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版本更新 · v2arXiv:2606.19741

Interpreting Neural Combinatorial Optimization via Evolving Programmatic Bottlenecks

Haocheng Duan, Yuxin Guo, Jieyi Bi, Anqi Xie, Sirui Li, Yining Ma, Cathy Wu

原文摘要

While Neural Combinatorial Optimization (NCO) achieves strong performance, its interpretability remains an open question. In this paper, we introduce Evolving Programmatic Bottlenecks (EPB), the first post-hoc, surrogate-based interpretability method for NCO, which explains a black-box policy by distilling it into an interpretable behavior surrogate. A natural surrogate is a Concept Bottleneck Model (CBM), yet standard concepts are input attributes, whereas NCO is better explained by decision rules that are hard to predefine. EPB thus uses LLMs to evolve a bank of programmatic decision rules as concepts, each producing an action distribution, where a learned state-dependent router then mixes them to form the surrogate action. We optimize EPB through an iterative framework: Block I fixes the bank size and introduces the hybrid textual-numerical gradient descent to jointly optimize the textual programs and the numerical router; Block II adapts the bank size via failure-targeted expansion and redundancy pruning. Extensive experiments demonstrate that the EPB surrogate closely matches the teacher's behavior and solution quality. We then analyze the distilled rules, reveal several NCO decision patterns, and verify the rules' faithfulness to the surrogate's decisions. Moreover, the surrogate allows targeted program edits that improve its generalization beyond the teacher. Our work advances interpretable NCO and establishes EPB as a promising direction for interpreting complex learned decision-making policies.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03523

DistDebug-Bench: Can LLM Agents Diagnose Bugs in Distributed Systems?

Yuxuan Jiang, Chenglin Liu, John Rose, Mason Miller, Ruthesh Thavamani, Juntao Wu, Yinwei Huang, Jiahao Xu 等 9 位作者

原文摘要

LLM agents show strong potential in software engineering tasks such as competitive programming and code synthesis. Existing benchmarks, however, largely focus on small-scale settings such as self-contained Python modules, leaving distributed systems, an essential class of production software, under-examined. We introduce DistDebug-Bench, a benchmark for evaluating agents' ability to diagnose complex-system failures at the code level. It curates 120 high-quality real-world bugs from 11 widely used distributed systems, spanning 5 programming languages and 10 failure patterns. Through an extensive evaluation of state-of-the-art LLMs and agents, we identify substantial gaps between current agents and real-world debugging tasks. A recurring gap is tunnel vision due to a lack of explicit reasoning structure and nondeterminism from inconsistent tool-use scope. The benchmark and evaluator are available at https://github.com/OrderLab/distdebug-bench, with agent traces and a leaderboard at https://distdebug.com.

cs.SE · cs.DC · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10772

Universal Approximation by Equivariant Message-Passing Graph Neural Networks with a Bilinear Readout

Pingbing Ming, Han Wang

原文摘要

We prove universal approximation for the bilinear-readout equivariant message-passing network (BREMP), a reference architecture for equivariant graph neural network interatomic potentials. Its layers combine equivariant convolutions with affine maps, and its readout forms invariants from products of the equivariant features. We construct a one-layer feature that determines the local environment up to permutations of atoms of the same type. The construction uses spherical harmonic filters of degree no larger than the largest number of neighbors at a common distance from the center. Iterating the construction recovers the environment reachable in as many hops as there are layers, using the same bound on filter degrees at the layer cutoff. The readout approximates every continuous invariant function of the features uniformly on compact sets. Together these give universal approximation of atomic energies at a prescribed physical range using a shorter layer cutoff. We assume that, throughout the admissible class, message passing reaches all neighbors in each physical environment within a common number of layers. Finally, DPA4, an interatomic potential architecture in production use, inherits this result by approximating the BREMP layers constructed in the proof and realizing the bilinear readout.

cs.LG · physics.chem-ph · physics.comp-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10770

How reliable is an embedding of the seabed? Repeat-look measurement of frozen encoders on synthetic aperture sonar

Harold Horsley, Oscar Pizarro, Hayat Rajani, Morten Omholt Alver

原文摘要

Pretrained encoders from self-supervised representation learning are increasingly applied to sonar imagery of the seafloor, where they are evaluated with protocols that take one embedding per target. A target patch of seabed, by contrast, is re-imaged from an alternate look on overlapping survey passes, so each embedding is one measurement of that target. Standard protocols cannot say how consistent those measurements are, or how much of their spread belongs to the patch and how much to the acquisition geometry. We treat this as a measurement-reliability problem. Using a synthetic-aperture sonar (SAS) survey, we measure the repeat-look reliability of embeddings from a frozen natural-image encoder (DINOv3 ViT-S/16). Standard single-look metrics, effective rank, clustering and a linear probe for coral, are all stable to the look, and the probe performs well (AUC 0.93). Yet the repeat-look reliability of the same embeddings is poor, with a distance-based intraclass correlation (dbICC) of 0.20 and nearest neighbours and clusters that barely agree across looks. This contradiction resolves as we find the probe learns a stable direction, whose ICC of 0.60 is 3.0 times that of the full embedding. Variance components, calculated from generalizability theory, attribute under a tenth of the within-cell spread to look acquisition geometry. The rest is cell-specific and cannot be separated without repeat passes at matched geometry. A sonar-pretrained encoder (BenthicDINO) and a classical SAS texture descriptor show the same pattern. For this dataset and the tested representations, uses that depend on patch identity fail, such as cross-pass comparison and retrieval, while uses at the level of habitat type (e.g. coral classification) do not. Because the standard metrics did not detect this, we argue that repeat-look reliability should accompany any evaluation of representations of the seafloor.

cs.CV · cs.LG · stat.AP

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.05339

Consistency-Preserving Concept Erasure via Unsafe-Safe Pairing and Directional Fisher-Weighted Adaptation

Yongwoo Kim, Sungmin Cha, Hyunsoo Kim, Jaewon Lee, Donghyun Kim

原文摘要

With the increasing versatility of text-to-image diffusion models, the ability to selectively erase undesirable concepts (e.g., harmful content) has become indispensable. However, existing concept erasure approaches primarily focus on removing unsafe concepts without providing guidance toward corresponding safe alternatives, which often leads to failure in preserving the structural and semantic consistency between the original and erased generations. In this paper, we propose a novel framework, PAIRed Erasing (PAIR), which reframes concept erasure from simple removal to consistency-preserving semantic realignment using unsafe-safe pairs. We first generate safe counterparts from unsafe inputs while preserving structural and semantic fidelity, forming paired unsafe-safe multimodal data. Leveraging these pairs, we introduce two key components: (1) Paired Semantic Realignment, a guided objective that uses unsafe-safe pairs to explicitly map target concepts to semantically aligned safe anchors; and (2) Fisher-Weighted Initialization for DoRA, which initializes parameter-efficient low-rank adaptation matrices using unsafe-safe pairs, encouraging the generation of safe alternatives while selectively suppressing unsafe concepts. Together, these components enable fine-grained erasure that removes only the targeted concepts while maintaining overall semantic consistency. Extensive experiments demonstrate that our approach achieves the best overall trade-off among baselines, providing effective concept erasure while preserving structural integrity, semantic coherence, and generation quality.

cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.22007

The Communication Map of a Transformer

Richard Zhe Wang

原文摘要

The components of a transformer communicate by writing to and reading from a shared residual stream, and the mechanistic interpretability literature has mapped these connections by hand, one circuit at a time. We present the communication map, which charts every potential communication channel from the geometry of the model's weights alone. Its unit of analysis is the whole transformer rather than the individual head or circuit. Every ordered writer-reader pair, across all 18 connection classes from head-to-head to neuron-to-neuron, receives a coupling coefficient that generalizes the composition score of Elhage et al. (2021), with an exact chance level. The census finds that 70-89% of head pairs are oriented far from chance, some coupled strongly and others actively avoiding each other. The map recovers the known induction circuits blind from the strongest head-to-head couplings and groups the heads into communities, and ablating one such community destroys the model's in-context copying. As an application, we pool the coupling coefficients of every head to identify a two-dimensional residual stream subspace, distinct from those found by activation PCA or outlier dimensions, whose deletion abolishes induction in six models up to Pythia-6.9B. We release the map, the statistical machinery, and the intervention suite.

cs.LG · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.31463

PithTrain: A Compact and Agent-Native MoE Training System

Ruihang Lai, Hao Kang, Haozhan Tang, Akaash R. Parthasarathy, Zichun Yu, Junru Shao, Todd C. Mowry, Chenyan Xiong 等 9 位作者

原文摘要

Mixture-of-Experts (MoE) has become the dominant architecture for frontier language models. To meet this demand, production frameworks have built optimized MoE training stacks over years of engineering effort. Yet evolving these stacks for new architectures and system optimizations remains expensive. With the rise of AI coding agents, they could automate parts of training-framework development and accelerate this evolution. But applying them to these existing frameworks carries hidden costs, invisible to today's throughput-only evaluations. We name this missing dimension agent-task efficiency (ATE): the cost of using coding agents to understand, operate, and extend a framework. Grounded in four agent-native design principles, we build PithTrain, a compact, agent-native MoE training framework supporting pretraining, supervised fine-tuning, and reinforcement learning. We further introduce ATE-Bench, covering real-world training-framework tasks. Our evaluation shows PithTrain achieves throughput competitive with production frameworks and higher agent-task efficiency on ATE-Bench. On new-feature tasks, PithTrain uses up to 62% fewer Agent Turns than Megatron-LM, and up to 44% and 64% less Active GPU Time than Megatron-LM and TorchTitan, respectively.

cs.LG · cs.AI · cs.CL · cs.DC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.10520

Distilling Graph Geometry: Knowledge Gap from GNNs to MLPs

Zhewei Chen, Hao Zhu, Jiaojiao Jiang, Ahad N. Zehmakan

原文摘要

GNN-to-MLP distillation aims to retain the predictive accuracy of a message-passing teacher while deploying a graph-free MLP at inference. Existing methods mainly transfer node-wise predictions or use confidence-based reweighting, but they do not specify where the student should preserve the teacher's graph-induced geometry. We show that this omission leads to two spectral failure modes in the student's representation space. On sparse graphs, the student suffers from spectral underfit, missing high-energy teacher directions induced by aggregation. On dense graphs, it suffers from spectral overfit, retaining spurious directions that the teacher has collapsed through aggregation. Motivated by an energy-weighted teacher-student alignment objective, we propose Graph Geometry-aware MLP (G^2MLP), a training-time distillation framework guided by Ollivier-Ricci curvature. Curvature acts as a local proxy for allocating supervision between prediction-level and representation-level alignment. The deployed model is a standard MLP with no graph access at inference. Across node-classification benchmarks, G^2MLP improves over graph-free distillation baselines in most settings, reduces the teacher-student rank gap in both regimes, and transfers to Graph Transformer teachers and link prediction.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10764

BAPLe++: Cross-Model Transferability and Prompt-Level Defense of Backdoors in Medical Foundation Models

Ufaq Khan, Umair Nawaz, Satyajit Tourani, Awais Rauf, Fahad Shamshad, Asif Hanif, Rao Muhammad Anwer, Muhammad Haris Khan 等 9 位作者

原文摘要

Medical foundation models (Med-FMs) are increasingly adapted to clinical tasks through prompt learning, which tunes a small set of prompt tokens while the backbone remains frozen. Recent work has shown that this efficiency introduces a vulnerability: jointly optimizing learnable prompts and an imperceptible noise trigger during few-shot adaptation implants a backdoor that leaves clean performance intact but steers triggered inputs to an attacker-chosen class. Since prompts are compact and readily shared, this raises two questions: whether a backdoored prompt remains effective on a different Med-FM, and whether it can be repaired without retraining the backbone. We address both in BAPLe++. First, we present the first study of cross-model transferability of prompt-based backdoors, quantified by two proposed metrics, Clean Accuracy Difference (CAD) and Backdoor Accuracy Difference (BAD). Second, we propose DePLe (Defense via Prompt Learning), which uses the infected prompt as a fixed anchor and learns a new prompt on a small clean set that preserves the clean task while diverging from the compromised embedding region. Experiments on four Med-FMs and six chest X-ray and histopathology datasets show that backdoors transfer across models, but asymmetrically and in a dataset-dependent manner. Compared with four training-free input transformations, DePLe reduces backdoor accuracy more consistently while preserving, and in several settings improving, clean accuracy. Our code can be accessed on https://github.com/Ufaqkhan/Baple-Plus-Plus

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.14095

Lyapunov-Based Sample Complexity Analysis for Weakly-Coupled MDPs

Tianhao Wu, Matthew Zurek, Weina Wang, Qiaomin Xie

原文摘要

We study the sample complexity of learning in average-reward weakly-coupled Markov decision processes (WCMDPs) and Restless Bandits (RBs) under a generative model. Naive reduction to a tabular MDP leads to high complexity bounds as the state-action space is exponentially large in the number of arms $N$. By exploiting the weakly coupled structure, we show that near-optimal policies can be learned with sample and computational complexities that are polynomial in $N$. Specifically, we analyze the plug-in approach, which applies an efficient planning algorithm to an empirical model estimated from data. For fully heterogeneous WCMDPs, we establish the first finite-sample PAC guarantee with polynomial complexity and an $O(1/\sqrt{N})$ optimality gap. For homogeneous RBs, we further prove that a smaller optimality gap is achievable under mild structural assumptions. A primary technical contribution is a novel Lyapunov-based analysis framework. Unlike classical approaches that rely on the difficult-to-control bias function, our framework uses an explicitly constructed Lyapunov function along with a drift transfer technique between the true and empirical models. A key step of independent interest in our framework is a fine-grained perturbation analysis for the underlying linear programming (LP) relaxation, which provides a general tool for analyzing LP-based policies and weakly-coupled systems.

cs.LG · math.OC · math.PR · stat.ML

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.11042

Characterizing the Trainability of Instantaneous Quantum Polynomial Circuit Born Machines

Kevin Shen, Susanne Pielawa, Vedran Dunjko, Hao Wang

原文摘要

Instantaneous Quantum Polynomial Circuit Born Machines (IQP-QCBMs) have been proposed as quantum generative models that combine a classically tractable training objective - based on the maximum mean discrepancy (MMD) - with a potential quantum advantage motivated by sampling-complexity arguments. While recent works have explored this model across various application domains, fundamental questions remain: does the model suffer from exponentially vanishing loss gradients, known as the barren plateau problem - a pervasive obstacle in quantum machine learning - and how do regimes of trainability relate to regimes of possible quantum advantage? Here, we address both questions analytically. To study trainability, we derive closed-form expressions for the variances of the partial derivatives of the MMD loss function and establish general upper and lower bounds. We explicitly characterize how trainability depends on the generator set and the spectrum of the chosen kernel, identifying regimes in which low Hamming-weight kernels avoid exponential gradient suppression under structured topologies. Regarding potential quantum advantage, we reformulate the anti-concentration property in terms of the same generator-set quantities that govern trainability, enabling a unified analysis. We show that sparse IQP architectures can produce classically intractable output distributions while simultaneously remaining trainable, at least at lower-weight frequencies. Our analytical results corroborate the numerical observations of prior work and provide principled guidelines for designing scalable IQP-QCBM architectures.

quant-ph · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10760

ConwayNet: Interpretability, Activation Coherence, and Noise Resilience via a Game-of-Life-Inspired Lateral Inhibition in Artificial Neural Networks

Yair Cohen, Scott M. Gilmore

原文摘要

Deep neural networks achieve universal function approximation by stacking interconnected nonlinearities, but as a consequence they are sensitive to noise and resist mechanistic interpretation. In contrast, the local update rules of cellular automata, such as Conway's Game of Life, give rise to not merely interpretable but programmable dynamics. We explore a bridge between these worlds: a lateral inhibition in neural networks based on continuous Game-of-Life rules that derives inhibitory and excitatory responses from the layer input and blends them into the activation map in a state-conditioned way. The mechanism is differentiable and architecture-agnostic, and its regulation is imposed rather than learned: the rule's parameters are fixed and are not optimised, while the rule's values and derivatives shape training throughout the network. Wired into fully connected, convolutional, wide-residual and graph convolutional networks, it gives rise to several properties. Activation maps become coherent across space, depth and seeds, with coarse structure persisting through depth while fine detail is renewed at every layer. The co-activation graph of the hidden units departs from an Erdős--Rényi random graph: it is laid out on the image and its giant component grows in steps, as in a spatial graph. Networks started from nearly identical initialisations, under label noise, disagree on thirty times fewer test images. Saliency maps rest on the object rather than the background, and resilience to pixel noise not seen in training is substantially improved.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2510.13068

NeuroRVQ: Multi-Scale Biosignal Tokenization for Generative Foundation Models

Konstantinos Barmpas, Na Lee, Dimitrios Chalatsis, William Raftery, Yannis Panagakis, Dimitrios A. Adamos, Nikolaos Laskaris, Alexandros Koliousis 等 10 位作者

原文摘要

Biosignals such as electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) encode physiological activity across multiple temporal and spectral scales, yielding representations that are rich but challenging for machine learning. Foundation models trained to predict masked signal tokens have shown promise in learning generalizable biosignal representations, yet their performance depends on the tokenizer's ability to preserve high-frequency dynamics and reconstruct signals with high fidelity. We introduce NeuroRVQ, a modality-adaptive biosignal tokenizer family designed for high-fidelity signal reconstruction. To capture the full frequency spectrum, NeuroRVQ decomposes biosignals into frequency-specific representations via multi-scale temporal convolutions, each encoded into hierarchical RVQ codebooks to preserve high-frequency detail, combined with a novel phase-aware training loss that respects the circular topology of Fourier phase. By tuning the temporal resolution, number and size of temporal kernels and RVQ depth, this design adapts to the spectro-temporal characteristics of each biosignal modality. To validate that tokenizer quality drives downstream performance, we train a simple masked-token foundation model for each modality (NeuroRVQ-FM) using the corresponding NeuroRVQ tokenizer. The NeuroRVQ-FM family achieves competitive or superior downstream performance compared to existing modality-specific foundation models, demonstrating that high-fidelity tokenization is a critical factor for effective biosignal modeling.

cs.LG · cs.AI · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10751

Constrained Look-ahead Guidance for Interference-Aware Flow Editing

Doudou Zhang, Qi Chen

原文摘要

Diffusion and flow models enable powerful generative modeling, but controlled editing remains challenging due to the need to balance source fidelity and edit compliance along the generative trajectory. We propose a principled, inversion-free framework for flow-based image editing from a trajectory centric perspective. Rather than relying on inversion or heuristic guidance, we introduce a one-step look-ahead mechanism that evaluates the effect of candidate updates on both edit and fidelity rewards estimated via denoising, and adjusts the current update based on this evaluation. At each step, our method applies reward-driven corrections guided by the look-ahead evaluation under an explicit fidelity constraint, resulting in stable and coherent trajectory evolution. This look-ahead design mitigates gradient interference between edit and fidelity objectives by anticipating their interaction in future states, leading to improved trade-offs. Experiments demonstrate that the proposed approach achieves stronger semantic edits while better preserving source content compared to existing strong baselines.

cs.LG · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10750

AERO: Adaptive Ensemble-Disagreement Routing for Oracle Feedback in Sample-Efficient Online RLHF

Shrey Singhal, Nannan Huang, Haytham M. Fayek

原文摘要

Aligning Large Language Models (LLMs) with human preferences via Reinforcement Learning from Human Feedback (RLHF) has driven substantial improvements across a wide range of tasks, but the cost of acquiring preference labels remains the dominant bottleneck in the alignment pipeline. While many recent alignment methods improve sample efficiency through active exploration over candidate responses, they typically query the oracle uniformly across contexts, even for contexts where the learned reward estimates are already reliable enough to provide synthetic labels. We propose Adaptive Ensemble-disagreement Routing for Oracle feedback (AERO), a fully online RLHF algorithm built on a Dyna-style model-based RL architecture, in which an ensemble-based Epistemic Reward Model (ERM) serves both as a reward posterior for exploration and as a learned preference-feedback model for synthetic labeling. AERO uses ensemble vote entropy, a Query-by-Committee disagreement measure, combined with an adaptive thresholding mechanism to decide which contexts require oracle feedback and which can instead receive ERM-based synthetic preference labels, concentrating expensive labels where they are most informative. Empirical results across different model families and scales show that AERO achieves highly competitive alignment performance while reducing the required oracle-query budget compared to online DPO and strong active-exploration baselines.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.05570

Poisson-GENERIC Neural Operators: Exact Metriplectic Structure in Function Space via Casimir Entropies

Jason Sulskis, Sathya Ravi

原文摘要

Existing thermodynamically consistent neural operators enforce GENERIC degeneracy by projecting the reversible operator, introducing state dependence without guaranteeing Jacobi. We instead use a compatible Lie--Poisson pencil $L=αD+λ(uD+Du)$ for nonlinear transport and constant Poisson multipliers otherwise. Augmenting the observed field $u$ with a latent entropy density $s$ makes $S=\int s$ a Casimir, so $L\,δS/δz=0$ without projection. The energy combines a fixed mechanical quadratic, a learned gauge-free potential, and a convex internal energy; the friction operator $M=AA^\top$ enforces $M\,δE/δz=0$ pointwise and supports diffusion and damping without a separate transport generator. Skewness, positivity, and both degeneracies hold for any parameters; the continuum pencil satisfies Jacobi, verified on the alias-free band; the closed-form heat and damped-wave friction operators are representable; physical energy is bounded under coercivity assumptions; and a discrete-gradient integrator preserves both laws in discrete time. On four PDEs in 1D and 2D with three backbones (FNO, Transolver, CNO), at equal optimizer steps the model wins 62/72 seed-level comparisons against unconstrained baselines, recovers the exact transport and wave symbols, matches heat and Burgers dissipation within 12\%, and dissipates nothing on advection; at equal wall-clock the unconstrained FNO is the more accurate ten-frame surrogate. What the structure gives at any budget is the energy balance: over a hundred frames, in and out of distribution, the model never raises the physical energy and conserves it where dissipation vanishes, whereas the unconstrained FNO loses a quarter of a conserved energy off distribution and projected GENERIC-FNO creates energy on most frames. Late-time under-damping on heat and explicit-integrator divergence on the wave are reported as limits.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10744

Writer-independent handedness identification from offline Bangla handwriting under controlled acquisition

Tanzim Hasan Prappo, Sayeed Hassan, Sayem Ali, Sukhendra Nath Dhar, M. Shahidur Rahman

原文摘要

Handedness is a soft-biometric cue for forensic document examination and writer profiling, yet reported accuracies from offline handwriting rarely keep writers, texts and image acquisition apart, so models may recognise the writer or capture conditions rather than the writing hand. Such controlled evidence is missing for Bangla. Using our BanglaHand dataset of 42 writers (21 left- and 21 right-handed), who each wrote six shared prompts and two own texts under controlled collection and photography, we evaluate handedness identification with all images of each test writer held out, whole-writer permutation tests and photometric controls. An ink-gradient descriptor identified the handedness of unseen writers with 77.4% balanced accuracy (95% writer-bootstrap interval 67.7-86.5%; p=0.001) and frozen ImageNet embeddings with 69.5-72.0%. Part of this signal is stroke darkness: in a post hoc analysis, right-handed writers' strokes were darker at their centres for equal stroke width, consistent with higher pen pressure and not explained by lighting drift. With ink density and brightness regressed out, the descriptor still reached 64.5% (p=0.027), whereas writing-only shape features were weaker (60.8%; p=0.090). Handedness is thus identifiable from offline Bangla handwriting under controlled acquisition, and photometric controls are essential to interpret such cues.

cs.CV · cs.CY · cs.LG

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2610.00329

Non-Commutative Lie-Algebraic State-Space Models: Finite-Division Variety Laws, Semidirect Cascades, and Gradient Saddle Escape

Zeyu Jia

原文摘要

Structured state-space sequence models (SSMs) achieve linear-time inference and logarithmic-time parallel training via associative scans, yet diagonal transitions generate solvable semigroups unable to track non-solvable group words or 3D rigid-body manifolds. We present Non-Commutative Lie-Algebraic State-Space Models (Lie-SSMs), parameterizing recurrent updates on Lie algebra tangent spaces via the matrix exponential. First, using an o-minimal cylindrical flag collar, we establish an unconditional single-layer finite-division criterion for contracting $SO(3)$ and $SU(2)$ heads, proving odd-prime Sylow abelianity, odd-$p$-core centrality, and exponent-30 commutator obstructions excluding $S_5$ in $SO(3)^m, SU(2)^m,$ and $SO(4)^m$ while identifying the sharp threshold $d^*(S_5)=d^*(A_6)=5$ in $SO(5)$. Second, Depth-2 semidirect and Schreier $2$-cocycle cascades resolve $S_5$, $2I\cong SL_2(F_5)$, and $\mathrm{Heis}(F_p)$ in $O(\log T)$ scan depth, whereas unnormalized contracting coupling collapses exponentially without Directional RMSNorm. Third, we characterize the $Θ(\varepsilon^{c-1})$ Chen--Magnus--Hall--Witt commutator saddle and $1/|[G,G]|$ coset-centroid dilution, proving generic first-order symmetry breaking at $\varepsilon=0$ and teacher-forced local Polyak--Łojasiewicz convergence under dense prefix supervision and factored readouts ($99.90\%/99.40\%$ on $S_5$ at $T=128$; $100.00\%$ on $D_{16}$). Across $SE_2(3)$ Left-Jacobian odometry, Hillis--Steele/Blelloch GPU scans ($48.24\times$ forward, $20.27\times/10.28\times$ full-layer speedup at $T=2048$), and three 3D IMU benchmarks (UCI HAR, UCI HAPT-PT, and 17-subject REALDISP), Lie-$SO(3)$ models reach $95.69\%\pm 0.14\%$ on HAR, $88.44\%\pm 0.83\%$ episode logit-vote ($83.97\%\pm 1.20\%$ C3 window in 30-subject 5-fold CV; $87.16\%\pm 0.64\%$ 9-subject split) on HAPT-PT, and $84.23\%\pm 1.00\%$ on REALDISP.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10738

Matchability-Aware Conformal Prediction for Open-Ended Language Model Generation

Ilia Azizi

原文摘要

Large language models (LLMs) often do not produce calibrated uncertainty estimates for their answers. We propose CAELUM, a framework that builds semantic prediction sets while separating aleatoric from epistemic uncertainty. CAELUM samples answers using an ensemble of clarification prompts, clusters them by semantic equivalence, and estimates the aleatoric and epistemic components via kernel-based von Neumann entropy before calibrating nonconformity scores with split conformal prediction. We use a weight $w_λ(x)=1+U_{\mathrm{ale}}(x)+λ\,U_{\mathrm{epi}}(x)$ to control for the contribution of the epistemic uncertainty of each instance and fit a coefficient $λ^\star$ to minimize prediction set size. When the objective is nonflat and selection is stable across splits, $λ^\star$ could be a diagnostic, where large values may indicate epistemic dominance (retrieve, abstain) and $λ^\star\to 0$ aleatoric dominance (clarify). For individual queries, a log ratio $R(x)$ compares the weighted epistemic component with the aleatoric component. Theoretically, we demonstrate that the coverage is capped by matchability, the fraction of items whose reference answer appears in any sampled cluster. Empirically, this matchability can be predicted from the original question features using a lightweight classifier. We use those predictions to define a stratum and derive a conformal coverage bound within it. Across 30 LLM and dataset combinations (4-32B), mean coverage is 0.74 in the learned stratum versus 0.65 overall, averaged over the 78 evaluations using the learned classifier. Project page: https://unco3892.github.io/caelum/

stat.ML · cs.CL · cs.LG · stat.ME

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2601.23258

Agnostic Language Identification and Generation

Mikael Møller Høgsgaard, Chirag Pabbaraju

原文摘要

Recent works on language identification and generation have established tight statistical rates at which these tasks can be achieved. These works typically operate under a strong realizability assumption: that the input data is drawn from an unknown distribution necessarily supported on some language in a given collection. In this work, we relax this assumption of realizability entirely, and impose no restrictions on the distribution of the input data. We propose objectives to study both language identification and generation in this more general "agnostic" setup. Across both problems, we obtain novel interesting characterizations and nearly tight rates.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.18078

Beyond Embedding Transfer: Component Roles in Grokking Transfer and Stability

Zeyu Jia

原文摘要

Warm-start transfer accelerates delayed generalization (grokking) on algorithmic tasks, yet how internal blocks, residual-stream depth, group non-commutativity, and continual optimization govern transferred circuits remains unclear. Across ten scale-matched batteries ($>1{,}800$ trajectories) on abelian $\mathbb{Z}_{113}$ ($L \in \{1,2,3\}$) and non-abelian $S_5$, transferring internal blocks alongside boundary carriers lifts early accuracy by $5.46$ percentage points and cuts validation confirmation latency by $616.7 \pm 129.0$ steps in 1-layer models ($p_{\rm Holm}=0.004$). In 2-layer models, full transfer compresses trigger onset $8.1\times$ ($+704.7 \pm 84.3$ integral units), whereas omitting donor embeddings incurs a $-4{,}475.6 \pm 76.8$-unit interface deficit. On ternary $S_5$ conjugation ($aba^{-1}$), two-token architectures stall in a unary conjugacy-class shortcut ($\mathrm{Cl}(aba^{-1})=\mathrm{Cl}(b)$, $34.48\% \pm 6.19\%$ peak accuracy). Combining three-token causal routing ($[a,b,a]$) with Peter--Weyl width ($d_{\rm model}=256 > \sum_λd_λ^2 = 120$) reaches $97.97\% \pm 1.01\%$ ($80.08\% \pm 7.81\%$ at equal $427\text{k}$ parameters) with $3.2\times$ faster onset than scratch. Under continued AdamW optimization, unshielded circuits relapse by $11.85\%\text{--}80.58\%$ drawdown, and depth-$L$ stability follows a graded $(L-1)$-prefix pinning hierarchy ($75.99\% \to 31.26\% \to 0.32\% \to 0.04\%$ drawdown at $L=3$). Finally, \textbf{Closed-Loop Curvature-Anchored Consolidation} ($η_k = η_0(1 - A_{\rm val}(k))$ with monotonic AMSGrad second moments) holds saturated drawdown to $0.00\%\text{--}0.12\%$ and lifts pre-saturation terminal accuracy by $+1.67\text{--}5.37$ points over fixed-budget micro-stepping ($87.06\% \pm 0.94\%$ vs.\ $81.69\% \pm 0.90\%$ on $S_5$, $p_{\rm Holm}=0.002$).

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.37351

Typed Edge-Tensor Non-Reciprocal Port-Hamiltonian Deliberation for Counterfactual Open-World Deductive Reasoning

Zeyu Jia

原文摘要

Scaling test-time computation in continuous latent spaces offers an attractive alternative to verbose token-by-token generation for multi-step deductive reasoning. However, unregularized recurrent latent updates suffer from a deliberation drift cliff when rolled out beyond training horizons, while strictly energy-conserving or symmetric message-passing dynamics cannot break parent-to-child causal symmetry or suppress cyclic modus-ponens amplification on cyclic distraction graphs. We introduce Typed Edge-Tensor Non-Reciprocal Port-Hamiltonian Deliberation (TE-BPHD), a neuro-symbolic continuous-time latent reasoning architecture that couples skew-symmetric port-Hamiltonian conservation with relation-typed non-reciprocal directional transport and monotone dissipative damping on a semantic entity-state manifold. We prove (i) unconditional input-to-state stability and exponential contraction of autonomous rollouts across arbitrary inference depths without external norm projection, (ii) strict directional DAG reachability separation over symmetric message passing, and (iii) exact cycle-suppression margins under finite cyclic horn perturbations. Across counterfactual open-world deductive benchmarks (PrOntoQA-OOD up to depth 15, ProofWriter-OWA Cyclic at depths 5-10, FOLIO-Symbolic, and Cluttr-Relational), TE-BPHD eliminates the deep-horizon drift cliff, improves depth-10 cyclic reasoning accuracy to 90.0% (+/- 0.8%) over symmetric port-Hamiltonian and standard Transformer baselines, and supports zero-retraining adaptive Lyapunov halting that reduces average solver evaluations by 44% with negligible accuracy loss.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10733

Spectrogram-Based CNN Autoencoder for One-Class Anomaly Detection in Machining Vibration Signals

Sebastian Zeleny, Christian Gawron

原文摘要

This work presents a one-class anomaly detection framework for vibration-based condition monitoring in machining processes using a convolutional neural network autoencoder. Three-axis vibration signals acquired during industrial broaching are transformed into time--frequency spectrograms and combined into a compact three-channel representation, enabling convolutional feature learning on multivariate time-series data. The autoencoder is trained exclusively on normal process data and detects anomalous strokes based on reconstruction errors. Labeled validation data are used for threshold calibration and an independent test set is reserved for final performance evaluation. To improve sensitivity to short-lived process instabilities such as chatter, a localized residual-based metric termed Segmented Vertical Error (SVE) is introduced. Experimental results on approximately 10{,}000 broaching strokes show a clear separation between normal and anomalous process conditions under strong class imbalance. The processing pipeline is additionally evaluated on an external public turning dataset as a proof of concept, indicating preliminary applicability beyond the original broaching setup. The results suggest that spectrogram-based autoencoder models can provide a practical and scalable basis for one-class anomaly detection in industrial vibration monitoring.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10730

Breaking Precision Walls in First-, Second-, and Zeroth-Order Optimizers: One Constant, Set in Advance

Lngel Kyo

原文摘要

Under power-law signal decay across directions, an optimizer can fit strong directions and miss weak ones while average loss hides it. A precision wall measures the steepest decay where the weakest direction recovers within 5% in a fixed budget. On low-rank matrix sensing and quadratics one constant sets the wall: once it dominates the denominator, Adam or grafted Shampoo is momentum descent at step $η/c$, setting the wall; in Shampoo, matching grafting's constant to damping restores the wall after three hypotheses failed. In zeroth-order Adam, $\varepsilon$ acts against $\varepsilon^\ast$ from a mean-square spectral model; below $\varepsilon^\ast$ error hits a noise floor. Set from true curvature or spectrum on new shapes, the rule raised the wall above the same-step default by a forecast amount: on two sensing shapes by $1.96, 1.15$ (Adam) and $1.59, 1.74$ (grafted Shampoo), and on two Hadamard-rotated quadratics by $1.12, 0.73$ (zeroth-order Adam, longer budget), rising on all seeds; fixed-step momentum at that step reaches those walls. A cosine-scheduled default (small constant giving weak modes large steps) is higher on one sensing shape, matched on new seeds of the other, and lower with the rule's constant. A default step retuned per budget catches up on rotated quadratics and population logistic regression, fails at 1 query/step on two diagonal quadratics (catching up at 16), and exceeds the rule on digits logistic regression (full-batch and minibatches, where gradient noise sets the $η= 0.01, 0.1$ default walls). On logistic regression the rule did not raise the first-order wall: same-step default Adam was higher (its wall on 4 new shapes at the forecast stability limit), while in zeroth order on the population loss setting $\varepsilon$ raised the wall by $0.98, 0.88$.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.11178

Oversmoothing as Representation Degeneracy in Neural Sheaf Diffusion

Arif Dönmez, Axel Mosig, Ellen Fritsche, Katharina Koch

原文摘要

Neural Sheaf Diffusion (NSD) learns a task-adapted sheaf geometry for node classification. We connect the study of these learned geometries with Geometric Invariant Theory (GIT) to analyse their representation-theoretic structure and develop mechanisms for geometric control during training. Identifying cellular sheaves with representations of the incidence quiver connects direct-sum decompositions to the harmonic space and motivates the study of trivial or low-complexity summands as potential degeneration modes. We make this perspective concrete by focusing on the trivial representation: on an embedded copy, the sheaf Dirichlet energy reduces to weighted scalar graph energy. The GIT stability framework provides a criterion for excluding such copies within the semistable class. Using its moment-map characterisation of polystability, we construct differentiable surrogates that encourage the corresponding balance conditions. We identify an equal-stalk obstruction to assigning a destabilising weight to the trivial representation and show how nonuniform stalk dimensions remove it. Controlled energy-minimisation experiments reproduce trivial-component formation, while native feature-conditioned NSD experiments on heterophilic graphs show how moment-map regularisation changes the balance and Dirichlet-energy trajectories of learned geometries during training, alongside their classification behaviour.

cs.LG · cs.AI · math.RT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.02956

The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset

Richard Schwarzkopf, Fabian Immel, Alexander Blumberg, Jonas Merkert, Nils Rack, Kaiwen Wang, Fabian Konstantinidis, Julian Truetsch 等 24 位作者

原文摘要

Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity. We present KITScenes Multimodal, a European dataset built around high-fidelity sensors and maps. Our fully synchronized sensor suite combines high-resolution global-shutter cameras, long-range lidar beyond 400 m, 4D imaging radar, and redundant GNSS/INS localization. Our HD maps are, to our knowledge, the most complete of any sensor dataset, validated through autonomous driving trials on open-source software. For the first time in a public dataset, all driving-relevant traffic elements, such as traffic lights, are mapped in 3D to a reprojection-accurate level with full topological connectivity. Recorded in cities with irregular street layouts and mixed traffic modes, our dataset complements existing datasets by broadening the available geographic diversity. We also introduce four benchmarks, each advancing spatial learning for embodied AI: online HD map construction, long-range depth estimation, novel view synthesis, and end-to-end driving. Project page: https://kitscenes.com/multimodal/

cs.CV · cs.LG · cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.14344

Zero-Shot Instruction Following in RL via Structured LTL Representations

Mathias Jackermeier, Mattia Giuri, Jacques Cloete, Alessandro Abate

原文摘要

We study instruction following in multi-task reinforcement learning, where an agent must zero-shot execute novel tasks not seen during training. In this setting, linear temporal logic (LTL) has recently been adopted as a powerful framework for specifying structured, temporally extended tasks. While existing approaches successfully train generalist policies, they often struggle to effectively capture the rich logical and temporal structure inherent in LTL specifications. In this work, we address these concerns with a novel approach to learn structured task representations that facilitate training and generalisation. Our method conditions the policy on sequences of Boolean formulae constructed from a finite automaton of the task. We propose a hierarchical neural architecture to encode the logical structure of these formulae, and introduce an attention mechanism that enables the policy to reason about future subgoals. Experiments in a variety of complex environments demonstrate the strong generalisation capabilities and superior performance of our approach.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10723

CyberBinGym: Can AI Agents Turn Binary Patches into Real Exploits?

Lambang Akbar Wijayadi, Zhaoqi Xiao, Zhun Wang, Hongwei Li, Wenbo Guo, Heng Yin, Zhenkai Liang, Dawn Song

原文摘要

Security fixes usually reach users as compiled updates, but deployment delays leave unpatched systems exposed. During this window, comparing patched and unpatched binaries can reveal how to exploit a vulnerability, turning a defensive update into actionable information for attackers. Existing benchmarks do not measure this capability: they expose source code, rely on human-crafted challenges, or evaluate binary comprehension without requiring exploitation. We introduce CyberBinGym, a benchmark containing 100 tasks derived from real vulnerabilities in 53 widely used projects, spanning six vulnerability families and three levels of analysis complexity. Agents receive stripped vulnerable and patched binaries without source code or vulnerability information, and must combine static and dynamic analysis to recover and exploit the vulnerability. Evaluation separately measures vulnerability recovery through differential crash testing and end-to-end exploitation through flag retrieval, with trajectory audits to reject reward hacking. The strongest evaluated agent recovers the target vulnerability in 88% of tasks and exploits it in 20%. Providing the patched binary raises its exploit rate from 10% to 25% on a matched 20-task subset; with standard binary hardening enabled, it still exploits two of its 20 previously successful tasks. These results demonstrate that current agents can turn released binary patches into working exploits in a controlled setting, although exploitation remains unreliable. These capabilities lower the barriers to targeting unpatched software and compress defenders' effective response window, increasing the urgency of timely patch deployment.

cs.CR · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.14068

CurveBench: A Benchmark for Exact Topological Reasoning over Nested Jordan Curves

Amirreza Mohseni, Mona Mohammadi, Morteza Saghafian, Naser Talebizadeh Sardari

原文摘要

We introduce CurveBench, a benchmark for visual topological reasoning. Given an image of disjoint closed curves, a model must recover the tree of adjacent regions rooted at the exterior. The benchmark contains 756 hand-drawn images, ranging from simple configurations to maze-like and densely packed arrangements. Each image has a manually verified reference tree. We evaluate predictions by exact tree matching with the exterior root fixed. Gemini 3.1 Pro, the strongest model in our evaluation, achieves 71.1% exact-match accuracy on CurveBench-Easy and 28.7% on CurveBench-Hard. A Qwen3-VL-8B checkpoint trained using reinforcement learning with verifiable rewards reaches 31.7% on Easy, compared with 21.7% for its Thinking base model at a 16,384-token generation budget. The same comparison yields a gain of 1.1 percentage points on Hard.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10718

FedSGN: Federated Sparse Grassmann Newton Learning for IoT Anomaly Detection

Jingjing Liu, Xiansen Ju, Xianchao Xiu, Wanquan Liu

原文摘要

Anomaly detection plays a critical role in securing the Internet of things (IoT). Although existing federated principal component analysis (PCA) methods can learn the subspace of normal samples without sharing raw data, they often suffer from feature redundancy that may degrade detection performance. To address this limitation, we propose a novel and effective federated sparse Grassmann Newton (FedSGN) learning method for IoT anomaly detection. Technically, our FedSGN leverages the $\ell_{2,0}$-norm constraint on the global projection matrix to eliminate redundancy and introduces an orthogonality penalty to preserve the intrinsic structure. Moreover, we develop an augmented Lagrangian solver integrated with a semismooth Newton scheme to enable parallel client updates and Grassmann projection for robustness. Numerical experiments demonstrate that the proposed method achieves favorable overall detection performance while reducing communication overhead. The source code is publicly available at https://github.com/xianchaoxiu/FedSGN

cs.LG

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arXiv ↗PDF ↗