Machine Learning
ASPIRE: Saddle-Point Discovery through Set Prediction and Physical Refinement
Yucheng Zhao, Quanyou Zhang, Shaoxiang Qin et al. (+2 more)
Predicting thermally activated
diffusion and defect
evolution with event-driven models requires identifying atomic rearrangement mechanisms and their activation barriers. Discovering the associated saddle points is a major computational bottleneck: multiple rearrangements may originate from one metastable state, while costly local searches can fail or repeatedly converge to the same saddle. To address this challenge, we introduce ASPIRE (Atomistic Saddle-Point Inference with Refinement for Events), a framework that predicts a set of saddle candidates from a single initial atomic environment and refines them through Dimer searches on the original interatomic potential. The framework's equivariant set predictor, Ev-Quiformer, integrates (i) geometry-conditioned scalar-vector event slots for generating multiple saddle-point proposals and (ii) a decoder that maps each slot to a full atomic displacement field by combining atom, slot, and anchor-relative vectors with invariant coefficients. We also contribute two
datasets: (i) BCCFE4VACAV-4000, comprising 4,000 four-vacancy body-centered cubic iron configurations and 65,450 reference events grouped by initial state for set supervision and post-refinement evaluation; and (ii) BCCFE-1TO4VAC, comprising 5,372 configurations with one to four vacancies each. Theoretically, we establish conditions for proposal equivariance. Experimentally, ASPIRE achieves 77.20% reference-event coverage on this
benchmark, compared with 75.73% for a conventional Dimer baseline, while requiring approximately half as many Dimer force evaluations. In a timing evaluation on 50 configurations, ASPIRE reduces wall time per configuration from 478.8 s to 176.3 s under the stated hardware settings.
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All authors Yucheng Zhao, Quanyou Zhang, Shaoxiang Qin, Haixuan Xu, Xiongye Xiao
Machine Learning
Spectrally Targeted Muon
Vishrut Goyal and Rohan Ramkumar
The
Muon optimizer orthogonalizes each update
matrix, setting all of its singular values to one, and has proven highly effective for training large
language models. It remains unclear, however, whether this success comes from amplifying small singular directions that gradient descent neglects or from suppressing large, degenerate directions that disrupt training. We introduce Spectrally Targeted
Muon, which orthogonalizes only the singular values above or below a threshold $\tau$, so that varying $\tau$ interpolates between normalized SGD and
Muon. It isolates the relevant singular subspaces with projections computed by Newton-Schulz iteration on a shifted Gram
matrix, so no SVD is needed. We evaluate these variants on the CIFAR-10 and NanoGPT speedruns, tracking the effective rank of gradient, update, and weight
matrices and a new metric, the
alignment of updates with the tangent space of the weight
matrix's isospectral
manifold. We find three things. First, the small singular values of the momentum are not noise. Orthogonalizing everything except the few largest singular values of each
matrix nearly matches
Muon while touching only a small fraction of the momentum, whereas orthogonalizing only the top falls well short even though it holds almost all of it. On
language models every momentum singular value is far below one, so targeted orthogonalization can only amplify, and
Muon wins by making directions that are too small to train on at their raw scale trainable. Second, shrinking the largest singular values is what keeps the parameter spectrum flat; this is cheap, and it is not what drives the loss. Third, AdamW differs from
Muon mainly in how slowly it builds structure, which explains its slower start and why warmup helps AdamW but only hurts
Muon.
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Robotics
iAm.md: Robot Skill Self-Assessment through Agentic Introspection for Unknown Open-Vocabulary Domains
Vincenzo Guarino, Emanuele Musumeci, Vincenzo Suriani et al. (+1 more)
Agentic AI based on Large
Language Model generalization capabilities offers a wide range of potential applications, including planning for
embodied tasks. For example,
embodied agents based on Foundation models can generate plausible plans in autonomous robotics scenarios. Due to limited context windows or hallucinatory phenomena in the next-
token prediction formulation, behaviors may be generated without establishing whether the deployed
robot and the observed environment actually support the requested operation, in what we call a "grounding failure". Thanks to the recent improvements in
reasoning capabilities of foundation models, autonomous
robot behavior generation problem can be formulated as a code generation problem. We present iAm.md, a Markdown standard and generation framework, that allows anchoring this process in complementary forms of deployment evidence. Through open-vocabulary semantic mapping, we combine local
vision-
language detections and object
segmentation and refer them to persistent object records in this intermediate standardized representation, allowing agentic introspection. We then study this new technique on a simulated TIAGo, on
navigation-and-manipulation tasks, showing how this standardized representation jointly supports skill self-assessment and executable task generalization.
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All authors Vincenzo Guarino, Emanuele Musumeci, Vincenzo Suriani, Daniele Nardi
Software Engineering
Cross-Provider Review as a Runtime Contract for Coding Agents: A Controlled Pilot and Fault-Injection Study
Bowen Xu and Boyu Chen
Coding
agents increasingly share a workstation while drawing on separate providers and subscription allowances. A second
agent can inspect a completed answer, but the call spends another pool and may provide no substantive finding. We describe an advisory cross-provider review contract: distinct resource pools, bounded execution, restricted reviewer capabilities, complete input delivery, usable semantic output, explicit failure states and durable per-attempt evidence. In a controlled,
agent-authored pilot of 20 paired development turns, eight had a material reviewer finding (95% exact interval 19.1-63.9%). A boundary-condition scan across both reviewer backends reproduced a previously discovered false success on partial input: four truncation levels passed historically and failed after repair. The scan also found and repaired cancellation during process reaping. In real CLI probes, Claude had no writing tools; Codex attempted writes in five of five read-only trials, each write tool failed, and no disposable repository changed. These tests cover specified paths and versions, not field reliability. A preregistered shadow study of metadata-only review allocation accrued 25 formal observations before an exact-runtime regression found a third defect: a reviewer exiting nonzero with a well-formed verdict was counted as complete. Exit status was not recorded per attempt, so exposure cannot be resolved retrospectively. The 25 formal and two pending records remain an audit cohort; the measurement-valid cohort restarted at zero and collection has begun. No gate result is reported.
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Computer Vision
LVSPM: Long Sequence View Synthesis and Pose Estimation Model
Xi Chen, Yachi Zhang, Linghao Chen et al. (+4 more)
We present LVSPM, a generalizable model that jointly estimates camera poses and synthesizes novel views from uncalibrated image collections. Trained with only RGB images and pose supervision, LVSPM avoids dense 3D ground truth and employs test-time training (TTT) layers to scale seamlessly to hundreds of input views. On RealEstate10k, Co3Dv2, and DL3DV, LVSPM surpasses VGGT in pose estimation across 16-256 views, with especially large margins at strict thresholds. For novel view synthesis under a practical protocol where more views cover larger scenes, LVSPM achieves state-of-the-art pose-free quality—surpassing even pose-dependent models in PSNR—and still maintains high quality as scene scale grows, while baselines collapse. The code is available at https://burningdust21.github.io/Projects/LVSPM .
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All authors Xi Chen, Yachi Zhang, Linghao Chen, Minghua Liu, Hao Su, Zexiang Xu, Xiaoshuai Zhang
Computer Vision
GPU-Accelerated Computation of Persistent Homology for Topological Analysis of Image Data
Fan Wang, Hubert Wagner, Rezaul Chowdhury et al. (+1 more)
In recent years, persistent homology has seen rapid adoption in deep learning, yet its computation remains a major bottleneck in network training. This paper introduces TopoGPU, a GPU streaming pipeline that computes persistence diagrams of cubical complexes induced by 2D and 3D images. TopoGPU streams the input image chunk by chunk, processing each chunk with massively parallel GPU kernels on a grid of GPU blocks; the resulting boundary relations are accumulated in host
memory, where the CPU performs the boundary
matrix reduction. TopoGPU introduces a stratification-aware discrete Morse matching that provably preserves persistent homology under streaming, together with a parallel topological sorting
algorithm and a parallel V-path parity
algorithm for deriving Morse boundaries on the GPU. TopoGPU outperforms Cubical Ripser, a state-of-the-art method for persistent homology computation, on every
benchmark evaluated, achieving an average end-to-end speedup of 53.24x and a maximum of 198.01x. We further integrate TopoGPU into a
topology-preserving deep network, demonstrating that it substantially reduces the cost of persistent homology computation during network training. TopoGPU is open source, with pre-built binaries, Google Colab notebooks, and Docker images available at the project's GitHub page: https://github.com/seravee08/GPU-Computation-of-Persistent-Homology-for-Image-Data.
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All authors Fan Wang, Hubert Wagner, Rezaul Chowdhury, Chao Chen
Systems and Control
EnergyNet in Practice: Long Grid, Short Grids and Mobility-Based Energy Peering
Jonas Birgersson, Max Collins, Marc A. Weiss et al. (+10 more)
Electrification is planned as a network problem: more demand, more upstream capacity, local resources waiting for reinforcement. We argue that a material part of that bill, in money and time, follows from architecture, not electrification itself, and propose the Short Grid as the deployment unit: a locally controlled building, block or campus whose demand, generation, storage and local
market operate behind Energy Routers enforcing an approved connection envelope to the Long Grid. Because the envelope governs exchange, not installed capacity, local resources can grow and many sites can build in parallel at the pace of PV and EV installation, not grid reinforcement. PV and EV batteries dominate today, but the architecture is general: any generation or storage, fixed or mobile, fits behind the same boundary and interfaces. EnergyNet Operators coordinate exchange among Short Grids and with the Long Grid; protection stays local; Vehicle-to-EnergyNet (V2EN) and Mobility-Based Energy Peering (MEP) make mobility-financed batteries accessible within and between them; priority-based operation protects critical services in shortage. We test it in models, not yet the field: a ten-building neighbourhood in Lund, Sweden; a bus-linked microgrid pair at Stanford and Half Moon Bay; and a proposed 20-site network in Rockaway, New York. Households and services are the largest demand category in Sweden, Denmark, Norway, California and New York (484 TWh/yr); halving their grid imports would be about 242 TWh. Europe's network investment to 2040 is estimated above EUR 1.2 trillion; modelling shows it depends on system design. Negagrid value, following the negawatt, counts network investment avoided, downsized or deferred, net of local substitute costs. "Don't pay twice": build coordinated local capability first; reinforce the Long Grid for what remains.
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All authors Jonas Birgersson, Max Collins, Marc A. Weiss, Jimmy Chen, Daniel Kammen, Tomas Kåberger, Mark Z. Jacobson, Newsha K. Ajami, Franklin Carrero-Martínez, Michael Menser, Henrik Ny, Lou Riordan, Jill G. Ferguson
Machine Learning
TRACE: A Governance Framework for Measuring Explainability Debt in Production AI Systems
Harish Kant Pathak
Production AI systems deployed in high-stakes domains accumulate a governance liability that existing monitoring frameworks fail to detect: the progressive inability to explain individual decisions when regulators, auditors, or affected individuals demand accountability. We introduce TRACE (Transparency, Risk, Accountability, Compliance, and Explainability), a seven-instrument governance framework for measuring, tracking, and remediating Explainability Debt in production AI systems. The foundational instrument, the Explainability Debt Score (EDS), quantifies the proportion of production decisions falling below a governance-defined explainability confidence threshold at any point in time. Complementary instruments include DART (Debt Accumulation Rate Tracker for breach
forecasting), SHIV (Scenario Health and Integrity Validator for daily governance), FDE (Feature Drift Evaluator for causal attribution), HVE (Human Validation Engine), AIDE (Audit Intervention Decision Engine), and ZERO (Zero Explainability Risk Optimiser for remediation). Through a twelve-month longitudinal case study of a production fraud detection system processing 50,000 daily financial transactions, achieving 98.46% accuracy and ROC-AUC of 0.9990, we demonstrate that an EDS of 0.23 on audit day was statistically predictable six months in advance using DART trajectory analysis (beta = 0.008/week, R-squared = 0.94, 95% CI: [0.006, 0.010]), and that 78% of Explainability Debt was concentrated in the highest-regulatory-risk decision category (transactions above $10,000), a risk asymmetry completely invisible to system-level metrics. TRACE provides the first quantitative operational architecture for EU AI Act Article 13 compliance in production AI deployment, establishing a new subdiscipline of explanation governance distinct from explanation generation.
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Information Retrieval
Learning Multi-Step Query Rewriting via Corpus Feedback for Conversational Search
João Coelho, Hong Wang, Jie Yuan et al. (+3 more)
Conversational Query Rewriting (CQR) turns a context dependent user turn into a standalone query for a retriever, and most methods do this in a single step from the dialogue history before retrieving once. The rewrite is therefore fixed before any corpus evidence is available to correct its reference resolution or its vocabulary. We recast CQR as a sequential
retrieval problem: an
agent rewrites the current turn, retrieves, and conditions its next rewrite on the returned passages. The
agent acts in a typed space of three rewriting operations, resolving conversational intent into a standalone query, generating lexical reformulations, or synthesizing pseudo-documents for document-to-document matching, together with a stop action that ends the episode. We train the policy with supervised fine-tuning followed by reinforcement learning against a single
retrieval-quality reward, using no human rewrite annotations. Across TopiOCQA and QReCC, the
agent outperforms several
retrieval-aligned baselines, while remaining effective across
retrieval backends and generalizing to the CAsT
benchmarks without additional training. Further analysis shows that, through
retrieval-reward optimization alone, the learned policy develops a behavior of grounding pseudo-documents in passages retrieved by earlier steps, substantially improving
retrieval.
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All authors João Coelho, Hong Wang, Jie Yuan, Zhuoer Wang, Samson Koelle, Wei Niu
Artificial Intelligence
Learning How to Search for Plans with Exponentially Less Space
Dominik Drexler, Simon Ståhlberg, Markus Fritzsche et al. (+1 more)
Heuristic search for a plan can store exponentially many states, even when its heuristic is almost perfect. We instead learn search control, one specification per domain, written as an indexical policy: a generalized policy with registers that hold objects and modes that sequence its rules. We add the choose rule, which loads an object into a register and marks a backtracking point, where one candidate suffices; every other rule must work for all of its outcomes and needs no search. Our main result is that structural termination, which rules out infinite executions, also bounds every execution by a
polynomial in the number of objects. A depth-first procedure then finds a plan in
polynomial space, however large the state space, with no list of visited states. The cost is time, exponential only in the choice depth, the number of real choices along an execution. Any class that such a policy solves therefore lies in NP, and in P at constant choice depth. We learn these policies with a
language model in a counterexample-guided loop that certifies termination, verifies the training tasks, and keeps the choice depth small. With the learned policies, the procedure solves 1,709 of 1,890 test tasks of the IPC 2023 Learning Track and the Autoscale Agile suite, more than LAMA, BFWS, and Levitron, and most of them within one second and 100 MiB.
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All authors Dominik Drexler, Simon Ståhlberg, Markus Fritzsche, Blai Bonet
Machine Learning
SPD-MetaFormer is what you need for small-data brain decoding
Zhida Wang, Wei Lyu, Guo Yu et al. (+1 more)
Brain signal decoding is challenging because neural recordings are noisy and vary across individuals, while labeled data are often limited. Recent
attention-based models on the symmetric positive definite (SPD)
manifold have nevertheless achieved strong performance using covariance and connectivity representations, yet the contribution of learned
token weighting remains unclear. We examine two representative architectures, MAtt (based on log-Euclidean geometry) and GBWAtt (based on generalized Bures–Wasserstein geometry), and find that their learned
attention weights remain close to uniform after training. We relate this behavior to bounded similarity parameterizations that, under the original softmax scaling, limit
attention-weight contrast. Moreover, replacing learned weights with uniform weights, throughout training and evaluation, has little effect on mean predictive performance while preserving each model's original aggregation geometry. Motivated by these findings, we introduce SPD-MetaFormer, an
attention-free architecture built on uniformly weighted Fréchet aggregation under log-Euclidean geometry. Its backbone uses a geodesic residual to update a summary
token and a shared spectral feedforward map to transform all
tokens, followed by a learned weighted readout.
Token states remain SPD-valued until tangent-space classification. Across three
EEG benchmarks, SPD-MetaFormer achieves competitive results relative to published Euclidean and
manifold baselines. Separate matched reproductions test learned versus uniform weighting within MAtt and GBWAtt. These results suggest that, in the short-sequence and limited-data regimes studied, carefully designed SPD architectures can provide a simpler and effective alternative to adaptive
manifold attention.
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All authors Zhida Wang, Wei Lyu, Guo Yu, Sui Tang
Data Structures and Algorithms · Cross-listed from math.CO
Smallest String Attractors and Minimal Coverage Certificates of Thue–Morse Words
Simone Faro, Francesco Pio Marino and Arianna Pavone
String attractors provide a compact way of representing the complete factor structure of a word: a set of positions is an attractor if every distinct factor has at least one occurrence crossing one of the selected positions. Although the minimum attractor size is known for several classical families of words, describing all optimal attractors is typically much more difficult, since it requires understanding the geometry of all factor occurrences rather than constructing a single optimal solution. We give a complete description for the finite Thue–Morse words. Earlier work proved that four positions are necessary and sufficient for every order $n\geq 4$, but the collection of all smallest attractors remained unknown. For every $n\geq 6$, writing $h=2^{n-3}$, we prove that the smallest attractors are exactly the two reflected families where the four offsets are chosen independently from $\{0,1\}$. Hence there are exactly $32$ smallest attractors for every $n\geq6$. The initial cases are genuinely exceptional: $t_5$ has $40$ smallest attractors and $t_4$ has $87$. We also study the attractor condition independently of optimality. For every $n\geq5$, we characterize the complete antichain of inclusion-minimal factor coverages of $t_n$. It consists precisely of the coverages of $aa$, $bb$, and the eight minimal unique substrings of every generation $t_m$, $4\leq m\leq n$. Thus there are exactly $8n-22$ canonical constraints, forming an irredundant exact certificate for attractors of arbitrary cardinality. When
attention is restricted to four-position sets, this linear-size system collapses to a constant one: it is enough to test the $24$ minimal unique substrings coming from three consecutive generations, and sixteen of these already force the two optimal families. We also show that three generations are necessary within this natural consecutive-generation hierarchy.
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Robotics · Cross-listed from math.OC
Noise-Induced Navigation in Non-convex Domains and Compact Manifolds
Karthik Elamvazhuthi
In this note, we study the problem of designing a feedback law that globally steers a system to a prescribed target configuration. Even if the system is fully actuated, topological obstructions generally prevent the existence of globally asymptotically stabilizing continuous feedback laws. We revisit this problem in a
stochastic setting by allowing noise to enter through the control channels. Using a criterion for asymptotic stability in the large that combines local Lyapunov stability with positive recurrence, we constructively show that one can construct elementary feedback laws that achieve global asymptotic stability in the large, of the target
equilibrium point in connected Euclidean domains with obstacles and
manifolds without boundary. For Euclidean domains with obstacles, we also show that the method extends naturally to the problem of finding the minimizer of a strongly convex function with non-convex constraints. Numerical experiments illustrate the effectiveness of the approach for Euclidean domains with circular obstacles and the two dimensional sphere. Additionally, we study the role noise strength when there is a non-convex obstacle, in which case the system might show metastability.
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Machine Learning
RH-Detect: A Unified Benchmark for Reward Hacking Detection
Junwei Quan, Evgenii Opryshko, Rohan Subramani et al. (+1 more)
Reward hacking, where a model exploits an evaluation signal without completing the intended task, threatens the reliability of deployed
language model systems. Existing
datasets use different labels, response formats, and metadata conventions, making detector results difficult to compare. We present RH-Detect, a
benchmark that combines reward-hacking-relevant subsets from eleven public
datasets, comprising 92,761 rows and six behavior categories, into a common schema. On 5,021 open-ended evaluation units, each comprising a task
prompt and a free-form model continuation, including multi-turn tool-use trajectories, we evaluate six off-the-shelf
language models from five families as reward hacking detectors without additional training. The best model achieves a pooled AUROC of 0.962, with accuracy above 93%. For the four strongest models, however, accuracy on the two multi-turn tool-use
datasets, MALT and TRACE, is 10.7-15.9 percentage points lower than on the other sources at a common decision threshold, highlighting a key gap for deployment-time monitoring. We find that different input formats have different effects across models. Removing thinking raises Qwen3.5-4B AUROC from 0.779 to 0.849, but lowers Qwen Flash from 0.977 to 0.950. We also evaluate the
benchmark as a training
dataset for detectors. Holding out each source in turn, single-
token SFT improves average AUROC on five of six held-out sources. A GRPO follow-up on that failure case yields a slight improvement in detection performance. Our results show that a single pooled score can conceal variation across data sources, detector inputs, and training procedures.
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All authors Junwei Quan, Evgenii Opryshko, Rohan Subramani, Igor Gilitschenski
Computation and Language
AI4Fire: Evaluating Large Language Models on Wildfire Tasks
Yue Zhao, Zuobin Xiong, Xiyang Hu et al. (+3 more)
Large
language models (LLMs) are entering wildfire management, where overstated evaluations can cost property and lives. How do they perform on wildfire tasks, with and without grounding? Bare means a model receives the task input alone. Grounded means it also receives one task-specific addition: for smoke detection, a smoke-free reference frame from the same camera. AI4Fire runs six core models bare and grounded on five wildfire tasks, zero-shot; a sweep adds 29 more. Our literature search on fire tasks found 138 works; none combines this roster, task coverage, and paired bare and grounded runs. We report three findings. (1) Grounding helped most where the addition carried the answer: a read-only SQL tool lifted every core model's database accuracy from at most 16 to at least 88 percent. (2) Simple rules were hard to beat: no core model outperformed repeating today's staffing count, and two open-weight models mostly copied the median of similar earlier fire-days, a worse forecast. (3) Public releases carry hazards: a fire-danger column separates the holdout perfectly, and 67 aerial fire frames carry smoldering or fire-free labels read from a clipped thermal maximum. We release
prompts, responses, scores, code, and the survey record.
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All authors Yue Zhao, Zuobin Xiong, Xiyang Hu, Zhangyu Wang, Xiongye Xiao, Ruolin Li
Computer Vision
GHARP: Real-time Gaussian Head Animation from Large-scale Reconstruction Prior
Ali Benlalah, Sepehr Johari, Patricia Vitoria et al. (+14 more)
We present GHARP (Real-time Gaussian Head Animation from Large-scale Reconstruction Prior), a method that animates 3D human heads in real time from a few input images of a subject and a driving expression signal. We decouple the problem into an identity stage that builds a representation of the subject's geometry and appearance offline, and an animation stage that predicts expression-dependent residuals on top of it at runtime. This separation offers a favorable trade-off with respect to fidelity, quality and runtime: the identity stage can be expensive while the animation stage runs a lightweight network, optimized for mobile devices. Our method performs animation in a semantically structured latent space of a pretrained reconstruction model, where expression changes remain spatially contained, making residual prediction efficient. This reconstruction prior provides a consistent spatial layout, allowing fusion of multiple input views into a compact, fixed-size canonical Gaussian representation. While this two-stage design improves the runtime-quality trade-off, it still inherits a problem common to all expression-driven avatar methods: expression codes describe only the face and thus omit body pose and clothing position, making these regions underspecified in the input. The animation network faces an ill-posed mapping and resorts to averaging over conflicting body appearances, producing blur and temporal flicker. We address this with a body
alignment network that learns to align the person's body in the target image with the input reference images, removing the ambiguity from the training signal. Our method achieves state-of-the-art quality on the Ava-256
benchmark while running up to 13x faster on an A100 GPU with 8x fewer Gaussians.
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All authors Ali Benlalah, Sepehr Johari, Patricia Vitoria, Armin Kappeler, Artem Sevastopolsky, Alexander Jung, Gabriele Fanelli, Kevin Mader, Manuel Breitenstein, Claudia Plüss, Jan Rüegg, Simon Biland, Thomas Etterlin, Dmitry Kostiaev, Mathias Deschler, Brian Amberg, Sebastian Martin
Numerical Analysis
A Neural-Network Approach for the Stability of Loosely-Coupled FSI Simulations in Hemodynamics
Francesca Renzi, Stefano Pagani, Paola F. Antonietti et al. (+1 more)
The development of accurate and efficient computational methods for fluid-structure interaction (FSI) problems remains an active research area due to its broad engineering and biomedical applications. Partitioned loosely coupled (LC) schemes are attractive for large-scale simulations because of their modularity and low computational cost. However, their applicability is limited by stability issues in high added-mass regimes, where fluid and structure densities are comparable, as in hemodynamics. In our recent work, we proposed an LC
algorithm based on Dirichlet-Neumann interface coupling, the LC-DN-{\alpha} scheme, which improves the stability of the coupling through a stabilization parameter {\alpha}. Although theoretical stability has been demonstrated under suitable constraints on {\alpha}, its optimal value depends on the physical and numerical characteristics of the FSI problem and is difficult to determine in practice. In this work, we propose a data-driven strategy to automatically estimate the optimal value of {\alpha}. We develop a feed-forward neural network to predict {\alpha} from the descriptors of the FSI problem. The model is trained on a
dataset of baseline FSI problems in large added-mass regimes and evaluated on more realistic configurations to assess robustness and generalization. Numerical results show that the proposed model generalizes across the considered FSI scenarios and provides robust estimates of {\alpha} under moderate variations in the problem setup, including changes in geometry and loading conditions, that do not completely alter the interaction mechanics represented in the training set. The methodology is intentionally developed and validated on simplified FSI problems, providing a proof of concept for data-driven strategies to improve the efficiency of partitioned FSI simulations and supporting their extension to more complex and realistic scenarios.
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All authors Francesca Renzi, Stefano Pagani, Paola F. Antonietti, Christian Vergara
Artificial Intelligence
StoreBench: A Live-Commerce Environment for Evaluating and Training Autonomous Operator Agents
Daksh Raghuvanshi, Ved Vedere and Yifan Wang
Reinforcement learning environments are now a primary lever for improving large
language model (LLM) capabilities in post-training, yet most agentic
benchmarks remain static: the world moves only when the
agent acts, the reward is a terminal verdict, and the pass bar is set arbitrarily. We introduce StoreBench, a live-commerce environment in which an
agent runs a mid-size online apparel store on a production-grade commerce backend, testing long-horizon planning and economic judgment under uncertainty. Customers order around the clock, suppliers reprice and fail, and
market shocks arrive with partial or no warning. The
agent acts through the same 29 merchant tools a human operator would use, under a windowed operation budget that makes simulated time a function of actions taken, so model latency cannot influence simulated time. Pass thresholds are calibrated against scripted anchor policies, the reward is hardened against a catalogue of reward hacks, and every episode replays identically given a sequence of actions. We evaluate seven frontier LLMs on 11 scenarios of 30 to 45 days and a full simulated year, over three world seeds at matched
reasoning effort. No model matches the scripted smart-triage policy on average: the best, DeepSeek-V4-Pro, passes 49% of task-seed
cells against the heuristic's 97%. Human experts working through the same tools and budgets outscore every model (mean composite 0.708 vs. 0.700). Over a full simulated year under the Claude Code harness, most models show dramatic performance improvement. In a GRPO post-training run, Qwen3.5-27B trained on only five disjoint tasks raises its mean composite on the held-out evaluation tasks from 0.136 to 0.373. We release five example training-split tasks, ten sample trajectories, and the scoring and verification tooling; the full environment and evaluation suite are withheld to keep the
benchmark uncontaminated.
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Robotics
Higher-Order Morphology Priors for Quadruped Reinforcement Learning Under Actuator Degradation
Derek You, Zafir Shamsi, Keqin Wang et al. (+1 more)
Actuator degradation turns quadruped locomotion into a coordination problem requiring joints to compensate for lost actuation. Prior work suggests that morphology-aware graph policies improve learning and generalization under body perturbations. We ask whether these benefits can be strengthened by explicitly modeling higher-order mechanical structure. We represent the Unitree Go1 as a
cell complex with limb- and body-level rank-2
cells and apply Hodge-based message passing. Under degradation training, the node-edge-face Hodge actor achieves the highest return on unseen actuator degradations, with higher survival and lower velocity-tracking error. These results support higher-order morphology as a useful inductive bias for whole-body compensation under actuator degradation.
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All authors Derek You, Zafir Shamsi, Keqin Wang, Christine Allen-Blanchette
Machine Learning
Rethinking the Tradeoff Between Temporal Encoding and Nonlinear Computation in Spiking Language Models
Hanfei Liu, Shuchang Feng, Yanxia Chen et al. (+2 more)
Spiking
language models face a tradeoff between representing continuous semantic features over short temporal windows and retaining costly nonlinear
attention operations. We introduce Spora, which jointly designs spike encodings and
attention operators. Binary temporal weights let $T$ spikes represent compositional values with up to $T$ bits of capacity, compared with $O(\log_2 T)$ bits for spike-count readout. Unipolar Binary Spiking (UBS) uses thresholds and spike-triggered residual decay to produce non-negative integer codes; Bipolar Binary Spiking (BBS) separates sign and magnitude and learns a scale for signed activations. These representations support accumulation-and-shift dot products and integer-exponent mappings in
attention. With four time steps, Spora achieves 76.6 average GLUE score and 44.1 CoLA MCC, improving over SpikeLM by 1.2 and 6.2 points, respectively. Extending BBS to six steps raises these scores to 78.2 and 47.4. Conditional-decay analysis, matched-budget activation-quantization comparisons, event-workload statistics, and fixed-point evaluation further characterize the connection between encoding fidelity and computational cost.
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All authors Hanfei Liu, Shuchang Feng, Yanxia Chen, Changzeng Fu, Shiqi Zhao
Machine Learning
World-Model Policy Arbiter for Goal-Conditioned Reinforcement Learning
Junwei Quan, Evgenii Opryshko, Nicholas Rhinehart et al. (+1 more)
Offline goal-conditioned reinforcement learning (GCRL) has produced a diverse set of goal-reaching
algorithms, yet no single
algorithm performs best across environments, goals, and even different phases of the same task. Rather than deploying only the best-performing policy, we ask whether a set of frozen goal-conditioned policies can be used collectively as a portfolio, deciding at every state which policy should act. Choosing a policy at each state is not straightforward. The policies' own value functions cannot be compared directly: they may use different scales, and some policies have no value function. We need to judge each policy by the states it is likely to reach, even though we can execute only one policy at a time. We also need to avoid switching so often that control becomes unstable. To address these challenges, we introduce World-Model Policy Arbiter (WMPA), a test-time framework that, given a set of frozen policies as input, rolls out each frozen policy in a learned state-space world model, evaluates the imagined futures with a shared goal-conditioned value function, and executes the highest-scoring policy for a short commitment interval before the next round of arbitration (policy selection). WMPA assumes access to a bank of frozen goal-conditioned policies and requires neither policy retraining nor privileged task-specific knowledge. Under the official OGBench evaluation protocol on 18 state-based
datasets spanning maze
navigation as well as cube, scene, and puzzle manipulation, WMPA improves the macro-average success rate from the 44% achieved by the best policy selected per
dataset to 58%, with statistically significant gains on 12
datasets. These gains include +33 percentage points on cube-double-play and +36 percentage points on scene-play.
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All authors Junwei Quan, Evgenii Opryshko, Nicholas Rhinehart, Igor Gilitschenski
Machine Learning
Coefficient Calibration as Selection Pressure in Symbolic Regression
Mattia Billa, Veronica Guidetti and Federica Mandreoli
In memetic symbolic regression, candidate structures are compared after coefficient calibration, so the calibration protocol itself contributes to evolutionary selection. Standard centralized calibration evaluates each structure at its pooled-sample optimum, ignoring how stable this calibration is under
covariate shifts, and can thus favor structures whose fit relies on sample-specific coefficients. We propose Dirichlet-Sinkhorn Constant Averaging (DSCA), a calibration strategy that partitions the optimization data into equally sized subsets with different
covariate distributions, calibrates each candidate independently on every partition, and evaluates it at the mean of the resulting parameters. We show that the excess loss of DSCA relative to centralized calibration vanishes at the population level for correctly specified, identifiable expressions, whereas under misspecification it persists when partition-specific calibrations do not aggregate to the pooled optimum. On synthetic
benchmarks and ten real-world
datasets, DSCA improves functional recovery and the accuracy-complexity trade-off over centralized Broyden-Fletcher-Goldfarb-Shanno and Levenberg-Marquardt calibration, under selection by negative log-likelihood and by the Akaike and
Bayesian information criteria. Mechanism analyses associate the DSCA excess loss with the generalization gap and show that the effect is not reproduced by repeated centralized fitting. These results indicate that controlled heterogeneous calibration provides a complementary source of selection pressure in symbolic-regression search.
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Cryptography and Security
Speedbumps: Rejection Attacks on Speculative Decoding
Adam Y. J. Jones, Yu Yuan and Sergio Maffeis
Speculative decoding is a popular technique for increasing the speed and reducing the costs of large
language model (LLM) inference by verifying multiple draft
tokens in a single target-model forward pass. The resulting benefit depends on the ability of the drafter to approximate the target model's distribution. In this work, we study Speculative Rejection Attacks (SRAs), a novel class of attacks that cause draft and target models to disagree more often, resulting in fewer draft
tokens being accepted per draft cycle. This leads to more target model forward passes needed per generated
token, slowing down inference and increasing costs for the victim. We introduce two attacks which append an
adversarial suffix to attacker-controlled content to degrade speculative decoding on a victim's
prompts. Both attacks optimise the expected length of the accepted speculative prefix, estimating per-depth acceptance from the target's probability of the drafted proposals (Speedbump-P) or from the overlap between the draft and target distributions (Speedbump-D). In some cases, attacks degrade speculative decoding to the point of being slower than autoregressive decoding. The degradation reduces the output quality - regularisation restores output quality but gives up most of the degradation, trading effectiveness for stealthiness. Additionally, the suffixes remain effective under sampling, and transfer across drafters (Speedbump-P) or across target models sharing a drafter (Speedbump-D). These findings identify the draft-target interaction of speculative decoding as a realistic attack surface through which
adversarial inputs can inflate inference costs.
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Machine Learning
Adaptive Multi-Discriminator WGAN Framework for Resource-Constrained Internet of Vehicles Using Reinforcement Learning and Game Theory
Farhoud Jafari Kaleibar, Amr M. Zaki and Marin Litoiu
Managing machine learning workloads as a network service introduces a resource-orchestration problem distinct from conventional model training; which nodes should be allocated to a task, how communication and computation budgets should be divided among them, and how service quality should be sustained as connectivity and node availability change with mobility. Deploying Generative
Adversarial Networks (GANs) in Internet of Vehicles (IoV) environments is a demanding instance of this problem; resource constraints, dynamic network
topologies, and competing optimization objectives mean that traditional GAN architectures cannot simultaneously achieve high accuracy, efficient resource use, low delay, and low communication overhead. This paper introduces an adaptive multi-discriminator Wasserstein GAN (MD-WGAN) framework that integrates reinforcement learning with game-theoretic coordination to address these challenges jointly. In our framework, roadside units host generators paired with Deep Q-Network (DQN)
agents that select discriminator subsets and manage distributed training across mobile vehicular nodes, while a game-theoretic coordination step allocates training epochs between generators and discriminators. A unified optimization objective ties
adversarial learning quality to resource efficiency, communication overhead, and latency under vehicular constraints, allowing the framework to continuously adapt its training behavior as network conditions change. Evaluation on real-world NGSIM trajectory data shows that the framework attains prediction accuracy comparable to state-of-the-art GAN baselines - the lowest RMSE (1.029) and MAE (0.894) among all evaluated methods - while markedly improving resource efficiency: average CPU utilization is reduced by roughly 28% and mean
memory usage by roughly 6%, at competitive communication overhead and latency.
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Hardware Architecture
The Missing Fourth Term for the Emulation Tensor Memory Equilibrium (TME) Model: The Residue Deconstruction Cost
Harun Bayraktar, John Gunnels and Peter Caday
The Tensor-
Memory Equilibrium (TME) model of "FP8 is All You Need (Part 1)" calculates the execution time of Ozaki Scheme II emulation of fp64 as the maximum of a tensor-core term and a High-Bandwidth
Memory (HBM) traffic term, plus a per-output reconstruction term. However, it omits the per-input deconstruction cost: every streamed fp64 operand must be scaled, rounded, and reduced modulo each of the $r$
moduli on SIMT pipes before any
matrix multiply can issue. In this note we add this fourth term, calibrate its constant from the cuBLAS emulation path, and derive a closed-form operational-intensity threshold $\mathrm{OI}^{*} = c_q r P_{\mathrm{fp64}}/(8P_{\mathrm{int}})$ below which emulation cannot match native fp64 regardless of tensor-core throughput. On the NVIDIA B300 GPU the threshold is $\mathrm{OI}^{*}\approx 0.56$ FLOP/B. As a result, GEMV, SpMV, and low-batch GEMV, which are the
memory-bound kernels the original paper claims to accelerate, are limited to 0.3-0.9x of native performance, and the 7-point stencil to 1.8x rather than the claimed 3.1x. Dense GEMM is unaffected as expected. We also show that precomputing and storing the residues moves the same cost into the bandwidth term, and we state the instruction count that an implementation would have to achieve to invalidate the bound.
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Machine Learning
Language Models for Page-Level Layout Decisions in E-commerce Search
Varun Joshi, Eva C. Song and ChengXiang Zhai
E-commerce search pages are critical touchpoints for millions of online shoppers. While traditional search engines return a ranked list of results, modern E-commerce search pages increasingly incorporate recommender system modules – for example, secondary stacks that surface alternative product groupings at specific positions. When introduced appropriately, secondary stacks can improve user engagement; however, suboptimal placement may disrupt browsing flow and degrade the primary results. Unlike traditional search ranking, where evaluation techniques such as interleaving are well established, evaluating page-level layout changes e.g., when and where to insert a secondary stack remains challenging without costly online A/B testing. To address this, we study offline methods for evaluating whether a given layout decision – specifically, the inclusion of a secondary stack at a particular position – is beneficial to users. We investigate
language models as scalable evaluators by comparing direct
prompt-based,
prompt-derived feature, and representation-based methods. Our results show that representation-based approaches consistently outperform
prompt-based judging in predicting user engagement, suggesting they provide a reliable foundation for offline layout evaluation in E-commerce search.
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Databases
Implementation Guidelines for Data Quality Metrics
Philipp Jung, Katinka Becker, Felix Biessmann et al. (+4 more)
Despite decades of data quality (DQ) research, a gap remains between DQ dimensions, such as accuracy or completeness, which the literature defines in textual form, and DQ tools, which typically implement low-level checks that are not aligned with these dimensions. ISO/IEC 25024 and ISO/IEC 5259 attempt to bridge this gap by defining DQ metrics for each dimension. However, these DQ metrics are hardly used, because the standards leave open how to implement them: for example, the metric for syntactic accuracy counts syntactically accurate values, but does not state how to decide that a value is syntactically accurate. This simply moves the problem to another level without solving it. As a result, DQ assessment currently cannot build on the standards. In this paper, we make the ISO DQ metrics executable. We classify all data-level metrics of both standards into (i) generalizable metrics that need no input beyond the data, (ii) parameterized metrics whose parameters can be learned from clean reference data or set by an expert, and (iii) non-generalizable metrics that need qualitative judgment and cannot be automated. For the metrics that can be automated, i.e., categories (i) and (ii), we propose implementation guidelines that resolve what the standards leave open. We realize the guidelines in dqmeasure, an open-source library of 20 metrics that learns these parameters from reference data instead of relying on manually defined rules. Our experiments on real-world and synthetic
datasets show that the metric scores decrease monotonically with an increasing number of injected errors, decline together with downstream ML performance, and scale linearly with the number of rows, which enables automated DQ monitoring based on the standards.
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All authors Philipp Jung, Katinka Becker, Felix Biessmann, Valerie Restat, Martin Seyferth, Daniel Schwabe, Lisa Ehrlinger
Computation and Language
Large Language Models for Machine Translation Quality Annotation: Humans and Models Are Both Challenged
Hala Almaghout, Christian Federmann and Qin Gao
Large
Language Models (LLMs) are considered to be a more efficient and cost-effective alternative to human judgment for Machine Translation (MT) evaluation. With MT evaluation spanning a large number of
language pairs, domains and levels of annotation granularity, LLMs must be thoroughly evaluated across these dimensions before being reliably used as alternatives to human evaluation. In this paper, we evaluate the performance of LLMs for two prominent MT quality evaluation schemes: Multidimensional Quality Metrics (MQM) and Error Span Annotation (ESA) by comparing their agreement with human annotators. We present results on a long-context test set of 70
language pairs and the publicly available WMT23 and WMT25 data, investigating both score and error span annotation agreement across a variety of
language pairs and domains. Our results show that while LLM agreement with human annotators exceeds agreement between human annotators for some evaluation tasks, both vary substantially across annotation schemes,
language pairs and domains and remain unreliable for most settings. Furthermore, we identify challenges facing both human and LLM annotators: humans are particularly challenged by fine-grained MQM annotations and low-resource
language pairs, while LLMs struggle with minor errors, wrong
language variants and error span annotation. Our results highlight the potential for improvement for both human and LLM annotation performance, possibly through human-LLM collaborative annotation pipelines that address the reliability issues identified in this work.
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Computers and Society · Cross-listed from physics.soc-ph
An ecology of participation for fusion energy development
Aditi Verma, Katie Snyder, Andrea Morales Coto et al. (+10 more)
Decisions that will shape future fusion facilities, including the production of waste, the management of tritium, the achievement of safety, and impacts on land, water, and local communities, are increasingly becoming sociotechnical in nature. For prior energy technologies, fission among them, such decisions were made top-down and met sustained public opposition. Research shows such opposition is rarely a deficit of public understanding and is instead rooted in place-specific environmental, health, sociocultural, and economic concerns and in distrust of how technologies are selected and sited. We argue that fusion developers have an opportunity to design differently, and we propose an ecology of participation: multiple, complementary modalities of engagement sustained over time, replacing the one-off public consultations now typical of large infrastructure projects. We synthesize human- and environment-centered design frameworks and introduce a framework that reinterprets opposition as a divergence between communities and technology developers in values, norms, or design characteristics. We present six case studies from our work: participatory technology assessment focus groups, participatory design workshops, immersive virtual-reality models of a fusion facility, the Global Fusion Forum platform, the Imaginary Energies speculative design platform, and Heartbeat, an arts-based sound installation. Mapping these onto the values-norms-design framework shows how each interrogates or closes a different part of the expert-public gap, and exposes two limitations of fusion public engagement efforts writ large: developers are seldom asked to articulate their own values and norms, and the environment is represented only indirectly. We close with five guiding questions for fusion researchers building their own participatory engagements.
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All authors Aditi Verma, Katie Snyder, Andrea Morales Coto, Nathan Kawamoto, Daniel Hoover, Ana Kova, Sara Eskandari, Stephanie O'Malley, Jared Owens, Mahmud Farooque, Gabrielle Hoelzle, Stephanie Diem, Kevin Daley
Robotics
When Listening Becomes Easier: Scrubbing Visual Cues for Shortcut-Free VLAs
Jasper Gerigk, Kenzo Aspuru-Takata, Chin-Hsuan Wu et al. (+3 more)
Shortcut learning is a prevalent issue in
robot learning. The limited diversity of
robot demonstration
datasets can mislead policies into exploiting spurious correlations between tasks and irrelevant features, such as viewpoint or background. Collecting sufficiently diverse
robot demonstrations is costly and inefficient, motivating algorithmic alternatives. We focus on
vision-
language-action (VLA) models and discover that different
vision-
language model backbones exhibit substantially different levels of susceptibility to visual shortcut learning. We find that model behavior correlates with our proposed representation-level metric, action margin, which requires no policy rollouts. Visual shortcuts consistently enter action representations in early layers, with models differing in the extent to which later layers correct them by incorporating
language information. To boost models'
attention to
language, we introduce task scrubbing, a new domain-
adversarial training method that decreases models' likelihood of using visual shortcuts and improves VLAs' generalization. Experiments in both simulation and the real world across multiple VLAs and visual cues show that task scrubbing improves out-of-distribution robustness and often eliminates visual shortcut learning.
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All authors Jasper Gerigk, Kenzo Aspuru-Takata, Chin-Hsuan Wu, Mohammad Mohammadi, Shuhong Zheng, Igor Gilitschenski