongside benign task performance without requiring harmful target responses. Across three communication
topologies and four safety
benchmarks, this attack raises the mean harmful-compliance score from 27.9 with benignly trained links to 76.9. Compared with direct supervised optimization, it also achieves higher average accuracy on two benign utility
benchmarks. Adapting the rewards toward safer behavior also enables repair of compromised links, substantially reducing harmful compliance across all evaluated attacks without updating the
agents. Overall, our results show that safety
alignment requires considering the multi-
agent system as a whole. Code: https://github.com/Muhammad-Huzaifaa/latent-safety
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Cryptography and Security
Privacy Foundations for Multi-Institutional Scientific Artificial Intelligence
Olivera Kotevska, Sumit Jha, Aur'elien Bellet et al. (+5 more)
Scientific artificial intelligence (AI), spanning foundation models (FMs) to
federated data-analysis pipelines, is becoming shared infrastructure across national laboratories, universities, hospitals, and industrial partners. This collaboration creates
privacy risks whose natural unit is often an institution's participation, research strategy, or technical capability rather than a single record. Differential
privacy (DP),
federated learning (FL), secure computation, trusted execution, and provenance each protect parts of the stack, but their guarantees rarely compose across mixed-trust institutions, access tiers, and autonomous
agents. This perspective recasts
privacy for scientific AI as an assurance problem defined by six elements: protected asset, observer, channel, permitted disclosure, guarantee, and evidence. We demonstrate the framing through a claim register for a composite cross-institutional scenario and use it to assess the model lifecycle. Two of the resulting gaps are specific to leadership-class facilities: scheduler, allocation, and telemetry metadata expose an institution's resource posture, and instrument-attached control loops leak research strategy through timing and contention on shared accelerators. We identify six research priorities: institution-level guarantees,
agent-communication
privacy, cross-tier information flow,
privacy-compatible reproducibility, leadership-scale accounting, and instrument side channels. The contribution is a common form for stating, comparing, and
auditing claims whose guarantees otherwise remain fragmented across the scientific AI stack.
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All authors Olivera Kotevska, Sumit Jha, Aur'elien Bellet, Rui Hu, Nathaniel D. Bastian, Rafael Ferreira da Silva, Ravi Madduri, Kibaek Kim
Computation and Language
Explore-over-Graph: Hybrid Embedding-LLM Reasoning for Knowledge Graph Question Answering under Incompleteness
Ola El Khatib and Djellel Difallah
Large
language models (LLMs) are increasingly combined with knowledge graphs (KGs) to ground
reasoning in structured evidence. However, most LLM-based KGQA methods rely on traversing existing graph edges and become unreliable when
reasoning paths are broken by missing facts. Alternatives that ask LLMs to generate missing knowledge risk introducing hallucinated evidence. We introduce XoG (eXplore-over-Graph), a framework for multi-hop question answering over incomplete KGs that recovers missing
reasoning paths from learned graph structure rather than LLM parametric knowledge. XoG combines type-level entity-relation statistics to identify candidate relations with KG
embeddings to retrieve plausible missing entities, using the LLM as a semantic selector and reasoner. These mechanisms are integrated into an iterative planning-exploration-
reasoning process. Experiments on WebQSP, CWQ, and the Wikidata-based BRINK
benchmark show that XoG remains competitive on complete KGs and consistently outperforms comparable methods without task-specific KGQA training under KG incompleteness. These gains persist across multiple LLM backbones, indicating that stronger LLMs alone do not resolve missing graph evidence. XoG also reduces LLM
token consumption by up to 33% compared with a closely related planning-based approach.
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Computer Vision
Seeing as Humans Do: Learning from Motion to Segment Anything Without Supervision
Weijian Jian, Xiaoyue Zhang, Bin Xiao et al. (+5 more)
The Segment Anything Model (SAM) relies heavily on massive manual annotations, creating a fundamental bottleneck for model scaling. While unsupervised methods attempt to learn object concepts from motion, they typically overfit to moving entities, lacking both multi-granularity understanding and the ability to generalize to static objects. To overcome this, we introduce Motion-Grounded Segment Anything (MoSA), a highly scalable unsupervised framework that learns a transferable objectness prior from unlabeled
videos. MoSA operates in three progressive stages: (1) automatically generating multi-granularity motion pseudo-labels from large-scale
video data; (2) training a Perceptual Grouping Model (PGM) via contrastive learning to internalize a generalized, appearance-driven concept of objects; and (3) transferring this learned prior into a
prompt-guided architecture for segment-anything-style inference on images. Extensive zero-shot evaluations across seven challenging
benchmarks (e.g., COCO and ADE20K) demonstrate that MoSA significantly outperforms existing unsupervised methods. Notably, despite using zero manual annotations, MoSA achieves
segmentation performance comparable to the fully supervised SAM. Our findings reveal that harnessing large-scale unlabeled motion is a feasible and highly scalable alternative to annotation-driven segment-anything pipelines.
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All authors Weijian Jian, Xiaoyue Zhang, Bin Xiao, Chunyu Xie, Yixiao He, Yutao Liu, Dawei Leng, Yuhui Yin
Machine Learning
CORD: Learning Reusable Degradation Representations Across Heterogeneous Physical Systems
Haibo Li and Zhiguo Zeng
Can heterogeneous physical degradation systems benefit from joint pretraining and move beyond system-specific prognostics toward reusable cross-system representation learning? CORD combines type-specific observation interfaces with a shared degradation backbone. Its two self-supervised objectives learn at complementary scales: Intra-Observation Structure Modeling (ISM) captures structure within observations, while Inter-Observation Dynamics Modeling (IDM) captures latent degradation
evolution across observation histories. We evaluate CORD under two transfer boundaries: Pretraining-Included System Types, where downstream
datasets and held-out units are unseen but their system types are represented during source pretraining, and Pretraining-Excluded System Types, where the entire turbofan-engine type is absent from pretraining. Across bearings, batteries, and cutting tools, CORD (Multi-domain) consistently improves over CORD (Single-domain) under Frozen adaptation, provides further gains under Full FT in most settings, and remains competitive with representative external baselines. Source-pretrained initialization also improves low-label adaptation to the pretraining-excluded engine type. Frozen-representation analysis further shows improved cross-unit lifecycle consistency after multi-domain pretraining. Joint pretraining across heterogeneous physical systems thus produces degradation representations reusable across devices,
datasets, and system types.
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Software Engineering
COMPASS: Predicting the Relationship of Multiple Patches for Vulnerabilities with LLMs
Yi Song, Dongchen Xie, Xiaoyuan Xie et al. (+4 more)
Modern software heavily relies on code reuse, so upstream vulnerability fixes do not automatically propagate to downstream codebases. Downstream maintainers must manually adopt patches to eliminate known risks. In practice, a single vulnerability often corresponds to multiple patches, which greatly complicates downstream patch adoption because different patch relationships imply different adoption strategies. To address this challenge, we first manually inspect large-scale multi-patch vulnerabilities (about 1K) in the real world and interview experienced developers, summarizing six typical types of patch relationships, i.e., Merge, Mirror, Better Solution, Fixing-of-Fixing, Collaboration, and Separation. Based on these observations, we propose COMPASS, an automated approach that predicts the relationships of multiple vulnerability patches with large
language models. Given a CVE as input, COMPASS follows a four-phase pipeline that (i) identifies the patch group and pre-scans explicit relationships, (ii) performs individual patch analysis, (iii) infers relationship instances via a hierarchy-guided
prompt, and (iv) validates completeness and consistency of the inferred results. As output, COMPASS reports the predicted relationships within the patch group and visualizes them as a relationship graph. We evaluate COMPASS on a
benchmark of 300 multi-patch CVEs and compare it against mainstream learning-based and LLM baselines. Results show that our method achieves strong and consistent prediction effectiveness and outperforms SOTA by 85.04% on average. We publicly release an online querying website to support community reuse of patch relationships knowledge: https://patch-relation.com.
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All authors Yi Song, Dongchen Xie, Xiaoyuan Xie, He Zhang, Lin Xu, Chunying Zhou, Zhi Jin
Databases
Scalable Approximate Algorithm for Dynamic Densest Subhypergraphs with Solution-Guided Maintenance
Jingbang Chen, Chenhao Ma and Yingli Zhou
Hypergraphs model interactions involving groups of entities, and finding highly connected groups is a fundamental task in analyzing these data. When interactions arrive and expire, maintaining a dense group can require frequent and expensive changes to the underlying representation. We propose CAP, a scalable
algorithm that explicitly maintains a $(1+\epsilon)$-approximate densest subhypergraph under hyperedge insertions and deletions. The
algorithm keeps a solution candidate together with an endpoint allocation that bounds the optimum density. The candidate's density guides maintenance: an update triggers repair only when this bound is too large to establish the required approximation. Local searches redistribute load or find a denser candidate, allowing CAP to retain useful solutions across many updates. We give an analysis that relates maintenance work to the size of the regions searched and the density lost by the maintained solution. It identifies conditions under which local repair remains inexpensive and provides a theoretical explanation for the observed efficiency. Experiments on real-world hypergraphs show speedups of up to nearly four orders of magnitude over the evaluated dynamic baselines, while supporting frequent queries and high accuracy. CAP also remains competitive with specialized dynamic graph methods on ordinary graphs, with speedups of up to approximately $64\times$ on the tested workloads.
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Machine Learning
GraphMAS: A Systematic Benchmark of Multi-Agent Coordination for Graph Learning
Jiayi Yang, Yifang Chen, Yuanfu Sun et al. (+2 more)
LLM-based multi-
agent systems coordinate specialized
reasoning through aggregation, interaction, and adaptive control, yet their potential for graph learning remains unexplored. Graph learning is a natural setting for such systems because useful evidence may arise from heterogeneous local, long-range, global structural, and semantic perspectives whose relevance varies across instances. Existing LLM-based graph learning approaches primarily rely on single-
agent reasoning, while multi-
agent coordination has been studied mainly in general
reasoning settings. Consequently, it remains unclear whether multiple specialized
agents can improve graph learning and how coordination strategies should be designed and evaluated. To address this gap, we introduce GraphMAS, a systematic
benchmark of multi-
agent coordination for graph learning. GraphMAS builds a shared pool of graph
reasoning specialists and organizes coordination along two dimensions, inter-
agent interaction and runtime adaptivity, yielding four paradigms and seven representative coordination methods. Under a unified protocol, we evaluate these methods across seven text-attributed graphs, three domains, and two graph learning tasks. We find that heterogeneous graph perspectives are complementary, and that coordinating specialists improves over individual specialists and single-
agent graph
reasoning, with gains from decomposing
reasoning across specialists rather than from broader evidence access alone. However, richer inter-
agent interaction does not reliably help, whereas instance-adaptive specialist selection yields the strongest accuracy-efficiency trade-off. We further show that coordination can be learned over a fixed specialist pool and transfers to held-out graphs. GraphMAS therefore provides a controlled evaluation framework and empirical principles for understanding when and how multi-
agent coordination benefits graph learning.
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All authors Jiayi Yang, Yifang Chen, Yuanfu Sun, Xinyan Ge, Qiaoyu Tan
Information Theory
Mission Efficiency Optimization in Low-Altitude Economy: Adaptive Power Allocation for Coordinating Heterogeneous Aircraft Swarms
Jiarui Zhang, Wei Feng, Chao Dong et al. (+2 more)
With the rapid development of the low-altitude economy, low-altitude operations are booming, where complex missions require collaborative efforts among multiple heterogeneous low-altitude aircrafts (LAAs). Specifically, different LAAs assume distinct roles: some for sensing, some for communication, some for computing, and others for mission execution, together forming a sensing-communication-computing-control (SC3) closed loop, akin to a reflex arc. To enable efficient coordination in such multi-LAA swarms, we introduce the concept of operational-capability
entropy (OCE) to quantify the effective work capability of operational LAAs. Accordingly, by jointly considering heterogeneous OCE and channel conditions among LAAs, we formulate the power allocation problem with the goal of minimizing the linear quadratic regulator (LQR) cost, which serves as a metric for mission efficiency. The resulting complex optimization problem is decomposed into two convex subproblems that are solved iteratively, with closed-form solutions derived for each. Simulation results demonstrate that the proposed mission-oriented adaptive power allocation scheme significantly outperforms traditional ones.
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All authors Jiarui Zhang, Wei Feng, Chao Dong, Ning Ge, Qihui Wu
Cryptography and Security
Path-Finding, Orbit State Preparation, and the Security of Invariant Quantum Money
Hans Schmiedel and Jiangshan Yu
The security of
quantum money from
knots, and of its generalization to invariant money, is based on the assumption that path-finding, exhibiting a sequence of moves between two equivalent objects, is hard. No proof of security from that assumption alone is known. The existing proofs add knowledge-of-path assumptions, which assert that any efficient
algorithm producing two objects with the same invariant implicitly knows a path between them. No attack can refute such an assumption, and it is not known to follow from security. We ask when path-finding is the right assumption. When each equivalence class is the orbit of an efficiently computable action of a group that can be superposed over, and every move acts as a group element, as for graphs, average-case hardness of path-finding is necessary for security. For
knots no such group is known, and a path-finder only reduces forgery to an equally hard state-preparation problem. With or without a path-finder, a forger must prepare a state that verification accepts, and we take the hardness of that task as the assumption. For schemes whose verification walk mixes in
polynomial time, the preparation assumption states that no efficient
algorithm, given the serial number of a freshly minted banknote and one object measured from it, prepares such a state. It is falsifiable, and it is equivalent to security against forgers that measure their banknote first. The transfer assumption, which security implies, states that measuring first costs a forger at most a
polynomial factor. Together the two are equivalent to security, so every proof of security must establish the preparation assumption. If the preparation assumption holds, no fully black-box reduction that calls the forger only at the serial number it is given can derive the transfer assumption from the preparation assumption.
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Machine Learning
Riemannian Flow Models with Reinforcement Learning for Molecular Crystal Structure Prediction
Thomas Egg, Harry Winston Sullivan, Maya M. Martirossyan et al. (+8 more)
Crystal structure governs material properties, making
crystal structure prediction (CSP) a fundamental problem in materials science. Generative models are a promising approach for solving this problem, but the prevalence of polymorphism, coupled with large unit
cells and complex packing geometry, makes the molecular CSP task challenging for existing models. To address this, we introduce Coarse-Grained Open Materials Generation (CG-OMatG), an equivariant Riemannian flow-based generative model. CG-OMatG predicts molecular
crystal structures via a coarse-grained, hierarchical representation. CG-OMatG treats molecules as rigid bodies—performing both inter- and intra-molecular message passing to construct a
geometric representation for molecular packings—and learns to reconstruct molecule centroid positions, orientations, and
lattice parameters, conditioned on chemical species and conformer geometry. We train the model on subsets of the Open Molecular
Crystals (OMC25) and Cambridge Structural Database (CSD)
datasets. Further, we fine-tune the model via policy gradient reinforcement learning to steer the model towards generating low-energy candidate structures. We validate the generated structures on the CSP blind test
benchmark, assessing agreement with experimentally determined
crystals using COMPACK packing-similarity analysis. CG-OMatG exhibits strong performance for generative molecular
crystal structure prediction, paving the way for accelerated polymorph screening and organic solid-state materials discovery.
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All authors Thomas Egg, Harry Winston Sullivan, Maya M. Martirossyan, Philipp Höllmer, Cheng Zeng, Adrian Roitberg, Mingjie Liu, Richard Hennig, Sapna Sarupria, Ellad B. Tadmor, Stefano Martiniani
Logic in Computer Science
Security Properties of Neural Networks as Decision Problems
Adrian Wurm
Certifying a deployed neural network raises decision problems that the verification literature has not classified: whether the model carries a
backdoor planted in its training data, whether a fault in its stored parameters can drive it into an unsafe state, whether its output leaks a private part of its input. We formalise eight such problems and classify what we can. The organising observation is a logical one. The function computed by a piecewise linear network, together with all its node values, is definable by a quantifier-free formula of real addition of size linear in the network, so a property of the network is a quantifier-alternation sentence, which Sontag's 1985
theorem places in the
polynomial hierarchy at the level of its prefix. Membership results are thus corollaries, and the argument makes plain what they need: that the quantified objects are inputs rather than the network's own parameters. Non-interference, monotonicity and counterfactual
fairness have exactly the complexity of network equivalence and of interval verification, all co-NP- complete over ReLU. Detection of
backdoor triggers from a quantised alphabet is Sigma_2^P-complete, one level above robustness certification, so it does not reduce to polynomially many robustness queries unless the hierarchy collapses. Inversion resistance is co-NP-complete for every l_p metric, p a fixed positive integer. Quantifying over parameters instead of inputs - the fault model of bit-flip attacks, radiation upsets and analog accelerators - makes verification exists-R-complete already for networks of identity nodes, for which every previously studied problem is in P, and it stays so when each parameter is confined to a box of inverse-
polynomial width; the corresponding safety question is forall-R-complete for ReLU.
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Machine Learning
How Does Local Landscape Geometry Evolve in Language Model Pre-Training?
Zhanpeng Zhou, Yuhan Sun, Bingrui Li et al. (+4 more)
The scale and expense of pre-training
language models make efficient hyperparameter tuning essential, yet a principled guidance is still missing. In this work, we analyze
language model pre-training dynamics from a local landscape geometry perspective. Our study reveals two distinct phases. In Phase I, sharpness of the local landscape is initially high, leading to instability and loss plateaus under large learning rates (LRs). The landscape shifts from sharp to flatter regions early in training. This dynamic explains the necessity of LR warmup and further suggests that larger peak LRs require proportionally longer warmup periods. In Phase II, the local landscape is governed by the gradient noise scale. Our theory identifies a depth flatness trade-off: high noise from smaller batches widens the loss basin, whereas reduced noise from larger batches deepens it. This theory motivates a dynamic batch-size (BS) scheduler that begins with a small BS and increases it late in training. Together, we provide a unified view of loss landscape
evolution, which translates into actionable tuning strategies for large-scale pre-training.
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All authors Zhanpeng Zhou, Yuhan Sun, Bingrui Li, Jinbo Wang, Huaijin Wu, Lei Wu, Junchi Yan
Computation and Language
MemCodex: Self-Programming Hierarchical Memory for Language Agents
Xiaoqiang Wang and Bang Liu
Agent memory faces heterogeneous access needs: a single-hop question may require one piece of evidence, whereas a multi-hop question must combine evidence from multiple sources. Predefined
memory workflows cannot adapt to these varying needs. Recent adaptive methods search or learn over
memory components and their compositions, but the design space itself remains predefined. We introduce MemCodex, a self-evolving hierarchical
memory system that organizes experience into executable
memory programs for summaries, relational knowledge, reusable skills, and latent
memory. Open-ended program
evolution searches the open design space of layer programs by rewriting how each layer is constructed, indexed, retrieved, and routed, thereby adapting both within-layer implementations and cross-layer composition. At query time, reads traverse the hierarchy from coarse to fine and stop once sufficient evidence is found, descending to the original history when needed. We further develop MemArena, a unified runtime that places heterogeneous data and
memory systems behind a common interface. MemCodex improves average task success by 10.1% relative to the strongest adaptive-
memory baseline, while using 3.4x fewer context
tokens and achieving 2.1x faster inference.
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Robotics
DiffWAM: A Fast and Efficient Navigation World Action Model
Mo Zhu, Yuze Wu, Xijie Huang et al. (+3 more)
Pretrained
video foundation models encode rich semantic and spatiotemporal priors for
embodied navigation, yet converting these priors into
UAV motion typically requires expensive future-
video synthesis and
geometric reconstruction. We investigate whether the motion implicit in future visual prediction can instead be recovered directly from the predictive representations of a frozen
video model. To this end, we present DiffWAM, a geometry-conditioned
navigation world-action model that directly transforms multi-level predictive features into continuous camera trajectories. Its Grid-Motion module preserves spatial-temporal motion associations, while Latent2Pose grounds them with first-frame geometry to recover metrically meaningful 3D motion. Complete
video rollouts and
geometric reconstruction are required only for offline supervision, eliminating future-
video decoding and multi-frame reconstruction during deployment. We further introduce FastDreamer, which overlaps predictive and
geometric computation with ongoing flight and performs timestamp-aware asynchronous trajectory handoff for continuous
UAV execution. DiffWAM achieves a trajectory RMSE of 0.3492 m and an endpoint success rate of 74.40% on the 1,000-sample DiffWAM-1000
benchmark, while representative real-world experiments demonstrate complex behaviors including constrained traversal, orbiting, S-shaped flight, and multi-stage
navigation. An onboard DiffWAM-Flash implementation further reaches 1.08 s model-pipeline latency on NVIDIA Jetson AGX Thor. These results demonstrate that predictive
video representations can be efficiently grounded into continuous 3D motion, providing a direct alternative to generate-then-reconstruct
navigation pipelines. Project page: https://zzmmzzm.github.io/diffwam.github.io/.
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All authors Mo Zhu, Yuze Wu, Xijie Huang, Xiao Cui, Fei Gao, Xin Zhou
Machine Learning
Revisiting On-policy Adversarial Black-Box Distillation: Calibrating Groupwise Reward Geometry for Effective Advantage Construction
Xiao Cui, Mo Zhu, Yulei Qin et al. (+3 more)
Black-box
distillation is a practical route for transferring capabilities from API-accessible large
language models that expose only text outputs into smaller student models. Recent on-policy
adversarial methods such as GAD improve over SeqKD by forming an
adversarial loop between a critic and a student, where the critic provides rewards for GRPO-based student policy optimization over the student's sampled responses. However, GRPO computes advantages from the within-group relative rewards of student samples for the same
prompt, whereas the critic is trained primarily to distinguish teacher responses from student responses. This objective mismatch can produce reward groups with collapsed scale or fragile margins, leading to brittle grouped optimization signals. We propose Groupwise Reward Geometry Conditioning (GRGC), a two-stage framework that improves advantage construction by shaping student-side reward groups during both critic training and policy optimization. To improve critic-side conditioning, Gaussian groupwise Optimal Transport calibration regularizes the critic during training to produce reward groups with non-collapsed spread and smooth rank-wise gaps by matching sorted
prompt-wise rewards to group-centered Gaussian
quantiles. Building on this conditioned reward geometry, policy-side group power modulation reshapes the
prompt-wise reward groups before they are converted into advantages, preserving the critic-induced ordering while increasing optimization-relevant margin separability. Extensive experiments across diverse teachers, student model families and scales, and training
datasets demonstrate the effectiveness of GRGC on both in-distribution and out-of-distribution evaluations, while introducing negligible overhead over GAD. The code is available at https://github.com/2018cx/GRGC.
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All authors Xiao Cui, Mo Zhu, Yulei Qin, Yuze Wu, Wengang Zhou, Houqiang Li
Computer Vision
Determining Vertical Displacement of Agricultural Areas Using UAV-Photogrammetry and a Heteroscedastic Deep Learning Model
Wojciech Gruszczyński, Edyta Puniach, Paweł Ćwiąkała et al. (+1 more)
This article introduces an
algorithm that uses a U-Net architecture to determine vertical ground surface displacements from unmanned aerial vehicle (
UAV)-photogrammetry point clouds, offering an alternative to traditional ground filtering methods. Unlike con-ventional ground filters that rely on point cloud classification, the proposed approach em-ploys heteroscedastic regression. The U-Net model predicts the conditional expected val-ues of the elevation corrections, aiming to reduce the impact of vegetation on determined ground surface elevations. Concurrently, it estimates the logarithm of the elevation cor-rection variance, allowing for direct quantification of the uncertainty associated with each elevation correction value. The
algorithm was evaluated using three metrics: the root mean square error (RMSE) of vertical displacements, the percentage of nodes with deter-mined displacement values, and the percentage of outliers among those values. Perfor-mance was assessed using the technique for order of preference by similarity to ideal so-lution (TOPSIS) method and compared against several ground-filter-based
algorithms across four
datasets, each including at least two time intervals. In most cases, the U-Net-based approach demonstrated a slight performance advantage over traditional ground filtering techniques. For example, for the U-Net-based
algorithm, for one of the test da-tasets, the RMSE of the determined subsidences was 6.1 cm, the percentage of nodes with determined subsidences was 80.5%, and the percentage of outliers was 0.2%. For the same case, the
algorithm based on the next best model (SMRF) allowed an RMSE of 7.7 cm to be obtained; for 77.3% of nodes, the subsidences were determined; and the percentage of outliers was 0.3%.
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All authors Wojciech Gruszczyński, Edyta Puniach, Paweł Ćwiąkała, Wojciech Matwij
Robotics
Experience-Driven Continual Learning of Terrain Traversability for Quadruped Robots
Luca Bricarello, João Carlos Virgolino Soares, Alberto Sanchez-Delgado et al. (+2 more)
Safe and efficient quadruped
navigation over unfamiliar terrain requires predicting terrain-
robot interaction before contact: geometry and visual appearance alone cannot reveal how the
robot will slip, load its feet, or expend energy. This paper presents a continual learning pipeline that uses locomotion experience to learn these interaction outcomes from pre-contact images and continually updates the predictions as new contacts are observed. Pre-contact descriptors, produced by a DINOv3 backbone model frozen during training, are mapped to five proprioceptive indicators weighted according to measurement reliability: planar foot slip, mean normal ground-reaction force, traction index, cost of transport, and touchdown loading rate. A compact evidential regressor allows us to predict these indicators together with aleatoric and epistemic uncertainty from the visual descriptors. Continual adaptation combines bounded experience replay with a validation gate: candidate models replace the deployed predictor only when they improve performance on recent held-out data while keeping degradation on historical held-out data within a prescribed tolerance. Predictions and epistemic uncertainty are projected into a local multilayer map and combined into a conservative traversability score map whose property weights can be adjusted without retraining. The resulting map is used for downstream
navigation tests. The ROS2 implementation supports evaluation on a Unitree Go2 in simulation and on hardware, with models trained separately in each domain. On a sequential hardware stream over three previously unseen terrains, gated replay reduces final anchor negative log-likelihood (NLL) degradation by 23.1% relative to replay without the gate while attaining similar new-terrain adaptation.
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All authors Luca Bricarello, João Carlos Virgolino Soares, Alberto Sanchez-Delgado, Fulvio Mastrogiovanni, Claudio Semini
Robotics
ChunkTrust: Adapting Execution Horizons for Robot Policies with Action-Expert Evidence
Fanding Huang, Jingyan Jiang, Shifeng Bao et al. (+13 more)
Robot foundation policies predict action chunks, but how many actions to execute before replanning depends on the current task phase. We introduce ChunkTrust, which treats the execution horizon as a latent variable inferred from action-expert evidence rather than a fixed hyperparameter. Its training-free Action-aware Horizon Selector (AHS) combines intra-chunk spectral stability of generation traces with inter-chunk continuity between executed history and predicted actions. An online Beta posterior with kernel forgetting tracks horizon preferences across replans. A lightweight Query-based Horizon Adapter (QHA) optionally learns a context-conditioned dense prior from complementary evidence, fused with current evidence and episode-local Beta
memory while the base policy remains frozen. Across RoboTwin2.0 and RoboCasa GR1 Tabletop, AHS improves overall task-averaged success for each evaluated base-policy configuration, including gains of +6.80 percentage points on $\pi_{0.5}$ over all 50 RoboTwin2.0 tasks and +9.67 percentage points on Qwen3GR00T in RoboCasa. AHS+QHA raises the gain over Base to +9.44 percentage points on the eight-task $\pi_{0.5}$ evaluation. On four real-world household tasks, AHS improves the equal-task mean normalized process score from 50.4% to 57.5%. Ablations examine the contributions of both evidence terms, temporal
memory, and the learned prior. Project page is https://hf618.github.io/ChunkTrust.github.io/
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All authors Fanding Huang, Jingyan Jiang, Shifeng Bao, Mingkang Pu, Shiwei Li, Jing Xu, Shijia Xu, Guanbo Huang, Chenghao Gu, Yuzhi Huang, Chenxin Li, Faisal Nadeem Khan, Huan Yang, Yan Wang, Cheng Chi, Zhi Wang