Systems and Control
Compute-Constrained Safety Filters with Neuromorphic Event Triggering
Tochukwu E. Ogri, Opeyemi Owolabi, Luke Fina et al. (+2 more)
Safety filters that rely on control barrier function (CBF) quadratic programs (QPs) are typically analyzed under the assumption that the QP is solved instantaneously, although nonzero computation time can render a computed input unsafe by the time it is applied. This paper develops a dual leaky integrate-and-fire (LIF) event-triggered safety filter for linear systems that accounts for this delay. Two LIF states monitor a shifted CBF residual and the deviation of the held input from the nominal feedback law. A computation is initiated when either LIF state reaches its threshold; the previous input remains applied until the prescribed application time, and any solution returned earlier is stored until then. A closed-form charging profile relates the safety-LIF threshold to residual-growth bounds, ensuring sufficient residual margin over the application delay. Under the stated assumptions, the resulting hybrid system guarantees forward invariance, completeness, and non-Zeno execution, with positive lower bounds on computation-initiation and input-application times. A spacecraft terminal-approach simulation demonstrates safety with fewer optimizer calls than a delay-tightened periodic filter.
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All authors Tochukwu E. Ogri, Opeyemi Owolabi, Luke Fina, Christopher Petersen, Rushikesh Kamalapurkar
Machine Learning
How Many Repeated Pairwise Comparisons Are Needed for Ranking under Heterogeneity?
Shashaank Aiyer and Han Shao
We study ranking models by population-average utility from pairwise comparisons when preferences vary across users and tasks. Prior work shows that a single comparison per user can be insufficient to identify the alternative with the highest average utility, even with arbitrarily many users (Golz et al., 2025). We investigate how many repeated comparisons within each user-task context are necessary and sufficient for ranking recovery. Under a heterogeneous Bradley-Terry model with fixed inverse temperature, we start with a naive MLE-based
algorithm that requires $\Omega(1/\Delta^2)$ repeated comparisons per context to ensure ranking recovery. We then present two MLE-based variants and a randomized Russian Roulette-style
algorithm that recover the ranking using $O(\log(1/\Delta))$ repeated comparisons per context, and we prove that this logarithmic dependence is optimal. Despite this worst-case requirement, our Russian Roulette
algorithm uses only $O(1)$ comparisons per context in expectation. Synthetic experiments and semi-synthetic experiments based on Arena data compare the four
algorithms in settings with varying levels of preference heterogeneity and under varying context distributions.
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Machine Learning · Cross-listed from stat.ML
Calibrating Ambiguity Set via Diagnostic Transport for Distributionally Robust Optimization
Wenbin Zhou, Elizabeth Cucuzzella and Shixiang Zhu
Distributionally robust optimization (DRO) protects decisions against distributional uncertainty by optimizing over an ambiguity set, but poorly aligned set geometry can require large radii and yield overly conservative decisions. We introduce diagnostic-transport DRO (DT-DRO), which uses held-out calibration data to adapt the ambiguity-set geometry to observed predictive errors. DT-DRO uses the conditional probability integral transform cumulative distribution function to diagnose systematic probability misallocation and translates this information into an outcome-level transport that jointly adjusts the ambiguity-set center and ground cost. The resulting formulation admits a computationally tractable dual reformulation. Theoretically, we derive valid ambiguity radii and decision-risk guarantees that tighten as estimation and approximation errors vanish, and show that DT-DRO can eliminate the nonvanishing robustness floor caused by model misspecification. Synthetic experiments and a power-outage application demonstrate improved decision quality, particularly under structural and tail misspecification.
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Programming Languages
Bayes Rule for Conditioning Probabilistic Programs
Feras A. Saad
Probabilistic programs provide a rigorous programming-
language foundation for expressing rich probabilistic models and offer
language-level support for solving challenging statistical inference problems. A key theoretical challenge that probabilistic programs inherit from probability theory is conditioning a probability distribution on an observation that has probability zero. Many
languages address this challenge by using computational rules for conditioning that may work well for some problems but are statistically unsound for others. This article introduces a principled mathematical approach for conditioning probabilistic programs. We first show that existing techniques based on "soft conditioning" and "neighborhood limits" exhibit semantic issues from the perspective of measure theory and produce inference results that disagree with sound statistical
reasoning. By leveraging tools from
geometric measure theory, we construct a novel disintegration-based semantics for probabilistic program traces that enables sound conditioning. The theory delivers an explicit Bayes rule representation of the conditional distribution, which is directly compatible with widely used probabilistic inference
algorithms that require access to unnormalized posterior densities. Our Bayes rule handles probabilistic models with loops, branches, discrete, continuous, and mixed-type distributions, observations on
manifolds, differentiable structure, and additional modeling motifs that arise in practice.
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Logic in Computer Science
An explicit shuffle construction of the homotopy span model of differential linear logic
Paul-André Melliès, Federico Olimpieri and Lionel Vaux Auclair
We give a direct and explicit construction of the homotopy span model of linear logic originally formulated by Melliès using abstract homotopy theory in the Quillen category Cat of small categories equipped with its natural model structure. Following the principles of homotopy theory, the formulas and proofs of linear logic are interpreted in the model as small categories up to categorical equivalence, the notion of weak homotopy equivalence underlying the natural Quillen model structure on Cat. Here, we explain how the original interpretation of proofs as fibrant spans can be replaced by a more liberal interpretation of proofs as separately fibrant spans. This relaxation from fibrant spans to separately fibrant spans enables us to give a simple and intuitive description of the homotopy span model, where the axiom and cut links are interpreted as identity spans, and where the
symmetries of the exponential modality are captured by a shuffle structure on the interpretation of proofs.
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Robotics
NavGPT-3: Harnessing Context in a Hierarchical Navigation Runtime
Gengze Zhou, Yicong Hong, Jiazhao Zhang et al. (+9 more)
Language models trained with long-horizon agentic reinforcement learning can generalize knowledge through
reasoning, express precise actions, and pursue goals over many steps, raising the ceiling on what an
embodied agent can understand and decide. Physical interaction, however, remains the domain of action policies, which provide dense, low-latency control. We present NavGPT-3, a harness that connects the two models, with an OS-like runtime built above it:
reasoning, acting, and monitoring run as threads with their own context, tools, and permissions, while the runtime schedules them and decides which thread controls the
robot's motion, so that the
robot can react to sudden real-world events through interruption and thread switching. Beneath it, our action policy NavGPT VLA, trained on 19.28M examples, allocates visual
tokens using codec allocation, in proportion to scene change; its 8B model alone reaches 76.94 SR on R2R-CE and leads RxR-CE with 82.39 SR. With the complete harness, NavGPT-3 sets the state of the art on R2R-CE (81.51 SR) and, for the first time, brings an autonomous
agent to human level: on RxR-CE it matches human followers in success (90.43 vs. 90.4 SR) and path fidelity (78.47 vs. 77.7 nDTW) at 1 min 22 s per episode, versus roughly 3 min for a human. We comprehensively ablate the harness design and the interaction between the two models, showing how tools and the action policy shape the path from
language-model
reasoning to physical control: when NavGPT VLA executes the route, the
reasoning loop shortens and the system's minimum reaction time falls from 3-19 s per
language-model decision to 0.5-1 s per action-policy step (1-2 Hz). These results show that designing this
embodied interface is central to connecting frontier
language-model intelligence with low-level physical control. We will release all models, code, and evaluation records.
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All authors Gengze Zhou, Yicong Hong, Jiazhao Zhang, Xunyi Zhao, Jian Zhou, Zixing Lei, Zun Wang, Chongyang Zhao, Xionghui Chen, Stephen Gould, Anton van den Hengel, Qi Wu
Artificial Intelligence
Plan-and-Patch: Diffusion Language Models for Agentic Planning
Syamantak Kumar, Jiang Guo, Hassan Hamad et al. (+6 more)
Planning is increasingly important for long-horizon
agents, where successful execution requires coordinating subgoals, tool use, and intermediate outcomes over many steps. Yet assumptions made during planning may be invalidated by the environment, tools may return unexpected results, or actions may fail. Effective
agents must therefore not only generate plans, but also revise them. Such revisions often affect only part of a plan, leaving the preceding and subsequent structure intact. Rather than regenerate the entire plan and risk unnecessary changes, repair can regenerate the affected region conditioned on the preserved prefix and suffix. We introduce Plan-and-Patch, a plan-and-act framework in which a
diffusion language model (dLLM) generates a structured, program-like plan through parallel unmasking and repairs it by filling in selected regions while keeping the surrounding steps fixed. We compare DreamReasoner-8B and Qwen3-8B as
diffusion and autoregressive (AR) planners. On Natural Plan without task-specific training,
diffusion (53.7%) achieves nearly twice the plan repair success rate of AR (27.0%). After task-specific training on agentic
benchmarks, ALFWorld and TextCraft, the planners achieve similar observed success in plan generation, while
diffusion reduces mean plan-generation latency by 39-46% relative to AR. Our results show that Plan-and-Patch provides a framework for faster plan generation and effective plan repair in long-horizon
agents.
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All authors Syamantak Kumar, Jiang Guo, Hassan Hamad, Hideo Kobayashi, Yi Xiang, Yezhou Yang, Yanjun Qi, Daniele Bonadiman, Jiarong Jiang
Computer Vision
VICO: Visual Environments Co-Evolving for Vision-Language Model Reasoning
Meng Lu, Ligeng Zhu, Olivia Xiao et al. (+8 more)
Reinforcement learning with verifiable rewards (RLVR) has become a standard recipe for post-training
vision-
language models (VLMs), but it typically assumes a static training environment. As the actor improves, fixed tasks drift out of its learning frontier: many become trivial, others remain unsolvable; and the learning signal collapses. We argue that VLM post-training should evolve the visual environment alongside the actor, not just the actor itself. We propose VICO, a co-evolutionary framework in which an actor and an Environment-as-Rewriter (EnvRewriter) are trained jointly: the EnvRewriter edits verifiable image-side structures, such as scene graphs, chart tables, or protected region masks, and re-renders them to produce label-valid training samples whose difficulty is calibrated to the actor's current ability through a pass-rate-based reward. This loop continuously realigns task difficulty with actor capability without any additional human annotation. Across nine
multimodal benchmarks spanning mathematical
reasoning and visually grounded understanding, VICO-8B improves over its base model by up to +5.0% on out-of-domain tasks, surpasses the strongest self-
evolution and text-editing co-
evolution baselines by +4.3% and +8.4% respectively, and stays comparable to chart-specialized RLVR methods using 16-160 times fewer labeled samples. By shifting from human-labeled supervision to image-editing co-
evolution, VICO offers a scalable path beyond static-corpus RLVR for visual
reasoning.
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All authors Meng Lu, Ligeng Zhu, Olivia Xiao, Yuchen Zhuang, Zihan Wang, Kuncheng Wu, Bangya Liu, Yu Wang, Charles Fleming, Wenqi Shi, Xuan Wang
Computational Geometry
Deterministic Distance Selection in $O(n^{4/3})$ Time
Haitao Wang
Let $P$ be a set of $n$ points in the plane. Given an integer $K$ with $1\le K\le {n\choose 2}$, the distance selection problem asks for the $K$-th smallest distance among all pairwise distances of the points of $P$. The problem has been studied extensively over the past four decades. Recently, Chan and Zheng (2023) gave a randomized
algorithm with $O(n^{4/3})$ expected running time, while the best known deterministic
algorithm runs in $O(n^{4/3}\log n)$ time. In this paper, we present a deterministic $O(n^{4/3})$-time
algorithm for the problem, matching the best known randomized complexity. We also consider the more general bichromatic version of the problem, where two point sets $A$ and $B$ are given, with $m=|A|$ and $n=|B|$, and the goal is to find the $K$-th smallest distance among the $mn$ distances between points of $A$ and points of $B$. For this problem, the best previously known
algorithm runs in $O((m\log n+n\log m+m^{2/3}n^{2/3})\log(m+n))$ time. We present a new deterministic
algorithm with running time $O(m\log^2 n+n\log^2 m+m^{2/3}n^{2/3})$.
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Machine Learning
MemoWM: How World Models Change What Agents Need to Remember
Bingfan Zeng, Zhisheng Chen, Chenbo Sang et al. (+6 more)
Long-term
agents face growing storage demands as they accumulate experience. World models capture reusable regularities that can reduce the information stored for each experience. We formulate the problem of
memory allocation conditioned on a world model and introduce MemoWM, a framework that uses shared predictions to compress retained information and reconstruct omitted content. Its task-aware allocation rule balances the expected impact of reconstruction errors against storage cost, retaining information with downstream value beyond the predictive prior. Across five long-term
agent-
memory benchmarks, MemoWM achieves 42.42% average answer accuracy, exceeding the strongest baseline by 2.62 percentage points, while reducing average experience-specific storage by 53.9% relative to MIRIX, the most storage-efficient baseline. Further analysis shows that stronger world models reduce per-experience storage at comparable task quality. Accounting for model parameters reveals a trade-off between shared model capacity and recurring storage costs, with the capacity that minimizes total storage increasing as more interactions are retained. Our code is available at https://github.com/Feld-maxiu/MemoWM.
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All authors Bingfan Zeng, Zhisheng Chen, Chenbo Sang, Zhengwei Xie, Jinpeng Wang, Xiangchen Guan, Rui Qian, Zheng Lu, Jingwei Song
Machine Learning
NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learning
Jay L. Kaplan, Samuel Varner, Rebecca Willett et al. (+1 more)
Equivariant graph neural networks have emerged as foundational architectures for machine-learned interatomic potentials, approaching
quantum-chemical accuracy at a fraction of the computational cost. These models describe local atomic environments accurately, but finite spatial cutoffs truncate long-range information flow, and stacking message-passing layers can lead to over-smoothing and over-squashing. Existing long-range extensions either prescribe a fixed analytical propagation kernel, restrict long-range communication to scalars or degree-preserving channels, are only approximately equivariant, or incur super-linear computational cost. Combining learnable long-range equivariant transport with multiscale many-body expressivity and efficient scaling for larger systems remains a central challenge. We introduce Neural Equivariant Multipole Operators (NEMORA), a neural equivariant extension of the Fast Multipole Method (FMM) for learning long-range tensorial representations. NEMORA generalizes the FMM's analytical multipole expansion and translation operators to learned equivariant counterparts on an adaptive spatial hierarchy. Its operators couple angular degrees and form many-body interactions across length scales, retaining the FMM's hierarchical organization and analytical radial factors as physical inductive biases while learning data-dependent long-range couplings. NEMORA evaluates in linear time and
memory complexity, allowing it to treat larger systems than other long-range methods reaching hundreds of thousands of atoms, and it augments both
symmetry-constrained and unconstrained short-range backbones. On non-local
benchmarks, it reduces force and energy errors relative to the short-range backbones by over an order of magnitude and up to three orders of magnitude, respectively, which is better than or competitive with existing long-range extensions in accuracy.
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All authors Jay L. Kaplan, Samuel Varner, Rebecca Willett, Juan J. de Pablo
Sound
AutoSynth: Learning to Generate Editable Synthesizer Programs from Audio and Text
Tristan Wu, Daniel Chin, Liwei Lin et al. (+2 more)
Audio generation models can translate natural-
language descriptions into sound, but their outputs are typically waveforms. Their audio quality is constrained by audio compression, and their outputs do not readily support direct edits to notes, timbral parameters, or modulation relationships. We present AutoSynth, which represents MIDI performance events, fixed synthesizer parameters, and variable-length modulation routes as a unified sequence for a synthesizer, and learns their dependencies with an audio-conditioned autoregressive model. A single model supports both tasks. Given reference audio, the model directly predicts a synthesizer program; given text, it uses a pretrained audio generation model and converts the generated audio into a program. Training consists of two stages: supervised learning on large-scale audio-program pairs automatically constructed from a small set of native presets, followed by group-relative policy optimization with a mixed reward combining semantic similarity, pitch-related features, acoustic similarity, and sound usefulness. The pipeline requires neither paired text-target-program annotations nor a differentiable synthesizer. Experiments show that AutoSynth produces complete, editable synthesizer programs and achieves competitive results in both synthesizer inversion and text-driven generation. Audio demos and source code are available at https://auto-synth.github.io/.
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All authors Tristan Wu, Daniel Chin, Liwei Lin, Junan Zhang, Gus Xia
Emerging Technologies · Cross-listed from quant-ph
Scaling Quantum Optimization to the Thousand-Qubit Scale with Distributed Quantum Sampling
Amana Liaqat, Ahmed Darwish, Stephen DiAdamo et al. (+6 more)
The Minimum Birkhoff Decomposition (MBD) seeks a sparse weighted sum of matchings and is a challenging optimization problem with applications in network scheduling and energy trading. We scale a
quantum-assisted decomposition method by combining single-layer QAOA sampling with Extended Fully-Corrective Frank-Wolfe (E-FCFW) optimization, spectral graph partitioning, and greedy feasibility repair. We demonstrate the pipeline on the 1,354-node PEGASE bus test case (representing a 1,354-bus transmission grid with 1,710 transmission lines), whose 1,710 edges define a native 1,710-variable matching optimization problem requiring 1,710
qubits in the unpartitioned encoding. Partitioning enables distributed execution on IBM superconducting
quantum processors. The best reported hardware result uses a 50-
qubit partition bound, while reducing partitions to 20
qubits degrades convergence in the partition-size comparison. Larger partitions are more demanding for
matrix product state (MPS) simulation, and bond-dimension tests show that truncating
quantum correlations reduces candidate quality. The results therefore motivate retaining a substantive
quantum sampling task within each partition as the overall problem scales. On the PEGASE-1354
benchmark, repaired QAOA samples achieve lower residual decomposition error than both simulated annealing and uniform random sampling baselines, while spatial
circuit packing reduces hardware execution time by approximately a factor of three. These results demonstrate that combining spectral partitioning, spatial
circuit packing, and classical feasibility repair provides an executable path for deploying gate-based
quantum sampling on thousand-variable constrained optimization problems on current hardware.
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All authors Amana Liaqat, Ahmed Darwish, Stephen DiAdamo, Dan Holme, Kieran McDowall, Emre Sahin, Naeimeh Mohseni, Giorgio Cortiana, Corey O'Meara
Human-Computer Interaction
AI-Mediated Self: How HCI Defines and Relates to the Self
Jenny Xiyu Fu, Qian Yang and Malte Jung
How might AI alter how we understand and experience the self? This scoping review analyzes 102 papers to examine how the self is defined in the field of human-computer interaction (HCI), how AI-self relationships are conceptualized, and what risks emerge when AI becomes entangled with selfhood. Our synthesis makes three contributions. First, we define AI-mediated self as a conceptual umbrella that connects dispersed work across education, workplace, health, and creative practices. Second, we consolidate six framings of the self with four domains of ethical risk-agency/autonomy, identity/authorship, relational capacity, and meaning-making-into a conceptual map that provides a reusable vocabulary across contexts. Third, we introduce the Inclusion of AI-Self framework, which situates AI-self relationships along a spectrum of proximity. Together, these contributions position selfhood as a central design space in HCI.
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Computational Engineering
Strategic Investment Decision Making for Value Creation in Energy Transition: A Reinforcement Learning Approach
Yasaman Cheraghi, Reidar B. Bratvold, Aojie Hong et al. (+2 more)
The global challenge of
climate change has driven significant steps to reduce CO2 emissions, guided by international agreements like the Paris Agreement of 2015. Acting too slowly could result in future losses and reputational damage, while moving too quickly could jeopardize shareholder value due to the marginal profitability or potential losses due to technology immaturity of many renewable projects. To navigate this complex transition, energy companies must adopt Sequential Decision Making (SDM) strategies to maximize value creation from decision flexibility under uncertainties. To support this, we developed a custom simulation environment to model the dynamic energy landscape up to 2050. Building on this, we designed a multi-criteria SDM framework that explores various decision strategies related to different portfolios for allocating funds across three sectors: oil & gas, renewables, and CO2 reduction. It aims to maximize value during the transition while accounting for uncertainties in productions, energy prices, and costs. This framework has three objectives: maximizing profit, minimizing CO2 social costs, and enhancing competitive advantage in the renewable energy sector. This research evaluates the use of Reinforcement Learning (RL) to identify optimal investment policies within the defined SDM framework. The
agent's sequential decisions shape a virtual dynamic environment by influencing key variables such as oil and gas production, renewable energy output, CO2 emissions, and revenues. Through repeated interaction, the RL
algorithm explores the state space and learns an optimal policy under uncertainty. We
benchmark the RL strategy against a set of manually defined baseline policies and find it consistently outperforms them in adaptability and long-term value creation.
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All authors Yasaman Cheraghi, Reidar B. Bratvold, Aojie Hong, Ressi B. Muhammad, Sergey Alyaev
Cryptography and Security
CPU-Auth: Device Fingerprinting for Authentication via DVFS Side-Channel
Ryan Swift
Lack of effective authentication has resulted in numerous security and
privacy breaches, including unauthorized access to protected information, identity theft, and fraud. One approach to mitigating such attacks is Multi-Factor Authentication (MFA), in which users must provide multiple pieces of information for authentication. Some secondary authentication factors include SMS text verification codes, biometrics, and
tokens. Each contains at least one notable flaw: SMS is notoriously insecure; biometrics rely upon access to sensitive personal data; and
tokens require dependence on third-party providers (e.g. OAuth providers). This work explores CPU-Auth, a novel authentication mechanism based on unique variations in the physical characteristics of the CPU of a computing device. By measuring the behavior of the Dynamic Voltage and Frequency Scaling (DVFS) governor remotely from within a browser, unique properties of the CPU can be leveraged to establish a hardware-based device fingerprint for use in CPU-Auth. The performance of CPU-Auth is evaluated on over 50,000 data traces using distance-based and deep learning methods. CPU-Auth is part of a larger research project, and the results provided in this report reflect only the contributions made to the project by members of this group.
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Machine Learning · Cross-listed from stat.ML
What can linear attention learn from nonlinear teachers in-context?
Mary Letey, Arman Rysmakhanov, Yue M. Lu et al. (+2 more)
Linear
attention is a tractable model for understanding the mechanisms governing in-context learning in
transformers. For linear regression tasks, recent asymptotic analyses have characterised its learning and generalisation behaviour. We extend this theory to nonlinear single-index targets, $y=f(x^\top w)+\varepsilon $. Our main result establishes a nonlinearity-noise equivalence: linear
attention extracts only the linear Hermite component of $f$, while the remaining nonlinear structure contributes to the generalisation error as effective noise. This reduction allows results from the corresponding linear theory to be transferred to nonlinear tasks. We illustrate its implications for finite pretraining data and for the transition from task memorisation to task generalisation as task diversity increases. These results identify a limitation of the reduced linear-
attention model and provide a tractable starting point for studying nonlinear in-context learning.
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All authors Mary Letey, Arman Rysmakhanov, Yue M. Lu, Cengiz Pehlevan, Jacob Zavatone-Veth
Computer Vision
DOGS: Design-Space Sampling for Prompt-Driven Logo Generation
Ganyu Zou, Chen Dai, Nathan Self et al. (+5 more)
Prompt optimization for text-to-image (T2I) generation has been pursued almost entirely as text rewriting, in which a short user brief is expanded into a longer, model-preferred
token sequence. We argue that such a
language-space formulation is ill-suited to structured visual design tasks such as logo creation, where a one-line brief leaves most design decisions unspecified. These decisions depend on relational priors that a linear sequence cannot encode, and they leave an uncontrolled channel through which protected marks may be reproduced. We therefore recast logo prompting as sampling within a structured design space, and instantiate this idea as DOGS (Design-space prompting with an Originality-aware GFlowNet Sampler). From a large corpus of real-world logos, we mine a typed, graph-structured design grammar whose edges record empirical co-occurrence. A GFlowNet sampler then generates design graphs with probability proportional to a terminal reward that combines recognizability, aesthetics, and corpus-relative originality. Every slot draws only from a closed design-level vocabulary, and any infringement-inducing or harmful
token is removed during parsing. The originality reward further penalizes proximity to existing logos, thereby incorporating infringement avoidance into the method by construction. On two open-source renderers and against nine baselines, DOGS produces logos that are more recognizable and aesthetic, substantially more diverse, and far less prone to trademark infringement.
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All authors Ganyu Zou, Chen Dai, Nathan Self, Kevin Piper, Ramachandra Rao Seethiraju, Karthik Shyamsunder, Chang-Tien Lu, Naren Ramakrishnan
Computer Vision
MESSENGER: Memory-Enhanced Sequential Scene Flow Estimation via Autoregressive Next-Frame Forecasting
Jiuming Liu, Jianing Li, Mengmeng Liu et al. (+3 more)
Scene flow can capture low-level 3D motion displacements in dynamic scenarios. Early pairwise
estimators relying on instantaneous two-frame motion lack long-term temporal correlation and also struggle with poor extrapolation ability in future prediction. Although some recent methods attempt to explore multi-frame scene flow estimation in a sequence-to-sequence manner, they typically suffer from heavy computational overhead with increasing input frames and long-horizon prediction degradation due to ineffective motion propagation. To address these problems, we propose a novel
memory-enhanced sequential scene flow pipeline, called MESSENGER. To sufficiently mine long-term temporal dependencies naturally within consecutive sequences, a
memory buffer is designed by explicitly storing multiple history flow estimates and latent states. For each input frame, the temporally stored flows and states are correlated and retrieved to predict the current initialized flow in a next-frame
forecasting manner. Furthermore, we develop an uncertainty-aware reweighting module to filter unreliable
retrievals and mitigate accumulated errors. Extensive experiments on nuScenes and Argoverse 2 demonstrate state-of-the-art performance of our MESSENGER, reducing EPE3D by 71.6% on nuScenes and 67.7% on Argoverse 2 in long-horizon future extrapolation. This superiority can be attributed to our designed autoregressive
forecasting paradigm, which naturally forces the network to progressively learn the next-frame distribution based on history observations. Code will be released at https://github.com/liujiuming123/Messenger.
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All authors Jiuming Liu, Jianing Li, Mengmeng Liu, Hongyang He, Hesheng Wang, Per Ola Kristensson
Computation and Language
Clarify, Then Focus: Statement Normalization for Conversation Analytics at Scale
Mikhail L. Arbuzov, Karan Dave, Evgeniya Dontsova et al. (+5 more)
Enterprise conversation analytics asks many questions of millions of interactions. Each question can require reconstructing what people mean and identifying which information matters, repeating costly interpretive work across the same transcripts. We propose a simple principle: clarify the text, then focus the reader. Statement normalization transforms dialogue into short, speaker-attributed statements with source references and semantic tags. The statements make meaning more explicit; the tags support selecting evidence for a particular question. Downstream models can use the full representation or a relevant subset, depending on what helps them make the decision. In an offer-suppression task on customer-service calls, normalization improves a supervised classifier without selection, while weaker prompted readers benefit from both normalization and selection. A small model can learn the normalization contract, while lightweight encoders handle tagging and downstream decisions. Sharing this preparation across questions supports an inference pipeline built entirely from small models, making analytics over millions of conversations substantially less expensive.
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All authors Mikhail L. Arbuzov, Karan Dave, Evgeniya Dontsova, Yaodong Hu, Vincent Lao, Navita Jain, Sisong Bei, Dmitry Dimov
Networking
Adaptive E2E Monitoring Path Selection with Deep Reinforcement Learning in Multi-domain Optical Networks
Soham Choudhury, Martin Bojinov, Jason P. Jue et al. (+1 more)
Network slicing over multi-domain optical networks enables resource isolation for high-bandwidth, virtually-dedicated networks. However, guaranteeing end-to-end (E2E) performance remains challenging when domains withhold internal
topology and performance metrics from external entities. Under this limited visibility, a control function (slice coordinator) needs to select E2E monitoring paths to detect and localize link failures. This paper formulates two complementary optimization problems, the initial and progressive monitoring path selection problems, which together realize closed-loop autonomous monitoring at the slice coordinator level. The initial problem maximizes failure coverage without prior information, while the progressive problem concentrates additional paths around suspected failure locations identified from prior monitoring. We propose a deep reinforcement learning (DRL)
algorithm that adaptively selects an optimal set of monitoring paths for each phase. Experiments using the GNPy optical network simulator demonstrate that our approach outperforms baseline
algorithms for both problems and achieves near-optimal localization in the initial selection, suggesting the feasibility of AI-driven closed-loop failure localization in future multi-domain optical network architectures.
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All authors Soham Choudhury, Martin Bojinov, Jason P. Jue, Genya Ishigaki
Cryptography and Security
Detection-Guided Adaptive Purification with Diffusion Models for Robust Audio Deepfake Detection
Muhammed Salih Kayhan and Qiben Yan
Audio deepfake detectors remain vulnerable to
adversarial perturbations that suppress the acoustic cues used for detection, allowing manipulated utterances to evade the detector. Although existing defenses can improve robustness, they require retraining the detector or introduce additional distortion.
Diffusion-based purification instead leaves the pretrained detector unchanged, but existing methods use the same purification strength for all inputs, creating a trade-off between removing
adversarial perturbations and preserving the subtle spoofing cues needed for detection. In this paper, we propose Detection-Guided Adaptive Purification (DGAP), a
diffusion-based defense that adjusts purification strength per input. Building on the observation that a light purification perturbs the detector score of an
adversarial input far more than that of a benign one, the framework uses the resulting score shift as a reference-free indicator of
adversarial manipulation. Inputs with small shifts are passed unchanged, whereas flagged inputs undergo stronger purification before final detection. We evaluate the framework against three
adversarial attack settings across three deepfake detectors, and compare it with nine existing defenses. Our results show that DGAP achieves the best defense performance across all detectors while leaving benign inputs nearly unaffected, and remains effective under the defense-aware adaptive attack.
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Sound
Listen-to-Reason: Listen with Experts, Retrieve over a Graph, Reason with LLMs
Pooneh Mousavi, Mirco Ravanelli and Cem Subakan
Large audio-
language models (LALMs) fuse an audio encoder into a large
language model (LLM) through multi-stage training. This coupling means that a new domain or a stronger LLM requires retraining, and their answers cannot be traced to what the model heard: a chain-of-thought is a post-hoc account. We propose Listen-to-Reason (L2R), an interpretable-by-design pipeline that passes audio to the LLM through an explicit, human-readable tree: small heads on frozen expert encoders map each chunk of a clip to semantically meaningful nodes on the tree (for
speech, music and environmental sound), and a frozen text-only LLM answers from these nodes and an ASR transcript without hearing the clip. Every answer can therefore be traced to the nodes and transcript it read, and the nodes are causal: replacing the deciding node with a distractor overturns 78% of correct answers on SAKURA. With a 7B reader, L2R outperforms all LALMs we compare against on SAKURA and trails them by 6-12 points on MMAU and MMAR, despite training about 1,400x fewer parameters on orders of magnitude less audio data. However, because any LLM can serve as the reader, we show that a stronger reader narrows this gap without retraining any audio component. A new domain is added with one small head: with five labelled clips per species, it outperforms QLoRA fine-tuning of an LALM on the same clips by 13-26 points.
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Robotics
TAPNAV: Humanoid Navigation through Tactile Active Perception
Huaze Liu, Zhenyu Wu, Jaehwi Jang et al. (+8 more)
Navigation in
vision-denied environments is challenging for
humanoid robots because proprioceptive odometry drifts and localization uncertainty accumulates rapidly. We present TAPNAV, a tactile active-perception framework that enables
humanoid navigation toward a goal by actively probing surrounding structures without relying on
vision. TAPNAV maintains a pose belief from odometry, IMU, and tactile contact observations, and couples uncertainty-aware global route planning with information-gain-driven local probing. The global planner searches for routes that keep predicted localization uncertainty bounded by exploiting opportunities for tactile correction, while the local planner selects probe actions that maximize expected information gain. A whole-body controller coordinates the
humanoid's locomotion and end-effector contact to execute the planned
navigation and probe motions. We evaluate TAPNAV in simulation and on a Unitree G1 across different floor plans and obstacle geometries. TAPNAV achieves lower state estimation error and a higher task completion rate than baselines. These results demonstrate that actively planning physical interactions with the environment can provide localization cues for reliable
humanoid navigation without
vision.
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All authors Huaze Liu, Zhenyu Wu, Jaehwi Jang, Junjie Sheng, Andrew Collins, Aaron Xie, Zhaoyuan Gu, Kaijie Zhu, Ding Jiang, Kevin Cai, Ye Zhao
Human-Computer Interaction
What does it mean to use AI critically? Unpacking critical AI literacy through students' evaluation of AI-generated content
Hyejeong Lee and Wonjin Yu
Although critical AI literacy has emerged as an important educational goal, the construct remains conceptually broad and insufficiently specified for guiding students' day-to-day interactions with AI. This study examines how students critically evaluated AI-generated content. Drawing on an analysis of students' chatbot interactions and written reflections, we first identified seven stages of AI-supported academic task completion and five functional roles assumed by AI. More importantly, we identified eight evaluative lenses that students used to assess AI-generated responses (accuracy, completeness, task
alignment/relevance, personalization, practicality, creativity, ethics, and bias). The findings suggest that critical AI literacy extends beyond verifying the factual accuracy of AI-generated information. Rather, critically engaging with AI involves situated and multidimensional judgment about the epistemic quality, contextual fit, practical feasibility, creative value, and ethical implications of AI-generated content. The framework offers both a conceptual contribution to the emerging literature on critical AI literacy and a practical tool for educators seeking to help learners move from passive acceptance of AI toward deliberate, context-sensitive, and responsible human judgment.
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Cryptography and Security
BRANCH: Bypassing Multi-Scanner AI Guardrails
William Hackett and Peter Garraghan
AI systems increasingly rely on Large
Language Models (LLMs) as core
reasoning engines, making them targets for
prompt injection and jailbreaks. Guardrails monitor and validate model inputs and outputs, yet their isolated, task-focused detection leaves gaps in their classification making them susceptible to bypasses. In response, guardrail systems formed by multiple scanners have emerged that collaboratively detect different types of malicious instructions, whereby shared latent representations across classification boundaries render established bypassing techniques ineffective. We propose BRANCH, a bypassing methodology designed for multi-scanner guardrail systems. Our method leverages a branching tree search approach that dynamically applies
adversarial perturbation against individual scanners, with subsequent perturbation optimization and technique selection based on overall improvement across all guardrail system scanners, effectively decoupling bypass evaluation from attack signal optimization. Our findings demonstrate that BRANCH achieves 100% attack success rate across 6 guardrail systems in 120 scenarios with 72% fewer queries and 4.5x reduced wallclock time compared to established techniques, while preserving semantic meaning within the bypass. We also show how bypasses generated by BRANCH transfer to 29 unseen guardrails, including 8 commercial black-box guardrails, improving attack success in some cases up to 100% with no additional optimization.
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Machine Learning
Conversational Task Disambiguation over Tabular Data: Leakage-Aware Formulation, Benchmark Suite, and Training
Nafiseh Ghoroghchian, Luis Scoccola, Tina Sedaghat et al. (+4 more)
Conversational task disambiguation over tabular data uses dialogue to resolve missing information about a user's intended task before producing a solution over tables or databases. Existing evaluation and training lack a leakage-aware foundation. Task success mixes the
agent's disambiguation and solution-generation capabilities and can also reflect oracle leakage, that is, information that a user simulator reveals beyond what a real user would. Existing
datasets also lack a shared representation of ambiguities and access boundaries. We introduce the notion of an ambiguous verifiable task, which formalizes ambiguities and resolutions, decomposing the
agent into an asking policy and a solution policy, and the environment into an oracle and verifier. This framework provides baselines and metrics for evaluating task disambiguation separately from solution generation, formal definitions of oracle leakage, judge-free leakage diagnostics, and a training objective for the asking policy. We instantiate the framework in text-to-SQL with AmbiTab, a
benchmark suite that unifies six ambiguous
datasets under a common representation specifying what the
agent, oracle, and verifier may access. We evaluate clarification strategies and oracle leakage, and train an asking policy with reinforcement learning. The trained asker improves our disambiguation metrics on all six
datasets and task success on five, and our leakage diagnostics measure how training affects oracle leakage.
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All authors Nafiseh Ghoroghchian, Luis Scoccola, Tina Sedaghat, Omid Vaheb, Hannah Chen, Dino D'Agostino, Keyvan Golestan
Computation and Language
Lossy Compressive Text Autoencoders
Vinko Sabolčec, Angelos Katharopoulos and David Grangier
Our work explores learning a compressed latent representation of text, at the intersection of data compression and representation learning. We propose an autoencoder architecture that performs residual downscaling and upscaling of hidden representations along the time axis, with a residual low-dimension discrete bottleneck. We analyze our approach for different quantization methods, training objectives, and
datasets. For different levels of compression, we evaluate the similarity between the original and reconstructed text both at the surface-level (BLEU) and at the semantic-level (LLM-based judge). Additionally, we evaluate our models on downstream question-answering and semantic text similarity
benchmarks. Our approach results in compressed representations which are on par with lossless text compression
algorithms at 2.24 bits per byte on web text data, while having good reconstruction and downstream task performance.
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Discrete Mathematics · Cross-listed from math.CO
Obstructions to $k$-colouring $H$-free graphs
Iain Beaton, Ben Cameron and Adam van Omme
A graph is $H$-free if it has no induced subgraph isomorphic to $H$. In 2020, Chudnovsky, Goedgebeur, Schaudt, and Zhong characterized all graphs $H$ such that there are only finitely many minimal obstructions to $3$-colouring $H$-free graphs. In general, the minimal obstructions to $k$-colouring $H$-free graphs are the $(k+1)$-vertex-critical $H$-free graphs, those are, the $H$-free graphs $G$ with $\chi(G)=k+1$ but $\chi(G-v)=k$ for every vertex in $G$. In this paper we complete the characterization for all $k > 4$ by showing that there are onky finitely $k$-vertex-critical $H$-free graphs if and only if $H$ is an induced subgraph of $P_4+\ell P_1$ for some $\ell \geq 0$.
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Cryptography and Security
DITTO: A Context-aware Pickle-based Pre-Trained Model Scanner for Effective Security Audits
Qiaolin Qin, Wanpeng Li, Benoit Baudry et al. (+3 more)
Pre-trained models (PTMs) are widely distributed as serialized binaries, but their reuse often exposes software supply chains to deserialization attacks. Despite the emergence of safer serialization formats, the unsafe Pickle format remains prevalent: our analysis of over 10,000 popular Hugging Face repositories reveals that 9.3% rely on Pickle. While many defense mechanisms have been proposed, state-of-the-art model scanners suffer from a coverage-precision gap, missing security-sensitive behaviors and generating excessive false alerts. In this paper, we introduce DITTO, the first stack-based, context-aware scanner for Pickle-based PTMs. DITTO faithfully tracks Pickle virtual machine state transitions and performs context-aware semantic analysis to infer model intentions. We also present PickleBench, a
benchmark of 959 benign and 92 malicious real-world models, including extension registry attacks previously missed by existing tools. Across multiple evaluations, DITTO achieves 100% scanning coverage, a 0% false-negative rate, and a 0.7% false-positive rate, yielding an F1 score of 0.966, significantly outperforming state-of-the-art scanners. By minimizing false alerts while preserving detection accuracy, DITTO generates actionable security reports with contextual evidence, enabling safe PTM reuse and strengthening software supply chain integrity.
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All authors Qiaolin Qin, Wanpeng Li, Benoit Baudry, Lorenzo De Carli, Heng Li, Ettore Merlo