Robotics
TempoBridge: Language-Guided Tempo Control for Vision-Language-Action Policies
Yeonseo Lee, Hyosup Shin, Guebin Hwang et al. (+1 more)
Vision-
Language-Action (VLA) models are effective at understanding what task to perform, but provide limited control over how it should be executed, such as moving quickly or slowly. We introduce TempoBridge, a lightweight framework that uses frozen VLA representations to modulate actions according to tempo cues in the instruction at each task phase, without additional tempo-conditioned
robot demonstrations or tempo-specific base-policy fine-tuning. TempoBridge extracts tempo cues from contextual VLM representations, aligns them with task progress through a causal phase router, and modulates nominal motion commands during execution. Across LIBERO tasks, TempoBridge improves Tempo Success Rate from 52.6% to 89.7% under canonical tempo instructions while retaining high task success. It also preserves near-baseline performance when no tempo cue is present and generalizes to unseen tempo expressions without additional training. Experiments on a physical
robot further demonstrate
language-conditioned tempo modulation in real-world manipulation.
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All authors Yeonseo Lee, Hyosup Shin, Guebin Hwang, Sungho Jo
Computer Vision
Iris-3B: Going Beyond the Latent with Pixel-Space Diffusion Training, Conversion and Fine-Tuning
Hanqiu Li Cai and Chema Garabito
Pixel-space
diffusion models avoid the lossy VAE of latent models, which suggests an advantage on downstream tasks where fine-grained detail matters. We test this claim along both routes to a pixel-space backbone. We pretrain Iris-3B, a 3B-parameter pixel-space text-to-image
transformer, from scratch through a $256\to512\to1024$ curriculum, after first ablating the prediction target and representation
alignment at $256^2$ to decide what to scale. We also convert a pretrained latent model, FLUX.2 Klein base 4B, to pixel space. We fine-tune both families for
monocular depth estimation and for image restoration/super-resolution. We find no significant improvement from using a pixel-space generative prior. Fine-tuned for depth with one matched direct-regression recipe, Iris-3B is level with the latent FLUX.2 Klein and the converted pixel FLUX.2 Klein falls behind it, and on $4\times$ DIV2K restoration neither pixel model beats a latent FLUX.2 Klein fine-tune, the converted one trailing it slightly. We document the recipes, the failure modes and the remaining confounds behind this negative result. Nevertheless, Iris-3B shows that pixel-space pretraining with the pixel-
transformer (PiT) head of PixelDiT scales to 3B parameters and to text-to-image quality competitive with latent models, matching Qwen-Image on OneIG under the official evaluators at $1024^2$. We release its weights and training code in the hope that they help pave the way for further work on pixel-space generation.
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Computer Vision
From Global Alignment to Local Grounding: Zero-Shot Chinese Character Recognition with Radical Verification
Yu-Heng Shih, Bing-Chen Wu, Tsz-To Wong et al. (+6 more)
Zero-shot Chinese character recognition (ZS-CCR) aims to recognize characters whose categories are never observed during training, and typically relies on the compositional structure shared between seen and unseen characters. Recent CLIP-style methods represent this structure with the Ideographic Description Sequence (IDS) and align it with glyph images in a shared
embedding space. However, they rely on a single global image–IDS similarity that discards the spatial layout of radicals and, being learned only implicitly from seen classes, generalizes poorly to unseen ones; moreover, global matching often retrieves the correct character within the top candidates yet fails to rank it first when characters differ only in subtle local radicals. To address these issues, we propose a global-to-local two-stage framework. In the first stage, STG-CLIP augments the IDS with explicit tree-position and radical-level
geometric priors, yielding a spatial-aware prototype that provides a consistent spatial description across seen and unseen categories for high-recall global
retrieval. In the second stage, the Radical Verification Module (RVM) uses the radical instances of each retrieved candidate as queries to verify whether the corresponding radicals can be matched to spatially compatible regions in the input glyph. A margin-based gating rule activates the RVM only when the leading global candidates receive similar similarity scores. Experiments on the ICDAR2013
benchmark demonstrate that our method achieves state-of-the-art performance under the character-level zero-shot setting, obtaining 83.06% top-1 accuracy with 2,755 seen classes. Ablation studies further show that the explicit
geometric priors and radical-level verification provide complementary improvements.
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All authors Yu-Heng Shih, Bing-Chen Wu, Tsz-To Wong, Ting-En Yen, Hong-Han Shuai, Bin-Hua Hsieh, Chien-An Chen, Yi-Ren Yeh, Ching-Chun Huang
Sound · Cross-listed from eess.AS
Beyond Token Revision: Investigating Mask-and-Replace Diffusion for Zero-Shot Text-to-Speech
Hounsu Kim, Joonyong Park, Yuki Saito et al. (+2 more)
Unlike autoregressive models, discrete
diffusion-based models for zero-shot text-to-
speech generate
speech tokens in parallel and can revisit earlier predictions. Mask-and-replace training extends mask-only training by randomly replacing some
tokens, and its gains are commonly attributed to self-correction, the ability to revise previously generated
tokens. However, exposure to randomly perturbed context during training may itself improve generation, raising the question of whether these gains require inference-time
token revision. To investigate this question, we use DeMaR, which combines mask-and-replace training with confidence-ranked mask-only sampling while preserving the total training corruption probability. Trained from scratch on LibriTTS, DeMaR achieves lower word error rates (WER) than autoregressive and mask-only
diffusion baselines using the same
speech tokenizer. This advantage persists when each
token remains unchanged after first being unmasked. Matched training conditions on two heterogeneous
speech tokenizers show that both noisy-context augmentation and replacement supervision improve WER under this restriction. These findings demonstrate training-side benefits of replacement beyond enabling inference-time
token revision.
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All authors Hounsu Kim, Joonyong Park, Yuki Saito, Satoru Fukayama, Juhan Nam
Hardware Architecture
BenchmarkAnything: Agent-Driven Construction of Simulator-Ready Microarchitecture Benchmarks
Xiangfeng Sun, Ceyu Xu, Chen Bai et al. (+1 more)
The selection of
benchmark workloads is of paramount importance in computer architecture, as it establishes the yardstick against which architectural innovations are measured and guided. Yet for decades, the SPEC
benchmark suites, comprising merely tens of workloads, have been the de facto standard in academic architectural research, where they are frequently treated as a principal evaluation and optimization target. When a suite this small is relied upon so heavily, it risks architectural overfitting; as our research and prior studies demonstrate, an overly narrow focus can mislead design decisions by overvaluing certain innovations, producing cores that excel on SPEC
benchmarks yet underperform on broader, realistic workloads. To mitigate this overfitting, adopting a large, comprehensive
benchmark suite is the natural solution. However, the immense engineering effort required to strip software into the clean, interference-free binary executables demanded by simulators often makes this highly impractical. In this work, we demonstrate that AI
agents provide an elegant solution to this challenge. Rather than manually curating yet another static
benchmark suite, we introduce an
agent-driven workflow capable of autonomously transforming arbitrary open-source repositories into simulator-ready executables. This automated approach makes workload collection highly scalable, allowing us to rapidly harvest hundreds of diverse applications from public repositories into our
benchmark suite. Through a comparative analysis of our
agent-generated suite against SPEC, we show that it not only achieves higher-fidelity performance assessments but also uncovers novel architectural insights that traditional, static suites fail to expose.
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All authors Xiangfeng Sun, Ceyu Xu, Chen Bai, Yuan Xie
Computation and Language
Arctic Questions, Missing Answers: A Dataset and Benchmark for LLM Abstention in Arctic Science
Benjamin Wilcox, Dawei Gao, Pradeeban Kathiravelu et al. (+3 more)
Large
language models (LLMs) should abstain from scientific multiple-choice questions when no option is valid, but frequent abstention alone does not demonstrate sensitivity to answer availability. We introduce ArcticQA, a
dataset of 194 questions derived from primary Arctic research, with automated checks of answer support and distractor contradiction against source evidence. We further develop ArcticAbstain, a paired
benchmark comparing answer-present and answer-absent conditions, with the correct answer replaced by a distractor in the latter and an explicit abstention option in both. We evaluate eight models from the Gemini, Claude, and ChatGPT families at high
reasoning effort, with three trials per condition, yielding 9,312 recorded responses. Answer-present abstention rates range from 0.0% to 63.0%, whereas replacing the correct answer increases abstention by 5.05 percentage points on average. These findings highlight substantial baseline differences and the need to evaluate abstention frequency and responsiveness jointly. The
dataset and
benchmark are available at https://github.com/BenWilcox8/arctic-qa.
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All authors Benjamin Wilcox, Dawei Gao, Pradeeban Kathiravelu, Douglas Causey, Kewei Sha, Yunhe Feng
Computer Vision
LighTROcc: Lightweight 4D Occupancy Forecasting via Instance-Centric 3D Gaussians
Hwanhee Jung, SeungHyeon Kim, Inkyu Koo et al. (+3 more)
Forecasting future 3D occupancy from surround-view cameras is essential for autonomous driving, yet existing approaches rely on dense voxel or bird's-eye-view representations whose cost grows rapidly with spatial resolution and prediction horizon. Because these representations do not explicitly maintain object identities, they also struggle to preserve instance consistency over time. We present LighTROcc, a lightweight instance-centric framework that represents movable objects with a compact set of learned queries and predicts present and future occupancy in a single forward pass. LighTROcc localizes each query through
attention-guided forward lifting, combining image-space cross-
attention, query-specific depth, and camera geometry to estimate its 3D center. Each instance is modeled as a mixture of anisotropic 3D Gaussians and propagated across future steps using predicted displacements, producing continuous, temporally consistent occupancy forecasts. Experiments on nuScenes and supplemented nuScenes-Occupancy show that LighTROcc outperforms the evaluated dense and instance-wise baselines in instance-level
forecasting accuracy while maintaining strong voxel-level occupancy quality. Across different model configurations, LighTROcc achieves a favorable balance between
forecasting accuracy and computational efficiency, demonstrating the potential of compact instance-centric modeling for camera-based 4D occupancy
forecasting.
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All authors Hwanhee Jung, SeungHyeon Kim, Inkyu Koo, Qixing Huang, Sang Ho Yoon, Sangpil Kim
Systems and Control
Restorative Control and the Limits of Safe Model Discrimination
Micheal Myers Jr
Restorative control seeks lasting recovery through an intervention that ends in finite time. We formulate this objective as a reach-avoid problem in which the input must bring the system safely into a desired basin of attraction before withdrawal. Two candidates share bistable baseline dynamics but accumulate irreversible damage through different allocations of drive-generated exposure. We derive a lower bound on the exposure required for basin transfer and a sufficient construction for safe restoration. When the lower bound exceeds the summed tolerances, every common open-loop input that restores either model causes failure under at least one. With sufficient tolerance, proportional allocation restores both without identification. For feedback from output samples with Gaussian noise, we bound safe-restoration probability in terms of failure risk under the alternative model. A numerical example illustrates the certificates under two sensing regimes.
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Computer Vision
TIRA: Tumor Immune Representation Adaptation for Zero-Shot Cross-Cancer MSI and TMB Prediction
Dasari Naga Raju
Microsatellite instability-high (MSI-H) and high tumor mutational burden (TMB-H) are clinically relevant biomarkers, yet their histopathological prediction remains challenging when models are transferred across morphologically distinct cancer types. Immune-associated spatial patterns can persist across cancers despite these morphological differences, but foundation-model-based predictors trained on a single cancer do not explicitly use this information, limiting cross-cancer generalization. To address this limitation, we propose TIRA (Tumor Immune Representation Adaptation), a target-free framework that refines frozen foundation-model representations using spatial immune
topology, without requiring target-domain data during model development or test-time adaptation. TIRA uses a
topology-supervised biology representation to condition tile-level
attention while pooling only morphological features for joint MSI and TMB prediction. We train TIRA on TCGA-COAD+READ and evaluate it zero-shot on CPTAC-COAD, TCGA-STAD, TCGA-UCEC, and CPTAC-UCEC, covering cross-site, cross-cancer, and combined cross-cancer-site distribution shifts under UNI2, CONCH, and Virchow2. With UNI2, TIRA improved zero-shot AUROC on TCGA-STAD from 0.633 to 0.766 for MSI and from 0.651 to 0.772 for TMB. Source-derived spatial immune
topology improved the cross-cancer robustness of frozen pathology foundation-model representations.
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Computer Vision
Mixture of Layers: Dynamic Layer Routing for Visual Reasoning
Jeonghwan Kim, Sofia Stoica, Jiwan Chung et al. (+5 more)
Pre-trained
vision encoders contain layer-wise visual representations that differ in spatial granularity, semantic abstraction, and sensitivity to local details. However, most
Multimodal Large
Language Models (MLLMs) rely on only the final or penultimate
vision encoder representations or fixed aggregation rules, making visual abstraction largely query-agnostic and limiting access to fine-grained cues such as small objects, spatial details, text, and subtle visual attributes. In this work, we propose Mixture of Layers (MoL), an instruction-conditioned layer routing approach at the visual patch level that dynamically aggregates query-relevant latent representations from intermediate
vision encoder layers. Given a text query, MoL predicts routing probabilities over
vision encoder layers and performs a top-k sparse aggregation over selected hidden states at either the image level, patch level, or through a hybrid routing mechanism. In doing so, MoL enables query-adaptive access to layer-specific visual features for fine-grained visual
reasoning. Our experiments across 7 fine-grained visual
reasoning tasks demonstrate substantial performance improvements, especially across fine-grained visual grounding and understanding tasks such as +18.9% improvement on V* in overall accuracy, +4.5% on HRBench4K, and +16.3% on CharXiv compared to the baseline MLLMs, without resorting to multi-resolution inputs, simple interleaving of multiple
vision encoders, or increasing the number of patch
tokens. We study
vision encoders' receptive field scales across different layers and their sampling behaviors to provide an in-depth analysis of why layer-wise sampling is helpful, demonstrating that conditional visual representations are a key step towards better visual perception and
reasoning in MLLMs. Our project page is available at https://wjdghks950.github.io/mol.github.io/.
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All authors Jeonghwan Kim, Sofia Stoica, Jiwan Chung, Ansel Blume, Hyeonjeong Ha, Zhenhailong Wang, Xin Luna Dong, Heng Ji
Computer Vision
InscriptionOCR: A Dataset and Method for Understanding Inscriptions
Jaidev Sanjay Khalane, Akbar Ali, V. N. Prabhakar et al. (+1 more)
Ancient script image restoration is a fundamental problem in computer
vision, as it directly affects the reliable analysis and interpretation of historical documents and inscriptions. Ashokan Brahmi is an ancient script extensively used during the reign of Emperor Ashoka in the 3rd century BC, primarily for inscriptions in Prakrit. These inscriptions, including major and minor rock and pillar edicts, constitute a valuable yet largely unexplored source of data for computational analysis. The degraded nature of inscription imagery and the lack of standardized digital resources pose significant challenges for automated processing. We present an end-to-end AI-based framework for understanding ancient inscriptions that encompasses image enhancement, optical character recognition (OCR), transliteration, and neural machine translation (NMT). The proposed pipeline processes low-quality images captured directly from stone inscriptions, performs image restoration and Brahmi script character recognition, maps the recognized characters to the Roman script, and finally translates the resulting Prakrit text into English. We also introduce two new
datasets: (i) InscriptionOCR
Dataset: the largest publicly usable digital OCR
dataset for Brahmi script to date, consisting of over 200,000 character images across about 600 classes, and (ii) a bilingual Prakrit-English parallel corpus comprising over 2,000 sentence pairs for NMT. We believe that the proposed framework and
datasets will facilitate future research in ancient script analysis, low-resource OCR, and digital epigraphy.
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All authors Jaidev Sanjay Khalane, Akbar Ali, V. N. Prabhakar, Shanmuganathan Raman
Computer Vision
Controllable Crowd Generation through World-Model Planning
JunGyu Lee, Jisu Shin, Seunghyun Shin et al. (+1 more)
Crowd simulation plays a central role in
robot navigation, autonomous driving, and urban planning. For these applications, realistic simulation requires crowds to adapt their behavior to environmental changes and user objectives. However, existing methods that rely on predefined control settings have limited flexibility in accommodating new user-specified objectives. To address this limitation, we propose Ctrl-CWM, a multi-
agent Controllable Crowd World Model that integrates crowd generation and run-time control. Our key idea is to adapt the world-model principle of planning using imagined futures to crowd simulation. To this end, Ctrl-CWM consists of an encoder that learns a representation of human motion dynamics, an actor that proposes pedestrian displacements, a critic that evaluates imagined crowd trajectories, and a planner that selects actions. We first learn human motion dynamics through trajectory prediction on real-world pedestrian
videos and then freeze the encoder to preserve them. Using this representation, the actor generates imagined crowd trajectories through repeated state updates, and the planner combines the critic's scores with user costs to select actions. Repeated planning advances the simulated crowd, while additional user costs introduce new control objectives without retraining. We extensively evaluate crowd generation under varied
agent arrival conditions and run-time control across avoidance and attraction scenarios. Ctrl-CWM outperforms the state-of-the-art method on most crowd realism and collision metrics, and adapts crowd behaviors to user-specified objectives introduced during simulation. The project page is available at https://jungyu0413.github.io/Ctrl-CWM
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All authors JunGyu Lee, Jisu Shin, Seunghyun Shin, Hae-Gon Jeon
Computational Complexity
Optimal (Parallel) Spooky Pebbling on Binary Trees
Mingyu Lee, Sanghyun Lee and Kabgyun Jeong
Pebble games model computations under a fixed space budget. Spooky pebbling allows
quantum memory to be released by measurement, with the resulting phases corrected later. We study two-input computations with binary-tree dependencies and determine the optimal work and parallel depth for complete binary trees. Our key idea is to clean up the tree in blocks, reducing repeated recomputation of intermediate values. For the complete tree $B_h$ with $n=2^h-1$ vertices and every space budget $h+1\le s\le n$, we give an
algorithm with asymptotically optimal work $\Theta(nh/\log(s+1))$. At the minimum budget $s=h+1$, this improves the $O(n\log n)$ bound of Kornerup, Sadun, and Soloveichik to $\Theta(n\log n/\log\log n)$, resolving their time-optimality question. We also construct a parallel schedule with optimal depth \[ \Theta\!\left(h+\frac ns \max\!\left\{\frac{h}{\log(s+1)},\,1+\log^*h\right\}\right). \] Our lower bounds hold for every full binary tree. We also show that achieving optimal parallel depth can require asymptotically more work than minimizing work alone. Applying our schedule to the RNS point-addition trees in the public implementation of Chevignard, Fouque, and Schrottenloher reduces their Toffoli/AND gate count by $20.86\%$ for a P-224 instance and $22.66\%$ for a P-256 instance, using the same arithmetic
circuits and peak workspace.
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Machine Learning
An extended deep energy method for thermo-mechanical crack propagation
Han Zhang, Mehrisadat Makki Alamdari, Babak Shahbodagh et al. (+4 more)
Thermo-mechanical fracture couples transient heat conduction on a cracked domain with a crack that grows as the temperature and the displacement evolve. Neural energy solvers have been proposed for phase-field fracture and later extended to represent a sharp crack through the network input, but heat conduction on the cracked domain and crack propagation under the resulting thermal stresses have not yet been treated together in these solvers. We present an extended deep energy method for thermo-mechanical crack propagation in which the crack remains a sharp polyline. Two networks represent the temperature and the displacement and receive the crack through a scalar
embedding function, discontinuous across the crack and smooth elsewhere, so that both fields can jump across it without a regularization length, and the displacement is enriched near the tip by the Williams expansion with trainable amplitudes. The two fields are obtained by minimizing an incremental conduction functional and the thermoelastic potential energy in a staggered sequence, with Monte Carlo integration on points stratified over background elements, densified near the tip and redrawn during training. The stress intensity factors are extracted by the interaction integral with the area term of Wilson and Yu and checked by a sweep of the contour radius, and the crack advances at the maximum hoop stress angle when the energy release rate of the kink reaches the critical value at the crack-tip temperature. On a stationary thermal edge crack the extracted stress intensity factor agrees with the published value to 0.11%, in a functionally graded shear test initiation agrees with an independent sharp-crack finite element solution to within one load step, and on a notched cruciform specimen the crack paths follow the published solutions under mechanical, thermal and combined loading.
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All authors Han Zhang, Mehrisadat Makki Alamdari, Babak Shahbodagh, Mohammad Vahab, Cosmin Anitescu, Timon Rabczuk, Elena Atroshchenko
Computer Vision
SkillCycle: Co-Evolving Agent Policies and Skill Banks
Ling Li, Qiuyu Shen, Zheng Jiang et al. (+3 more)
Internalizing external skills changes a
language agent's capabilities and, with them, the value of its remaining guidance: rules can become redundant, misleading, or insufficient for newly encountered decisions. This creates a coupled problem of learning from skills and adapting the skills that supervise further learning. We introduce SkillCycle, a framework for co-evolving
agent policies and skill banks through a feedback loop between skill internalization and rule revision. Our central contribution is to give
distillation feedback a second role:
token-level contextual differences help locate rules for inspection, while interaction outcomes guide edits to their content and applicability. SkillCycle alternates between two phases: policy learning with a fixed skill bank and router, and rule revision with a frozen policy. Candidate edits undergo rule-level and whole-bank environment comparisons before they guide the next learning cycle. On WebShop, SkillCycle with a 3B model achieves a success rate of 74.74% and a score of 88.37 without inference-time skill inputs, representing relative improvements of 0.73% and 3.96% over the state-of-the-art (SOTA) model, respectively. In Cycle 3 ablations on ALFWorld and WebShop, SkillCycle's no-skill success rates improve by 10.18% and 18.11% relative to a static skill bank, and by 2.41% and 2.50% relative to a single bank update, respectively. These results show that continually revising skill guidance as the
agent's capabilities change helps transform external skills into policy capabilities that require no skill inputs at inference. We will release code, configurations, skill banks, and evaluation protocols.
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All authors Ling Li, Qiuyu Shen, Zheng Jiang, Qinwei Ma, Yuxuan Liu, Zhidong Deng
Systems and Control
Hierarchical Defense in Leader-Defender-Attacker Games: Nested Equilibria and Receding-Horizon Control
Honglei Guo, Hanfang Zhang, Guochuan Zhang et al. (+1 more)
We formulate a discrete-time leader-defender-attacker game that combines hierarchical interaction within a protective group with competition against an external attacker. The leader seeks to reach a prescribed demand point while avoiding capture, whereas the defender seeks to intercept the attacker while remaining near the leader. To capture their distinct objectives and asymmetric interactions, we introduce a nested
equilibrium concept combining a leader-defender Stackelberg relationship with a Nash-type best-response condition for the attacker. For linear dynamics and quadratic objectives, we derive affine response laws and a backward recursion, together with sufficient conditions for unique stagewise solutions. We further express arrival, capture, and interception conditions as quadratic inequalities in the initial joint state, characterizing winning regions under the resulting policy. A receding-horizon
algorithm implements the computed control laws using updated state information. Numerical experiments over randomized initial configurations examine the influence of the prediction horizon and compare the proposed method with an independent-control baseline, illustrating differences in game outcomes and the
agents' realized costs.
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All authors Honglei Guo, Hanfang Zhang, Guochuan Zhang, Yuhan Zhao
Computer Vision
Event-Aligned Visual Action Reasoning for World Action Models
Xiaomeng Yang, Yushu Wu, Yi Gao et al. (+4 more)
World-Action Models (WAMs) utilize future visual prediction as an intermediate
reasoning process to guide action generation. However, existing WAMs typically structure visual imagination according to predefined temporal intervals, without explicitly accounting for the different roles of task-critical interactions and connecting transitions. We argue that effective visual foresight should align directly with task-relevant interactions and their corresponding
reasoning demands. To this end, we introduce an event-aligned visual action
reasoning framework that organizes visual-action prediction around interaction events. Through event-aligned visual-action supervision, WAM learns to generate event-aligned visual context in each imagined rollout, placing greater emphasis on critical state changes that inform action generation. This shapes the visual
reasoning granularity according to the underlying interaction dynamics, with detailed
reasoning around task-critical events and coarser progression through connecting transitions. Furthermore, we introduce an execution validity head that identifies the valid portion of each predicted action sequence, avoiding redundant actions during chunked inference. Experiments demonstrate a 10.26 percentage point improvement in DOMINO success rate over baseline and competitive performance on RoboTwin 2.0. It also transfers from DOMINO Level 1 to Levels 2 and 3 without target-level adaptation.
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All authors Xiaomeng Yang, Yushu Wu, Yi Gao, Yuhao Lei, Xuan Zhang, Pu Zhao, Yanzhi Wang
Artificial Intelligence
RSI-Forge: From Research Papers to Environments for Recursive Self-Improvement
Renxiong Wang, Darvin Yi, Abril Herrlein et al. (+16 more)
Environments are the foundation of recursive self-improvement: they provide the problems
agents work on and the feedback used to evaluate progress. Yet constructing challenging research environments with reliable evaluation still depends on domain experts, limiting their scale and disciplinary coverage. We introduce RSI-Forge, a multi-
agent pipeline that turns published papers into executable environments for self-improvement. Three
agents coordinate construction, reproduction, and review to produce tasks with automated evaluators; each paper's method is independently reimplemented to establish a baseline score. We present 210 environments across 18 fields, including 90 reviewed by independent human domain experts. Both experts and
agent judges give high ratings to the potential for improving the provided starting solutions and the evaluators' ability to distinguish solution quality, whereas experts are more critical of shortcut resistance, faithfulness to the source paper, and whether a single idea can exhaust a task. To validate their use for repeated improvement, we evaluate four models over 3 successive attempts on 120 environments, with each attempt inheriting prior code and notes while model weights remain fixed. At least one model improves after the first attempt in 84% of environments. Models also outperform the reproduced paper methods in 68 of the 120 environments, demonstrating room for gains beyond these baselines. Transcript analysis identifies work beyond parameter tuning in 95% of these successful attempts. Analysis of the resulting trajectories shows that models scoring lower on these tasks explore less, more often accept gains smaller than the reported standard error, and rely more heavily on tuning to the development set. RSI-Forge provides a scalable approach to constructing research environments for training and evaluating self-improving
agents.
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All authors Renxiong Wang, Darvin Yi, Abril Herrlein, Anas Mahmoud, Advait Gosai, Lisiman Hua, MohammadHossein Rezaei, Xingang Guo, Anisha Gunjal, Utkarsh Tyagi, David J. Lee, Minglai Yang, Haris Riaz, Chenguang Wang, Huaxiu Yao, Daniel Yue Zhang, Aakash Sabharwal, Tong Zhao, Yunzhong He
Machine Learning
What a Reporting Convention Hides: A Matched-Budget Audit of Quantum Natural Gradient with an Exactly Computed Metric
Lu Wei, Yufeng Wang and Haibin Ling
Several published comparisons of variational
quantum optimizers time only runs that reach a target loss, or read the verdict at a single target. Either convention could decide whether an optimizer's costlier steps pay off. We measure how much each convention changes verdicts among Adam, simultaneous perturbation
stochastic approximation (SPSA) and
quantum natural gradient (QNG), on initializations held out from the selection of settings. We compute the exact metric that preconditions QNG, price every step in
circuit evaluations and give every method the same budget. In a median pooled over
circuit widths, cost families and a sweep of settings with common misses, SPSA needs more than twice Adam's evaluations to reach a loose target. Dropping the censored runs that miss the target hides this gap. On the global-cost family we hold fixed the settings selected for a strict target. QNG then usually reaches the loose target after Adam but the strict target first. QNG's strict-target lead disappears when the metric's simulator price, linear in the number of parameters, is replaced by an assumed hardware count quadratic in that number. We recommend charging every miss the budget and reporting verdicts across targets.
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Distributed Computing
Democratizing MoE inference on commodity GPUs with CoMoE
Ruwen Fan, Yuezhi Zu, Minhui Xie et al. (+5 more)
Deploying Mixture-of-Experts (MoE) models relies heavily on Expert Parallelism, which generates intense inter-GPU communication. Consequently, state-of-the-art inference systems require high-bandwidth, P2P interconnects (e.g., NVLink) in datacenter GPUs to handle massive
token routing, making deployment prohibitively expensive. Consumer GPUs offer comparable compute power at significantly lower cost, promising to democratize MoE inference for individuals and enable
privacy-preserving local deployments. However, their bandwidth-limited (only weak PCIe bus bandwidth) and host-mediated interconnects (no P2P support) introduce severe communication bottlenecks. We present CoMoE, a communication-efficient MoE inference system that resolves this mismatch through novel host-centric routing. Our key insight is that the unique communication
topology provides the opportunity to elevate the host to an active routing hub, which can fundamentally reduce communication volume and eliminate global synchronization-induced stalls. Specifically, for
token dispatch, we introduce host-backed
token multicast to write shared
tokens to the host exactly once, eliminating outbound transmission redundancy. For
token combine, we propose a fine-grained,
token-level aggregation mechanism using host staging buffers, which replaces rigid global synchronization and mitigates straggler effects. Evaluation on RTX 5090 GPUs shows that CoMoE improves inference throughput by up to 1.46x, approaching the performance of NVLink-capable A800 GPUs at only 23.4% of the hardware cost.
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All authors Ruwen Fan, Yuezhi Zu, Minhui Xie, Junru Li, Qingda Hu, Xinjun Yang, Jiwu Shu, Youyou Lu
Information Theory
Physics-Aware Power Optimization for Rydberg Quantum Arrays via Barankin Bound Minimization
Yixiao Li, Jiayi Zhang, Bokai Xu et al. (+1 more)
Rydberg-atom
quantum uniform linear arrays (RAQ-ULAs) offer a promising sensing architecture for direction-of-arrival (DOA) estimation in terahertz (THz) beam
alignment. Existing studies often model the
quantum receiver as a macroscopic linear block and rely on the Cramer-Rao bound (CRB) for array evaluation or power allocation. However, as a local variance bound, the CRB cannot capture threshold breakdown caused by spatial ambiguities in low-signal-to-noise-ratio (SNR) regimes. This paper proposes a physics-aware power optimization framework for RAQ-ULAs based on
stochastic Barankin bound minimization. We derive a closed-form
stochastic multipoint Barankin bound (BRB)
matrix under a low-SNR integrability condition and introduce an arcsine-prior calibration to characterize bounded-domain error saturation. By incorporating a Lindblad-guided electromagnetically induced transparency (EIT) readout model, we couple the ambiguity-sensitive BRB with
photon shot noise, power broadening, and
laser Rabi frequencies. A CRB-optimized analytical baseline is further derived to expose the limitation of local-SNR-based allocation. To solve the resulting nonconvex problem, we develop a backtracking majorization-minimization (MM)
algorithm with a Lipschitz-based quadratic surrogate. The
algorithm ensures monotonic decrease and converges to a feasible stationary point. Simulations show that the proposed framework predicts threshold breakdown more accurately than the CRB and enlarges the reliable operating region under severe THz attenuation.
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All authors Yixiao Li, Jiayi Zhang, Bokai Xu, Yin Zhang
Computer Vision
Spatial Latent Reasoning for Embodied Reference Understanding
Ling Li, Jianhui Zhong, Wei Liu et al. (+3 more)
Pointing-gesture visual grounding requires connecting hand geometry with the visual identity and extent of a referred object. A central challenge for continuous latent
reasoning is how to organize these complementary cues into useful intermediate supervision. We propose Spatial Latent
Reasoning (SLR), a framework that structures this supervision around an ordered sequence of
geometric and visual states. A spatial ray state is supervised by fingertip position and pointing direction, followed by four states aligned with target-region features. To construct the visual targets, we introduce parity pooling, which applies polyphase grouping to average region
tokens on four interleaved spatial supports. All states are generated recurrently during training and inference; auxiliary annotations are required only during training. On EgoPoint-Ground, the framework improves mIoU over same-backbone supervised fine-tuning by 2.8, 17.5, and 21.1 percentage points on Qwen3.5-4B, Qwen2.5-VL-7B, and Qwen3-VL-8B, respectively, with improvements on both hard subsets. On YouRefIt, it achieves 77.6% precision at IoU 0.5, a numerical margin of 5.2 percentage points over the reported state of the art under differing evaluation protocols. Ablations support joint
geometric and visual supervision on the standard and similar-object sets, and favor parity over three alternative pooling operators on the standard set. These results support task-structured supervision for continuous pointing grounding. We will release the code and supporting materials.
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All authors Ling Li, Jianhui Zhong, Wei Liu, Zheng Jiang aand Yuxuan Liu, Jingyu Li, Zhidong Deng
Computer Vision
trACT: temporal revelation Airborne Camera Trap
Oliver Bimber, Rakesh John Amala Arokia Nathan, Mohamed Youssef et al. (+3 more)
Effective remote monitoring and surveillance using drones are frequently impeded by severe environmental and thermal clutter, dynamic vegetation, target camouflage, and system latency. Drawing inspiration from the hunting strategies of birds of prey that hover and stabilize their
vision to isolate subtle ground motion, we introduce trACT (temporal revelation Airborne Camera Trap), a lightweight, real-time aerial robotics framework designed for autonomous consumer drones. The system integrates Temporal Max Pooling (TMP), a low-level signal processing method that transforms imperceptible movement across a rolling integration window into robust value and time encodings, with self-supervised motion anomaly detection to isolate target motion from background environmental motion caused by wind gusts and drone drift. To overcome mechanical and processing delays, trACT combines motion prediction with automated gimbal-stabilized optical zoom verification and equitable multi-target verification balancing. Extensive real-world field experiments in densely forested wildlife habitats and surveillance scenarios demonstrate that trACT successfully bridges the gap between wide-area aerial monitoring and precise, autonomous target verification under challenging operational conditions.
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All authors Oliver Bimber, Rakesh John Amala Arokia Nathan, Mohamed Youssef, Vinayak Lal Bhatnagar, Ralf Berger, Klaus Hackländer
Artificial Intelligence
Hanru Bai, Faissal Izermine, Oscar Davis et al. (+1 more)
Flow
Language Models (FLMs) have emerged as a continuous-state alternative to discrete
diffusion language models, yet the role of their continuous representations in
reasoning remains unclear. We investigate this question by comparing the
reasoning efficiency of FLMs and discrete
diffusion models, measured by solution accuracy under matched denoising steps. Unlike discrete
diffusion, which passes categorical states between denoising steps, FLMs evolve a continuous sequence representation throughout denoising and decodes it into discrete
tokens only at the end. Our theoretical analysis shows, from a superposition perspective, how information retained in these continuous states can benefit
reasoning. Intermediate-state interventions provide further empirical support for this theoretical account, showing that removing information about alternative candidates reduces subsequent solution recovery. Together, these findings show that FLMs allow evidence for multiple candidates to persist and inform subsequent
reasoning before a discrete answer is produced. Furthermore, our experiments on maze planning and Sudoku tasks show that FLMs achieve greater
reasoning efficiency in the few-step regime: FLMs achieves higher sequence accuracy than discrete
diffusion baselines at matched model sizes and small denoising steps. On maze planning tasks, FLMs can also achieve comparable accuracy with smaller models. For example, on Maze15, FLM reaches the 95% accuracy target at 64 denoising steps with 36.5% fewer parameters than MDLM. These findings point to continuous state spaces as a promising foundation for
reasoning models that require fewer refinement steps.
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All authors Hanru Bai, Faissal Izermine, Oscar Davis, T. Konstantin Rusch
Machine Learning
Self-Consuming Generative Models with Co-Evolving Human Preferences
Xiukun Wei, Tian Xie, Ding Zhu et al. (+1 more)
Generative models are increasingly trained in self-consuming iterative loops, where users curate preferred samples from model-generated candidates and the curated samples are used to train future generations of the model. Prior work has largely assumed fixed user preferences, but in practice exposure to model outputs gradually reshapes what users perceive as desirable, creating a feedback loop in which model distributions and user preferences co-evolve. We take a first step toward understanding the long-term behavior of such coupled dynamics. We show that when training relies entirely on user-curated synthetic data, iterative curation amplifies initial biases and drives the system toward one of multiple singleton
equilibria in which the instance holding an initial advantage eventually dominates. In contrast, injecting reference data into training at a sufficiently large rate fundamentally changes the dynamics and yields a unique globally attracting
equilibrium. Building on this insight, we study how reference-data injection can be used to control long-term outcomes, and propose an efficient
algorithm that jointly selects a reference distribution and its mixing weight to steer the coupled system toward
equilibria that preserve desired attributes while minimizing data collection costs.
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All authors Xiukun Wei, Tian Xie, Ding Zhu, Xueru Zhang
Databases
Indexing Designs and Adaptive Data Distribution Optimization for In-Memory Databases on Hybrid DDR–CXL Memory
Huijie Cao, Shun Yang, Yinan Zhang et al. (+3 more)
CXL
memory expands single-node
memory capacity for in-
memory databases but has higher latency and lower bandwidth than local DDR. Hybrid DDR–CXL databases require joint indexing and data distribution design. Indexing designs differ in access and migration paths and constrain the placement of indexes and tuples, creating trade-offs in runtime performance, DDR
memory efficiency, and system integration complexity. Their performance impact is thus difficult to determine. Indexes and tuples may differ in access characteristics: placing them in the same tier may reduce DDR efficiency, while separate management adds tracking and decision overhead. We propose five indexing designs based on two approaches: treating the two tiers as a unified data space or managing them separately. We analyze their trade-offs and underlying causes and develop adaptive data distribution optimization for single-index designs in which the database explicitly manages distribution. The method manages index objects and tuples separately, reduces metadata overhead through hierarchical hotness tracking and an index object residency policy, and extends benefit-aware admission and randomized eviction to determine and adjust their placement according to each type's expected benefits and
memory footprints. We implemented the designs in a hybrid DDR–CXL database prototype and evaluated them using YCSB. The single-index design with direct addressing achieves the best performance under most workloads and system configurations. The dual-index design performs better mainly when DDR capacity is limited or accesses are highly concentrated, and has relatively low integration complexity. Adaptive distribution optimization improves DDR
memory efficiency and system performance, enabling single-index designs to achieve up to $1.59\times$ the throughput of their counterparts with non-separated management.
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All authors Huijie Cao, Shun Yang, Yinan Zhang, Huiqi Hu, Xuan Zhou, Weining Qian
Information Retrieval
Finding the Right Balance: Relevance and Diversity in LLM Retrieval
Guillaume Brouillette, Faustin Kagabo, Usef Faghihi et al. (+1 more)
Retrieval diversification is widely available in
retrieval-augmented generation (RAG) frameworks, yet prior studies disagree on whether it improves
retrieval and answer quality. We show that its effectiveness varies primarily with candidate-pool redundancy, in a pattern consistent with the number of distinct evidence pieces a query requires. Using controlled near-duplicate injection and production-style overlapping chunking, we find that diversification harms relevance, evidence coverage and answer quality on clean pools, but becomes beneficial on multi-evidence tasks when redundancy causes nearest-neighbor
retrieval to select repeated passages. We therefore introduce a query-adaptive rule that diversifies only when the effective number of distinct documents in the nearest-neighbor top-$k$ selection falls below the query's evidence requirement. Computed from existing
embeddings, the rule captures most of the achievable gain, transfers across
datasets and encoders and automatically reduces to nearest-neighbor
retrieval for single-evidence queries. We also introduce RNG-Score, a
geometric reranker with an exact nearest-neighbor fallback whose margin indicates duplicate structure. Overall, we conclude that diversification should be used selectively, based on observable redundancy and evidence requirements.
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All authors Guillaume Brouillette, Faustin Kagabo, Usef Faghihi, Nadia Ghazzali
Machine Learning
Shared Geometry As A Rosetta Stone: Cross-Modal Alignment Without Paired Data
Dominik Schnaus, Thomas Dagès, Daniel Cremers et al. (+2 more)
Multimodal representations enable zero-shot classification and
retrieval, but aligning independently trained models usually requires large amounts of paired data. Yet, the Platonic Representation Hypothesis suggests that models trained on different modalities may converge spontaneously toward a shared representation geometry. But then, do we even need paired examples for cross-modal
alignment? Remarkably, we show that paired examples are unnecessary for coarse cross-modal
alignment. Our simple Wasserstein Procrustes method with a coarse
geometric initialization aligns two disjoint
embedding sets by estimating a single orthogonal map without seeing any pairs. Across
datasets, modalities, and unimodal models, we show that we can consistently align independently trained representations without pairs, and standard
geometric alignment metrics accurately predict when this is possible. Nevertheless, we can naturally benefit from paired examples. In the very few-pair regime, our method substantially outperforms existing ones, while staying competitive with pair-based methods with more added examples. Finally, we demonstrate that the resulting
alignments can enable text-to-image generation without paired examples. These results show that independently trained models often share enough geometry to establish cross-modal correspondence with little or no paired data.
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All authors Dominik Schnaus, Thomas Dagès, Daniel Cremers, Xi Wang, Phillip Isola
Data Structures and Algorithms
Generating cyclic pivot Gray codes for well-ordered $k$-degenerate graphs in constant amortized time
Lei Dong, Dennis Wong, Bowie Liu et al. (+4 more)
A graph $G$ is $k$-degenerate if there exists an ordering $v_1, v_2, \dots, v_n$ of its vertices such that each vertex $v_i$ has at most $k$ neighbors $v_j$ in $G$ with $j < i$. A well-ordered $k$-degenerate graph is a labeled graph on the vertex set $\{1, 2, \dots, n\}$ in which every vertex $i$ has at most $k$ neighbors among $1, 2, \dots, i-1$. We present the first simple
algorithms that generate, rank, and unrank cyclic pivot Gray codes for well-ordered $k$-degenerate graphs, where consecutive graphs differ by the addition, removal, or pivoting of a single edge. Our
algorithm generates each well-ordered $k$-degenerate graph in constant amortized time per graph, using $O(n^2)$ space, while ranking and unranking take $O(n^2)$ time and $O(n^2)$ space.
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All authors Lei Dong, Dennis Wong, Bowie Liu, Rui Bao, Lin Chen, Chan-Tong Lam, Sio-Kei Im
Machine Learning
Stability and Diversity of Networked Self-Consuming Generative Ecosystems
Xiukun Wei, Yang Zhang and Xueru Zhang
The widespread deployment of generative AI has made it increasingly difficult to distinguish synthetic content from real data. Consequently, synthetic data is inevitably incorporated into the training pipelines of future model generations, forming a self-consuming training loop. Prior work has studied the effects of such recursive self-consuming training, but analyses have largely been limited to isolated models, where a model consumes only its own synthetic data, or to simplified interactions between two models. This paper takes a first step toward understanding networked self-consuming generative models, in which multiple models consume synthetic data generated by one another through complex interaction pathways. We introduce a theoretical framework representing models as nodes in a directed, weighted graph, with edge weights governing the flow of synthetic data among models. Using this framework, we analyze the long-term behavior of networked models under retraining dynamics, establishing conditions for convergence and characterizing the resulting fixed points. We further investigate how the system's long-term stability and diversity are shaped by each model's access to real data, cross-model data consumption, and the structure of the interaction graph.
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