Emerging Technologies
Reading While Writing: Baseline Information Requirements for Molecular Neural Interfaces
Hongbin Ni and Ozgur B. Akan
Molecular neural interfaces that deliver and sense the same neurotransmitter must estimate endogenous release in the presence of their own chemical input. We study two exchanging regions with shared saturable uptake and concentration change measurements. Known delivery and exchange permit identification of apparent uptake at the driven region, but an unknown resting concentration difference leaves the source region's incremental uptake uncertain. We characterize all admissible source histories consistent with both ideal records and show how delivery and baseline information affect release classification. A dopamine-inspired example produces identical ideal concentration-change records for mean release-rate changes of -8.99 and +1.31 nanomolar per second. Stronger delivery changes whether all compatible sources imply suppression, with the true source held fixed. Under a Gaussian observation model, we bound discrimination based on chemical fluctuations. A baseline reference with an assumed standard deviation of 3.3 nanomolar reduces root-mean-square error from 10.41 to 1.21 nanomolar per second compared with an incorrect fixed baseline under exact gain calibration. Classification errors remain substantial with weak excitation, gain errors or responses near the category boundaries.
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Human-Computer Interaction
LeanSide: A Formally Verified Co-Reasoning System for Natural-language Proofs
Chenjun Guo, Manooshree Patel, Arnav Mehta et al. (+7 more)
Large
language models are increasingly used as collaborators on deductive-
reasoning tasks, but their outputs can hallucinate or pull users away from intended
reasoning. Formal proof assistants provide machine-checked verification, but have a steep learning curve and require more granular
reasoning than human written proofs. We explore an interface that combines these strengths, allowing users to write and revise free-form natural-
language proofs while a verified backend checks their
reasoning and returns feedback at the user's granularity. We study this interface in the context of undergraduate mathematics education by developing LeanSide, a formally verified co-
reasoning system, which auto-formalizes student
reasoning into Lean and informalizes verifier output into understandable feedback. We conducted user studies through classroom deployment and analyzed which system properties helped students make progress and which caused them to get stuck. We use these findings to derive design implications for using a formally verified backend in human-AI co-
reasoning systems.
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All authors Chenjun Guo, Manooshree Patel, Arnav Mehta, Krishiv Kothari, Thomas Lu, Niels Voss, Rayna Bhattacharyya, Peter Donovan, Bjoern Hartmann, Gireeja Ranade
Cryptography and Security
Crossing the Cyber Divide: Sim-to-Sim and Sim-to-Real Transfer for RL Agents
Sabrina Saika, Yinuo Du and Aritran Piplai
Cyber attack
agents are typically trained and evaluated within a single simulator, making it unclear whether learned policies transfer beyond the environments in which they were developed. This limitation hinders both deployment and fair comparison, as cyber simulators differ substantially in their state representations, observation models, and action spaces. In this paper, we study policy transfer across cyber environments and argue that simulator-to-simulator and simulator-to-real transfer can be viewed as instances of the same underlying
alignment problem. We propose a framework that separates state
alignment from action translation, enabling a policy trained in one environment to operate in another without retraining. We evaluate transfer across four cyber platforms, CyberBattleSim, NetSecGame, CyberWheel, and NASim, including emulated deployments in NASim. Our experiments show that zero-shot transfer is feasible, fully preserving source-policy performance in closely aligned environments and achieving 45.2% win rates when transferring policies whose source performance is 60.5%. In emulated virtual machine environments, transferred policies exhibit a Jensen-Shannon divergence of 0.085 from native policies, indicating strong behavioral similarity. Code and
benchmarks are available at: https://anonymous.4open.science/r/RL-Transfer-between-env-4F47/.
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Machine Learning
Scalable Multi-Task Inverse Reinforcement Learning
Allen Tran, Jia Wan, Nathan Kallus et al. (+1 more)
By learning transferable rewards, inverse reinforcement learning (IRL) enables counterfactual evaluation of
agents under modified environments. Such transfer places strict requirements on coverage since target environments affect
agents' state occupancy. We propose a multi-task IRL method that pools data across multiple
agents with different rewards in the same environment under a low-rank assumption. In addition to alleviating coverage requirements, so each task need not visit every state as long as others do, the method offers scalable evaluation of multiple tasks under new environments as computationally intensive planning scales with rank rather than the number of tasks. We provide finite sample guarantees on reward recovery and on policy learning in new environments. Experiments show our method is robust to limited coverage, recovers rewards on and off of each task's support, transfers to target environments at lower regret than baselines, with its computational advantage over per-task methods widening as tasks grow.
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All authors Allen Tran, Jia Wan, Nathan Kallus, Aurélien Bibaut
Computer Vision
Video Evidence Indexing: Learning Where to Look from Video Previews for Token-Budgeted Long-Video Question Answering
Haowen Guan, Shengzhi Li and Shichao Pei
Long-
video question answering is limited by the high cost of visual
tokens and by the fixed context width of current VLMs. A long-
video question may require broad temporal coverage, but the answer is often supported by only a compact set of moments. To locate these moments efficiently, we propose
token-budgeted
Video Evidence Indexing (VEI): given a dense low-resolution
Video Preview, the model constructs a compact high-resolution Evidence Set for final
reasoning. We treat VEI as a policy that must jointly solve evidence localization, which finds question-relevant moments, and budget planning, which decides where to spend the limited high-resolution frame budget. We implement this idea with an inference pipeline: the
Video Preview provides cheap global coverage,
Video Evidence Indexing constructs the Evidence Set, and Answer Generation combines both inputs for final VQA. To address missing frame-level supervision, we adopt privileged self-
distillation, where an answer-aware teacher guides the normal test-time policy on student-generated indexing traces. We explore previews at 1, 6, 12, and 24 visual
tokens per frame, training a single policy that supports all four resolutions. Experiments show that
Video Evidence Indexing improves accuracy under limited visual budgets, and self-
distillation further improves both QA accuracy and temporal evidence localization.
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Machine Learning · Cross-listed from stat.ML
Learning to Price Electricity for Optimal Demand Response
Jing Shang, Mohammad Mehrabi, Xinyang Zhou et al. (+3 more)
There is considerable interest in using time-varying electricity prices to shape consumer demand response, and better align energy demand with renewable production. However, optimal prices generally vary over time in response to complex signals such as weather forecasts, sunrise/sunset times, and day-of-week patterns; and existing methods are not able to make efficient use of such rich contextual information. Here, we propose a neural-network-based
algorithm for contextual energy pricing, modeling pricing as a Stackelberg game and leveraging a mean-field solution representation from Mehrabi et al.(2024). The approach learns constrained mappings from contextual features to feasible price signals. We validate our approach by simulating the energy grid in several US cities, and show that incorporating contextual information can considerably increase the value of the demand response programs.
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All authors Jing Shang, Mohammad Mehrabi, Xinyang Zhou, Mahmoud Saleh, Andrey Bernstein, Stefan Wager
Sound · Cross-listed from eess.AS
MAV-C: A Framework for the Joint Objective Estimation of Audio-Visual Complexity in Immersive Virtual Environments
Luca Resti, Amelia Gully, Michael McLoughlin et al. (+2 more)
We present the Motion-Aware Audio-Visual Complexity Metric (MAV-C), a reference-free framework for the joint objective estimation of audio-visual complexity. The metric combines
entropy-based audio features (temporal, spectral, and spatial) with visual features (Sobel gradient magnitude, chromatic uniqueness, and optical flow) via a parametric fusion stage, producing a continuous joint complexity score CAV (t) [0,1]. We validate MAV-C on two
datasets: a controlled synthetic corpus (SYN) of stimuli with known signal characteristics and a naturalistic gameplay corpus (GAM) of 60 clips drawn from the SAFEPLAY-X
dataset. On SYN, the metric exhibits strong validity: the audio score CA and visual score CV are each insensitive to changes in the opposite modality (CoV < 0.003), the joint score CAV spans [0.00,0.90] across all parameter combinations, and single-axis feature sweeps produce monotone trajectories (Spearman up to 0.995). On GAM, CV differs significantly across content categories (Kruskal-Wallis p = 0.021) while CA does not, and the two sub-scores are uncorrelated (r = 0.03), confirming they operate on independent signal dimensions. OFAT sensitivity analysis identifies a two-tier parameter hierarchy, with modality balance (wa) and visual regularization (v) as most significant tunable parameters. Full subjective calibration is planned as future work.
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All authors Luca Resti, Amelia Gully, Michael McLoughlin, Gavin Kearney, Alena Denisova
Machine Learning
Increasing Width Allows Greedy Layer-wise Training to Rival End-to-End Backpropagation in Self-Supervised Learning
Syon Mansur and Joel Zylberberg
End-to-end backpropagation has been the dominant mode of training in deep learning, allowing for the coordination of parameter updates across layers of a neural network. Prior studies have explored alternative – and, in some cases, simpler – training mechanisms, showing that they can sometimes achieve performance similar to backpropagation. However, the architectural conditions under which locally optimized networks, which avoid end-to-end backpropagation of error, can learn representations comparable to those learned through end-to-end training remain unclear. We aim to answer this question in the context of self-supervised learning, an important framework for large-scale pretraining in artificial intelligence. Here, we investigate how network width and depth affect the efficacy of greedy layer-wise and end-to-end self-supervised training in convolutional networks. We find that in wider networks, the benefits of end-to-end backpropagation over greedy layer-wise training shrink: in relatively shallow and very wide networks, we even observed higher performance in models trained with greedy layer-wise training. Subsequent analysis of the representations formed by these networks shows that very wide greedy-trained networks exhibit more favorable representational geometry than do networks trained end-to-end with backpropagation. This work shows that width can compensate for restricted credit assignment and identifies differences in representational geometry as a potential mechanism for their improved performance.
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Networking · Cross-listed from eess.SP
ARCTAN: Arbitrary RF Containment Using Tactical Aerial Networks and Differentiable Ray Tracing
Samuel Rivera, Zhihui Gao, Yiming Li et al. (+1 more)
Aerial base stations (ABSs) can rapidly establish connectivity in ad hoc, infrastructure-deprived environments, but their broadcast, line-of-sight transmissions leak far beyond the intended service area, exposing communications to passive eavesdropping and interference. Prior physical layer defenses based on cooperative jamming typically assume known eavesdropper locations, simplified statistical channels, or continuously repositioned jammers. We instead pose the problem as a
radio frequency (RF) containment: confining usable signal to a user-defined, arbitrarily-shaped target zone while denying it elsewhere independent of eavesdropper location. We present ARCTAN, a gradient-based optimization framework that jointly optimizes the position, orientation, and transmit power of stationary ABSs and cooperative jammers (CJs) by backpropagating through site-specific, differentiable 3D ray traced channels. Evaluated in a high-fidelity digital twin across three target zone geometries, ARCTAN achieves a mean in-zone SINR of approximately 10 dB while reducing mean out-of-zone SINR from 13-16 dB to -3-5 dB, and suppressing signal-leakage ratios from over 93% to below 46% requiring at most 10 of 12 candidate CJs.
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All authors Samuel Rivera, Zhihui Gao, Yiming Li, Tingjun Chen
Machine Learning
Signal-Noise Factorization Isolates Nuisance Variation into Removable Subspaces
Sakin Kirti and Joel Zylberberg
Recent theoretical work identified fundamental properties of representation geometry that shape inference ability of deep neural networks. These include signal-noise factorization (SNF), the ability to segregate signal from noise, and signal-signal factorization (SSF), the ability to segregate task-specific and task-irrelevant signals. Here, we built regularizers that reinforce these two properties during training. We compared networks trained with these regularizers to $L_2$-regularized baseline networks on the CIFAR-100 classification task to understand how our regularizers shape representation geometry and impact performance on a well-known computer
vision baseline. Enhancing SNF via regularization improved model performance but enhancing SSF did not. Motivated by biomedical applications, we investigated how our regularizers affected performance on the BloodMNIST
dataset treated with MedMNIST-C corruptions at five severity levels, and found even larger performance gains using the SNF regularizer. To understand the mechanism by which SNF-regularization produces improved performance, we analyzed the nuisance subspaces across regularization regimes, finding that the SNF-regularized models represent noise in distinct subspaces, separate from class-relevant signal. Because this geometry is explicit, the dominant corruption-induced directions can be estimated on held-out data and projected out of the representations. This manipulation led to a substantial gain in accuracy. These results show that regularizers that enforce signal-noise factorization can produce substantial improvements on computer
vision tasks that contain out-of-distribution image distortions at inference time. They also highlight how shaping representations affects model performance: isolating nuisance variables from categorical ones is more important than maintaining factorized representations of categorical variables.
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Computer Vision
What Builds the Scene? Luminance Dominates Geometry Formation in 3D Gaussian Splatting
Rezvan Joshaghani and Steven Cutchin
Standard 3D Gaussian Splatting (3DGS) learns geometry and appearance jointly from RGB supervision, making it difficult to isolate how luminance and chroma contribute to the learned representation. We study this by training models under different channel supervision, freezing their non-appearance parameters (position, scale, rotation, and opacity), and re-estimating appearance with the same solver before comparing held-out reconstruction. Across eleven
benchmark scenes with four independent runs each, geometry learned from luminance alone supports held-out reconstruction 0.085 dB below RGB-trained geometry on average. If chroma is deleted from a trained model, a sufficiently expressive solver can re-fit it on the frozen geometry to the original quality or slightly better. Higher-order spherical harmonics contribute much more reconstruction quality to luminance than to chroma, improving PSNR by 1.44 dB versus 0.19 dB on average, although on mirror-like surfaces hue does still change with viewpoint. The luminance advantage is even larger when geometry is being formed. Chroma-only supervision produces geometry 3.9-5.5 dB worse than luminance-only supervision after the same appearance solve; densification explains part of this gap. Overall, geometry formation in standard 3DGS is strongly luminance-dominated but not luminance-exclusive, and much of the chromatic appearance can be recovered after spatial support has formed.
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Machine Learning · Cross-listed from q-bio.BM
StabilityArc: Decoding Protein Sequence Embeddings into Generalizable Stability Landscapes
Aaron L. Feller, Andrew D. Ellington and Claus O. Wilke
Every
protein has a unique stability landscape, but the physical consequences of mutation are governed by recurring biochemical constraints. We test whether a shared decoder, trained on measurements from diverse
proteins, can interpret these constraints in an unseen target, enabling cross-
protein transfer for initial experimental round prescreening. We present StabilityArc , which maps frozen ESMC-600M residue representations through a shared RoPE
transformer to an Lx20
matrix of substitution effects; a symmetric, contact-aware residual aids in predicting epistasis in simultaneous substitutions. In 66 strict leave-one-
protein-out evaluations covering 134,794 ProteinGym variants, StabilityArc achieves 0.7134 Spearman correlation, exceeding the strongest zero-shot baseline, ProSST-2048 (0.6526), by 0.0608. We further explore the utility of this method by providing the score as a prior for Kermut, achieving Spearman correlation of 0.8280 across three supervised split schemes, improving on Kermut's reported 0.8167.
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Hardware Architecture · Cross-listed from math.OC
Q-MINO: A Minimal-Norm Method for Quantization-Aware Training
Don Li
The Straight-Through
Estimator (STE) is a widely used heuristic for Quantization-Aware Training (QAT), but its surrogate gradients can exhibit substantial mismatch with the underlying quantized objective, leading to noisy updates and parameter oscillations, particularly in ultra-low-bit regimes. We propose the Quantization-Aware Minimal-Norm Optimizer (Q-MINO), a temporal bundle method that combines gradient consensus, state-drift regularization, and an
alignment constraint to construct stabilized, minimum-norm update directions from recent optimization states. Q-MINO solves the resulting constrained subproblem using a warm-started Frank–Wolfe procedure with a feasible fallback initialization. Theoretically, via a
stochastic Lyapunov Kurdyka–Łojasiewicz (KL) framework, we show that Q-MINO achieves asymptotic neighborhood convergence. Moreover, we detail numerical experiments with Q-MINO at various quantizations.
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Computer Vision
Personalized Image Generation with Reasoning and Reflection
Bo Ni, Ngoc N. Tran, Qinwen Ge et al. (+10 more)
Personalized image generation has remained narrowly focused on conditional synthesis from curated visual exemplars, rather than capturing who a user is. In practice, however, a user's personal context is much richer, comprising reviews, posts, images, captions, and metadata accumulated over time. A truly personalized generator should leverage this history to produce images aligned with the user's lifestyle and aesthetic preferences. To this end, we introduce the first unified
benchmark for personalized image generation from user histories. The
benchmark comprises two complementary tasks and a multi-axis evaluation protocol that assesses target fidelity, visual quality, user distinguishability, semantic
alignment with the user's history, and task-specific utility. Grounded in real-world e-commerce and social media settings, the
benchmark includes: (1) Personalized Scene Generation, which places a given object in a scene that reflects a user's preferences and lifestyle, motivated by personalized product presentation; and (2) Personalized Creative Generation, which generates a novel image on a specified topic that is faithful to a user's aesthetic and visual identity, motivated by social media content creation. We further propose PEARL, which couples a
multimodal reasoner with a frozen image generator in an interleaved reason-reflect loop optimized with differential data reward. Across both tasks, PEARL outperforms strong baselines, achieving an average improvement of 15% across personalization metrics.
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All authors Bo Ni, Ngoc N. Tran, Qinwen Ge, Franck Dernoncourt, Seunghyun Yoon, Samyadeep Basu, Sungchul Kim, Puneet Mathur, Nedim Lipka, Tong Yu, Yu Wang, Ryan A. Rossi, Tyler Derr
Emerging Technologies · Cross-listed from quant-ph
Beyond Feasibility: Finite-Depth Accessibility in Constrained QAOA
Rushikesh Ubale, Gregory T. Byrd, Yasar Mulani et al. (+1 more)
Constraint-preserving mixers keep QAOA within a feasible subspace, but feasibility and global mixer connectivity do not determine which feasible configurations are available to a particular shallow
circuit. We study constrained QAOA at the level of the
circuit actually executed: a specified feasible initial state, finite depth, and finite schedule of mixer interactions. We define the finite-depth accessible set $R_p(M,x_0)$ and show that the computational-basis support of the ideal QAOA
circuit is contained within it. This gives a parameter-independent objective ceiling $f_R^\star=\max_{x\in R_p}f(x)$ and a normalized reachable-quality diagnostic $Q_R$, separating accessible volume from the objective quality contained within it. We evaluate this framework using resource-matched block-local XY mixer schedules for constrained portfolio optimization and synthetic block-constrained quadratic problems. Across 207 favorable-tail CVaR QAOA configurations, $Q_R$ shows the strongest association with realized shallow-QAOA performance among the structural diagnostics considered, with Pearson and Spearman correlations of $0.7003$ and $0.8057$; the relationship remains positive under controlled and dependence-aware analyses. The distinction persists across changes in feasible initialization, portfolio size, and objective family. A compilation-only study further shows that, for the dense portfolio
Hamiltonians considered here, phase-separator routing dominates the compiled two-
qubit resource scale, while mixer schedules with comparable compiled two-
qubit cost can exhibit markedly different finite-depth accessible quality. These results motivate objective-relevant finite-depth accessibility as a complementary diagnostic for resource-bounded constrained QAOA.
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All authors Rushikesh Ubale, Gregory T. Byrd, Yasar Mulani, Sangram Deshpande
Robotics
Measuring Asset and Scene Reconstruction Effects in Real-to-Sim Robot Evaluation
Sanya Verma, Luca Cilio, Velissarios Christodoulou et al. (+4 more)
Simulated evaluation is increasingly used alongside real-world evaluation of
robot policies because it is cheaper and easier to repeat; however, its value depends on how closely its outcomes track the real
robot's. We test whether our reconstruction pipeline, combining metrically scaled object geometry, authored physical parameters and scene reconstruction, reduces disagreement between simulated and real
robot scores relative to a default open-source recipe. We constructed two simulated versions of one bimanual
robot cell: an authored reconstruction, using object geometry at estimated metric scale, projected textures, authored physics and our own scene splat; and a baseline, referred to as the default reconstruction, using the open-source recipe of a generative single-image mesh, engine-default physics and a Gaussian-splat scene. Both reconstructions use the same object photographs and scene
video. Two policies ran five tasks each, giving ten task-policy pairs, which we call
cells; each
cell was run twenty times in each reconstruction with all other settings held fixed. Both reconstructions were scored against the same real trials, graded by a third-party evaluator. Pearson correlation between the ten simulated and real
cell means is r = 0.90 for the authored reconstruction and 0.51 for the default. Mean score error is 6.97 percentage points for the authored reconstruction and 17.54 for the default, a reduction of 10.56 percentage points. These results show that improving the quality of the environment reconstruction through higher visual fidelity, authored physics and metric scale makes the simulation more faithful to the real world and narrows the sim-to-real gap. We release the harness, the per-trial scores, every reported run's configuration, and the assets and scenes of both reconstructions.
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All authors Sanya Verma, Luca Cilio, Velissarios Christodoulou, Tyler Fermelis, Qi Ting Ng, Rachel Chung, Sofia Liang
Machine Learning
Reformulation-Contrastive Learning for Mixed Integer Programs
Ousema Bouaneni, Mathis Le Bail, Clément Elliker et al. (+2 more)
Mixed-integer linear programs (MILP) model many real-world decision problems, motivating machine-learning methods that exploit recurring structure to accelerate MILP solving. MILPs can admit many equivalent formulations: integrality-preserving changes of variables and the addition of redundant constraints can alter their formulations while preserving the optimization problem. We leverage these reformulations as a source of self-supervision for learning general-purpose representations of MILP variables and constraints. We characterize the affine reformulations that are valid for every input instance, and distinguish re-descriptions, which leave variables unchanged, from substitutions, which transform them predictably. Building on equivariant self-supervised learning, we introduce ReMILP (reformulation-contrastive MILP representation learning), which jointly trains a graph neural network and a hypernetwork to predict how variable
embeddings transform under changes of variables. Without solver-derived labels, ReMILP learns representations that exhibit the intended invariance and equivariance on unseen problem classes. Across binary solution, constraint activity and integrality gap prediction, these representations carry task-relevant information when frozen and provide a useful initialization for fine-tuning.
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All authors Ousema Bouaneni, Mathis Le Bail, Clément Elliker, Maël Jenny, Sonia Vanier
Machine Learning
Reward as Observation: Learning Reward-Based Policies for Rapid Adaptation
Morgan Byrd, Jacob Blevins, Maks Sorokin et al. (+2 more)
This paper explores a reward-based policy to achieve zero-shot transfer between source and target environments with completely different observation spaces. While humans can demonstrate impressive adaptation capabilities, deep neural network policies often struggle to adapt to a new environment and require a considerable amount of samples for successful transfer. Instead, we propose a novel reward-based policy only conditioned on rewards and actions, enabling zero-shot adaptation to new environments with completely different observations. We discuss the challenges and feasibility of a reward-based policy and then propose a practical
algorithm for training. We demonstrate that a reward policy can be trained within three different environments, Pointmass, Cartpole, and 2D Car Racing, and transferred to completely different observations, such as different color palettes or 3D rendering, or Stretch
robot navigation in Habitat-Sim, in a zero-shot manner. We also demonstrate that a reward-based policy can further guide the training of an observation-based policy in the target environment.
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All authors Morgan Byrd, Jacob Blevins, Maks Sorokin, Robert Wright, Sehoon Ha
Machine Learning
Benchmarking Generative Models for Near-Surface Data Assimilation on Real Station Observations
Ruizhe Huang, Qidong Yang, Jonathan Giezendanner et al. (+1 more)
Weather reanalysis products rely on computationally intensive numerical weather predictions followed by data assimilation that corrects the forecast toward observations. Recent advances in deep generative models offer a cheaper alternative that shifts much of this cost from inference to offline training. However, existing generative approaches have been evaluated on synthetic observations or under different
datasets and evaluation schemes, making it unclear which design choices improve real-world data assimilation. We present the first controlled
benchmark of generative data assimilation for single-time near-surface analysis from real weather station observations. Using 11,849 NOAA MADIS stations across the contiguous United States and four near-surface variables, we hold the
dataset, observation operator, and deep learning architecture fixed, and measure spatial generalization at held-out stations. The
benchmark compares the major design choices proposed for generative data assimilation, including
diffusion versus flow matching, pixel versus latent-space formulations, and multiple inference-time conditioning strategies, against a classical 3D-Var baseline. The
benchmark reveals three conclusions. First, the best generative methods outperform 3D-Var (35.7% vs. 33.3% RMSE improvement over ERA5), although 3D-Var receives the ERA5 field at the analysis time as its background and the generative methods receive none. Second, full-gradient guidance consistently outperforms stop-gradient and initial-noise optimization. Third, other choices provide little measurable benefit:
diffusion and flow matching perform nearly identically under matched conditions, and latent-space variable mixing does not help. Both advantages widen when stations are sparse. Together, these results identify which components of generative data assimilation improve spatial generalization in near-surface analysis.
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All authors Ruizhe Huang, Qidong Yang, Jonathan Giezendanner, Sherrie Wang
Robotics
CF-JEPA: Improving Robustness of JEPA World Models via Controllability Factorization
Morgan Byrd, Robert Wright and Sehoon Ha
Controlling an
agent with
vision requires being able to separate useful information from irrelevant background information. JEPA-style latent world models seem like a natural approach for this, as they do not perform pixel-level reconstruction; however, they are still sensitive to these distractor signals and experience latent collapse. In this work, we introduce Controllability Factorized JEPA (CF-JEPA), a JEPA-style world model which splits the latent space into controllable and uncontrollable subspaces. This factorization allows us to capture all the distractor information into the uncontrollable region, while we use the control-relevant latent information for our task. With this, we show comparable performance across 2D and 3D control tasks under nominal conditions and improved performance under distracted conditions, where CF-JEPA is the only model that does not experience latent collapse. We also validate our model under distracted conditions for a simulated
robot task, highlighting the practical application of such a scheme.
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Sound
Where the Body Keeps the Beat: Structured Motion Conditioning and Music Dynamics Supervision for Dance-to-Music Generation
Changchang Sun, Lu Cheng and Yan Yan
Dance-to-music (D2M) generation aims to synthesize musically plausible soundtracks whose temporal structure aligns with a given dance performance. Representative D2M methods do not explicitly distinguish motion cues across body parts and frequency bands, while standard flow matching lacks a dedicated objective for supervising local music-latent dynamics. To address these issues, we propose Dyna2Music, a latent flow-matching framework that combines structured motion conditioning with explicit supervision of music-latent dynamics. An empirical analysis on AIST++ quantifies how spatial partitioning and frequency separation affect raw music-to-kinematic beat
alignment and motion-reference density, informing the conditioning design. Accordingly, Dyna2Music decomposes joint velocities into slow and fast components and hierarchically fuses the resulting part-wise motion energy with pretrained joint features to condition music generation. To complement this representation, we introduce latent dynamics consistency (LDC), an auxiliary objective that matches adjacent-frame change magnitudes between a single-step clean-latent estimate and the paired reference. LDC makes local music-latent variation an explicit training target without adding trainable parameters or inference computation. Dyna2Music supports variable-length music generation, and experiments on AIST++ and TikTok demonstrate improved rhythmic
alignment and audio quality over representative D2M baselines.
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Computation and Language
Reason in Style: Discovering and Controlling Style in Language Models
Ioana Marinescu, Eric Karl Oermann and Kyunghyun Cho
Language models learn content and style jointly, making stylistic variation in their outputs difficult to identify and control. We study whether recurring styles in model responses can be discovered without supervision and explicitly controlled. We design an
algorithm that learns to separate representations of content and style from
language models' outputs and validate its effectiveness on math questions in a controlled setting. By applying this method to over 100K verified traces from nine distinct teacher models, we discover six recurring yet imbalanced styles. We then fine-tune smaller student models to follow these styles when explicitly conditioned on them, using importance weighting to balance the contribution of the styles represented in the corpus. This approach improves Pass@$k$ over standard fine-tuning on the same data across six math
reasoning benchmarks, demonstrating that we can diversify the style of answers effectively. We confirm that this also results in strong correspondence between requested and realized styles. We find that style affects correctness: the probability of solving a problem depends on the style we condition on, and different problems benefit from different styles. In summary, our results show that stylistic variation in model-generated data can be discovered in an unsupervised way, and made explicit, providing a source of both control and improved
reasoning performance.
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Machine Learning
JEPA-TTT: Persistent Test-Time Training of Latent World Models for Planning under Dynamics Shifts
Zheyuan Zhang, Suyu Ye, Nakul Agarwal et al. (+5 more)
World models enable
agents to plan by predicting future states of the environment, but their predictions can become unreliable when test-time dynamics differ from those seen during training. We present JEPA-TTT, which adapts the latent dynamics predictor of a pretrained action-conditioned Joint-
Embedding Predictive Architecture world model throughout test time. Self-supervised updates accumulate across episodes, while the visual encoder and reward head remain fixed, preserving the pretrained representation and task objective. Planning requires neither a goal image nor online environment reward. JEPA-TTT uses dense replay, which forms prediction windows at every temporal offset, retains them in a growing buffer, and samples minibatches from that buffer for predictor updates. Across eight dynamics shifts in four continuous-control environments, JEPA-TTT improves planning on every shift. After 500 test-time episodes, it reduces autoregressive latent prediction error by 83% on average and improves planning performance by 153% over the frozen JEPA world model. These results show that persistent self-supervised test-time training can adapt a pretrained latent world model under changed dynamics.
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All authors Zheyuan Zhang, Suyu Ye, Nakul Agarwal, Hossein Nourkhiz Mahjoub, Ehsan Moradi Pari, Daniel Khashabi, Tianmin Shu, Vaishnav Tadiparthi
Game Theory
Outer Diversity of Condorcet Domains
Piotr Faliszewski, Jan Jabrocki, Mateusz Słuszniak et al. (+3 more)
A Condorcet domain is a set of rankings over a given candidate set, such that every election that consists only of (an odd number of) votes from the domain has a transitive majority relation. We study outer diversity of Condorcet domains, i.e., a measure that quantifies expected swap distance from a random vote to a closest one in the domain. We numerically analyze outer diversity for maximal Condorcet domains with few candidates, and then we establish its asymptotic behavior for several special domains, mostly obtaining theoretical results.
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All authors Piotr Faliszewski, Jan Jabrocki, Mateusz Słuszniak, Krzysztof Sornat, Stanisław Szufa, Tomasz Wąs
Neural and Evolutionary Computing
Neuromorphic Pseudo-Random Number Generators with a Low Power Hardware Implementation
Jafar Shamsi, Navid Akbari, Sonia Sennik et al. (+2 more)
Pseudo-random number generation often requires trade-offs among quality, power consumption, and bandwidth to produce unpredictable sequences of numbers. The
brain, on the other hand, efficiently generates unpredictable output complex network dynamics occurring in a high-dimensional state. This state, which is hypothesized to be chaotic, relies on the balance between excitation and inhibition. Here, we investigated if computational models of these chaotic balanced states can be harnessed for Neuromorphic Pseudo-Random Number Generators (NPRNGs) in low power hardware. We successfully constructed a balanced spiking neural network model consisting of leaky-integrate-and-fire
neurons that could be readily implemented in low power FPGAs and used as a NPRNG. The prototyped NPRNG consumed 3.24 mW during operation and produced pseudo-random numbers at 120kbps. In both hardware and software instantiations, NPRNGs produce high-quality random numbers as validated by standard metrics for testing RNG quality.
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All authors Jafar Shamsi, Navid Akbari, Sonia Sennik, Aaron Gruber, Wilten Nicola
Robotics
Toward Humanoid Robots in Construction: A Teleoperation Feasibility Study
Parastoo Ali Pour, David R. Martin, Chang Min Hur et al. (+6 more)
We present a teleoperation system that enables a single operator to perform construction tasks on a Unitree G1
humanoid, combining extended reality (XR) based upper body control with pedal-based locomotion to enable simultaneous manipulation and locomotion. Motivated by persistent labor shortages, hazardous working conditions, and challenges in
humanoid autonomy, we investigate teleoperation as a practical near-term approach for reducing physical strain on workers while generating high quality demonstration data. We evaluate the system on two representative construction tasks drawn from O*NET occupational database, and report task success and completion time relative to a manual baseline. The system achieved 100% success on tool transport and 80% success on surface painting, with teleoperation requiring substantially more time compared to manual execution.
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All authors Parastoo Ali Pour, David R. Martin, Chang Min Hur, Bo Zhang, Tommy Zhou, Brandon Thomas Lichter, Shane Stanfield, Pramod Khargonekar, Mohammad Abdullah Al Faruque
Computation and Language
Sequential Functional Structured Tucker Compression for Large Language Model Attentions
Jiangfeng Chen, Xinyu Wang, Tianshuo Yan et al. (+4 more)
Post-training compression of LLM
attention is often formulated as independent
matrix approximation, ignoring both the shared structure among
attention projections and the representation shift introduced by earlier compression. We propose FTC, a sequential structured compression framework that adapts the approximation to the current compressed model while jointly exploiting the native Q/K/V head structure under a fixed storage budget. The output projection is handled separately to account for the changed post-
attention representation. FTC requires neither fine-tuning nor gradient-based recovery. Across seven decoder-only LLMs from 6B to 32B parameters, FTC achieves the lowest WikiText-2 perplexity among the compared methods at every tested keep ratio on five modern GQA models, with the largest gains under aggressive compression. The improvements transfer to downstream tasks and remain substantial at the 32B scale.
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All authors Jiangfeng Chen, Xinyu Wang, Tianshuo Yan, Hanwei Wu, Xiao-Wen Chang, Yang Zhang, Lei Ding
Artificial Intelligence
Robust Nash Alignment under Preference Uncertainty
Shihab Ahmed, Debamita Ghosh, David Tang et al. (+3 more)
Preference-based
alignment methods typically optimize against a single preference model, and can therefore be brittle when pairwise preferences are uncertain: noisy, heterogeneous, or shift after deployment. To address these issues, we propose Robust Nash
Alignment, a game-theoretic framework for
alignment to uncertain pairwise preferences. Our formulation has a major learner seeking a policy with a large worst-case win rate against both an
adversarial competitor and any preference kernel lying in an ambiguity set around a nominal preference. When the ambiguity set captures the uncertainty in preferences, the resulting robust objective of the game directly yields a certified lower bound on worst-case performance. However, we note this problem is computationally challenging to optimize, and to address this, we introduce a four-player primal-dual proxy game involving the leader policy, follower policy,
adversarial kernel, and dual variable, and develop a single-loop optimistic mirror descent-ascent
algorithm for it. We show that the proxy always lower-bounds the truncated hard-constrained objective, quantify the proxy-to-hard gap, and characterize an exactness condition under which the proxy recovers the robust objective. We then prove an \(\mathcal{O}(1/\sqrt{T})\) average-iteration convergence for the proxy-game duality gap, which implies a near-optimal robust policy for the original robust objective. Experiments on controlled tabular games and LLM
alignment with uncertain preference further validate the convergence theory and show improved performance over nominal baselines.
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All authors Shihab Ahmed, Debamita Ghosh, David Tang, Yudan Wang, Alvaro Velasquez, Yue Wang
Distributed Computing
MANTA: Machine Learning Augmented Tiering Advisor
Johannes Freischuetz, Kiet Pham, Sujay Yadalam et al. (+3 more)
Memory tiering has been used to expand
memory capacity, particularly in datacenters, by combining fast DRAM with slower tiers, including CXL-attached
memory. Its effectiveness depends on keeping useful pages in the fast tier, but existing heuristic policies can lag behind changing hot sets in phased or bursty workloads. To explore these limitations, we introduce ChOMP, a scalable offline optimizer that minimizes placement and bandwidth-sensitive migration costs. We then develop a trace-driven simulator that uses this reference to identify performance opportunities for online policies. Motivated by these results, MANTA predicts future page usefulness from runtime access features and integrates a lightweight learned model into ARMS. Across eight workloads on emulated CXL, MANTA achieves
geometric-mean speedups over ARMS of 1.12$\times$ and 1.08$\times$ at 4~GB of fast
memory on Linux 6.2 and 6.18, respectively; across six Optane workloads, it achieves 1.69$\times$. On individual workloads, MANTA is up to 1.25$\times$ faster than ARMS with emulated CXL on Linux 6.2, 1.21$\times$ on Linux 6.18, and 5.6$\times$ with Optane.
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All authors Johannes Freischuetz, Kiet Pham, Sujay Yadalam, Konstantinos Kanellis, Michael Swift, Shivaram Venkataraman
Machine Learning
WOMBAT: Whitebox Oracle for Molecular Benchmarking and Attribution Testing
Dominik Matuszek, Bartosz Zieliński, Tomasz Danel et al. (+1 more)
When a graph neural network (GNN) explainer produces an unexpected attribution on a molecule, the attribution alone cannot reveal whether the explainer has failed or the model has learned a shortcut. We introduce WOMBAT, a
benchmark of 14 whitebox GNNs, each with message-passing weights set by hand to detect a specific SMARTS motif. Each model's decision rule is known by construction, providing attribution ground truth against which explainer errors can be identified and studied. We validate the models on millions of PubChem molecules and evaluate post-hoc explainers including GNNExplainer, PGExplainer, and Integrated Gradients. Guided by our qualitative analysis, we construct a model that causes Integrated Gradients to spread attribution across the graph, even though the model reliably detects the intended motif. We release the
dataset, models, and evaluation code to help researchers in the development of newer XAI tools for GNNs.
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All authors Dominik Matuszek, Bartosz Zieliński, Tomasz Danel, Dawid Rymarczyk