Numerical Analysis
Inferring constitutive forces from noisy Allen-Cahn movies with moment equations
Soobeen Jung and Hyunju Kim
Inferring an Allen-Cahn constitutive force from images requires more than an accurate optimizer: nonlinear observation noise alters the mean of force features, shared noise couples instruments to the response, and a temporal balance borrowed from another solver can remain biased even on clean data. We develop observation-aware, noise-adjusted moment equations that address these effects before inversion. The surface-tension-calibrated constitutive
transformer (SCCT) couples the resulting moments to a positive Bernstein inverse and can adapt its prior and precision from images and moments. A conditional stability bound separates moment residuals from prior error. Synthetic tests show that correcting the estimating equation matters more than increasing regularizer flexibility; the benefit of learned adaptation is smaller and depends on the training comparison. Forces inferred from planar movies predict unseen planar and three-dimensional evolutions. A ring-cage breakup shows why diffuse-interface accuracy distinguishes forces even when their topological predictions agree, while external surface tension sets the energy scale separately.
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Machine Learning
LEAP: Learning Efficient Action Proposals For LLM Agents
Zhen Xu, Qizheng Zhang, Gerry Wan et al. (+2 more)
LLM
agents are known to be slow in rollouts. An
agent completes a task one step at a time. At each step, it reasons and then chooses an action to execute. The next step and action cannot start until the previous one has finished. Speculative decoding accelerates the rollouts at the reason phase by drafting and verifying the inference
tokens. Recent works have also started to apply similar ideas at the action phase. These works use off-the-shelf models, usually large, to draft action proposals for target model to verify. Large drafters match the target more often but take longer to propose, while small off-the-shelf models are fast but rarely make the same decision as the target. We ask a more general question: what determines the end-to-end speedup of action speculation? To answer it, we develop a latency framework for the speculative round. The framework compares what a round gains with what it costs. The gain depends on how well the drafter predicts the target and on how many steps the task can take before it ends. The cost comes from drafting, from waiting for target verification and from executing tools. Guided by the framework, we introduce LEAP (Learning Efficient Action Proposals) which keeps the drafter small and makes it accurate by training it on the target actions sequences. With a small 0.6B model, LEAP agrees with the target on most decisions and makes
agents up to 60% faster in end-to-end wall clock time, with no systematic change in task success. Across various
datasets, target models and draft models, the framework accounts for most of the measured speedups. We also show the draft model can be online trained with no prior trace collection and match the performance of offline training, making LEAP practical to deploy in the real world.
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All authors Zhen Xu, Qizheng Zhang, Gerry Wan, Shang Zhu, Ce Zhang
Robotics
A Passive AI System for Verifying Physical State on Automated Liquid Handlers
Junqiong Joanne Qiu, Zeckria Kamrany, Ananya Anand et al. (+7 more)
Automated
liquid handlers execute digital protocols, but operators must assemble the deck and confirm that the physical setup matches the intended experiment. We present the Labware Setup Checker, a passive
vision system that verifies deck preparation on an Opentrons Flex without modifying its hardware or firmware. A consumer webcam mounted outside the
robot supplies images to a browser application. The checker segments the deck into slots, corrects slot assignments using the known deck geometry, classifies the labware, and compares the observed setup with a reference protocol library to report missing, misplaced, or unexpected labware. It operates independently of the
robot controller and does not interrupt or gate a run. During SwabSeq Respiratory Viral Panel (RVP) pre-PCR deck preparation, the checker tracked a slot from empty to a base and then to the completed plate-and-base assembly, identifying the completed assembly in all 29 frames before
robot motion. Across these frames, 95.1% of the 348 labware classifications for slots requiring exact matches agreed with the protocol reference. In 480 frames with partially occluded reservoirs, all 6,720 slot assignments were correct. Across three camera positions and two lighting conditions, slot assignment was 100% correct, and 97.73% of labware classifications matched the protocol reference. Median verification time was 2.10 s on a laptop using only its CPU and 4.24 s from frame capture to receipt of results during AWS deployment in the laboratory. These results show that an external camera and independent software can provide verification of deck setup against protocol requirements within seconds, allowing operators to review the physical setup before starting a run.
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All authors Junqiong Joanne Qiu, Zeckria Kamrany, Ananya Anand, Emma Vidal, Yunjia Johanna Bai, Nathan S Chen, Sean Son, Varada Abhyankar, Taryn Jakub, Eleazar Eskin
Computer Vision
CHASE-VLA: Post-Training Quantization Framework for Vision-Language-Action Models with Chunk-Aware Scale Estimation
Jin Hyun, Jung Gyu Min, Gyuhyun Jung et al. (+1 more)
Vision-
Language-Action (VLA) models map visual observations and
language instructions to continuous
robot actions, but a
diffusion-based action expert (AE) poses a key challenge for low-bit post-training quantization (PTQ). The AE is repeatedly invoked across denoising steps and policy queries, where fixed calibration scales can be mismatched with activation ranges that vary with denoising progress and intended motion. We propose CHASE-VLA, a chunk-aware PTQ method that exploits a VLA-specific signal readily available from the policy: the generated action chunk, including its unexecuted future suffix. Rather than relying only on static scale matching for AE layers, CHASE-VLA combines the previously generated chunk as causal action context with denoising step group information to adapt AE activation scales. This enables W4A4 quantization of both MLP and
attention projections in the repeated AE without modifying the pretrained policy. On LIBERO, CHASE-VLA achieves 97.3% average success rate on $\pi_{0.5}$ when both MLP and
attention projections in the AE are quantized to W4A4, restoring FP16-level performance. CHASE-VLA also reduces the weight storage of the quantized AE linear layers by 73.4% and their single-chunk
memory traffic by 70.9% and 71.2% on $\pi_{0.5}$ and GR00T N1.6, respectively, with a predictor overhead of at most 1.26% of the saved storage.
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All authors Jin Hyun, Jung Gyu Min, Gyuhyun Jung, Youngjoo Lee
Computation and Language
Zhihan Yang, Wei Guo, Jean-Marie Lemercier et al. (+14 more)
Despite the success of discrete
diffusion language models (dLMs) for fast parallel decoding, their non-smooth, high-dimensional space hinders trajectory steering for
reasoning and inference acceleration. To overcome this, we present Sigma, the first large-scale (3B/8B) continuous dLM built on steerable, low-dimensional ODE/SDE latent trajectories. Trained blockwise via likelihood optimization, Sigma jointly denoises Gaussian-corrupted
token embeddings while learning an optimal
embedding geometry. To accelerate training, Sigma leverages pre-trained weights from autoregressive (AR) models for warm-starting. During inference, we identify classifier-free guidance and score temperature as essential for high-fidelity
reasoning and coding. Across comprehensive math
reasoning and coding evaluations against state-of-the-art discrete counterparts (masked dLMs and AR baselines), Sigma achieves competitive performance with discrete models on standard
benchmarks (e.g., GSM8K, Minerva, HumanEval, MBPP) after pre-training and on challenging
reasoning tasks (e.g., MATH-500, AIME) after supervised fine-tuning. Beyond performance parity, we uncover key structural properties unique to continuous dLMs: (i)
embedding-space steering effectively governs the quality-diversity trade-off, yielding strong pass@k performance and (ii) continuous trajectories enable graceful degradation for low NFEs and efficient
distillation. These establish continuous dLMs as a promising paradigm for efficient
language generation.
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All authors Zhihan Yang, Wei Guo, Jean-Marie Lemercier, Simon Welker, Yonggan Fu, Mohammad Mahdi Kamani, Sajad Norouzi, Julius Berner, Tomas Geffner, Karsten Kreis, Yongxin Chen, Molei Tao, John Thickstun, Pavlo Molchanov, Ante Jukić, Arash Vahdat, Morteza Mardani
Artificial Intelligence
A GHOST in Long-Horizon Agents: Governance Hazard from Overlooked Safety Constraints across Turns
XinPeng Shen, Lan Zhang, Yixiao Huang et al. (+4 more)
Long-horizon
agents are now playing an increasingly significant role in assisting humans with complex problem-solving. However, it is exactly their extended interaction history that introduces an underexplored execution-safety concern. Under benign interaction conditions, an
agent may execute an action that violates a safety constraint specified many turns earlier. We term this failure mode Governance Hazard from Overlooked Safety Constraints across Turns (GHOST), which may cause irreversible damage. Our experiments reveal that GHOST events are not isolated cases: this failure mode, occurring precisely under benign interaction conditions, yields an occurrence rate of 11.5% on GPT-5.5. Furthermore, we theoretically show that if the residual conditional violation hazard along each safe prefix is bounded below by a non-summable sequence, the execution enters the hazard region almost surely. Leveraging this theoretical insight, we further propose
STAR-Guard, a two-layer defense coupling historical semantic safety constraint restoration with pre-execution audit.
STAR-Guard restores applicable safety constraints to reduce unsafe proposals, while its deterministic audit layer prevents residual violations from reaching the environment. Consistent with this two-layer design, we observe no GHOST events in our experiments under the GPT-5.5 setup.
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All authors XinPeng Shen, Lan Zhang, Yixiao Huang, Haoran Cheng, Jiewei Lai, Leilei Chen, Haoxiang Deng
Artificial Intelligence · Cross-listed from stat.ML
Generalization Bounds for Flow-matching Generative Models for Intrinsically Low-dimensional Data
Saptarshi Chakraborty, Quentin Berthet and Peter L. Bartlett
Despite the remarkable empirical success of flow-matching models, their statistical generalization guarantees remain underdeveloped. Existing analyses often impose restrictive assumptions on the estimated velocity field and yield convergence rates that fail to reflect the intrinsic low-dimensional structure common in real data, such as natural images and molecular geometries. In this work, we study the statistical generalization of flow-matching models for learning an unknown distribution $P_{\mathrm{data}}$ from finitely many samples. We derive finite-sample error bounds on the learned generative distribution, measured in the Wasserstein-$p$ distance, for all $p\geq 1$. Specifically, given $n$ i.i.d. samples from $P_{\mathrm{data}}$, we show that, for every $d>d_p^\ast(P_{\mathrm{data}})$ and appropriately chosen network architectures and hyperparameters, the learned distribution $\widehat{P}^{\mathrm{FM}}$ satisfies $ \mathbb{W}_p(\widehat{P}^{\mathrm{FM}},P_{\mathrm{data}}) \lesssim n^{-1/d}+n^{-1/(2p)}\bigl(\log(1/\xi)\bigr)^{1/(2p)}$ with probability at least $1-\xi$, where $d_p^\ast(P_{\mathrm{data}})$ denotes the Wasserstein-$p$ dimension of the target measure. Our results demonstrate that flow matching naturally adapts to the intrinsic geometry of data and mitigates the curse of dimensionality, as the convergence exponent depends on the intrinsic rather than ambient dimension. These guarantees remain meaningful in high-dimensional regimes and provide a theoretical explanation for the empirical success of flow matching on structured data distributions under substantially more relaxed assumptions than those in existing analyses.
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Machine Learning
Mind the Refinement Gap: When Safe High-Level Robot Plans Produce Unsafe Executions
Stabak Das, Priyesh Ranjan, Xiangfang Li et al. (+1 more)
Language-enabled
robot systems increasingly combine semantic-graph planning with temporal-logic safety monitors. We investigate a trace-completeness assumption in these systems: whether the high-level action sequence checked by a monitor represents the
navigation and implicit action effects induced during execution. We audit this assumption in RoboGuard by comparing its verdict on a surface plan with its verdict on a graph-refined trace under the same Linear Temporal Logic (LTL) specification. Our evaluation comprises 28 controlled cases spanning five action-abstraction families and 14 end-to-end cases in which SPINE [1] generates plans from natural-
language instructions while RoboGuard generates scene-grounded safety specifications. In the controlled evaluation, all 12 targeted abstraction cases exhibit the predicted surface-versus-refined discrepancy while all 16 controls behave as expected, motivating graph-based trace refinement as a lightweight mitigation and a diagnostic tool for physical-AI safety monitors.
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All authors Stabak Das, Priyesh Ranjan, Xiangfang Li, Lijun Qian
Machine Learning
AIGS: Adaptive Incremental Gating System for Online Representation Learning in Non-Stationary Data Streams
SiRui He, Kai Liang Lew, Chui Zi Ong et al. (+1 more)
Real-time data streams in Web of Things (WoT) and edge computing environments often evolve through latent regime changes. For online representation learning under strict computational constraints, the central problem is resolving the stability-plasticity dilemma: keeping useful historical knowledge while rapidly reacting to concept drift. Existing methods employ fixed update schedules or rolling windows. However, they suffer from parameter ossification during sudden shifts and waste computational resources when the stream remains stable. This paper proposes the Adaptive Incremental Gating System (AIGS), a lightweight closed-loop state-aware adaptation framework. AIGS introduces the Shock Ratio, an endogenous residual feedback mechanism that normalizes current reconstruction error against recent variation. This signal drives a Continuous Plasticity Controller that smoothly interpolates between learning plasticity and
memory retention. By treating representation learning as a closed-loop control mechanism, AIGS avoids catastrophic forgetting and maintains a strictly linear $\mathcal{O}\left(k\cdot d\right)$ per-step complexity suitable for latency-sensitive edge devices. Experiments on real-world smart city dynamic streams-spanning traffic networks, meteorological systems, and industrial infrastructure-demonstrate distinct domain-dependent advantages. On Electricity
Transformer Temperature
datasets, AIGS achieves preventative early-warning lead times of 8.31 (ETTm1) and 9.88 (ETTm2) steps under gradual degradation. On Performance Measurement System traffic
datasets, it shows significantly faster post-shift recovery after abrupt mutations. On the highly noisy Weather
dataset, it improves anomaly recall while resisting
stochastic noise overfitting. These findings establish AIGS as a practical, plug-and-play adapter for resource-constrained edge monitoring systems.
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All authors SiRui He, Kai Liang Lew, Chui Zi Ong, Chean Khim Toa
Computer Vision
SpectralCache: Accelerating Diffusion-Based World Models via Spectral Feature Caching
Zhendong Mi, Pu Zhao, Ziyu Hu et al. (+4 more)
Diffusion-based world models enable high-quality interactive environment generation but suffer from substantial inference overhead due to repeated
Transformer evaluations during denoising. Existing caching methods mainly exploit temporal redundancy at the feature or
token level, leaving the underlying mathematical structure of
diffusion features largely unexplored. In this work, we reveal that world-model features exhibit highly stable singular subspaces across nearby denoising steps, while their singular values follow predictable
evolution patterns. Building on this observation, we propose SpectralCache, a training-free spectral caching framework that reuses stable singular subspaces and estimates only low-dimensional singular values through linear extrapolation. We further exploit the spectral consistency between neighboring full-computation features to skip selected expensive backbone evaluations via singular value scaling. Extensive experiments on representative world models demonstrate that SpectralCache consistently improves inference efficiency while preserving generation quality. On HunyuanWorld-Voyager-13B, SpectralCache achieves 5.22x acceleration while maintaining a WorldScore of 65.90 for static scenes, substantially outperforming existing training-free caching methods in inference efficiency.
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All authors Zhendong Mi, Pu Zhao, Ziyu Hu, Xiaodong Yu, Yanzhi Wang, Grace Li Zhang, Shaoyi Huang
Machine Learning
Distributed Learning with Selective State Space Models: Architecture-Aware Convergence Analysis
Adam Piaseczny, Md Kamran Chowdhury Shisher, Shiqiang Wang et al. (+1 more)
Modern state space models (SSMs), such as Mamba2, provide a compelling alternative to
transformers by combining linear-time sequence modeling with recurrent state-space dynamics. However, the behavior of SSMs in distributed learning settings remains poorly understood. In particular, the existing standard
federated learning methods are largely architecture-agnostic, and do not account for the stability, selectivity, and state-space parameterization that characterize modern selective SSMs. To address this, we derive architecture-aware gradient and smoothness bounds for single- and multi-layer selective SSMs, and convergence bounds for FedAvg and FedProx, characterizing how recurrent stability, input-dependent discretization, and state projection norms affect
federated optimization. We then numerically validate the single-layer bounds on sequences generated by a teacher SSM, using a learner that follows the analyzed recurrence. We use this analysis to formulate expectations about the effects of local training and client heterogeneity, and examine these expectations by comparing nine
federated learning
algorithms on Mamba2
language modeling across six text domains. These experiments illustrate how SSM-specific bounds can provide a basis for interpreting the behavior of practical
federated learning
algorithms.
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All authors Adam Piaseczny, Md Kamran Chowdhury Shisher, Shiqiang Wang, Christopher G. Brinton
Distributed Computing
Annotation-Driven Migration of CUDA Programs to Tenstorrent Blackhole
Ayumi Ohno and Shinya Takamaeda-Yamazaki
Tenstorrent Blackhole combines distributed local
memories, explicit inter-core communication, and Tensix cores decoupling data movement from computation. CUDA offers a substantial HPC software base but leaves physical data placement and scheduling largely implicit. Migrating CUDA HPC kernels to Blackhole requires spatial mapping across cores and per-core coordination of compute and data-movement kernels. We present an MLIR-based compiler deriving data placement, inter-core communication, and tile computation from a statically shaped affine CUDA subset. Declarative annotations express choices not fixed by the source, including compute/DM operation placement and streaming granularity. We evaluate it on Gaussian elimination, five-point stencil, and symmetric rank-k update. Relative to our compiler's default realizations, the best measured configurations achieve a 4.2x speedup for BF16 Gaussian and 2.1-2.2x for FP32 Gaussian and Jacobi. A choice's performance impact can reverse with surrounding policies, precision, and loop schedule, motivating comparison of alternative per-core realizations rather than independent policy selection.
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Machine Learning
Context-Tower Conversion Preserves Generation While Freezing Retains Knowledge: Low-Budget AR-to-Diffusion Conversion of MoE LLMs
Wentao Lu, Tianyu Zhu and Jesse Clark
Converting a pretrained autoregressive (AR) model to a
diffusion language model (dLLM) enables parallel generation without pretraining a new model. Published conversion methods differ by roughly three orders of magnitude in training data and have not been compared under a common protocol. We compare two conversions of the same 30B Mixture-of-Experts (MoE) parent, holding the corpus, supervised-
token budget, trainable parameter set and evaluation harness fixed, each under its own training recipe. The in-place model updates a subset of the parent's weights using denoising and representation-
alignment losses; the frozen-tower model instead conditions through cross-
attention on a frozen causal copy of the parent. With 1B training
tokens, the frozen-tower model scores 71.60 on HumanEval pass@10 against 6.19 for the in-place model, an 11.6x improvement. At the same budget it also keeps 95% of the parent's GSM8K score and 99% of its MMLU-Pro score. A dense-parent experiment reproduces the HumanEval separation. Within the two-tower design at about 500M
tokens, freezing the context tower retains substantially more MMLU-Pro performance than training it, while both give similar observed HumanEval scores. Our theoretical analysis establishes that both conversion classes contain an exact sampler for the AR parent under a hard
attention mask and left-to-right commitment of one position per round. Under a shared loss, freezing removes the gradient contribution through the context states. Furthermore, evaluation protocol substantially affects a published 500B-
token conversion's scores in both directions across tasks, while its AR parent's scores vary by less than three points, so comparing dLLMs needs a common protocol. These results show that, in the tested low-budget regime, the frozen-tower configuration retains substantially more of the parent's generation performance than in-place conversion.
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Artificial Intelligence
Coherence-Driven Belief Formation and Population Dynamics of Contagion in LLM Agents
Tathagata Banerjee and Nima Moghaddas
Models of social contagion usually assume how individuals adopt beliefs and derive population behavior from it. We instead empirically measure belief adoption in
language model
agents, quantifying the probability an
agent adopts a claim given how many peers endorse it. We find this adoption kernel to be sigmoid, a characteristic of complex contagion, with a threshold that is sensitive to three sources: the claim's plausibility, the source's reliability, and the
agent's disposition. These three dimensions are well approximated by a single effective dimension which we propose can be understood as the coherence of the incoming belief with the LLM
agent's prior beliefs. Further, we observe a characteristic of complex contagion in the collective dynamics of belief adoption in a system of AI
agents: further spread on clustered than random networks. These systems also exhibit a bifurcating cascade window, and self-sustaining hysteretic consensus which lead to consensus being far harder to remove than to establish.
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Databases
RaBitQ-SSD: Split Codes and Pipelined I/O for SSD-Resident Vector Search
Yuexuan Xu, Jianyang Gao, Michael Norris et al. (+5 more)
SSD-resident approximate nearest-neighbor search is essential when vector collections exceed DRAM capacity. The challenge is to reduce SSD reads and hide I/O latency through concurrent reads and overlap with computation. However, for graph-based search, progressive candidate discovery limits advance I/O planning while IVF search reads inverted lists in full, incurring unnecessary SSD reads. In this paper, we present RaBitQ-SSD, an extension of IVF-RaBitQ for SSD-resident vector search. Specifically, we propose Split-RaBitQ, which retains a configurable prefix of each binary code in DRAM, allowing in-
memory code storage below one bit per dimension. Using this partial representation, it provides an unbiased distance
estimator with a probabilistic error bound. Once the in-
memory coarse quantizer identifies candidate lists, this bound supports
pruning individual candidates within them, enabling finer-grained SSD access. We also design an asynchronous search pipeline that coordinates candidate
pruning with SSD read scheduling to reduce unnecessary reads while overlapping I/O with computation. On
datasets ranging from $5$ million to $1$ billion vectors, RaBitQ-SSD delivers up to $1.74\times$ the throughput of graph-based baselines at $90\%$ recall, while reducing SSD page reads by up to $3.8\times$. Its on-SSD indexes are up to $7.0\times$ smaller and $10.0\times$ faster to build than DiskANN. We also build and search an index of $10$ billion vectors on a single machine with one SSD, achieving over $3{,}000$ queries per second at $90\%$ recall.
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All authors Yuexuan Xu, Jianyang Gao, Michael Norris, Alibek Zhakubayev, Pankaj Singh, Junjie Qi, Matthijs Douze, Cheng Long
Artificial Intelligence · Cross-listed from physics.chem-ph
Equivariant Flow Matching for Electron Density Prediction
Chenxing Liang, Chengdong Wang, Yuchao Lin et al. (+2 more)
Machine learning surrogates for density functional theory (DFT) have been increasingly used to reduce the cost of first-principles calculations. In this arena, predicting real-space electron densities offers a scalable and transferable initialization for self-consistent field (SCF) procedures. However, current methods face a clear dilemma. That is, grid-based architectures incur a high computational cost, while basis-set methods fail to capture the structural correlations inherent in the coefficient space. Here, we develop OrbFlow, an $\mathrm{SE}(3)$-equivariant generative model that predicts Gaussian-type orbital (GTO) coefficients via flow matching. OrbFlow retains the efficiency of a compact atom-centered basis while replacing pointwise regression with a learned probability path over the full coefficient space. It is trained through a two-phase trajectory curriculum that mitigates discretization drift during numerical integration. OrbFlow achieves state-of-the-art accuracy on QM9, reducing density error by 13.6% relative to the previous best model, and reduces error by 51% to 63% on every molecule of the MD
benchmark relative to the strongest prior method sharing its basis. The predicted density also cuts SCF iterations by up to 68% with zero-shot transfer to unseen exchange-correlation functionals and recovers dipole and quadrupole moments to within a few percent of DFT references without any SCF calculation.
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All authors Chenxing Liang, Chengdong Wang, Yuchao Lin, Xiaofeng Qian, Shuiwang Ji
Information Theory
Random Quantum LDPC Codes Approaching the Gilbert-Varshamov Bound
Tushant Mittal, Shashank Srivastava, Madhur Tulsiani et al. (+1 more)
We show that for any $R,\epsilon >0$ and any
prime $p\geq 2$, there exists an infinite family of $p$-ary
quantum low-density parity-check (QLDPC) codes, rate $R$, checks of weight $O_\epsilon(1)$, and normalized distance at least $\delta_{\mathrm{GV}}(p,R)-\epsilon$. Here, $\delta_{\mathrm{GV}}(p,R)$ denotes the
quantum Gilbert-Varshamov (GV) bound for $p$-ary stabilizer codes. In fact, we construct an ensemble of such QLDPC codes, such that a random code from this ensemble is close to the GV bound with high probability. Moreover, this ensemble matches the performance of random stabilizer codes on several
quantum channels. Specifically, it approaches the
quantum capacity of the erasure channel and the hashing bound for memoryless Pauli channels, including the depolarizing channel. A significant challenge in working with QLDPC codes is that they are necessarily degenerate, i.e., contain many low-weight stabilizers. A key contribution of our work is a construction of QLDPC codes with quantitative control on their degeneracy. These codes are obtained by combining known constructions of asymptotically good QLDPC codes with the expander-based distance amplification procedure of Alon, Edmonds, and Luby [FOCS'95]. Our random ensemble is constructed by starting with these low-degeneracy QLDPC codes near the
quantum Singleton bound and concatenating each coordinate with a random inner code. This can be viewed as a
quantum analogue of Thommesen's construction, and as an LDPC version of a result of Ouyang, with an appropriately designed outer code.
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All authors Tushant Mittal, Shashank Srivastava, Madhur Tulsiani, Mary Wootters
Computer Vision
Capturing Dynamics: The 4D Facial Expression Intensity Dataset
Zesheng Wang, Alexandre Bruckert, Pierre Lebreton et al. (+3 more)
The estimation and analysis of facial expression intensity play a crucial role in affective communication and human-computer interaction. Previous research has primarily focused on detecting and estimating facial expression intensity from frame-level 2D representations. However, this limitation restricts a comprehensive understanding of real-world facial expressions, as they are inherently 3D and temporally continuous. This paper investigates the perception of facial expression intensity by introducing the 4D Facial Expression Intensity
Dataset (4DFEID). We employ a parametric face model and compile a total of 2,869 mesh sequences with controlled
geometric variations, generating 4D data instances with diverse peak intensities and identity attributes. Using a Likert scale, we collect more than 90,000 subjective intensity perception ratings via a crowdsourcing platform. We explore various architectures and aggregation methods to establish baselines for episode intensity estimation on the new
dataset, revealing that spatial-temporal graph models consistently outperform traditional frame-aggregation methods. In contrast to existing
datasets that rely on 2D static imagery, the proposed 4D-FEID
dataset provides the community with a unique and vital resource for investigating the perception of facial expression intensity through the use of dynamic 3D stimuli. By offering high-fidelity, spatio-temporally coherent facial data, 4D-FEID establishes a new foundation for research into more nuanced and naturalistic expression analysis, thereby addressing a gap in the current landscape of affective computing and human-computer interaction studies. The
dataset is available at link.
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All authors Zesheng Wang, Alexandre Bruckert, Pierre Lebreton, Patrick Le Callet, Yante Li, Guoying Zhao
Multiagent Systems · Cross-listed from physics.soc-ph
Performance of Zero-determinant Strategies in Repeated Games without Discounting
Masahiko Ueda
Zero-determinant (ZD) strategies are a class of strategies in repeated games, which unilaterally control payoffs. It has been shown that several ZD strategies promote cooperation in social dilemma games. It has been widely believed that the performance of ZD strategies is expressed as “unilaterally enforce linear relations between payoffs”. However, in order to interpret properties of ZD strategies in repeated games without discounting, previous studies assumed that the limit of the Cesaro average of the probability distribution of the action profile exists. Here, we explain the performance of ZD strategies without this assumption, which leads to a stronger result than previous ones.
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Cryptography and Security · Cross-listed from quant-ph
When Normalization Selects the Sign: Auditing Robustness Ablations in Quantum Attention
Owen Friedewald, Srikar Alla, Ali Shiri Sichani et al. (+1 more)
Removing an input-scaling module changes both a classifier and the perturbations reaching its encoder. A robustness difference can therefore reflect the comparison rule as well as the module. We demonstrate this problem in a four-
qubit quantum-
attention detector on generated power-grid trajectories. A learned scaling module appears beneficial at a fixed physical attack budget, but matching an upper bound on perturbations at the encoder reverses the ordering. Neither comparison alone establishes a robustness benefit caused by the module. The initial test also perturbs clean examples into attacked examples while retaining their original labels; tests restricted to already attacked examples do not establish a benefit. Replacing a trained model's input scales disrupts detection. Retraining its linear classification layer restores the detection rate, but changes individual predictions, leaving the comparison descriptive rather than causal. Two further design checks explain why the input
quantum Fisher information regularizer cannot train this model's query parameters, and why removing confidence bounds does not establish a larger certified radius. The evidence is limited to ten seeds, exact simulation, synthetic data, and a restricted set of attacks; classical baselines achieve better clean prediction. The practical lesson is to specify which perturbation budget is fixed, check that attacks preserve labels and interventions preserve predictions, and distinguish exploratory controls from confirmatory evidence.
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All authors Owen Friedewald, Srikar Alla, Ali Shiri Sichani, Chi-Ren Shyu
Artificial Intelligence
Batched Speech Decisions Without Decoding: Single-Token Supervision Lets a Frozen LLM Hear Beyond the Transcript
Jie Jin, Ziyin Ma, Min Yin et al. (+4 more)
Full-duplex voice
agents make many small, closed decisions, which current systems answer by slow autoregressive decoding. We propose DuplexJev, which feeds ASR-encoder hidden states through a small connector into a frozen LLM and reads each question as a single-
token distribution over its options. Nothing is decoded, and an 8-GPU node answers 80 decisions about eight utterances in about 0.1 s. With a last-layer connector, spoken QA stays close to reading the transcript (90% vs. 91%). DuplexJev also hears the speaker: gender and
emotion accuracy both reach 90% (from 55% and 28%) with a cross-
attention connector, whose spoken QA drops by only 1 point (83% to 82%). We train decisions with cross-
entropy on the read-out answer
token, instead of the usual transcript
distillation, whose teacher never hears the voice, and keep
distillation for content. Encoders and LLMs are interchangeable; we release weights, training recipe, a batched-inference pipeline for full-duplex serving and a bilingual spoken-QA set.
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All authors Jie Jin, Ziyin Ma, Min Yin, Jinyu Chen, Haigang Song, Zhikun Pang, Xiaowen Zhang
Cryptography and Security · Cross-listed from quant-ph
Where Quantum Fourier Sampling Stops Short: A Three-Gate Audit Protocol for Delay-PUF Security Models
Owen Friedewald, Ali Shiri Sichani and Chi-Ren Shyu
Quantum Fourier sampling may help audit the spectral learnability of delay-based physical unclonable functions (PUFs). We ask whether that promise survives access matching, a strong classical comparator, and oracle synthesis. Three gates structure the evaluation. Structure: low degree is not small support at reachable challenge lengths; for 4-XOR at $n=14$, degree $\le d_f(0.1)$ admits $91\%$ of all $2^n$ characters and the median $90\%$-mass set spans a third of the spectrum. Algorithmics: constructing the phase oracle logically implies classical membership access, making Kushilevitz–Mansour the correct baseline; across 45 tasks it exhausts each finite domain, and no 4-XOR ideal-sampling case reaches $90\%$ mass within $2^n$ calls. A
quantum-kernel diagnostic appears more favorable, with
geometric difference rising to $2.151$ at $N=512$ challenges, but it correlates $0.991$ with $1/\sqrt{\lambda_{\min}(K_C)}$ for the classical Gram
matrix $K_C$, and the 4-XOR label-complexity ratio does not exceed a balance-preserving permutation null ($p=0.930$). Trace-normalized
geometric difference can therefore grow through classical ill-conditioning alone, without task-label
alignment. Implementation: a simulator-validated fixed-point phase oracle based on the
quantum Fourier transform admits an $18.9\%$ routed-depth reduction, yet the least certified precisions have estimated durations of $1.18$–$1.55\times$ the median dephasing time $T_2$ of the mapped
qubits on a static backend snapshot, without hardware execution. We find no end-to-end advantage in the evaluated regime, although ideal sampling does use fewer coherent calls on the thresholded task. The contribution is the Three-Gate
Quantum Audit Protocol: a reproducible procedure separating an ideal query advantage from a realizable security benefit. This is not a claim about deployed silicon and not an impossibility result.
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Machine Learning · Cross-listed from math.OC
A hybrid CNN-adjoint optimization framework for reconstruction of viscoelastic tissue properties in magnetic resonance elastography
Anwesa Dey, Johann Rudi and Elena Cherkaev
Magnetic resonance elastography (MRE) is a noninvasive imaging modality for quantifying the viscoelastic properties of soft tissues from shear wave propagation. Recovering the complex-valued shear modulus from measured displacement fields leads to a severely ill-posed inverse problem, particularly in the presence of noise and limited boundary excitations. We investigate adjoint-based optimization, convolutional neural network (CNN) reconstruction, and a hybrid framework combining both approaches. The forward model is based on a scalar form of the modified stationary Stokes system with a complex shear modulus. We establish well-posedness of the forward problem, existence of minimizers, and first-order optimality conditions for the adjoint-based formulation, and implement a nonlinear conjugate-gradient method with Armijo line search. While PDE-constrained optimization can accurately refine coefficient reconstructions, its performance depends strongly on initialization. We therefore construct a two-dimensional CNN that maps complex-valued displacement measurements to spatially varying complex shear modulus fields and provides rapid, informative initial reconstructions. The proposed hybrid method uses the CNN reconstruction to initialize the adjoint-based optimization, yielding faster convergence and improved accuracy. The CNN is trained on coefficient fields containing individual perturbations and tested on both individual and previously unseen combined configurations. Numerical experiments demonstrate that the CNN generalizes to these more challenging configurations, while subsequent PDE-constrained optimization further refines the reconstructed coefficient. These results demonstrate the potential of combining data-driven initialization with physics-based optimization for efficient and accurate MRE reconstruction.
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Machine Learning
Online Verification of Language Model Responses Under Cost Constraints
Erfan Hajihashemi and Yanning Shen
As large
language models are increasingly deployed for multi-step
reasoning, verifying the correctness of their outputs has become essential for maintaining reliability at scale. Verifying the correctness of large
language model outputs is often done by querying a costly ground-truth oracle, which is impractical to invoke at every step in an online setting. Prior work addresses this by querying a single weak verifier on every step, and using its score to decide whether the costly strong verifier needs to be queried as well, reserving strong verification for only a small fraction of the steps. However, a single fixed weak verifier may not perform consistently well as the subject matter or difficulty of incoming queries changes over time, and committing to one in advance risks either overly costly or inaccurate verification. We introduce OMVV (Online Multi-Verifier Verification), an
algorithm that maintains a pool of $K$ candidate weak verifiers with differing cost and verification performance, and adaptively routes each round's decision to a verifier selected via an online score combiner and an exponential-weights routing policy. OMVV provides a distribution-free, finite-time guarantee on false-accept and false-reject rates across the full pool of verifiers, and further achieves sublinear regret against the best fixed verifier in hindsight under a combined cost and consistency objective. Experiments on
reasoning dataset benchmarks show that OMVV achieves higher accuracy at lower verification cost than any single fixed verifier, across a range of operating budgets.
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Artificial Intelligence
Designing the Future of User Feedback for Generative AI
Alisa Frik, Julia Bernd, Amitis Karami et al. (+1 more)
Post-deployment feedback from users can be a cost-effective, scalable, and representative means to monitor and improve generative AI systems and features. When implemented effectively, giving such feedback can increase users' engagement with and trust in GenAI systems. Government regulations and industry guidelines call for post-deployment user engagement, but there is little guidance on designing mechanisms that are usable for consumers and provide actionable input for product teams. We conducted a multi-phase study as a collaboration between academic researchers and eBay. Our
benchmark evaluation of current industry approaches identified common issues including lack of discoverability, unclear terminology, and inattention to user value. Based on these findings, we developed best-practice recommendations and designed and tested a prototype feedback-collection tool. The tool aimed to provide users with an efficient, flexible, and positive feedback-giving experience, and provide product teams with rich data on performance and potential problems in a usable format.
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All authors Alisa Frik, Julia Bernd, Amitis Karami, Mohammad Tahaei
Artificial Intelligence
Lost in the Request: How Communication Variation Disrupts Retrieval and Action in Email Agents
Feng Chen, Ritam Dutt, Atnaz Taheri et al. (+1 more)
An email assistant should not complete less work simply because a user phrases the same request differently. Yet most
benchmarks test each task with only one canonical request, leaving this form of robustness largely unmeasured. We test whether email assistants remain reliable when the requested information, available evidence, and expected outcome stay fixed, but the communication style or English variety changes. We construct validated variants along five communication-style axes and four rule-based dialect conditions, and evaluate them on three
benchmarks: a
retrieval-augmented generation (RAG) pipeline and two tool-using
agents. Indirect requests reduce performance on all three
benchmarks, while formal requests reduce performance on both agentic
benchmarks. Examining the systems more closely shows that these failures have different causes. Verbose requests mainly hurt a lexical retriever by making the relevant email harder to find. By contrast, indirect and dialect variants remain harmful even when the relevant email is retrieved. In the agentic setting, indirect and formal requests mainly cause the
agents to omit required actions, not to take more unsupported actions. These results show that a successful response is not enough to establish robustness: evaluations should vary how requests are expressed and separately measure whether
agents complete the requested work.
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All authors Feng Chen, Ritam Dutt, Atnaz Taheri, Alex Williams
Computer Vision
Imagine the Future, Internalize the Gist: Efficient VLA Reasoning via Internalized Spatiotemporal Imagination
Shenglan Li, Zhendong Mi, Hengyi Zhu et al. (+6 more)
Vision-
language-action (VLA) models increasingly incorporate intermediate
reasoning to improve robotic manipulation, yet existing approaches primarily reason about observed states without explicitly anticipating future scene
evolution. Extending such
reasoning to explicit future rollouts at every inference step, however, introduces substantial computational overhead. We propose IG-VLA, a VLA
reasoning framework that enables models to imagine the future and internalize the gist. Our Latent Spatiotemporal
Reasoning learns to imagine task-relevant future scene
evolution directly in visual representation space, guiding action prediction without costly pixel-level
video generation. To further reduce inference overhead, we introduce Scene Gist
Memory, which internalizes
reasoning-derived scene-behavior associations into a compact Scene Gist
Token, preserving the benefits of future
reasoning while bypassing explicit future imagination at inference. Extensive experiments on LIBERO, LIBERO-Plus, and VLABench demonstrate the effectiveness and efficiency of IG-VLA. On the LIBERO-Plus
Language suite, both the
reasoning and gist policies outperform the strongest baseline by nearly 6% in success rate. The gist policy also achieves up to 6.38x speedup over baselines, reducing inference latency from 1081ms to 169.5ms per action chunk on a single NVIDIA A6000 GPU. These results demonstrate that future spatiotemporal
reasoning can be effectively internalized for efficient VLA deployment.
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All authors Shenglan Li, Zhendong Mi, Hengyi Zhu, Jingwu Luo, Chun Kit Chan, Geng Yuan, Yanzhi Wang, Pu Zhao, Shaoyi Huang
Programming Languages
Bao: Automatic Region Placement and Memory Allocation for Intermittent Computing
Byeongjee Kang, Jan Hoffmann, Limin Jia et al. (+2 more)
Intermittent computing enables batteryless embedded devices to operate in harsh environment, but frequent power failures interrupt program execution and require careful management of state across power cycles. The core challenge is to guarantee both
memory consistency and forward progress while minimizing the energy overhead of checkpointing. Recent compile-time approaches identify regions that fit in the energy buffer. At runtime, the device waits and recharges between the regions. However, they still rely on greedy or path-local heuristics that commit to local decisions and can miss globally lower-overhead boundary placements. To address this limitation, we present Bao, a system that jointly finds optimal energy-aware region formation and
memory allocation decisions with formal correctness guarantees, by formulating it as a mixed-integer linear program. We prove that any feasible solution guarantees energy safety and implement our approach in LLVM. Our evaluation on 13
benchmarks across 3 capacitor sizes shows that Bao outperforms existing baselines, achieving 10% faster execution and 52% fewer region boundary hits on average compared to the best baseline.
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All authors Byeongjee Kang, Jan Hoffmann, Limin Jia, Brandon Lucia, Feras A. Saad
Systems and Control · Cross-listed from math.OC
Data-Driven Static Output-Feedback Control for Multi-Agent Systems
Paul Serna-Torre, Adam Sedlak and Patricia Hidalgo-Gonzalez
This work proposes a data-driven framework that synthesizes output-feedback control for linear time-invariant multi-
agent systems (MAS). We first develop two
algorithms that use offline data samples of state and input trajectories to determine a Nash
equilibrium (NE) for MAS. Next, we formulate a semidefinite program that, given the NE solution, computes the static output-feedback controllers for the
agents in MAS. Compared to prior work, virtues of our framework include weaker controllability assumptions, the absence of online information and data-driven estimation of states. Next, we use the proposed framework to design control schemes for voltage and frequency regulation for nonlinear multi-nodal electrical networks with grid-forming and grid-following inverters. Thus, compared to prior work that implements their data-driven frameworks in small-scale numerical examples, we validate the performance of our data-driven framework in high-fidelity nonlinear models of MAS. This implementation in realistic MAS provides insights that small-scale numerical examples may fail to capture. Results show successful grid stabilization and provision of regulation services from the inverters in the grid using our proposed framework. It can attain up to 78% less overshoot and 30% faster settling time than traditional strategies such as proportional-integral control.
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Artificial Intelligence
CuBEs: Culturally-Situated Behavioral Evaluations and the Limitations of Culture-Blind LLM Judges
Hoda Ayad, Tanu Mitra and Abhishek Mukherji
Evaluating the occurrence and triggers of large
language model (LLM) behaviors - such as sycophancy, self-preference, or over-confidence - is critical for predicting real-world model deployment risks. However, existing situated behavioral evaluations typically ignore cultural context, limiting their generalizability across an increasingly global user base. To address this gap, we propose CuBEs - Culturally-situated Behavior Evaluations that probe for response patterns across diverse user cultures. We first extend an automated testing pipeline to inject cultural context into behavioral test scenarios and subsequent evaluation. We assess the cultural adaptability of this pipeline by building a human-labeled
dataset that captures nuanced dimensions of behavior understanding across 12 distinct cultures. Our
dataset reveals significant cross-cultural variations that one-size-fits all judgments fail to capture. Through evaluating 13 open- and closed-source LLMs, we find that introducing cultural situatedness in the evaluation scenario creates significant variation in the presence of a behavior. For example, while our baseline experiments testing for political bias capture localized Western political dimensions like the American conservative-progressive divide, non-Western culturally situated evaluations surface entirely different axes of bias such as religious and colonial political issues. Our findings demonstrate that standard, culturally-agnostic evaluations fail to capture these shifts, highlighting the necessity of culturally situated behavioral testing for global deployments.
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