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新论文 · v1arXiv:2611.03916

When Latent Dynamics Meet Data Assimilation: A Benchmark of Koopman-Type Representations

Zhiwei Zhang, Wenxuan Yuan, Xiaoyuan Cheng, Pouria Behnoudfar, Chuanqi Chen, Charlotte Moser, Yuta Tarumi, Jin-Long Wu 等 13 位作者

原文摘要

Koopman representations describe nonlinear dynamics through linear evolution in a transformed space, offering an attractive framework for their linear simplicity and generality. Yet these advantages do not necessarily translate into improved performance in deep latent data assimilation (LDA). We systematically compare seven representative methods and structural baselines across five systems spanning approximately periodic to strongly chaotic dynamics and a broad range of state dimensions. We evaluate forecast and assimilation accuracy, physical state recovery, latent-trajectory behavior, and dynamical consistency, and assess Gaussian error assumptions, objective convexity, and uniqueness of analysis solutions. Our results reveal a trade-off between representational flexibility and optimization tractability. Linear evolution of the latent state can stabilize short-term forecasts but restrict expressiveness in strongly chaotic and high-dimensional systems, without consistently improving assimilation accuracy. Parameterized linear operators offer greater flexibility, yet their joint optimization with the encoder--decoder remains nonconvex. Methods with closed-form analysis updates generally perform better and exhibit greater robustness in our experiments, although matrix inversion remains a computational bottleneck. These findings show that the practical benefits of Koopman structure depend on both sufficient observational information and the choice of assimilation procedure.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.16610

Tensor Cookbook: Mastering Tensors through Diagrams

Beheshteh T. Rakhshan, Guillaume Rabusseau

原文摘要

High-dimensional data arise naturally in many areas of science and engineering, including machine learning, signal processing, computational physics, and statistics. Such data are often represented as tensors, multi-dimensional generalizations of matrices. While tensors provide a natural representation for multi-modal structure, their direct manipulation quickly becomes challenging as the order grows: the number of parameters increases exponentially, and algebraic expressions involving many indices become difficult to interpret and implement. Tensor networks (TNs) provide an effective framework for addressing these challenges. Originally introduced by Penrose and developed extensively in quantum physics, the graphical language of tensor networks encodes contractions as edges in a graph, reducing notational overhead and revealing structural properties obscured by index notation. Despite the central role of high-dimensional tensors in modern machine learning and numerical analysis, tensor network diagrams remain underutilized outside quantum computing, partly due to the lack of a self-contained mathematical reference accessible to a broad technical audience. This manuscript provides a self-contained guide to tensor networks and their use in tensor algebra. We present the main operations on tensors, contractions, products, and reshaping through, graphical notation, and show how classical tensor decompositions and related computations are naturally expressed in this framework. We also illustrate how tensor networks simplify the derivation of gradients and the manipulation of high-dimensional probability distributions. Throughout, we show that the diagrammatic approach yields genuinely shorter and more transparent proofs of classical identities, rank bounds, and gradient formulas that would otherwise require laborious index manipulation.

cs.LG · cs.GL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03912

From Triples to Cards: A Web Component for Human-Readable Linked Open Data

Puyu Wang, Hang Dong, Rui Zhao

原文摘要

JSON-LD is getting increasing attention for publishing Linked Data, especially within cultural heritage. We present LODox Data Viewer, a reusable web component library for data presentation and visualization in semantic portals. It renders JSON-LD as human-readable, themeable cards, embeddable in any semantic portal page with a single script tag. Package URL: https://www.npmjs.com/package/lodox-data-viewer

cs.DL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.02258

Universal Conceptual Structure in Neural Translation: Probing NLLB-200's Multilingual Geometry

Kyle Elliott Mathewson

原文摘要

Do multilingual translation models organize meaning around a shared conceptual space, or do they primarily encode language identity and surface form? We probe the encoder geometry of Meta's 600M-parameter NLLB-200 model across 135 languages and 101 Swadesh concepts in contextualized carrier-frame and decontextualized bare-word regimes against null and confound controls. First, two-way variance partitioning reveals an additive language-shift plus conceptual-manifold geometry: language shifts account for 45.7% of variance on bare words vs. 13.7% in context, and mean-centering improves the between-to-within-concept distance ratio (vs. a 0.99 permutation null) by 2.14x on bare words (1.15 to 2.46; 2.27x across disjoint scripts) and 1.19x in context (2.25 to 2.69), with conceptual separation peaking at Layer 10 (mutual k-NN overlap jumping 4.35x). Second, semantic category explains 25.4% of Swadesh convergence variance ($F(7,93)=4.53$, $p=2.21\times 10^{-4}$; partial $F=7.74$ controlling for fertility and length)---over 20x orthography or phonology ($R^2\le 0.012$)---while the advantage of Swadesh over non-loanword concrete nouns ($d=0.54$--$0.69$) is mediated by Zipfian subword compactness ($p=7.43\times 10^{-7}$). Third, language distances track ASJP NLD and LDND phonetic distances within subfamilies under block-permutation and cross-script controls ($ρ_s=0.65$--$0.67$, block $p=10^{-4}$; global bare-word Mantel $ρ_s=0.24$, $p=0.001$), and CLICS$^3$ colexified concepts lie closer across string-distinct, within-category ($ρ_s=0.32$), and bare-word ($d=1.79$) controls ($d=1.43$, $p=1.55\times 10^{-33}$). Finally, basic color terms recover the Berlin and Kay hierarchy ($ρ_s=0.91$--$0.93$, $p\le 2.18\times 10^{-4}$), and semantic offsets exceed distance-matched controls ($p=3.55\times 10^{-5}$, $d=1.06$). We release InterpretCognates, an interactive toolkit and reproducible suite.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.34270

Bayesian Active Learning for Intent Disambiguation in Interactive Robot Planning

Huao Li, Carson Sobolewski, Augustinos Saravanos, William Tan, John Karigiannis, Chuchu Fan

原文摘要

Interactive robot planning requires robots to infer and execute human intentions from natural language instructions that are often ambiguous, incomplete, or underspecified. Although large language models (LLMs) provide a powerful interface for clarification, relying on the generative model to drive an multi-turn conversation can introduce systematic failures. We propose a Bayesian framework that treats clarification as an active learning problem over grounded Signal Temporal Logic (STL) task specifications. Our method uses LLMs to initialize candidate formal specifications and translate informative contrasts into natural-language clarification questions, while Bayesian optimization maintains uncertainty estimation over user intent and selects queries that maximize information gain. After convergence, the inferred STL specification is passed to a formal planner to synthesize a verifiable robot trajectory. Across four simulated and real-world task domains, our approach generally achieves higher task satisfaction and requires fewer clarification rounds than LLM baselines, while helping smaller models close the performance gap against larger reasoning models.

cs.RO · cs.AI · cs.HC

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2608.28469

Distributed Cross-Layer Optimization for Covert Multi-Hop, Multi-Modal Networks: Exponentially Fast Convergence and Robust Tracking

Sirin Chakraborty, Andrea Panebianco, Yuchen Tian, Kevin S Chan, Fikadu Dagefu, Yin Sun, Ness B. Shroff

原文摘要

This paper develops the first distributed cross-layer algorithm for joint congestion control, routing, scheduling, and power control in covert multi-hop, multi-modal wireless networks, where adversarial wardens (Willies) monitor radio modalities via energy detection. The Detection Error Probability (DEP), the probability that a Willie fails to reliably detect ongoing transmissions, is generally non-concave in the transmit powers, making DEP-based covert network optimization challenging. We resolve this by constructing the tightest concave lower bound on the log-DEP, yielding a conservative convex problem that guarantees satisfaction of the original DEP constraints and unifies hard covertness constraints and covertness-utility maximization in a single problem. We develop a Parallel Proximal Alternating Direction Method of Multipliers (PP-ADMM) algorithm for the resulting cross-layer problem and prove global Q-linear convergence, i.e., exponentially fast convergence, to the set of optimal solutions under standard regularity conditions. Numerical results confirm linear convergence and demonstrate robust tracking performance under channel fading and Willie mobility.

cs.IT · eess.SP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03906

Proper Conflict-Free Coloring on Structured Graph Classes: Bounds, Exact Algorithms, and Hardness

Kamal Santra

原文摘要

A proper conflict-free coloring is a proper vertex coloring in which every non-isolated vertex has a neighbor whose color appears exactly once in its open neighborhood. We study this coloring on chordal and ordered bipartite graph classes. For well-partitioned chordal graphs, we prove the tight bound $ω(G)\leq\pcf(G)\leqω(G)+1$ and give an $O(n^2)$ construction from a partition tree. Every assignment of lists of at least $ω(G)+1$ colors also admits a PCF coloring that respects the lists. Deciding whether $ω(G)$ colors suffice is NP-complete, even when the partition tree is a star. This also proves NP-completeness on chordal graphs when the number of colors is part of the input. For interval graphs, we prove that lists of size $ω(G)+1$ always suffice, improving the previous ordinary-coloring bound of $ω(G)+2$. We use matchings to characterize when $ω(G)$ colors suffice and give an endpoint algorithm taking $2^{O(ω(G)^2)}n^{O(1)}$ time. We also characterize the exact value when the clique number is at most three and give a linear-time algorithm from sorted interval endpoints. For biconvex graphs, we prove a tight five-color bound and give a direct exact algorithm taking $O(n^{33})$ time. For bipartite permutation graphs, a smaller dynamic program gives an exact $O(n^{17})$ algorithm. Finally, if a bipartite graph is convex over an ordered shore $X$, we prove the sharp bound $\pcf(G)\leq\lceil\log_2(|X|+1)\rceil+3$ and construct a coloring in linear time from the order.

cs.DM · math.CO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03900

Clean-anchored excess-emission control for CTC adaptation under competing speech

Yuanbo Zhou, Junjie Xiong

原文摘要

Competing speech can cause excess emissions in a connectionist temporal classification (CTC) recognizer. We penalize the amount by which the noisy student's expected collapsed token count exceeds a frozen teacher's count on the paired clean utterance. The one-sided objective caches one scalar per utterance and adds no inference module. With 147,456 trainable parameters in wav2vec 2.0, it improves competing-speech word error rate (WER) over matched CTC-only adaptation across three development seeds. On a locked LibriSpeech test, speech-interference WER falls from 45.198% to 44.323%, while clean WER changes by 0.004 percentage points. Unseen VCTK interferers yield a 0.307-point improvement. Ablations favor collapsed-count control over frame-mass regularization. Longer training increases the speech gain but worsens noise and music performance, identifying an insertion-deletion tradeoff rather than uniform robustness.

cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03899

EpiLoRA: Fusing Many LoRA Adapters into Low-Precision GEMMs for Serving and RL

Hieu Tran, Hong Yu

原文摘要

Low-rank adapters (LoRA) let one base model serve many fine-tuned variants: multi-tenant engines keep a single copy of the base and batch the requests of many tenants, each with its own adapter. As base models move to 8-bit (FP8) and 4-bit formats, their general matrix multiplications (GEMMs) become cheaper, but the adapters do not: engines run them as separate small GPU kernels beside the GEMM, whose latency takes a growing share of every decoding step. An adapter projects its input down to a low rank and back up, and our key observation is that the GEMM needs the down-projection only in its epilogue, the final stage in which it writes its output. EpiLoRA, a plugin for the vLLM engine, therefore launches the down-projection first and the low-precision GEMM as a dependent kernel that streams its weights meanwhile and adds every tenant's up-projection in its epilogue. On NVIDIA Hopper GPUs, EpiLoRA decodes for a single adapter with up to 24% lower time per token than vLLM's fastest configuration, and faster than SGLang and TensorRT-LLM; with many tenants, its adapter kernels deliver more throughput than vLLM's on most GPU-model pairs we tested. The same low-precision base also trains: a LoRA reinforcement-learning trainer that computes on the engine's FP8 weights needs no 16-bit copy, so a 27B model trains on one 48 GB GPU, and an engine that then keeps activations in 16 bits removes most of the remaining mismatch between the model generating training data and the one trained.

cs.DC · cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.00672

ORBIT-FMIB: Tracking Order-Resolved Epistatic Information Through ESM-2

Maryam Rahimimovassagh, Waldemar Karwowski, Ivan Garibay, Niloofar Yousefi

原文摘要

Protein foundation models support mutation-effect and structural prediction, but predictive performance alone does not reveal which forms of biological interaction information remain accessible through model depth. We ask whether ESM-2 retains higher-order epistatic information as strongly as first- and second-order information across its representation hierarchy, introducing ORBIT-FMIB, a diagnostic framework combining Walsh-based interaction decomposition with subset-conditioned neural dependence estimation. The method is validated on synthetic landscapes with known interaction structure before being applied to the dense four-site GB1 fitness landscape using frozen ESM-2 representations. An initial production run suggested ESM-2 retains higher-order epistatic information less well than lower-order information ($Δ_{\mathrm{HO-LO}}=-0.107$). An independent replication of the complete measurement grid, under matched GPU hardware and identical critic seeds, substantially reduced this contrast ($Δ_{\mathrm{HO-LO}}=-0.017$), and its sign was unstable across otherwise-defensible evaluation-pairing choices applied to the same trained critics ($-0.011$ to $+0.015$). We therefore do not currently have robust evidence that ESM-2 selectively loses higher-order epistatic information, nor that retention is equal across orders; the directional question remains open. The measurement protocol itself, including its documented removal of a positional-subset shortcut in pooled critics, remains validated and is unaffected by this finding. ORBIT-FMIB is offered as a diagnostic framework for probing interaction structure in protein foundation models; this study's own replication result illustrates why such probing requires adequately-powered reproducibility checks before its output is treated as a biological finding.

cs.LG · q-bio.QM

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2510.14716

Approaching the Continuous from the Discrete: an Infinite Tensor Product Construction

Antonio Lorenzin, Fabio Zanasi

原文摘要

Increasingly in recent years, probabilistic computation has been investigated through the lenses of categorical algebra, especially via string diagrammatic calculi. Whereas categories of discrete and Gaussian probabilistic processes have been thoroughly studied, with various axiomatisation results, more expressive classes of continuous probability are less understood, because of the intrinsic difficulty of describing infinite behaviour by algebraic means. In this work, we establish a universal construction that adjoins infinite tensor products, allowing continuous probability to be investigated from discrete settings. Our main result applies this construction to $\mathsf{FinStoch}$, the category of finite sets and stochastic matrices, obtaining a category of locally constant Markov kernels, where the objects are finite sets plus the Cantor space $2^{\mathbb{N}}$. Any probability measure on the reals can be reasoned about in this category. Furthermore, we show how to lift axiomatisation results through the infinite tensor product construction. This way we obtain an axiomatic presentation of continuous probability over countable powers of $2=\lbrace 0,1\rbrace$.

math.CT · cs.LO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03892

Isothermal-Isobaric Molecular Dynamics with a Foundation Interatomic Potential in LAMMPS across NVIDIA and Intel GPUs

Xiaoli Yan, Eliu Huerta

原文摘要

Foundation machine-learning interatomic potentials (MLIPs) offer one model for many chemistries, but their deep, globally coupled architectures do not fit the spatial decomposition that scales classical and short-ranged MLIPs. We present pair_style uma, a LAMMPS pair style that evaluates the Universal Model for Atoms (UMA) through LibTorch on NVIDIA and Intel GPUs from a single source tree. It reimplements FairChem's edge-sharded graph parallelism in C++ over exported TorchScript modules, with one MPI rank per device, NCCL or oneCCL collectives, and the whole energy and force path in double precision. The strain-gradient virial is carried through the exported modules and the cross-device reductions, so that fix npt runs on both vendors' hardware. We measure NPT capacity and cost on one 4xA100-40GB node, one 8xH200 node, and one 12-tile Intel node, and on up to 32 Intel nodes. Without activation checkpointing, one node holds NaCl cells of about 8,000 atoms (A100, 0.21 ns/day), 22,000 atoms (Intel, 0.10 ns/day), and 47,000 atoms (H200, 0.16 ns/day). Graph parallelism raises the capacity of a single device by 8.0x on H200 and 4.6x on A100. Across nodes, capacity grows to about 779,000 atoms on 32 Intel nodes, with a slower speed at 0.007 ns/day. On H200 and A100, a FLOP count of FairChem's graph-parallel predictor gives lower bounds of 31% and 51% of FP64 peak. From one omat checkpoint and with no refit, nine 100 ps NPT heating ramps (NaCl, equiatomic Pt-Ru, and equiatomic (HfNbTaTiZr)N, on all three node types) ran to completion.

physics.chem-ph · cs.CE

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2511.15238

A Quantitative Approach to Reach-Avoid Reachability Analysis for Discrete-Time Nonlinear Control Systems

Ihab Tabbara, Eliya Badr, Hussein Sibai

原文摘要

A reach-avoid specification requires a dynamical system to reach a target set of states while avoiding a failure set. Reachability analysis partitions the state space into parts starting from which the specification can be satisfied by following some control signal and those from which it cannot despite the best control effort. In this paper, we present a quantitative approach for reachability analysis to analyze the satisfaction of avoid and reach-avoid specifications for discrete-time nonlinear control systems. In such an approach, a value function assigns a value to each state that determines how robustly the system can satisfy the specification starting from it and following the optimal control signal. However, computing such a value function is difficult. Instead, our approach constructs discrete abstractions on which it performs value iteration to compute sound bounds on the desired value function. The resulting lower bound enables a sound under-approximation of the reach avoid set (RAS) and, in the avoid case, a sound over-approximation of the backward reachable set (BRS). When the control set is finite, the upper bound additionally enables a sound over-approximation of the RAS and, in the avoid case, a sound under-approximation of the BRS. The lower bound also yields quantitative guarantees on safety margins and safe target-reaching times. Our approach further refines the abstraction by splitting the grid cells that could not be classified as within or outside the BRS (or RAS) to improve the usefulness of the bounds. We evaluate our approach on five example systems with up to five-dimensional state spaces.

eess.SY · cs.FL · cs.SC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.00514

Generate in Reconstruction Space, Match in Semantic Space: Transport Geometry for One-Step Generation

Hugues Van Assel, Edward De Brouwer, Saeed Saremi, Gabriele Scalia, Aviv Regev

原文摘要

Generative modeling and self-supervised representation learning (SSL) optimize structurally different objectives: generative training rewards distributional fidelity, while SSL rewards semantic coherence. Yet recent work repeatedly finds that SSL features improve generative training, though the mechanism of this synergy remains unclear. Here, we study the benefits of SSL in generative modeling in the framework of one-step generation where the role of representation is explicit: frozen SSL features are used to match generated samples to real data. We use the Sinkhorn divergence in that feature space, providing a tractable surrogate for the Wasserstein distance, the population-level discrepancy approximated by Fréchet-style evaluation metrics (such as FID). We find that this objective becomes highly effective when computed in a semantically structured SSL feature space (a 39$\times$ reduction in ImageNet FID). We trace this behavior primarily to matching estimation: semantic SSL features that suppress nuisance reconstruction details induce a more compact geometry, making distribution matching more tractable. As a consequence, the best training SSL features need not match the features used by the evaluation metric. In particular, we show that using Inception as the feature extractor can improve FID while degrading matching stability and sample quality, revealing a form of metric hacking. Using extensive experiments on ImageNet, we identify which SSL feature families lead to best generation performance and show that matching stability is a quantitative criterion for selecting them. Code is available at https://github.com/Genentech/semantic-transport-generation.

cs.LG · cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.02036

Global Coherence: When Every Agent Is Right and the Team Is Still Wrong - A Local-to-Global Semantic Foundation for Multi-Agent Collaboration

Xin Heng

原文摘要

AI agents share code, documents, tools, and analyses. No participant sees the whole state, so every step can look right while the combined result is wrong. We call this the global coherence problem. It is a problem of state, not only of intelligence: a stronger model decides better locally, but cannot see what is missing from its context. The Observation-Aliasing Impossibility Theorem gives the exact condition. An agent can always act validly exactly when every world consistent with its context allows a common action. When look-alike worlds need different actions, no reasoning, role, message, or sample can guarantee the right one; with k such worlds whose valid actions are disjoint, the best randomized policy succeeds with probability 1/k. Finding the smallest sufficient context is NP-hard, but refinement finds one and keeps it current as rules change. A second failure survives full information: valid pieces can fail to fit into one world. The same theorem sets the conditions for coherence; sheaves and groupoids are one way to meet them. Models propose, and the harness decides what is valid. Ten studies test it. On TeamBench, teams overspend a shared budget in 5/5 runs; showing them the live count still leaves 4/5, and a harness that owns the budget leaves 0/5. Identity collisions that no pairwise check can see appear in 23 of 45 public alignment networks; split-view agents create them in 9 of 40 merges, and a network check removes all nine. A frontier LLM solves 40/40 revision tasks when it sees the deciding event; with the event hidden, no arm that reasons longer, votes, or splits into roles significantly beats chance, and a companion study replicates this on models from a second provider. In tau2-bench Telecom, after a silent revert, the harness scores 1.00 against 0.07 for call-time policy checks. Refinement keeps a sufficient context at 28% of the cost of sharing every field.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03891

Hardware-Aware Design and Layout Selection in Quantum Reservoir Computing for Insurance Claims Forecasting

Renáta Rusnáková, Eugen Šlapak, Gaurav K. Pandey, Juraj Gazda

原文摘要

Quantum reservoir computing (QRC) processes temporal information through quantum dynamics, but it remains unclear whether small QRC models can forecast real data competitively with classical models and whether their predictions survive the transfer from ideal simulation to noisy hardware. We address both questions for a six-qubit gate-based reservoir that forecasts next-day motor-insurance claim counts. Five reservoir candidates were selected and frozen using hardware-native interaction graphs, Trotterized Ising dynamics, memory and measurement-robustness criteria, circuit cost, and calibrated-noise screening. On 232 held-out test days, the ideal candidates reached root-mean-square errors (RMSEs) of 5.66-6.12 claims/day, compared with 5.36 for ridge regression. For four of the five, the difference from ridge was not significant under a 95% block bootstrap, and their accuracy was comparable with classical recurrent and reservoir baselines. On the 156-qubit IBM Kingston processor, we ran 63 evaluations in 21 paired rounds, comparing hardware-aware, random-native, and Qiskit optimization level 1 layouts. Hardware-aware placement gave lower RMSE than Qiskit in every round, with a maximum transfer distortion of 7.59 claims/day (11.84 for Qiskit), and avoided the severe transfer failures seen for some random layouts. Most of the hardware distortion appeared as a systematic forecast shift, which a short hardware probe on training inputs detected before the test runs. Longer replay windows raised circuit cost without a consistent gain in forecasting accuracy or hardware transfer. Small QRC models can thus reach competitive accuracy on this task, and hardware-aware layout selection makes their transfer to IBM Kingston more consistent.

quant-ph · cs.LG · q-fin.ST

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2409.14591

Non-Cartesian Guarded Recursion with Daggers

Louis Lemonnier

原文摘要

Guarded recursion is a framework allowing for a formalisation of streams in classical (as opposed to concurrent, probabilistic, quantum) programming languages. The latter take their semantics in cartesian closed categories. However, some programming paradigms do not take their semantics in a cartesian setting; this is the case for concurrency, reversible and quantum programming for example. In this paper, we focus on reversible programming through its categorical model in dagger categories, which are categories that contain an involutive operator on morphisms. We show how to introduce the framework of guarded recursion into dagger categories with sufficient structure for data types, also called dagger rig categories. First, given an arbitrary category, we build another category shown to be suitable for guarded recursion in multiple ways, via enrichment and fixed point theorems. We then study the interaction between this construction and the structure of dagger rig categories, aiming for reversible programming. Finally, we show that our construction is suitable as a model of higher-order reversible programming languages, such as symmetric pattern matching, to which we add guarded recursion features.

cs.LO · cs.PL · math.CT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03887

The Information Limits of Neural Controlled Differential Equations Under Sparse Observations

Rostislav Gusev, Evgenia Romanenkova, Alexey Zaytsev

原文摘要

Neural controlled differential equations (Neural CDEs) evolve hidden states along continuous paths constructed by interpolating discrete observations. Sparse measurements leave the input between observation times undetermined, and admissible continuous paths consistent with the same data can produce different terminal states. We characterize this ambiguity for planar drivers with bounded speed. The set of compatible terminal states admits a leading-order zonotopic approximation determined by feasible signed areas and transported Lie brackets. Its projection onto a scalar prediction yields a computable, target-specific ambiguity certificate. We also study the number of measurements needed to reduce the remaining uncertainty. For nondegenerate planar CDEs, the optimal worst-case radius over deterministic adaptive sampling policies scales as $N^{-1}$ when speed is bounded and $N^{-2}$ when both speed and acceleration are bounded, where $N$ is the number of observation intervals. Synthetic experiments assess the certificate using admissible continuous completions and examine the role of path regularity.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03885

Self-Consistent Neural Control: Learning Trajectories for Implicit Equilibrium

Hengyi Zhou, Haobo Fang, Xiaonan Huang, Dezhong Tong, M. Khalid Jawed

原文摘要

One big challenge in physical AI is interacting with systems whose states are defined by implicit equilibrium. Learning in these systems is difficult because their responses can be multistable, path-dependent, and sensitive over long horizons. Adjoint learning provides an efficient way to optimize trajectories without unrolling iterative equilibrium solvers, but its gradient relies on the frozen-tangent approximation, which neglects higher-order sensitivity terms and can become inaccurate when the local equilibrium varies rapidly. In this work, we introduce Self-Consistent Neural Control (SCNC), optimizing trajectories along which sensitivity variations are explicitly controlled. We derive a mathematical bound on the resulting gradient error and develop a practical finite-difference approximation. We validate the framework on deformable objects, which are representative of implicit equilibrium systems, ranging from simple quadratic energy models such as elastic rods to complex systems including thin-walled tubes, measuring tapes, and Slinky-like structures. Compared with the adjoint learning and the gradient-free iCEM baselines, SCNC achieves faster convergence and more stable optimization by reducing switching to undesirable equilibrium branches, doubling the stable optimization rate over adjoint learning 83.3% vs. 41.7% and reducing the average loss by more than 1.5 orders of magnitude on thin-walled tubes, where sensitivity variations are pronounced.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03884

Timely Ground-Station Status Delivery via UAV-Assisted Data Collection and Hierarchical RL

Xiangjian Gao, Mohammad Moltafet, Hamid R. Sadjadpour

原文摘要

We consider a UAV-assisted status-update system in which a rotary-wing unmanned aerial vehicle (UAV) collects updates from multiple sensors, stores the newest update of each sensor, and forwards selected buffered updates to a ground station (GS). We minimize a weighted sum of the time-average GS-side age of information (AoI) and UAV energy consumption over a finite horizon. The optimization is subject to half-duplex operation, sensor and UAV energy-causality constraints, transmission-rate and power limits, and a terminal-location constraint. Because collection changes the information age at the UAV but does not reduce the GS-side AoI until the buffered update is forwarded, mobility, collection, and forwarding decisions are coupled over time. We use a hierarchical AoI-energy actor-critic (HAE-AC) controller in which a coarse-timescale actor selects return-feasible UAV movements, while a deterministic per-slot scheduler constructs feasible collection or forwarding sets and assigns the minimum transmit power required for each scheduled transmission. In a 100-sensor deployment, HAE-AC reduces the average GS-side AoI by about 22% relative to an adaptive staleness-gated benchmark. Compared with forward-upon-collection, selective buffered forwarding reduces the number of downlink bursts by roughly 30% while increasing the number of collections per sensor by more than 23%.

cs.IT · eess.SP · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03882

ProofPaths: An AI Agent Framework for Assessing Student Reasoning

Mohsen Hariri, Vipin Chaudhary

原文摘要

Students can use large language models to complete assignments, but a correct solution does not establish whether they understand the reasoning behind it. Individualized interviews can probe that understanding but require recurring staff effort. We introduce ProofPaths, an AI agentic framework that combines rubric-based grading with individualized and interactive interviews. Multiple grading agents assess a submission against instructor guidance, exchange critiques, and revise their judgments. Their scores determine which review supplies the grade and feedback. An interview agent asks students to justify steps in their submission and uses their answers to guide follow-up questions. Each question is linked to a retrieved submission excerpt, so instructors can compare the student's explanation with the cited passage. The system tracks assessment progress and stores grading reviews and interview transcripts for each submission. Several deterministic tests of the assessment controllers check source handling, score aggregation, and limits on correction attempts. Instructors can inspect all reviews and transcripts and override grades within the assignment workflow.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2508.20054

Between Markov and restriction. Two more monads on categories for relations

Cipriano Junior Cioffo, Fabio Gadducci, Davide Trotta

原文摘要

The study of categories abstracting the structural properties of relations has been extensively developed over the years, resulting in a rich and diverse body of work. In a previous paper we offered a survey providing a modern presentation of these ``categories for relations'' as instances of gs-monoidal categories, showing how they arise as Kleisli categories of suitable symmetric monoidal monads. The end result was a taxonomy that organised numerous related concepts in the literature, including in particular Markov and restriction categories. This paper further enriches the taxonomy: it proposes two categories that are once more instances of gs-monoidal categories, yet more abstract than Markov and restriction categories. They are characterised by an axiomatic notion of mass and domain of an arrow, the latter one of the key ingredients of restriction categories, which generalises the domain of partial functions. The paper then introduces mass and domain preserving monads, proving that the associated Kleisli categories in fact preserve the corresponding equations and that these monads arise naturally for the categories of semiring-weighted relations.

cs.LO · math.CT

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新论文 · v1arXiv:2611.03880

The Hyperclique Hypothesis is False

Nick Fischer

原文摘要

The Hyperclique Hypothesis postulates that, for all constants $k > h \geq 3$, detecting a $k$-clique in an $h$-uniform hypergraph with $n$ vertices requires time $n^{k-o(1)}$. This hypothesis is false: there is an algebraic algorithm that detects (and even counts) $k$-cliques in $h$-uniform hypergraphs in time $O(n^{(1-ε_h)k})$. Via known reductions, this also refutes the recently proposed $k$-AP Hypothesis. As a consequence, many problems with conditional lower bounds based on these two hypotheses may admit faster algorithms after all. This result is almost entirely due to AI.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03876

Embedding Semantic Entropy: NLI-Free Semantic Uncertainty Estimation with Geometry-Aware Clustering

Akshiya Raveendran, Praveenasarma Baskarakurukkal, Pragatheeswaran Vipulanandan, Kamal Premaratne, Uthayasanker Thayasivam

原文摘要

Large Language Models (LLMs) often generate fluent yet incorrect responses, making un- certainty estimation important for reliable deployment. Semantic Entropy (SE) estimates uncertainty by clustering responses based on semantic equivalence, but it relies on hard entailment-based clustering. We propose Embedding Semantic Entropy, which replaces entailment-based clustering with K-Medoids in semantic embedding space and uses dynamic cluster selection to account for intra-cluster compactness and inter-cluster separation. We evaluate the method on TriviaQA, SQuAD, NQ-Open, SVAMP, and BioASQ using Falcon- 7B-Instruct, LLaMA-2-7B, LLaMA-2-7B-Chat, and Gemma-7B. Results show competitive AUROC and AUARC performance, suggesting that NLI-based entailment clustering can be replaced with embedding-space clustering with limited degradation while improving the inspectability of semantic grouping. This matters because semantic uncertainty scores depend on underlying semantic groups; by making these groups explicit, compact, and separable in embedding space, Emb-SE turns uncertainty estimation from a black-box en- tailment decision into an inspectable clustering problem.

cs.LG

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新论文 · v1arXiv:2611.03875

INSIGHT-HPC: An Interpretable System Intelligence for Global HPC Telemetry

Rupak Kadel, Jie Li, Yong Chen

原文摘要

Modern high-performance computing (HPC) clusters integrate multi-generation CPUs and GPUs, which generate rich, high-dimensional telemetry streams from out-of-band baseboard management controllers (BMC), job schedulers, and facility power distribution units. Detecting anomalies within this telemetry is critical to prevent energy waste and costly node failures. Existing approaches often rely primarily on statistical deviations and require stronger labels than actual production environments can provide. In addition, these detectors have a limited operational context for interpreting alerts. We present INSIGHT-HPC, a multi-plane anomaly-detection architecture that combines statistical, workload-aware, and physics-guided evidence into operator-interpretable episodes. INSIGHT-HPC integrates heterogeneous, large-scale telemetry streams and transforms noisy minute-resolution observations into de-duplicated fault episodes with operator-facing explanations. This design allows the system to distinguish workload-explained behavior from genuine infrastructure anomalies while producing interpretable alert summaries for operators. On production HPC telemetry with weak operational labels and controlled HPC-relevant fault injections, INSIGHT-HPC achieves a stronger precision-recall balance than all evaluated baselines. The system achieves a precision of 0.913, a recall of 0.817, an F1 score of 0.862, and an AUROC of 0.896. These results demonstrate the value of combining workload context, physical consistency, and explainable episode-level fusion.

cs.DC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.23979

SPELUNKA: A Framework for Finding Unanticipated Associations in Vision-Language Models

Cassandra Parent, Isha Agarwal, Kumail Alhamoud, Charikleia Moraitaki, Rafiya Javed, Walter Gerych, Marzyeh Ghassemi

原文摘要

Vision-Language Models (VLMs) are the backbone of many state-of-the-art multimodal systems, but often encode spurious and harmful associations from their training data. While prior work focuses on removing known biases from VLMs, methods for discovering unanticipated associations for retrieval tasks remain underexplored. We introduce SPELUNKA, an automated, inference-time framework for finding unanticipated distributional biases for retrieval tasks in VLMs without pre-specified labeled subgroups, inference-time human supervision, or model fine-tuning. Given a concept of interest, SPELUNKA generates captions for related images, extracts relevant entities from captions, and selects semantically meaningful associations. A frequency-based detection rule then identifies spurious associations by measuring over- or under-representation of these attributes in a VLM's top-ranked outputs relative to their full dataset distribution. We evaluate SPELUNKA across four datasets, and find that it 1) successfully recovers both synthetic and naturally occurring spurious correlations, 2) achieves an average AUROC of 0.93 across four datasets in identifying images strongly associated with a query, and 3) outperforms baselines in finding features that are strongly associated with a given query.

cs.CV · cs.LG

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版本更新 · v3arXiv:2603.04530

Complete Diagrammatic Axiomatisations of Relative Entropy

Ralph Sarkis, Fabio Zanasi

原文摘要

Relative entropy is a fundamental class of distances between probability distributions, with widespread applications in probability theory, statistics, and machine learning. In this work, we study relative entropy from a categorical perspective, viewing it as a quantitative enrichment of categories of stochastic matrices. We consider two natural monoidal structures on stochastic matrices, given by the Kronecker product and the direct sum. Our main results are complete axiomatisations of Kullback-Leibler divergence and, more generally, of Rényi divergences of arbitrary order, for each such structure. Our axiomatic theories are formulated within the framework of quantitative monoidal algebra, using a graphical language of string diagrams enriched with quantitative equations.

cs.LO · cs.IT · math.CT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.21034

Can Autonomous Vehicles Keep Their Cool? A Survey of Thermal Side-Channel and Perception Attacks

Harsh Aaryan, Ce Zhou

原文摘要

Autonomous vehicles (AVs) couple safety-critical perception to thermally constrained compute. Physical adversaries can spoof thermal infrared (IR) sensors used for night driving, while unprivileged software co-tenants can fingerprint perception workloads from thermal, dynamic voltage and frequency scaling (DVFS), and thermal event interrupt (TEI) telemetry required for package safety. These paths differ in mechanism and privilege, but both exploit interfaces that AV stacks cannot disable without compromising Safety of the Intended Functionality (SOTIF). Existing microarchitectural and CPS surveys cover cache timing, adversarial ML, and camera or LiDAR spoofing; none frames physical thermal IR attacks and unprivileged silicon thermal telemetry as one residual-risk class. This survey systematizes thirteen works into thermal perception integrity, telemetry leakage, and model-information extraction, comparing them by privilege, timescale, co-tenancy, deployability, and AV relevance while mapping defenses to residual risk. A commodity proxy supports the software co-tenant branch only; physical IR evidence comes from cited literature. We identify open problems in on-target automotive evaluation, fusion trust under IR spoofing, chiplet thermal coupling, capability-based telemetry, and standards-ready threat analysis, motivating co-design across sensors, operating systems, silicon, and automotive standards.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03872

Learning Almost Complex and Complex Structures

Edward Hirst, Tancredi Schettini Gherardini, Tomás S. R. Silva, Jakob Stein

原文摘要

We study the numerical constructions of almost complex and complex structures on spheres and products of spheres up to real dimension six. Our general set-up parametrises almost complex structures via a global matrix field in an ambient Euclidean space, compressed by the tangent projector. The projector's algebraic residual measures the almost complex condition, while its Nijenhuis tensor is evaluated in the intrinsic norm of the round product metric. Polynomial features and neural networks are trained without prescribed targets to perform general searches for these structures. The almost complex search passes numerical admissibility checks on all nine examples that admit such structures and rejects $S^4$, where a topological obstruction is known. A matrix correction then enforces the almost complex identity, subject to a novel uniform admissibility condition. For integrability, a free polynomial representation with Levenberg-Marquardt optimisation learns the complex structures on $S^2\times S^2$, $S^3\times S^1$, $S^3\times S^3$ and $S^5\times S^1$, with the largest Nijenhuis norms found by adversarial search all below $10^{-9}$. These general searches were unsuccessful on $S^6$, $S^4\times S^2$ and $S^4\times T^2$. Therefore, for these three cases direct numerical reconstructions were built in torus-fibred coordinates. They are integrable by construction, satisfy their compatibility conditions to better than $10^{-11}$, and corroborate the recent constructions on $S^6$ and $S^4\times S^2$.

math.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v8arXiv:2508.01415

RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Interactive Environmental Learning in Physical Embodied Systems

Mingcong Lei, Honghao Cai, Yuyuan Yang, Yimou Wu, Jinke Ren, Zezhou Cui, Liangchen Tan, Junkun Hong 等 19 位作者

原文摘要

Long-horizon embodied tasks require agents to learn, reason, and generalize robustly across complex real-world environments. However, existing approaches often struggle with partial observability, fragmented spatial reasoning, and inefficient integration of heterogeneous memories, limiting their capacity for long-horizon adaptation. To address this, we introduce RoboMemory, a brain-inspired framework that unifies Temporal, Spatial, Semantic, and Episodic memory for efficient long-horizon planning and interactive learning. Its core innovations are a comprehensive memory system designed for embodied tasks, and a dynamic spatial knowledge graph for scalable, consistent memory updates. Extensive experiments on EmbodiedBench show that RoboMemory improves the average success rate of Qwen2.5-VL-72B-Ins by 26.5 percentage points and achieves performance comparable to strong closed-source VLM agents. Real-world trials further suggest RoboMemory's potential for interactive environmental learning by reusing accumulated experience across repeated task attempts. These results demonstrate RoboMemory as a scalable memory-augmented framework for embodied agents, improving long-horizon planning and cumulative learning in complex interactive environments.

cs.RO · cs.AI

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