Artificial Intelligence
MRVQ: One Resident Index for Dimension- and Rate-Elastic Vector Search
Sean Culatana, Shang-En Huang and Kang Li
Dense-
retrieval services must switch among
embedding-prefix dimensions and index bit rates as latency, quality, and
memory budgets change. Tuning a quantizer separately for each rate gives the best quality, but the
retrieval tier then holds several code streams and quantizer states at once. We introduce Matryoshka Residual Vector Quantization (MRVQ), a post-hoc residual quantizer for frozen
embeddings. Its maximum-rate code can be truncated two ways: dropping residual stages lowers the rate, and dropping
embedding coordinates lowers the dimension. One resident artifact therefore serves every (dimension, rate) pair we evaluate. Across FiQA and NFCorpus, four
embedding families, and {4, 8, 16}-byte codes, MRVQ is the lowest-RAM design we evaluate. It uses 17.8-22.0x less
memory than three separately trained QINCo2 indices, and 1.89-2.02x less than a lean shared-model steelman. The saving is not free: per-rate QINCo2 is 0.026-0.107 nDCG@10 better on FiQA. But MRVQ beats PQ, OPQ, and AdANNS-OPQ at matched code size. We also evaluate a low-build-cost PCA-scalar design that attains quality comparable to RaBitQ and its extension while fitting 420x faster at the median. Finally, we report two negative results: QINCo2 collapses when trained at high rates, and a ranking-bound hypothesis misses its pre-specified acceptance criteria. MRVQ is therefore a low-
memory operating point for elastic
retrieval, not a universal quality winner.
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Cryptography and Security
PoCoFL: POlicy-COmpliant Federated Learning
Dominik Roy George, Varesh Mishra and Aysajan Abidin
Federated Learning (FL) is a
privacy-oriented learning paradigm that enables collaborative model training while keeping training data local to participating clients. However, it does not guarantee that clients submit policy-compliant contributions or that aggregators process admitted contributions correctly. Existing verifiable FL systems tailor validation rules to specific FL settings, learning workflows, and cryptographic constructions, limiting their applicability across network
topologies, participant roles, and aggregation semantics. In this paper, we present PoCoFL, a policy-compliant
federated learning framework that separates three aspects: (i) FL type, (ii) policy semantics, and (iii) cryptographic realisation. We provide a formalisation that captures client and aggregation requirements as policy-dependent relations. Clients prove compliance of their contributions using commitments and non-interactive zero-knowledge proofs, while aggregators prove that the recorded set of admitted contributions was processed according to the selected aggregation policy. We demonstrate PoCoFL through four formal instantiations: (i) vanilla, (ii) continual, (iii) personalised, and (iv) threshold-encrypted
federated learning. We evaluate the effects of policy enforcement on the learning objectives of vanilla, personalised, and continual FL. We further implement proof-of-concept realisations of all four instantiations, demonstrating the versatility and practical feasibility of PoCoFL. Overall, these results show that PoCoFL can capture complex policy representations while remaining network-
topology agnostic.
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Computer Vision
On-Board Anomaly Detection for Efficient Marine Environmental Monitoring
Thomas Goudemant, Clotilde Szywala, Benjamin Francesconi et al. (+4 more)
Marine ecosystems are impacted by various threats such as oil spills, algal blooms, and sediment floods, which disrupt habitats, wildlife, and human activities. Advances in satellite imagery and Artificial Intelligence (AI) have enhanced our capabilities for early detection and mitigation of such hazards. In this paper, we propose a marine event detection pipeline for Earth observation satellites equipped with multi- or hyperspectral sensors. Our approach includes a self-supervised neural network encoder that compresses satellite images into a reduced latent space, enabling efficient onboard processing. A machine learning anomaly detection model identifies deviations from normal sea patterns to detect environmental anomalies. We compare its performance against traditional
algorithms such as Isolation Forest, One-Class Support Vector Machine and Local Outlier Factors. Our lightweight, resource-efficient pipeline is optimized for deployment on satellites with limited computational resources, ranging from embedded CPUs to AI hardware accelerators. By prioritizing the transmission of critical information, our solution enhances system responsiveness and optimizes satellite communication bandwidth. Demonstrated through current integration across multiple missions, including European Space Agency's (ESA) Phisat-2 mission and Microsoft/Thales Alenia Space IMAGIN-e mission, our pipeline aims to improve marine environmental monitoring by providing timely alerts and efficient data reduction.
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All authors Thomas Goudemant, Clotilde Szywala, Benjamin Francesconi, Michelle Aubrun, Yves Bobichon, Marjorie Bellizzi, Adrien Girard
Computational Complexity
Separating QMA from QCIP with a Classical Oracle, or, the Power of Quantum Proofs over Classical Interaction for Quantum Verifiers
Alper Cakan
Whether some problems require
quantum proofs has been a central question in
quantum complexity (Aharonov and Naveh, 2002; Aaronson and Kuperberg, CCC 2007). Recently, breakthrough work of Bostanci, Haferkamp, Nirkhe, and Zhandry (STOC 2026), followed by a simpler separation due to Bostanci, Huang, and Vaikuntanathan (FOCS 2026), established a classical oracle separation between $\mathsf{QMA}$ and $\mathsf{QCMA}$. However, while they are not in $\mathsf{QCMA}$, the problems used in both separations still lie in $\mathsf{AM}$: they admit a two-message public-coin proof system with a classical verifier. In this work, we ask whether some problems truly require
quantum proofs. More formally, we consider the complexity class $\mathsf{QCIP}$, introduced by Buhrman, Le Gall, and Weggemans (2024), where an efficient
quantum verifier interacts with an unbounded prover over a classical channel for an arbitrary
polynomial number of rounds. We construct a classical oracle $\mathcal{O}$ such that $\mathsf{QMA}^{\mathcal{O}}\not\subseteq\mathsf{QCIP}^{\mathcal{O}}$, thus showing that some
languages indeed require
quantum proofs, with no classical replacements. Since $\mathsf{QCMA}=\mathsf{QCIP}[1]$, this strengthens the earlier $\mathsf{QMA}$–$\mathsf{QCMA}$ separations, which now follow as a special case of our result. As a technical contribution, we extend to the complexity theory setting the techniques developed by Cakan, Goyal, and Shmueli (CRYPTO 2026) in the context of cryptography for analyzing classically interacting
quantum machines. We believe this may be of independent interest.
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Machine Learning · Cross-listed from stat.ML
Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models
Maksym Tretiakov, Sarah Filippi, Vincent Fortuin et al. (+3 more)
Many machine learning methods aim to approximate the lower-dimensional
manifold on which the data lives. A desirable feature of such methods is that they should capture the epistemic uncertainty of this learned
manifold. One model that achieves this is the Gaussian Process Latent Variable Model, in which a Gaussian Process (GP) mapping from the latent space provides an estimate of the uncertainty of the
manifold. However, the effectiveness of this uncertainty estimation is limited by the mean-field variational approximation between the GP inducing points and the latent variables. In this work, we apply Amortized Structured
Stochastic Variational Inference to allow the variational posterior for the latent space to be conditionally dependent on the value of the inducing points. We demonstrate that this more flexible variational posterior improves several metrics relating to the reconstruction of points on the data
manifold.
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All authors Maksym Tretiakov, Sarah Filippi, Vincent Fortuin, Ruth Misener, Ruby Sedgwick, James Odgers
Machine Learning
When May a Bandit Leave Its Anchor? E-Process-Authorized Thompson Sampling under Non-stationarity
Mayand Gulati, Kerong Wang and WeiChen Au
Stationarity rewards
memory, but after a change the same history can mislead. We ask when forgetting should be permitted. E-process-authorized Thompson sampling (e-ATS) gives each arm full-history and discounted Beta states. An anytime-valid e-process first authorizes the discounted state, then a reversible relevance score controls its influence. Before authorization, e-ATS exactly follows optimistic Thompson sampling (OTS). Under a Beta-Bernoulli prior-predictive stationary model, e-ATS's probability of ever departing from OTS is at most the chosen $\alpha_E$, without fitted thresholds. Relative to e-ATS, removing authorization increased mean normalized dynamic pseudo-regret by $38.4\%$ on the registered suite but reduced it by $7.5\%$ on the literature-derived replay suite. Therefore, evidence controls when adaptation begins, not whether it always helps.
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Computational Complexity · Cross-listed from quant-ph
Exponential quantum space advantage in random data streams
Adam Bouland, Matthew Ding and Siddhartha Jain
We show two unconditional
quantum space advantages in the random-order streaming model. First, we show the Yamakawa–Zhandry Code Intersection problem admits exponential
quantum advantage in the streaming model when its inputs are streamed in random order. This means
quantum computers exhibit exponential space advantage even when simply receiving $(x,f(x))$ pairs for a uniformly random function $f$ in a uniformly random order. Our lower bound is shown using density-restoring partitions as in the work of Göös, Gur, Jain, and Li (STOC 2025) combined with a convex potential, similar to the work of Raz (J.ACM 2018) on parity learning and its generalization by Garg, Raz, and Tal (STOC 2018). Second, we use our framework to show
quantum space advantage for the Optimal
Polynomial Intersection (OPI) problem in certain regimes via a streaming version of the Decoded
Quantum Interferometry
algorithm (Nature 2025; arXiv:2510.10967). In particular, we show that for degree $d$ and $n$ evaluation points, attaining $1/2 + \Omega(\sqrt{d/n})$ fraction of satisfied OPI constraints via streaming requires $\Omega(n)$ classical bits of
memory but only $O(d\log n)$
qubits. This yields provable
quantum advantage in a "low-rate" regime when the number of evaluation points is much larger than the degree, with a space advantage that can be as large as exponential in certain parameter settings.
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Machine Learning
On the Convergence of Success Conditioning for Policy Optimization
Matthew Brun and Xu Andy Sun
Success conditioning is a strategy for improving decision-making policies in
stochastic environments; it updates a policy by increasing the probability of taking actions that yield successful outcomes. Success conditioning is common to many reinforcement learning applications, yet its limiting behavior and convergence rates are not well understood. In this work, we demonstrate that success conditioning converges to an optimal policy on a broad class of Markov decision processes (MDPs). We also derive convergence rates in some common settings. For discounted MDPs, we prove convergence within $\mathcal{O}(1/\varepsilon^p)$ iterations to an $\varepsilon$-optimal policy, where the exponent $p$ depends on problem data. For single-period MDPs, such a policy is obtained within $\mathcal{O}(\log(1/\varepsilon))$ iterations.
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Machine Learning
IDRF: Inverse-Distilled Reward Fine-tuning of Masked Discrete Diffusion Models
Vladislav Gromadskii, David Li, Samson Gourevitch et al. (+4 more)
Masked discrete
diffusion models offer a promising alternative to autoregressive generation, but iterative sampling can be costly, and intractable sequence likelihoods complicate reward fine-tuning. We introduce IDRF, a framework for reward fine-tuning of few-step masked discrete
diffusion generators. Starting from a standard reverse-KL-regularized objective, IDRF replaces the intractable sequence-level KL penalty with inverse-
distillation regularization. With an optimal auxiliary denoiser, we prove that the population inverse-
distillation loss upper-bounds the sequence-level KL divergence to the reference distribution. IDRF optimizes a trajectory-based surrogate of this loss without reference-model rollouts, so the student keeps its own few-step sampler. We view few-step generation as a finite-horizon Markov decision process and optimize reward with a clipped policy-gradient objective over the student's trajectories. Across DNA, image, and text generation, IDRF achieves high reward with up to $32\times$ fewer denoising steps than the reference while mitigating reward hacking and preserving sample quality.
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All authors Vladislav Gromadskii, David Li, Samson Gourevitch, Yazid Janati, Eric Moulines, Maxim Panov, Alexander Korotin
Machine Learning
Broken scale symmetries in undercomplete linear autoencoders
Farhad Pashakhanloo and Jacob A. Zavatone-Veth
Neural network loss landscapes have many
symmetries, which are preserved by gradient flow but broken by finite-stepsize
stochastic gradient descent (SGD). A canonical example of such a
symmetry is scale in homogeneous networks: one can scale up the parameters in one layer and down in the next without changing the network output. Previous work has documented cases in which SGD breaks this
symmetry in favor of balancing gradient noise or minimizing fluctuations. Here, we show that the solution geometry of undercomplete linear autoencoders instead selects a preferred sign for scale drift: on the PCA solution
manifold, SGD favors large decoder weights. This directed scale drift occurs on a slow timescale, and its dynamics admit an analytically-tractable effective description. However, it cannot continue indefinitely: increasing scale eventually drives the dynamics towards a finite-stepsize stability boundary. The resulting solutions are sharper than a balanced baseline in the sense of the maximum
eigenvalue of the loss Hessian, but different sharpness measures can move in opposing directions. Thus, undercomplete autoencoders give a concrete illustration of how loss geometry can convert residual gradient noise into directed motion along a
manifold of functionally-equivalent solutions.
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Artificial Intelligence
Do Large Language Models Know Colombian Law? A Reliability Benchmark for the Colombian Legal System
Rubén Manrique, Michelle Castellanos, Jorge Morales et al. (+3 more)
Large
language models (LLMs) are increasingly used to support legal practice, education, and research, yet their reliability in national legal systems outside the United States remains largely undocumented. We introduce an expert-validated
benchmark for evaluating LLM reliability on the Colombian legal system. The
benchmark comprises 1,042 items spanning ten areas of law and three question formats (closed multiple-choice, semi-open, and open-ended IRAC), built through a human-in-the-loop pipeline with multi-stage expert review. We evaluate 15 contemporary proprietary and open-weight models with format-appropriate metrics. Accuracy on closed questions ranges widely, from 0.905 (Gemini 3.1 Pro) to 0.577, but on free-text legal answers factual correctness never exceeds 0.45 (on a 0-1 scale) for any model. We find a dissociation between answer relevancy and correctness (Spearman rho = -0.46): models reliably sound responsive while frequently being wrong, a pattern of particular concern for non-expert users. Closed-question accuracy and free-text correctness are strongly rank-correlated (rho = 0.94), so cheap multiple-choice screening predicts model ranking but overstates absolute reliability. An independent rubric-based LLM judge and blind human expert scoring both reproduce the free-text ranking (rho >= 0.88). The judge further reveals that only about half of the norms models cite are correct; the rest are wrong or non-existent. Reliability varies systematically by legal area and follows an inverted-U across question complexity. Our results indicate that current LLMs require expert supervision for Colombian legal tasks, and that grounding answers in authoritative sources is a promising path to higher reliability. We release the
benchmark construction pipeline to support reproducible evaluation.
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All authors Rubén Manrique, Michelle Castellanos, Jorge Morales, Juan David Gutiérrez, Antonio Barreto Rozo, Joaquín Vélez Navarro
Computer Vision
LoGo: Local-Global Rewards for Consistent Long-Horizon Video Generation
Ziqi Ma, Shreya Sharma, Mohamed El Banani et al. (+8 more)
Camera-controlled
video models are rapidly advancing toward long generation horizons and complex camera control. A key failure mode is 3D inconsistency: as the camera moves, objects lose permanence and scene structures shift. Existing post-training techniques, which assign a single scalar reward to the entire generation, are poorly suited to correcting these inconsistencies over long horizons. We introduce LoGo, which blends global and spatially localized rewards for camera-controlled
video models. The local reward provides fine-grained credit assignment, which substantially improves 3D consistency, while the global reward preserves camera following and
video quality. Across three base models, LoGo shows a clear advantage on DL3DV and TrajectoryBench, a new
benchmark for long-horizon, complex-camera-control generation that current evaluations lack. LoGo effectively reduces local object shifts, artifacts, and global scene changes, illustrating the importance of credit assignment in post-training
video models. Project website: https://ziqi-ma.github.io/logo-website/
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All authors Ziqi Ma, Shreya Sharma, Mohamed El Banani, Katja Schwarz, Chongjie Ye, Chao-Yuan Wu, Li Fei-Fei, Ben Mildenhall, Georgia Gkioxari, Justin Johnson, Gowthami Somepalli
Artificial Intelligence
Credit Where It Matters: Dependency-Aware Policy Optimization for Terminal Agents
Yu Li, Guangfeng Cai, Long-Fei Li et al. (+5 more)
Terminal-using
agents benefit from reinforcement learning (RL) in coding, debugging, and other multi-step terminal tasks. In these tasks, later commands often depend on information or intermediate results produced by earlier commands. However, existing trajectory-level and step-level credit assignment methods do not explicitly trace the read-write dependencies through which commands affect the final outcome. Consequently, training signals could still be assigned to irrelevant operations, weakening learning from relevant steps. In this paper, we propose Dependency-Aware Group Policy Optimization (DepGPO), which uses execution dependencies between commands to guide credit assignment for terminal
agents. Specifically, we construct a command dependency graph from execution traces and trace backward from the resources inspected by the task verifier. We then assign credit to relevant writes and their supporting reads along these paths, and use it to redistribute trajectory advantages across steps. Extensive comparative experiments and ablation studies demonstrate that DepGPO improves task performance and training stability on complex terminal tasks.
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All authors Yu Li, Guangfeng Cai, Long-Fei Li, Shuo Han, Shengtian Yang, Han Luo, Kaibing Yang, Lei Feng
Computer Vision
Yiqi Liu, Ruifeng Yuan, Yang Wang et al. (+7 more)
Physical fidelity has received increasing
attention in world models and
video generation, yet how
video representations encode physical information remains less understood. We introduce the World
Embedding Benchmark, comprising 8,000 controlled simulation cases from 80 families spanning fluid mechanics, solid mechanics, dynamics, and optics & electromagnetism. Each case pairs a rendered
video with simulation-derived physical annotations, supporting three complementary tasks: text-
video retrieval, physical-property regression, and multiple-choice
video-description pair classification. We use these tasks to distinguish cross-modal physical
alignment from the recoverability of quantitative physical information. Evaluated pre-trained omnimodal
embedding models show weak
retrieval and near-chance within-family pair classification, while lightweight probes recover useful physical information from frozen
video embeddings. Continual contrastive training with physics-specific
video-text pairs improves
retrieval and pair classification but degrades physical-property regression, revealing a trade-off between
alignment and quantitative information recoverability. Finally, we use the
embeddings to retrieve reference
videos for
retrieval-augmented generation with MiniMax-H3. Retrieved references improve the physical fidelity of generated
videos, with stronger
retrieval models yielding larger gains in our experiments. Together, these findings highlight the need to evaluate physical
alignment and property recoverability jointly, and demonstrate the utility of physical representations for improving
video generation.
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All authors Yiqi Liu, Ruifeng Yuan, Yang Wang, Long Li, Fengyu Cai, Hou Pong Chan, Jialin Yu, Hao Zhang, Chenghua Lin, Chenghao Xiao
Artificial Intelligence
NeutronGym: Physics-Graded Neutron Instrument Design for LLM Agents
Lijie Ding and Changwoo Do
Designing a scientific instrument tests whether
language-model
agents can do physics rather than recall it, provided the grading cannot be argued with. We introduce NeutronGym, to our knowledge the first executable environment for
neutron instrument design:
agents build instruments through validating tools, McStas ray-traces what they build, and a level-resolved ladder grades syntax, runtime, structure and science with no LLM judge. Procedural families supply unlimited instances of a fixed layout whose design parameters the
agent must set, with held-out parameter regimes; a curated slice, McStasBench, adds 16 tasks from published instruments behind memorization probes and a sandbox. Seven models reproduce at most 7 of the 16, none retrieves a reference, and none meets an improvement target. The environment also trains. Reinforcement learning on its reward takes Qwen3-8B from 11% to 77% of held-out instances of a family whose targets come from a hidden design (69% at a second seed), past an untrained Qwen3-32B, and the recipe holds, at one seed each, on three further gated families. The analysis says what that gain is. Without the ladder's partial credit it collapses by 60 points. From reward alone the trained model reaches what a classical optimizer reaches, at the
agent's simulation budget, only when handed the closed-form physics (77% against 81%, a gap that does not separate at this size), while frontier models still solve 98-99%. Getting a trustworthy result meant failing four task designs that no-model baselines could solve, and we release the probes that found them.
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Information Theory · Cross-listed from quant-ph
Entanglement of purification for Werner states: canonical purification and nonadditivity
Zahra Baghali Khanian and Amir-Reza Negari
We prove that the von Neumann
entanglement of purification is nonadditive. For two-
qubit Werner states $W(f)$ with singlet weight $1/64\le f\le1/4$, we show that canonical purification is globally optimal: its
entanglement, also known as reflected
entropy, gives the exact
entanglement of purification. In contrast, the regularized
entanglement of purification lies strictly below the reflected
entropy for $0<f<f_\ast$, where $f_\ast\approx0.017$. Together, these results establish nonadditivity throughout $1/64\le f<f_\ast$. Our canonical-optimality proof generalizes the proof in [1], which established Rényi nonadditivity for $0\le\alpha<1$ using classical two-
qubit states. We thus resolve the additivity question at $\alpha=1$. Remarkably, nonadditivity is again witnessed by separable states and no
entanglement in the underlying states is required.
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Artificial Intelligence
Depth as Time in One-Step Generative Models
Arnold Caleb Asiimwe, William Yang, Sanghyuk Chun et al. (+2 more)
The recent wave of one-step generative models, which compress the multi-step trajectory of
diffusion via either
distillation or learned flow maps, has reached an inflection point where they can generate high-quality images. Here, we ask a natural question that follows from these advances: what happens to the denoising trajectory of multi-step
diffusion when generation is compressed into a single forward pass? We offer an empirical observation we call depth as time: the denoising computation that multi-step
diffusion performs across sampling steps appears to unfold across the depth of a single forward pass, and can be recovered by decoding intermediate layers with the model's own output head. Most interestingly, we show that this depthwise computation depends on the transport task a flow map is trained to solve. The most surprising case is MeanFlow, where probing shorter transport intervals reveals both denoising and renoising within a single network evaluation. In contrast, generators trained without a time-indexed transport task, such as drifting models, do not exhibit the same depthwise denoising. Consequently, we show that models that exhibit the depthwise denoising phenomenon are more compressible across the layerwise computation: a MeanFlow SiT-L/2 model can be compressed by $16.6\times$ in parameters into a single time-conditioned block. We offer an explanation for this denoise-then-renoise behavior and show that, when we treat the layerwise computation explicitly as a flow, a single time-conditioned block can be trained to denoise across layers, compressing a MeanFlow SiT-L/2 model by $16.6\times$ in parameters. Together, these results suggest that the temporal computation of
diffusion is not eliminated by one-step generation, but reorganized across network depth.
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All authors Arnold Caleb Asiimwe, William Yang, Sanghyuk Chun, Esin Tureci, Olga Russakovsky
Computation and Language
FALCON: A Model and Dataset Agnostic Framework for Synthetic Data Generation for NL2SQL Pairs
Darian Lee, Shannon Rumsey, Jack St. Clair et al. (+3 more)
Relational databases are among the most widely deployed forms of structured knowledge, and natural
language access to them requires grounding
language onto schema entities and relations while handling the ambiguity inherent in how people phrase requests. Existing synthetic NL-to-SQL data generation methods largely ignore this ambiguity and produce oversimplified queries that fail to prepare models for the complexity of real-world structured knowledge access. We present FALCON, a framework that generates realistic, ambiguity-aware NL-to-SQL data matching the complexity of challenging real-world
benchmarks, at low cost using compact open models. Our approach combines reserved-word SQL seeding and persona-based prompting to generate structurally complex queries, while
alignment-based filtering preserves difficulty by distinguishing genuinely incorrect examples from complex but valid queries. Human evaluation confirms consistent high quality across model sizes, and our generated data exceeds existing
benchmarks in both SQL complexity and natural
language richness. Difficulty-stratified analysis shows models trained on FALCON data increasingly outperform baseline-trained models as query complexity increases, validating our pipeline's success in generating challenging training data. When combined with a small proportion of existing
benchmark data, mixed training recovers performance on simpler queries while preserving these advantages on complex ones. The model- and database-agnostic design enables organizations to generate high-complexity NL-to-SQL training data locally without external APIs.
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All authors Darian Lee, Shannon Rumsey, Jack St. Clair, Xinyi Tang, Aditya Bansal, Yuanming Shi
Networking
Beyond Idealized Orbits: Realistic Network Topology and Spatio-Temporal Traffic Emulation for LEO Satellite Constellations
Suryansh Aryan, Josh Smith, Alexa Houck et al. (+5 more)
Large low-earth orbit (LEO) satellite constellations are revolutionizing network access globally by delivering potential high-capacity, low-latency communication, fueling innovation and accelerating research in advanced space-based non-terrestrial network architectures. Consequently, a research testbed that allows flexibility in designing realistic network routing schemes, traffic load balancing and inter-shell communication methods to emulate real-world multi-shell network becomes an important tool for industry developers and academic researchers. This study broadens the scope of the SpaceNet testbed, now including emulation of multi-shell constellations and computation of routes with the existing global web of terrestrial terminals. Furthermore, the testbed produces accurate trends in return trip time (RTT) and throughput metrics due to its support of high-level congestion modeling and the necessary local and global congestion routing. To evaluate the efficacy of the testbed, the network performance of emulated results for the Starlink constellation is evaluated and compared with data from real Ping and iPerf tests between Starlink terminals. Two important communication scenarios have been discussed that highlight the testbed's potential for realistic multi-shell
topology generation and handling various types of traffic distribution with well-informed congestion routing. Predictions of network performance using SpaceNet accurately addresses temporal behaviors for different traffic levels with strong temporal predictability in its throughput and RTT time-series, as well as enhanced ground truth coverage within a 95% confidence interval.
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All authors Suryansh Aryan, Josh Smith, Alexa Houck, Rahul V. Chintalapati, Alexander Kedrowitsch, Jeffrey H. Reed, Nishith D. Tripathi, Samantha Parry Kenyon
Robotics
CORNAV: Construction-Aware Reasoning for Robot Navigation on Active Worksites
Parastoo Ali Pour, Deepak Prakash Kumar, Tommy Zhou et al. (+2 more)
The construction industry faces persistent labor shortages, low productivity that costs the global economy over $1.6 trillion annually, and one of the highest injury rates among major industries. These factors motivate the use of autonomous
robots to improve efficiency and worker safety. Existing
language-grounded
navigation systems, however, rely on semantic scene understanding alone and lack access to construction-specific context such as architectural plans, evolving work schedules, and safety constraints. As a result, they localize permanent building features unreliably and cannot safely navigate active jobsites. We present CORNAV, a blueprint-grounded, schedule-aware
navigation framework that operates from 2D CAD drawings and project schedules without requiring a Building Information Model. CORNAV aligns architectural blueprints against hierarchical open-vocabulary 3D scene graphs to ground object queries, converts project schedules into time-varying
navigation constraints, and validates requests through an LLM-based safety module that escalates hazardous zones before planning. An A* planner then enforces mandatory exclusion zones while preferentially avoiding higher-risk areas. Across an indoor office and a real construction site, blueprint grounding raises task success from 13.0% to 72.2% over semantic
retrieval alone, schedule awareness eliminates all hard-zone violations, and the safety module correctly rejects hazardous requests arising from mislabeled project schedules.
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All authors Parastoo Ali Pour, Deepak Prakash Kumar, Tommy Zhou, Pramod Khargonekar, Mohammad Abdullah Al Faruque
Game Theory
Normal-Form Correlation in Markov Games
Ioannis Anagnostides, Constantinos Daskalakis, Gabriele Farina et al. (+3 more)
There has been a surge of recent work on correlated
equilibrium concepts in Markov games. However, existing results focus on concepts weaker than normal-form correlated
equilibria (NFCEs), leaving open the more challenging question of computing such
equilibria, which goes back to the seminal work of Papadimitriou and Roughgarden (JACM'08). Here, we establish the first efficient
algorithm for NFCEs in finite-horizon Markov games with a fixed number of players $n$. In particular, with $S$ states, horizon $H$, and at most $A$ actions per player, it computes an $\epsilon$-NFCE in time $S(AH/\epsilon)^{O(n)}$. This is the first
algorithm polynomial in $1/\epsilon$ and the description of the game for NFCEs in an interesting class of problems beyond the normal-form setting. Moreover, under the usual assumption that recommendations are independent across states, we show PPAD-completeness—that is, computational equivalence to Nash
equilibria—either in many-player games or when the precision is exponentially small. The key idea behind our approach is to run backward induction on a sequence of auxiliary stage games, but with the twist that in each step we compute a constant-expectation correlated
equilibrium. This is a natural refinement of correlated
equilibrium in which the conditional expected payoff from obeying is independent of the recommendation. In fact, our reduction goes both ways, establishing an equivalence between constant-expectation CEs and NFCEs in Markov games. For a fixed number of players, we observe that a constant-expectation CE can be computed approximately by combining linear programming with suitable discretization. In contrast, it is PPAD-hard in i) polymatrix (many-player) games at constant precision, and ii) two-player games at exponentially small precision. The latter result follows from an unexpected connection to rank-2 two-player games.
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All authors Ioannis Anagnostides, Constantinos Daskalakis, Gabriele Farina, Noah Golowich, Tuomas Sandholm, Brian Hu Zhang
Machine Learning
UniIntervene++: An Adaptive Intervention Agent for Efficient Real-World Reinforcement Learning
Yudong Lin, Haoyuan Deng, Zhuoxuan Yuan et al. (+3 more)
Online reinforcement learning (RL) enables
robot policies to improve through physical interaction, but the assistance they require changes as their competence evolves. Existing intervention strategies based on offline estimates or fixed decision rules can therefore become mismatched to the current policy. To address this, we propose UniIntervene++, an adaptive intervention
agent that learns to allocate control between autonomous execution and heterogeneous assisted behaviors during online RL. Specifically, UniIntervene++ first formulates the evolving RL policy, trajectory correction, and a task-structured CodePolicy as Options in a unified semi-Markov decision process and learns their relative values online. Building on this, competence-adaptive intervention periodically probes the RL policy through unassisted execution, keeping control allocation responsive to its evolving capability. Finally, coupled experience learning allows assisted behaviors to improve the RL policy, whose evolving outcomes in turn reshape future intervention decisions. In this way, UniIntervene++ jointly determines when to intervene, how to intervene, and when to return control as the RL policy improves. Across five real-world manipulation tasks, UniIntervene++ achieves an average success rate of 89.67%, outperforming all baselines by at least 6 percentage points, while reducing human intervention to 0.77%, a relative reduction of at least 94.6% from the best baseline. Code is available in our \href{https://github.com/dannyyudong/An-Adaptive-Intervention-
Agent-for-Efficient-Real-World-Reinforcement-Learning}{GitHub repository}.
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All authors Yudong Lin, Haoyuan Deng, Zhuoxuan Yuan, Zaijia Yang, Yuanjiang Xue, Ziwei Wang
Artificial Intelligence
Video-Time Priors for EEG-fNIRS Emotion Regression on Familiar Videos
Minghao Kong, Ying Gao, Jiurun Chen et al. (+4 more)
Do viewer-specific physiological signals improve continuous
emotion regression beyond the stimulus-driven response shared across viewers? We examine this question by predicting
emotion ratings for new viewers watching
videos that also appear in the training set. As a stimulus-only baseline, we define a
video-time prior:the smoothed median of training labels for each
video at each second. We then combine this prior, using a fixed fusion weight, with a graph-based model of electroencephalography (
EEG) and functional near-infrared spectroscopy (fNIRS) signals. On MER-PS 2026, five-fold cross-validation grouped by participant over 24 participants yields a mean absolute error (MAE) of 29.01 for fusion, compared with 29.06 for the prior alone. On an external cohort of four participants excluded from training, the corresponding MAEs are 27.72 and 28.04. In this cohort, most of the MAE reduction relative to a global-constant baseline comes from the prior, that is, from
video identity and playback time alone.Fusion lowers MAE for three of the four held-out participants and for nine of the fifteen
videos. The added value of physiological signals is therefore small and varies across participants and
videos. Because the model uses physiological signals recorded after each prediction time, it supports only offline prediction. Generalization to unseen
videos remains untested.
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All authors Minghao Kong, Ying Gao, Jiurun Chen, Muyao Chen, Zheng Ge, Jianxin Wu, Rongjie Wang
Computer Vision
DEPICT: Scoring Text-to-Image Alignment by Answer Agreement
Vasco Ramos, Sandra Godinho Silva, Joao Magalhaes et al. (+2 more)
Image-text
alignment is a core problem in computer
vision with applications in caption evaluation,
hallucination detection, data curation, and the benchmarking of text-to-image (T2I) generators. As T2I models improve, benchmarking has become demanding, requiring metrics capable of finding a series of issues like missing objects, swapped attributes, miscounts, and ignored negations. Recent work addresses this by fine-tuning evaluators on preference data or by prompting a
vision-
language model, either holistically with the caption or with decomposed verification questions. However, existing approaches fall short: fine-tuned metrics remain bound to one backbone and training distribution; holistic metrics miss fine-grained details; and decomposed metrics rely on a fixed-YES assumption that penalizes faithful images whenever that assumption fails. In contrast, we propose DEPICT, a training-free metric that replaces fixed reference answers with expected agreement between image-based and caption-only answers, weighting questions by how decisively the caption determines them. By replacing fixed references, our agreement rule increases negation accuracy from 19% to 88%. To recover the context lost during decomposition, DEPICT merges this agreement score with a holistic score. We evaluate DEPICT on five
benchmarks and eleven backbones from three model families and find that it surpasses all training-free metrics and exceeds fine-tuned evaluators on two out of three human-correlation
benchmarks.
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All authors Vasco Ramos, Sandra Godinho Silva, Joao Magalhaes, Ricardo Rei, Pedro Henrique Martins
Robotics
LLA-MPC on Embedded Hardware: Rapid Adaptive Control with Thousands of Parallel Models
Henry Z. Liao, Maitham F. AL-Sunni and John M. Dolan
We present a generalized implementation of Look-Back and Look-Ahead Adaptive Model Predictive Control (LLA-MPC), a learning-free framework for real-time, rapid adaptive system identification and control. The original formulation was demonstrated only in simulation, for autonomous racing with a fixed model structure. Our implementation has modular dynamics and integrators, and we validate it on the F1TENTH platform under constrained computation and noisy state estimation. The system identifies tire parameters online by evaluating thousands of candidate models in real time on an embedded computer. Experiments with low-friction tires across changing surfaces show that LLA-MPC completes high-speed tracking tasks where a fixed nominal model fails. Code,
videos, and our related work are available at: https://lla-control.github.io.
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Robotics
Bridging Frontier Reasoning and Robot Execution: From Autonomous Demonstration Generation to Dense Language Supervision
Bosung Kim, Alexander Trevithick, Ruiyi Wang et al. (+1 more)
Recent advances in frontier models enable
robot manipulation from only a few demonstrations, but high inference latency limits their use for real-time
robot control. To bridge this gap, we study two complementary approaches that connect frontier
reasoning with low-latency local execution. First, we use a frontier model to autonomously generate demonstrations that supplement human demonstrations for training a fast local policy. We augment its in-context examples with corrective demonstration segments that show how to recover from physical errors, improving generation reliability. Generation time and cost decrease as successful examples accumulate in context, suggesting a path toward more efficient data collection. At deployment, a harness combines frontier-generated instructions with a fast local policy, enabling efficient execution while preserving the frontier model's ability to guide and correct actions. With low-latency execution delegated to the local policy, the bottleneck shifts to its capacity to reliably follow the frontier model's diverse instructions. Our second bridge introduces dense
language supervision across three nested granularities—primitive, atomic, and composite—with multi-aspect descriptions at each level. Across long-horizon tasks in RoboCasa 365 and BEHAVIOR-1K, where instructions change as execution progresses, the combined supervision achieves the highest performance under both oracle and frontier-model instructors, demonstrating more reliable instruction following through the policy's
language interface. Finally, we evaluate both bridges together on a crossword task that combines semantic planning and manipulation within a fixed time budget. These results support autonomous demonstration generation and dense
language supervision as complementary components for connecting frontier
reasoning to low-latency local execution.
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All authors Bosung Kim, Alexander Trevithick, Ruiyi Wang, Prithviraj Ammanabrolu
Robotics
World Action Learning via Interaction-Centric Spectral Latent Guidance
Zhiming Liu, Yikun Miao, Ying Chen et al. (+7 more)
Learning general-purpose
robot policies requires large-scale real-world interaction data, yet collecting
robot demonstrations remains expensive and difficult to scale.
Egocentric videos offer abundant human interaction experience with task-relevant semantics for robotic manipulation, but direct transfer is challenging for two reasons: latent actions inferred from frame reconstruction can be dominated by nuisance variation such as ego-camera motion, and human and
robot behaviors often exhibit different temporal dynamics. We propose WING (World Action Learning via INteraction-Centric Spectral Latent Guidance), a framework for transferring interaction knowledge from
egocentric videos to
robot policies. WING first separates observer-induced motion from hand-object interaction and distills the interaction-centric component into latent actions. It then exploits the observation that cross-embodiment task semantics are concentrated in slowly varying temporal structures, identifying shared low-frequency components between
egocentric latent actions and
robot behaviors in the spectral domain and using them to guide action generation. WING achieves average success rates of 99.20% on LIBERO, 93.80% on RoboTwin 2.0, and 57.7% on RoboCasa-GR1, and also performs strongly across four real-world manipulation tasks under diverse generalization settings. These results show that interaction-centric spectral guidance provides an effective and scalable way to transfer physical interaction knowledge from human
egocentric video to
robot control. Project page: https://mikuz12.github.io/wing/
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All authors Zhiming Liu, Yikun Miao, Ying Chen, Hongrui Yin, Fangqi Zhu, Xiaoyi Pang, Quanxin Shou, Zhengyang Yan, Haodong Wang, Song Guo
Machine Learning
Mastering Atari 2600 Games with Discovered Options
Erik M. Lintunen and Marlos C. Machado
Temporal abstractions, often instantiated as options, have long been regarded as a mechanism for accelerating credit assignment, facilitating exploration, and enabling generalisation in reinforcement learning (RL). However, developing general option discovery methods that are effective in large-scale, high-dimensional domains remains a fundamental challenge. Existing option discovery methods are either confined to relatively simple domains, depend on handcrafted or quasi-symbolic representations, or offer little improvement over learning without options. We present Wayfarer, a general, domain-agnostic, online deep RL
agent that discovers options through
Laplacian representation learning from high-dimensional observations and leverages them for control. We show that the resulting options simultaneously improve exploration, accelerate credit assignment, and generalise effectively to unseen settings, enabling substantially faster learning of complex policies. Wayfarer achieves state-of-the-art performance among single-stream
agents on the most challenging Atari 2600 games, with the largest gains in games that require long-horizon exploration and strategic behaviour, such as Montezuma's Revenge and Private Eye.
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Sound · Cross-listed from eess.AS
Evaluating Inference-time Algorithms for Semantic Sound Scene Segmentation
Sripathi Sridhar, Gordon Wichern, Yoshiki Masuyama et al. (+2 more)
Spatial semantic
segmentation of sound scenes (S5) involves the detection and extraction of target sound events in audio files. However, in typical detection-extraction pipelines, errors in the detection module propagate to the extraction module, degrading overall performance. In this work, we develop a multichannel detection-extraction model and evaluate inference-time
algorithms to improve the class detection and target sound extraction in a multi-stage setup. We evaluate two fitness functions: binary cross
entropy and mixture consistency; two relabeling strategies: naive and conditional confusion
matrix relabeling; and three source estimate evaluation models: a multi-label classifier, a fine-tuned single-source classifier, and an off-the-shelf audio judge. We thoroughly evaluate the impact of these design choices on the DCASE 2025 Task 4
dataset, and validate
entropy-based gating of inference-time updates on the held-out evaluation set, laying the groundwork for inference-time updates of S5 systems in real-world scenarios.
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All authors Sripathi Sridhar, Gordon Wichern, Yoshiki Masuyama, Mark Cartwright, Jonathan Le Roux
Computer Vision
ManifoldSplat: Language-Guided Semantic Shape Editing of 3D Gaussian Head Avatars
Antonio Canela and Jordi Sànchez-Riera
High-fidelity 3D head avatars have reached near-photorealistic quality. While recent methods enable text-driven manipulation, they struggle to provide fine-grained localized control, often entangling features or lacking
geometric consistency. Modifying geometry through natural
language currently requires slow per-
prompt optimization or compromises identity and rigging. We present ManifoldSplat, the first end-toend framework for
language-guided semantic shape editing of animatable 3D Gaussian Splatting avatars reconstructed from
monocular videos. By performing edits within the structured FLAME
manifold rather than directly optimizing an unstructured Gaussian cloud, we strictly preserve identity and animation. We introduce DeltaRegion, a per-region disentangled Conditional Variational Autoencoder (CVAE) delivering feedforward shape deltas, alongside a refining stage to recover view-consistent details. ManifoldSplat reconstructs and edits an avatar in ~90 seconds on a consumer GPU, rendering at ~800 FPS. Extensive evaluations demonstrate our approach sets a new state-of-the-art in localized
prompt alignment,
geometric coherence, and identity preservation. Project page and code: https://a-canela.github.io/manifoldsplat/
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