Networking
Pricing IoT Data Delivered via LEO Satellites
Rahul Ramachandran and Suman Banerjee
IoT terminals served by LEO satellite constellations transmit data to passing satellites in discrete uplink windows. That data is delivered to buyers only when the satellite reaches a ground station. This store-and-forward structure makes the achievable price a discontinuous function of the delivery completeness threshold (a phenomenon we call a pricing cliff) and creates a geographic pricing asymmetry between polar and equatorial terminals that depends on constellation size. We present an integrated pricing model combining a full SGP4 orbital simulation of the deployed Kinéis constellation with a buffer flow model and a value decay framework with decay constants derived from operational decision-window timescales in the application literature and anchored to observed satellite imagery
market prices. We show that polar and equatorial terminals are dominated by structurally different latency components requiring different infrastructure interventions, formally characterize the pricing cliff phenomenon and its commercial significance, and derive a geographic pricing premium that is exactly independent of the residual value assumption. Going from 1 to 25 satellites reduces the polar–equatorial premium by 25x, with diminishing returns beyond n=10; at full constellation size the premium falls below 1% of peak data value and remains below 1.5% across the full plausible range of buyer urgency.
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Information Theory
Mutual Information Constrained Chernoff Bottleneck
Dier Tang and Guangyue Han
The classical information bottleneck (IB) measures the relevance of a representation $U$ of $X$ to a target $Y$ by $I(U;Y)$, which does not directly characterize the error of downstream decisions. For a binary hypothesis $Y$ inferred from many separately encoded observations, the optimal error exponent is the Chernoff information between the two conditional distributions of $U$ given $Y$. We study the mutual information constrained Chernoff bottleneck, which seeks an encoder that maximizes this Chernoff information subject to a rate constraint $I(U;X) \leq R$. We show that its optimal value $C(R)$ increases strictly up to $R = H(V)$, where $V$ merges the symbols of $X$ with equal likelihood ratio, remains at the uncompressed exponent beyond, and, unlike the IB curve, need not be concave. We further show that $k+1$ outputs suffice to attain $C(R)$, where $k$ is the cardinality of $V$. We propose an alternating
algorithm that updates the encoder via a generalized Blahut–Arimoto
algorithm and the Chernoff parameter $s$ via a nonlinear equation, and prove that its iterates remain feasible, with nondecreasing and convergent Chernoff information. Comparing with the classical IB for $n$ samples, we show that, whenever the two objectives disagree, the IB encoder has the smaller equivocation $H(Y|U^{n})$ at $n = 1$, whereas the Chernoff bottleneck has the smaller error probability and equivocation for all large $n$. Numerical experiments confirm the theory, and on real topic-detection data from the 20 Newsgroups corpus, compressing each word to only $17\%$ of its
entropy retains $90\%$ of the error exponent and nearly the accuracy of the uncompressed classifier.
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Computation and Language
BITEM at the NTCIR-19 R2C2 Task: Predicting Confidence from Agentic RAG Pipeline Signals
Julien Knafou, Luc Mottin, Alexandre Flament et al. (+3 more)
The BITEM team entered both subtasks of the NTCIR-19 R2C2 task with a single agentic pipeline, in which a model searches, reads and records evidence over a movie corpus while an orchestrator holds the record and rules on what may be submitted. A claim is admitted only once an entailment cascade has checked it against the passage it cites, and an answer is released only once enough checked evidence stands behind it. Each question is run three or four times, every pass retrieving from a corpus stripped of what the earlier passes have already seen. The confidence filed with each answer is computed by the orchestrator from what the run leaves behind and is never asked of the model, which is offered no way to rate itself. The two
retrieval runs placed 4th and 5th of 22, pooling the passes was worth 0.0709 nDCG@20, and the gain was largest on the multi-hop and post-processing-heavy questions, where the organisers rank the pooled run top of the field. Sixteen of the 25 answer runs were built on passages these two runs supplied, 12 of them filed by other teams. HMR rewards a system whose confidence is high where it answers right and low where it answers wrong. The pipeline reached an accuracy of 0.9219, 6th of 25, while the confidence filed with those answers gave an HMR of 0.4915, 13th. A few rules crafted over those same recorded signals, with no further model call and no further
retrieval, raise that to an accuracy of 0.9375, 5th, and an HMR of 0.6985, 9th. Ranking on HMR alone can reward a system for answering wrongly with low confidence, so we propose accHMR, the accuracy multiplied by HMR, which reports the reward in proportion to the accuracy, and on which the revised rules would have scored 0.6549, 5th. For future work, fitting a model on the numbers the pipeline already produces, rather than writing such rules by hand, would be a real step forward.
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All authors Julien Knafou, Luc Mottin, Alexandre Flament, Paul van Rijen, Esteban Gaillac, Patrick Ruch
Systems and Control
Complete Characterization of Minimum-Order Functional Observers from Darouach to Luenberger
Tyrone Fernando
This paper gives an order-by-order characterization of linear functional observers between the minimum dimension permitted by the algebraic observer condition and the upper endpoint determined by functional observability. Functional observability indices determine the minimum algebraically admissible order $$ q_0=\sum_{j=1}^r\eta_j, $$ where $r$ is the number of independent functionals to be estimated and $\eta_j$ is the functional observability index associated with the $j$th functional. For an $n$-dimensional system with $p$ independent measured outputs, let $n_0$ denote the rank of the observability
matrix. Under functional observability, the upper endpoint of the functional-observer order range is $n_0-p$. The algebraic and spectral observer conditions are characterized at each prescribed order between these two endpoints. A nullspace representation in the original state space yields rank and
matrix-pencil characterizations of the algebraic and spectral conditions, respectively. The minimum observer order is obtained as the smallest spectrally feasible order in the resulting order spectrum. The two endpoints include the classical Darouach and Luenberger orders as special cases: the lower endpoint reduces to the Darouach order when $q_0=r$, while, under complete observability, the upper endpoint reduces to the Luenberger order $n-p$.
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Artificial Intelligence
Which Attention Heads are like the Human Head? Not the Ones that Compute
Christopher Pinier, Gustaw Opiełka, Hannes Rosenbusch et al. (+3 more)
Brain-AI
alignment is often interpreted as a sign that model and
brain perform similar computations. Whether the aligned units are causally involved in model computation is rarely checked. On an abstract pattern-completion task (AAABAAA $\rightarrow$ B), we compare LLM
attention-head representations with human
EEG and test how ablating those heads affects task performance.
Alignment and causation dissociate:
brain-aligned heads contribute to performance, but their removal is substantially less disruptive than removal of heads selected via attribution patching. We compare two head sets that prior interpretability work defines without reference to the
brain: concept vectors (CVs), which represent abstract patterns across formats, and function vectors (FVs), selected for their contribution to correct-answer prediction.
Brain alignment shows little association with FV scores, while its association with CV scores varies across models. Among
brain-aligned heads, we find recurring
attention profiles: one emphasizes distinctive elements (novelty heads), the other repeating elements (repetition heads). The novelty family tracks salience and attends to the same elements that humans look at, yet its removal is less damaging than random ablation on average. Repetition heads contribute modestly to performance and are associated with abstract-pattern representation (CVs). Across 17 models spanning 3B-72B parameters, FV-ranked removal is substantially more disruptive than
brain-ranked removal.
Brain alignment thus captures how the model reads the stimulus, and only faintly captures how it represents the pattern and solves the task.
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All authors Christopher Pinier, Gustaw Opiełka, Hannes Rosenbusch, Taylor Webb, Michael D. Nunez, Claire E. Stevenson
Machine Learning
TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models
Deqing Fu, Huangyuan Su, Rajat Sen et al. (+4 more)
Tabular foundation models achieve strong zero-shot accuracy on structured data by pretraining on synthetic tables, but they ignore the column names, task descriptions, and auxiliary files that carry
dataset semantics. Meanwhile, self-evolving machine learning engineering (MLE)
agents train models from scratch on each
dataset, yet jointly searching over features, architectures, and hyperparameters is noisy and prone to overfitting. We introduce TabFM-Auto, which pairs a tabular foundation model, TabFM, with a
language model
agent that evolves the data pipeline around it. Guided by
dataset metadata and validation feedback, TabFM-Auto iteratively refines data cleaning, feature engineering, context selection, and post-processing to reduce TabFM's error. Across all 51
datasets of the TabArena
benchmark, five TabFM-Auto configurations with different
agents and
language models take the top five overall positions, and the best raises TabFM from 1785 to 2013 Elo. The discovered pipelines also transfer to other frozen tabular foundation models (+69 to +143 Elo) with no further search. On the 8 tabular competitions of MLE-Bench, TabFM-Auto ranks first overall among MLE
agents.
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All authors Deqing Fu, Huangyuan Su, Rajat Sen, Taman Narayan, Sujay Sanghavi, Abhimanyu Das, Weihao Kong
Artificial Intelligence
KV-Kaizen: Learning Context-Adaptive Cache Compression Choices
Joao Monteiro, Louis Béthune, Anastasiia Filippova et al. (+3 more)
As the context size of text processed with an LLM grows, the size of KV caches can outstrip the
memory allocated for the original model weights. This impacts LLM throughput negatively, since decoding is
memory-bound and decode cost grows with cache size. Recent work alleviates this bottleneck by discarding the least relevant
tokens. Eviction introduces a tension, since a one-off decision to discard content may prove detrimental later. Instead, we focus on alternative choices that can lead to cache compression without evicting
tokens. We achieve this by learning a selector that is able to produce, based on context, a per-layer cache configuration towards an overall compression budget. The selector operates along three axes: sharing one cache across layers (depth), caching at fewer bits (precision), or truncating the low-rank latent cache representations (rank). We call the resulting method KV-Kaizen, for the many small per-layer choices it compounds. We observe that these interventions taken independently and uniformly over all layers limit achievable compression because they degrade accuracy. Crucially, composing them locally and adaptively to the context can instead preserve accuracy while achieving large
memory savings. At inference, the selector runs once, before pre-fill. In evaluations on instruction following and
reasoning tasks, our selectors reach the Pareto frontier of accuracy against cache size, against learning-free and post-hoc baselines. On long-context tasks, KV-Kaizen improves on eviction and can be composed with it, reaching a 32x smaller decode-time cache on a 14B model while preserving accuracy. A 4x cache size reduction incurs no accuracy degradation from 7B parameters up, and a compressed model is more accurate than a smaller uncompressed one with the same cache size. Together, these findings support pre-training large models and compressing them only afterwards.
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All authors Joao Monteiro, Louis Béthune, Anastasiia Filippova, Sonia Laguna, David Grangier, Marco Cuturi
Computer Vision
ORMA: Optimization-based Monocular 4D Reconstruction of Articulated Animals
Xuyi Hu, Francesco Palandra, Shangzhe Wu et al. (+3 more)
Recovering articulated 4D representations of animals from
monocular videos remains challenging due to the large diversity of quadruped morphologies and lack of animal 4D supervision data. Existing learning-based reconstruction methods operate on individual images and rely on synthetic or model-fitted 3D supervision, which inherits the constraints of strong parametric priors and limits generalization to out-of-distribution species. When applied to out-of-distribution animals, they often recover a plausible pose while producing inaccurate geometry because the underlying shape model cannot faithfully represent the observed instance. We present ORMA, a training-free reconstruction framework that decouples articulation from shape, using the predicted pose as reference for optimization while leveraging generative 3D priors for accurate shape reconstruction. Given a reference image, we reconstruct the animal geometry and register it to the parametric model SMAL+, yielding an articulated shape adapted to the observed instance. We then combine per-frame articulated pose estimates with globally consistent camera poses to recover animal motion in a shared world coordinate frame, and further refine the reconstruction using self-supervised DINO correspondences and temporal consistency. To enable quantitative evaluation, we introduce PAW4D, a synthetic multi-species
benchmark with ground-truth 3D geometry and camera motion. Experiments on PAW4D, PFERD, and challenging in-the-wild
videos demonstrate that ORMA improves reconstruction accuracy while recovering globally consistend animal motion across diverse quadruped species.
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All authors Xuyi Hu, Francesco Palandra, Shangzhe Wu, Daniel Cremers, Riccardo Marin, Silvia Zuffi
Software Engineering
Merged, Not Measured: An Empirical Study of Performance Issues Fixed by Coding Agents
Zhenyu Qi, Haotang Li, Jinfu Chen et al. (+6 more)
Coding
agents open pull requests (PRs) that claim to speed up software, but studies of human performance fixes say little about how maintainers respond to such a fix or whether its claim holds. From the 71,677
agent PRs of AIDev v4, a text filter and codebook coding by
language models and by the authors select 1,262 performance issues fixed by six
agents in 582 repositories. We code each issue and its tests and re-execute 23 rejected and 30 merged fixes. (1) 57% of closed fixes are merged, 61% of rejections give no stated reason, and only 6 of the 23 re-executed rejected claims held under our three-run pilot on mostly
agent-built workloads. (2) Acceptance rises with the
agent's track record in the repository (31-37% to 70%) and with the repository's pre-opening merge rate on its other
agent PRs (33% to 84%). Merged fixes delete a larger share of the lines they change (0.26 versus 0.15), a difference that holds within
agent and within repository, with no such difference detected in the coded content, description, tests or measurements. (3) Repeated computation and redundant data processing cause 44% of the issues, and 46% of fixes are architectural-level. (4)
Agents change tests in 37% of fixes and 11% carry a performance test or
benchmark; of the 30 merged fixes, 18 met our delivery criterion, 3 fell short of the claim, 9 showed no significant gain or regressed, and 14 change behavior on untested inputs. The outcome tracks the repository's history with the
agent rather than the coded content of the fix, and a merge does not show that the fix delivers what it claims.
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All authors Zhenyu Qi, Haotang Li, Jinfu Chen, Huashan Chen, Yutong Zhao, Derui Zhu, Tomas Cerny, Bo Liu, Sen He
Data Structures and Algorithms
Can We Break Fine-Grained and NP-Hardness Barriers if We've Seen the Graph Before? The Isomorphic-Priors Model
Dani Dorfman, Simon Döring, Martin G. Herold et al. (+4 more)
If we run a heavy-duty computation on prior data, can we avoid repeated computation for similar future inputs? Inspired by this question, we introduce a new computational model for graph problems called
algorithms with isomorphic priors. Solving a graph problem $\Pi$ in this model involves two phases: (i) The preprocessing phase quickly analyzes prior graphs $G_1, ..., G_k$ along with the (previously computed) exact optimal values OPT$(G_i)$. (ii) Subsequently, given a new graph H, a fast query phase must either (a) output the exact solution OPT(H), or (b) correctly report that H is not isomorphic to any $G_i$. Can we avoid computing OPT(H) from scratch when H is isomorphic to some $G_i$? We show that this is the case for a number of problems; for many others, we establish conditional lower bounds. $\textbf{(1)}$ Some NP-hard problems, including Constrained Shortest Path and $\ell_p$-Shortest Path and Constrained Spanning Tree, admit
polynomial preprocessing and query times in our model. In contrast, almost all of Karp's 21 NP-complete problems and $(2-\varepsilon)$-approximate $k$-Center, for every fixed $\varepsilon>0$, admit no such
algorithms unless Graph Isomorphism (GI) is in P, even with O(1) priors. $\textbf{(2)}$ In contrast to conditional $n^{3-o(1)}$ fine-grained lower bounds, our framework achieves an $O(n^\omega)$ query time for Negative Triangle and a near-linear query time for Replacement Path. It also achieves near-linear query time for Maximum Flow. $\textbf{(3)}$ While it remains a major open problem whether infinite-duration games (Paritiy Game, Mean Payoff Game, Energy Game, and
Stochastic Game) admit
polynomial-time
algorithms, they can be easily solved in near-linear time within our model. Our proofs rely on a simple combination of existing tools and are accessible to readers without specialized background.
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All authors Dani Dorfman, Simon Döring, Martin G. Herold, Danupon Nanongkai, Daniel Neuen, Joachim Spoerhase, Zihang Wu
Discrete Mathematics · Cross-listed from math.CO
There is no $8$-regular $K_3$-irregular graph
Artem Hak, Sergiy Kozerenko and Andrii Serdiuk
A graph is $K_3$-irregular if its vertices belong to pairwise distinct numbers of triangles. We prove that no $8$-regular $K_3$-irregular graph exists, settling the last unresolved case. Following the initial discovery of such graphs for regularities $r \in \{10,11,12\}$ (Stevanović et al., 2024), our previous work (Hak et al., 2025) showed that no such graphs exist for $r \le 7$, provided the first example for $r=9$, and proved that any $8$-regular candidate must have between $17$ and $22$ vertices. We exclude these possible orders for $r=8$ by combining careful analysis of triangle degrees with integer linear programming techniques. Meanwhile, a recent construction (Zhang, 2026) established that regular $K_3$-irregular graphs do exist for all $r \ge 9$. Together with our results, this establishes that an $r$-regular $K_3$-irregular graph exists if and only if $r\geq 9$.
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Machine Learning
$S^3$: Spectral Null-Space Swap Makes Reasoning Models Efficient
Hongbo Ma, Sansheng Cao, Jiajun Fan et al. (+2 more)
LLMs trained with Chain-of-thought excel in
reasoning capability, but often come with excessive
token cost. We find that the core of
reasoning capacity lies in the Thinking model's weight component within the null space of a projection defined by the corresponding Non-thinking model's dominant singular directions, and removing the subspace component can largely improve
reasoning efficiency without hurting the accuracy gained during thinking-mode post-training. Unlike existing efforts that mostly operate within the dominant subspace, we are the first to unveil the critical role of the null space and harness it for model optimization. Motivated by this finding, we propose Spectral Null-Space Swap ($S^3$), a training-free composition of paired Non-thinking and Thinking checkpoints. Our method keeps the Non-thinking model inside its own dominant subspace and takes the Thinking checkpoint outside it, improving
reasoning efficiency while maintaining accuracy. We extensively evaluate $S^3$ on 2B-30B dense and mixture-of-experts (MoE) architectures spanning 28 evaluation environments across mathematical,
multimodal, and audio
reasoning domains. $S^3$ establishes new empirical Pareto Frontiers among training-free model composition strategies: across all settings, it reduces inference
token overhead by an average of 27.4% compared to full Thinking models while simultaneously improving overall task accuracy by 1.0 percentage point (e.g., yielding +8.3% accuracy on HMMT25 alongside a 33.0%
token speedup). We further use
attention entropy for explanation and find that the retained component produces more concentrated
attention, and we use a simplified analytical model about optimization to demonstrate why null-space can effectively reduce
attention entropy, thereby improving the efficiency of
reasoning.
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All authors Hongbo Ma, Sansheng Cao, Jiajun Fan, Bangji Yang, Ge Liu
Machine Learning
On Trajectory-Aware Training for Masked Diffusion Language Models
Manuel Madeira, Amitis Shidani, Alice Bizeul et al. (+6 more)
Masked
diffusion models (MDMs) generate text by unmasking several
tokens per step, but they are trained and sampled under different conditions. The model is trained on randomly masked sequences, whereas inference follows a trajectory shaped by the model's own predictions. Additionally, each step has no access to what the previous one computed. Recent methods narrow these limitations from separate angles, leaving open how these choices interact. We introduce PUMBA, a unified framework for trajectory-aware training that trains the denoiser on consecutive steps of policy-induced trajectories, passes information between steps, and optimizes them jointly by backpropagation through time. A controlled study of this design space shows that i) exact train–inference
alignment fails due to local overfitting, whereas a looser
alignment still brings training masks closer to those seen at inference; ii) passing continuous information outperforms discrete gradient
estimators through the commitment at each step; and iii) performance improves as backpropagation through time spans more steps, which we support theoretically. Combined, these components match the best checkpoint of a same-size autoregressive model. Building on these findings, we scale PUMBA to supervised fine-tuning of LLaDA-8B, where it improves the trade-off between performance and number of function evaluations (NFEs) in both full-canvas and block
diffusion generation. At matched performance, it needs up to 22% fewer NFEs than standard fine-tuning with twice the budget in full-canvas generation, and up to 26% fewer than standard fine-tuning for the same number of steps in block
diffusion.
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All authors Manuel Madeira, Amitis Shidani, Alice Bizeul, Victor Turrisi, Louis Béthune, Bhavika Devnani, Dan Busbridge, Pierre Ablin, João Monteiro
Cryptography and Security
Dagger: Decoupling-based Model Stealing Attack against Graph Neural Networks
Ying Song, Xiaowei Jia and Balaji Palanisamy
As Graph Neural Networks (GNNs) are widely deployed as Machine Learning-as-a-Service (MLaaS) APIs, model stealing attacks have emerged as a critical security threat. By querying a victim model's black-box API, an adversary can construct a functionally equivalent surrogate model, compromising proprietary intellectual property and downstream security. Existing GNN stealing attacks, however, rely on overly permissive assumptions, such as soft-label outputs, large query budgets, full-graph query access, and prior knowledge of victim backbones that rarely hold in real-world deployments. In this work, we formalize a strictly constrained black-box, hard-label and backbone-agnostic threat model for GNN stealing attacks under a tight query budget. Given these realistic restrictions, we identify four fundamental challenges: sparse local structures and isolated nodes that degrade victim label quality, insufficient supervision signals, systematic imbalance with incomplete class coverage, and backbone mismatch. To address these interlocking barriers, we propose Dagger, a novel two-phase decoupling-based attack framework. Specifically, in Phase 1, Dagger pre-trains a surrogate using decoupled information propagation to preserve structural context over sparse local subgraphs while handling isolated nodes, combined with
manifold-level node mixup to synthesize continuous supervision signals and smooth decision boundaries. In Phase 2, Dagger freezes the encoder and fine-tunes the classifier head via class-balanced sampling paired with logit adjustment to rectify severe query imbalance without requiring extra victim queries. Extensive experiments across four
benchmark graphs and four GNN backbones demonstrate that Dagger consistently outperforms state-of-the-art GNN stealing attacks, achieving up to 18.16% higher fidelity while only utilizing 12.23$\times$ fewer queries than the strongest baseline.
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Robotics
Comparing Utility of Inertial, Occupancy, Semantic, and Intent Information in Human Motion Prediction During Daily Tasks
Max Burns, Maisha Khanum, Monroe Kennedy III et al. (+1 more)
Accurate human motion prediction is crucial for robotic systems operating around people, particularly in complex indoor spaces. In this study, we assess the relative importance of different sources of information in indoor motion prediction with a human motion
diffusion model. We collected a
dataset of nine naive human subjects conducting simulated indoor daily activities while wearing a pair of Meta Aria glasses. This
dataset includes ten buildings from a university campus, and encompasses 238 minutes of
navigation between daily tasks. Overall, we demonstrate a 42% improvement beyond a constant velocity baseline. Including body motion, scene representation, and eye gaze fixation data significantly reduced prediction error. Semantic information was found to be useful for indoor motion prediction, but to a lesser degree than in outdoor
navigation. Providing explicit intent information reduced error beyond any other addition, suggesting that incorporating explicit intent estimation or user input are fundamental for finer prediction of indoor motion. One of the few indicators of intent, eye gaze fixation, was found to be especially useful in predicting deceleration, and provided basic spatial information to the model in the absence of an occupancy map. These results are a first step towards predicting human motion in highly ambiguous indoor scenarios. Code will be made public upon acceptance. Project page: https://human-motion-
diffusion.github.io/
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All authors Max Burns, Maisha Khanum, Monroe Kennedy III, Steven H. Collins
Robotics
PhysWAM: Physically Consistent World Action Model for Autonomous Driving
Dhruv Parikh, Fengcheng Yu, Quankai Gao et al. (+11 more)
World-action models (WAMs) jointly predict how a scene will evolve and how an
agent should act, however joint generation alone does not necessarily impose a shared
geometric constraint on these predictions. We present PhysWAM, a unified world-action model for autonomous driving that co-denoises multiview
video, metric depth, and ego motion within a single flow-matching
transformer. To ground world and action generation in measured scene geometry, we introduce Coupled Point Projection (CPP) that unprojects the generated depth into 3D points, transforms them using the generated $\mathrm{SE}(3)$ ego motion, and minimizes their distance to
LiDAR points transformed using the recorded ego motion. This
geometric constraint promotes physical consistency with the measured scene by jointly supervising generated depth and motion alongside their standard flow-matching objectives. At inference, trajectory selection relies only on a simple label-free consensus rule, with no learned scorer or simulator feedback. We evaluate PhysWAM across NAVSIM v1 and v2 planning, zero-shot closed-loop transfer, and future
video and metric-depth prediction. Despite PhysWAM's simple selection procedure, it achieves strong planning performance and transfers zero-shot to unseen driving environments. It also generates accurate metric depth and temporally coherent
video, with CPP improving both planning and depth prediction. Together, these results demonstrate that the
geometric relationship between scene depth and ego motion provides a direct way to couple world and action generation within a simple unified model.
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All authors Dhruv Parikh, Fengcheng Yu, Quankai Gao, Jiawei Yang, Junjie Ye, Maulik Bhatt, Thang Vu, Charles Ochoa, Rowan McAllister, Igor Vasiljevic, Rajgopal Kannan, Viktor Prasanna, Vitor Guizilini, Yue Wang
Computer Vision
SoL-Refiner: Speed-of-Light One-Step Refinement for High-Resolution Video
Haozhe Liu, Tian Ye, Shuchen Xue et al. (+9 more)
High-resolution
video generation is expensive, as its cost grows rapidly with the number of spatiotemporal
tokens. A practical alternative first generates a lower-resolution
video and then applies a refiner, but conventional multi-step refinement introduces a second sampling bottleneck. We present SoL-Refiner, a one-step
video refiner that transforms low-resolution model outputs into 4K
videos with a single denoising step. Our three-stage recipe combines high-resolution continual training, reinforcement learning (RL) post-training, and a final one-step
distillation. We introduce Refiner-Bench, a
video refinement
benchmark constructed from the outputs of different
video generators, and use a shared-input protocol to compare refiners at approximately 2K output resolution. At 2K, the one-step SoL-Refiner outperforms all external refiners on the VBench and UniPercept averages, while at $3840\!\times\!2176$ it improves both metrics over the three-step LTX-2.3 Refiner. With the complete acceleration stack, SoL-Refiner achieves an $8.91\times$ speedup in refinement latency over the same baseline in our 2K latency setting.
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All authors Haozhe Liu, Tian Ye, Shuchen Xue, Yitong Li, Junsong Chen, Haopeng Li, Jincheng Yu, Duomin Wang, Ruihua Zhang, Lei Zhu, Song Han, Enze Xie
Artificial Intelligence
SelfSearch: Reward-Free Search for Self-Improving Agents
Jungwoo Yang, Injin Kong and Yohan Jo
Advances in the coding capabilities of LLM
agents allow them to inspect and modify their own instructions, tools, and execution procedures. Existing approaches use this ability to search for improved
agents through repeated downstream evaluation, which incurs substantial costs and ties the search to the evaluated tasks. We introduce SelfSearch, a reward-free search procedure in which
agents modify themselves using records of previous self-improvement episodes. These records capture the
reasoning, tool actions, and outcomes of earlier modification attempts, providing concrete experience for improving both task solving and self-modification. Without downstream reward signals during search, SelfSearch improves population-mean success over the initial
agent in all six model–
benchmark settings, with individual
agents gaining up to 11.2 percentage points on Terminal-Bench 2.1. On SWE-bench
Multilingual, an
agent improves success by 5.0 percentage points while reducing execution cost by 38.5% on tasks solved by both the initial and evolved
agents. SelfSearch achieves competitive task success with evaluation-guided search baselines at lower search cost. With only \$4.03 in search cost, it produces a harness that solves 82.0% of Terminal-Bench 2.1 tasks with DeepSeek V4 Flash under the settings of a public nine-harness comparison, matching the top-scoring harness, Codex. These results suggest that experience gained through self-modification can improve
agents' downstream capabilities and efficiency.
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Human-Computer Interaction
A Task-Driven Framework for Multiscale Ocean Flow Dynamics through Integrated Simulation and Visualization
James Kress, Jithendra Nadimpalli, Shehzad Afzal et al. (+2 more)
Internal waves are large-amplitude
gravity waves that occur below the ocean surface and propagate along interfaces separating water layers of different densities. Understanding their generation, propagation, and
evolution is essential, as these waves play a vital role in the ocean system by contributing to nutrient transport, biological productivity, and the transfer of energy across the ocean and continental shelf. Domain scientists use high-resolution numerical ocean models, to study internal-wave dynamics and associated coastal and nearshore processes on hybrid computational grids. These models generate large-scale, three-dimensional spatiotemporal
datasets that capture internal wave flow behavior and interactions with multiple ocean variables. These
datasets are generally analyzed using command-line tools with limited interactivity. To address these challenges, we in collaboration with domain scientists designed a task-driven visualization methodology for analyzing multiscale, multivariate flow data on hybrid grids. The framework incorporates a hybrid-grid volumetric reconstruction method, enabling continuous 3D analysis and a coordinated multi-view design that supports interactive exploration of complex flow structures. An insight-based evaluation with domain experts demonstrates that the system enables the identification of previously difficult-to-observe phenomena, including transverse wave propagation, energy transport pathways, and shoaling-driven mixing. Beyond the application domain, our contributions provide generalizable techniques and design principles for visual analysis of multiscale, multivariate flow data on irregular grids.
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All authors James Kress, Jithendra Nadimpalli, Shehzad Afzal, Sohaib Ghani, Ibrahim Hoteit
Machine Learning
TabFM: A Zero-Shot Foundation Model for Tabular Data
Weihao Kong, Erez Louidor Ilan, Shuxin Nie et al. (+6 more)
Tabular machine learning typically relies on per-
dataset workflows, fitting tree ensembles or running AutoML searches from scratch for every task. We present TabFM, a 400M-parameter tabular foundation model that formulates supervised tabular prediction as in-context learning. TabFM produces calibrated zero-shot predictions in a single forward pass without task-specific tuning. Trained entirely on synthetic tables generated from structural causal models, TabFM learns general tabular representations that transfer zero-shot to real-world tasks. Across all 51
benchmark datasets in TabArena (38 classification and 13 regression), zero-shot TabFM ranks first among default tabular foundation models and outperforms tuned AutoML pipelines. Two extensions over the same frozen weights improve performance further on both tracks: multi-view feature expansion with ensembling and post-hoc calibration (TabFM+), and LLM-guided,
dataset-specific data processing and feature engineering (TabFM-Auto).
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All authors Weihao Kong, Erez Louidor Ilan, Shuxin Nie, Taman Narayan, Rajat Sen, Yichen Zhou, Deqing Fu, Samet Oymak, Abhimanyu Das
Machine Learning
Kolmogorov-Arnold Classifier Systems as Universal Approximators
Hiroki Shiraishi, Hisao Ishibuchi and Masaya Nakata
As the input dimension $n$ grows, rule-based machine learning, such as Learning Classifier Systems (LCSs), faces a fundamental scalability bottleneck for function approximation: both rule count and parameter count grow exponentially with $n$. Traditional LCSs partition the $n$-dimensional input space directly, requiring $\mathcal{O}(m^n)$ rules for adequate coverage, where $m$ is the per-variable resolution. This article breaks from this paradigm by reorganizing rules dimension-wise, guided by the Kolmogorov-Arnold representation
theorem: any continuous $n$-dimensional function can be expressed as a finite superposition of one-dimensional functions. The proposed Kolmogorov-Arnold Classifier System (KACS) decomposes the target function into one-dimensional subproblems and assigns a dedicated ruleset to each, reducing the worst-case rule count from $\mathcal{O}(m^n)$ to $\mathcal{O}(mn^2)$ and replacing $n$-dimensional local models with one-dimensional models requiring only two parameters per rule, independent of $n$. We also provide the first constructive proof that an LCS, namely KACS, is a universal approximator for continuous functions on compact domains. Evaluated against a direct $n$-dimensional input space partitioning approach under otherwise identical conditions, KACS achieves competitive accuracy in many settings while using only 2% to 40% of the parameters. Our implementation is available at https://github.com/YNU-NakataLab/KACS.
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Artificial Intelligence
BrainNet Studio: A Unified Toolkit for Brain Network Construction, Intelligent Analysis, and Visualization
Xiwei Zeng, Shengrong Li, Yiheng Liu et al. (+3 more)
Brain networks characterize structural and functional relationships among
brain regions and support research on cognition,
brain disorders, and
brain-computer interfaces. Their time-varying
topology and higher-order spatiotemporal dependencies are not adequately represented by conventional static networks. Existing tools primarily focus on static connectomes and provide limited integration of dynamic network modeling with modern graph and sequence learning methods. We present BrainNet Studio, an integrated toolkit for static and dynamic
brain network analysis. It provides a unified workflow encompassing network construction, feature extraction, predictive modeling, candidate biomarker identification, visualization, and assisted interpretation. The toolkit integrates 27
algorithms, including deep learning, graph neural networks, and spatiotemporal sequence models, to support classification and the identification of discriminative
brain regions and connections. A large
language model generates researcher-verifiable summaries of functional connectivity, structural connectivity, and structure-function coupling at individual and group levels. Within a consistent computational framework, users can configure analytical tasks, compare methods, inspect outputs, and extend functionality without repeatedly assembling application-specific pipelines. BrainNet Studio provides a practical and extensible platform for connectome analysis in cognitive neuroscience, exploratory studies of
brain disorders, and
brain-computer interfaces. The toolkit is publicly available at https://github.com/xbrainnet/Brainnet-Studio.
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All authors Xiwei Zeng, Shengrong Li, Yiheng Liu, Chunwei Tian, Daoqiang Zhang, Qi Zhu
Artificial Intelligence
Topological Coherence for Self-evolving Multi-agent Systems
Sen Zhao, Ruiqi Kong, Zuyu Zhang et al. (+4 more)
Complex tasks inherently couple workflow structure,
agent responsibility, collaboration, and
memory access: task regions delimit responsibility and tool scope, cross-region dependencies give rise to handoffs, and ownership boundaries delimit private and selectively shared
memory. Existing methods can jointly optimize
agent and communication structures, yet such optimization does not by itself require responsibility, handoff, and
memory boundaries to remain consistent with task dependencies. We term this requirement topological coherence. We introduce TOCOMAS, a
Topology-Coherent Multi-
Agent System. TOCOMAS grounds a task graph in tool interfaces, organizes compatible task nodes into reusable responsibility domains, and derives dependency-induced and profile-conditioned collaboration together with boundary-regulated
memory visibility. During online self-
evolution, TOCOMAS proposes coupled changes to
agent, collaboration, and
memory policies, retaining for subsequent tasks only candidates that satisfy structural constraints and improve evaluated reward. Across BBEH, WorkBench, SWE-Bench-Verified, and CoMemBench, TOCOMAS improves task success over baselines across backbones. CoMemBench also shows gains over the self-evolving baseline in verified progress, handoffs, and
memory isolation.
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All authors Sen Zhao, Ruiqi Kong, Zuyu Zhang, Lifeng Shen, Xinyu He, Xu Zhang, Qinghua Zhang
Human-Computer Interaction
Towards the Threshold: A Fall-Risk Anchored Pareto Framework for Virtual Reality Gait Feedback Selection for Individuals with Multiple Sclerosis
Nafisa Anjum, John Quarles and M. Rasel Mahmud
Reduced walking speed in people with multiple sclerosis (MS) is associated with an increased risk of falls. However, virtual reality (VR)-based rehabilitation studies often emphasize performance improvements without considering the cognitive and physical effort required to achieve them. This study introduces a threshold-anchored efficiency framework that jointly evaluates gait performance and overall effort. A normative walking-velocity target of \(T=1.29\) m/s was derived from the mean walking speed of non-fallers in an independent MS gait
dataset and used as a clinically motivated reference point. Thirty-four adults with MS were evaluated across eight VR feedback conditions spanning unimodal, bimodal, and
multimodal feedback. For each condition, we quantified the proportion of the velocity gap to the target that was closed and the associated cognitive and physical burden. Pareto efficiency analysis identified five non-dominated conditions: Static Visual, Spatial Auditory, Auditory+Visual, Auditory+Vibrotactile, and
Multimodal, whereas Spatial Vibrotactile and Vibrotactile+Visual were dominated. Spatial Auditory showed a favorable performance-effort trade-off, closing 72.7% of the velocity gap while maintaining below-average burden.
Multimodal feedback achieved the greatest gap closure (95.6%) but also imposed the highest burden. These findings demonstrate that greater gait improvement does not necessarily correspond to greater rehabilitation efficiency and provide a quantitative framework for comparing VR feedback strategies according to both performance gains and participant burden.
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Machine Learning
Scene-Consistent Illumination Transfer for Inserted Advertising Graphics
Rameshwar Mishra, Bishshoy Das, A. V. Subramanyam et al. (+1 more)
Replacing a visible advertisement in a broadcast frame is geometrically straightforward but photometrically delicate. A pasted graphic can have the correct perspective and still appear detached when its brightness, shading, or shadow disagrees with the surface beneath it. This paper presents Ad-Relight, an inference-only procedure for transferring scene illumination to a supplied advertising graphic without collecting a banner-specific training set. The procedure first separates slowly varying shade from graphic structure, then probes a pretrained
diffusion relighter with two nearly identical backgrounds to isolate the contribution of the target region. A final pass combines this residual with a smoothed luminance field and a soft attenuation mask. Across 560 generated placements, the approach improves structural similarity, perceptual distance, and illumination agreement over
geometric compositing and direct relighting baselines. Human judgments and an automated preference study show the clearest gains on floor-mounted graphics with nonuniform lighting. The current study is image based; temporal stabilization remains an open extension.
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All authors Rameshwar Mishra, Bishshoy Das, A. V. Subramanyam, Guan-Ming Su
Artificial Intelligence
Video-RSI: Recursive Self-Improvement of Video Understanding Agents via Harness Evolution
Bingjun Luo, Jialin Guo and Siqi Li
Video understanding
agents acquire evidence through an executable harness that controls what they observe and how they use those observations. However, execution traces contain only the evidence acquired by the current harness, leaving competing explanations for failure unresolved and limiting the basis for self-improvement. We introduce
Video-RSI, a framework for recursive self-improvement in which a
video understanding
agent uses its own
language model to revise its harness. Through active
video investigation, the model revisits the original training
videos to test competing failure explanations with additional observations, grounding proposed changes in evidence beyond the existing trace. Cost-aware harness
evolution turns these diagnoses into reusable revisions and determines which revisions to retain by considering both answer accuracy and visual cost. Across our evaluation settings on
video understanding
benchmarks, the evolved
agent improves accuracy while processing fewer frames and achieves competitive accuracy-efficiency trade-offs against existing
video understanding
agents. These results demonstrate the potential for
video understanding
agents to improve their own evidence acquisition and use through harness
evolution. Code is available at https://github.com/bingjunluo/
Video-RSI .
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Machine Learning · Cross-listed from stat.ML
Identifiability Guarantees for Drivers and Dynamics of Delayed Physical Systems
Julien Boussard, Antoine Debouchage and Théo Saulus
A wide range of methods have been proposed, including physics-informed neural networks, which are powerful but do not guarantee identifiability of the dynamics, symbolic regression, which requires a set of precomputed operations, and causal discovery, which is more principled but usually relies on strong assumptions that physical systems may violate. In this work, we develop a theory-grounded method and prove that under a set of permissive assumptions, the structural drivers and drift of
stochastic delayed differential equations are identifiable. Our method outperforms others on a
benchmark for driver identifiability, and on a second
benchmark to evaluate physical consistency of the learned dynamics.
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Machine Learning
An Efficient Machine Learning Approach for Degradation Forecasting in AEM Water Electrolysis
Marco Veneriano, Ani Gjergji, Sebastiano Bellani et al. (+3 more)
This study provides a data-driven analysis of a novel
dataset of single-
cell Anion Exchange Membrane water electrolyzers (AEMWE), operated under constant current load across multiple heterogeneous experimental campaigns. We train and evaluate a range of machine learning models with different complexity, including linear baselines, LSTMs and CNNs, to perform medium-term
forecasting of the
cell voltage degradation curve. The models are assessed within a rigorous training and evaluation framework specifically designed for heterogeneous industrial data.
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All authors Marco Veneriano, Ani Gjergji, Sebastiano Bellani, Andrea Riva, Vito Paolo Pastore, Matteo Santacesaria
Computer Vision
Does Local Video Understanding Transfer Across Encounters? The EgoGears Benchmark
Yuedong Tan, Lei Qi, Yu Liu et al. (+13 more)
Embodied systems must make knowledge acquired during one encounter usable in another despite changes in viewpoint, motion, and illumination. Yet aggregate cross-
video accuracy conflates failures of local perception with failures to preserve observation identity, establish correspondence, and compose evidence, obscuring whether local
video understanding actually transfers. We introduce EgoGears, a complementary single- and multi-
video benchmark designed to diagnose this transition. It contains 567 single-
video and 1,487 multi-
video questions derived from 126 human-collected
egocentric recordings covering 39 outdoor routes. Repeated traversals across movement speeds and lighting conditions ground comparisons in shared physical environments; 531 questions require
alignment across independent recordings. Single-
video questions measure the local visual, spatial, and motion evidence available to a model, while multi-
video questions test whether evidence remains bound to the correct observation and can be composed into consistent route relationships. We report 29 single-
video and 31 multi-
video MLLM configurations across six model families in the main leaderboard. Among the 20 configurations evaluated comparably on both splits, every model performs worse on multi-
video questions, with a mean decrease of 22.5 percentage points, and the gap persists when answer format and scoring are held fixed. The gap is not explained simply by additional
videos or recording boundaries. The central bottlenecks are observation–evidence binding and ordered route-state tracking. The code and
benchmark are publicly available at https://github.com/lei-qi-233/EgoGears.
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All authors Yuedong Tan, Lei Qi, Yu Liu, Di Wen, Ruiping Liu, Xiaoye Wang, Yufan Chen, Junwei Zheng, Chengzhi Wu, Chen Zhang, Zhihang Chen, Haiwen Sun, Zongwei Wu, Radu Timofte, Danda Pani Paudel, Kunyu Peng
Computer Vision
Look Closer: Patch-wise Supervision for AI-Generated Image Detection
Zhida Zhang, Tao Wu, Siyu Liu et al. (+1 more)
How much of an image does a detector need to see? Small RGB regions can retain useful evidence of image synthesis even when they reveal little of the full scene. Motivated by single-patch detection, we study patch-wise supervision: a shared backbone classifies explicit crops, each crop receives its own loss, and patch probabilities are averaged only at inference. The procedure requires neither handcrafted residual filtering nor a learned image-level fusion module. Experiments span single-patch selection, multiple generator collections, and four CNN and
Transformer backbones. On GenImage, the reported patch-wise variants improve average accuracy over their whole-image counterparts across all four backbones. Comparisons of supervision granularity, source resolution, crop size, and inference coverage further characterize the approach, while post-processing tests and difficult-image evaluation reveal its limitations. The historical experiments include evaluation-based model selection, so their scores are not presented as a uniformly selected leaderboard comparison. Overall, the study identifies explicit local input and patch-level supervision as a simple, useful combination for investigating generalizable AI-generated image detection.
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All authors Zhida Zhang, Tao Wu, Siyu Liu, Jie Cao