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新论文 · v1arXiv:2611.13016

Permutation-Based Pre-trained Model Merging via Reciprocal Orthogonality

Akira Ito, Daiki Chijiwa, Atsutoshi Kumagai

原文摘要

Ito et al. (2026) showed that sufficiently wide, independently trained neural networks can exhibit linear mode connectivity (LMC) with softmax temperature calibration, even without permutation. LMC means that a low-loss linear path connects the models' parameters. This behavior is explained by layerwise exponentially weighted connectivity (LEWC), whereby each merged layer outputs an exponentially weighted sum of the original models' corresponding representations, yielding ensemble-like predictions. However, prior work observed LEWC with substantial widening, such as 8192 units per hidden layer for MLPs and $16\times$ width for VGG-11, raising the question of whether such widths are necessary. We therefore search for permutations that encourage reciprocal orthogonality (RO), which suppresses interference between each model's weights and the other model's representations. This allows both models' computations to coexist in the merged network, promoting LEWC at smaller widths. We formulate the search as independent layerwise quadratic assignment problems and approximately solve their continuous relaxations using Frank--Wolfe. Experiments show that our method facilitates LMC with less width expansion. It also merges models trained on different data distributions, where weight matching, which maximizes weight similarity through permutations, struggles. The merged predictions approximate those of a logit ensemble of the original models.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.26092

TryOnCrafter: Unleashing Camera Trajectories for Realistic Video Virtual Try-on via a Renderable 4D Try-on Proxy

Hao Sun, Hao Yan, Mengting Chen, Quanjian Song, Yu Li, Juan Cao, Jinsong Lan, Xiaoyong Zhu 等 10 位作者

原文摘要

While Video Virtual Try-on (VVT) has achieved remarkable progress in synthesizing realistic garment overlays on dynamic subjects, existing paradigms remains fundamentally constrained by a passive dependency on source camera trajectories, failing to accommodate the requisite interactive freedom for omnidirectional viewpoint exploration. To address this limitation, we define a pioneering research frontier: Camera-controllable Video Virtual Try-on (CaM-VVT). Unlike conventional VVT, CaM-VVT not only necessitates viewpoint-agnostic texture hallucination but also strict structural synchronization between non-rigid human dynamics and background contexts under arbitrary, unconstrained camera movements. To tackle these challenges, we present TryOnCrafter, the first unified DiT-based framework specifically architected for the CaM-VVT task. Departing from implicit pixel-space manipulation, we introduce a Renderable 4D Try-on Proxy that explicitly decouples the human subject from the environment. This is achieved by distilling high-fidelity 2D try-on priors into a clothed 3DGS-based avatar, which is subsequently animated via SMPL-X sequences and metric-aligned into a reconstructed background point cloud. This proxy establishes a robust structural foundation with superior texture density and motion integrity. Our Proxy-Anchored Video DiT leverages this robust structural foundation as a primary geometric anchor, ensuring that the synthesized photorealistic videos are strictly constrained by prescribed trajectories and physically plausible deformations. Benefiting from the inherent editability of the 4D proxy, TryOnCrafter facilitates diverse downstream applications, including human relocalization, ``bullet time'' effects, and $360$-degree orbital viewing.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11847

Severity-Controlled Prediction Sets for Medication Recommendation

Yu Gu, Zijun Yu, Chi-Kuang Yeh, Xinyu Wang, Ziyang Song

原文摘要

Medication recommendation is a multi-label prediction task. However, existing prediction-set evaluation and calibration methods rely mainly on exact-overlap metrics and therefore treat all missed medications as equally severe, overlooking how closely the prediction set matches them within the medication hierarchy. To address this, we introduce Conformal Severity Control (CSC), a post-hoc framework for calibrating medication prediction sets under hierarchy-aware mismatch severity. CSC uses the Anatomical Therapeutic Chemical (ATC) hierarchy to quantify mismatch severity and controls patient-level severity without retraining the predictor, supporting both average severity and upper-quantile severity for the highest-severity mismatches. Experiments on MIMIC-IV show that CSC tracks user-specified severity tolerances and shifts prediction sets away from distant mismatches toward exact or close matches.

cs.LG · stat.AP · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11846

MOTIF: LLM-Defined Material Taxonomies for OOD Property Prediction via Invariant Feature Selection

Gwangseon Jang, Sieun Yun, Dong Ho Lee, Kyong-Ha Lee, Chanuk Lim, Jin Woo Choi, Han Seul Kim

原文摘要

Out-of-distribution (OOD) generalization remains a central challenge in materials property prediction, where models must extrapolate to novel chemistries beyond the training distribution. While large language models (LLMs) have demonstrated strong scientific reasoning, their direct use for quantitative prediction remains limited. To address this, we systematically evaluate multiple roles for LLM assistance, finding that only taxonomy-based grouping consistently improves OOD generalization over a strong kNN+GBT baseline. Building on this, we here propose MOTIF, a framework that leverages LLMs not as predictors, but as generators of candidate taxonomies that partition materials into chemically meaningful environments. These taxonomies are evaluated via training-only pseudo-OOD validation, and only those that improve generalization are retained. Invariant feature selection is then applied within each accepted taxonomy, followed by gated aggregation across taxonomies. We evaluate on 2D halide perovskites, where bandgap prediction must generalize across distinct organic spacer families. On this benchmark, MOTIF reduces mean OOD MAE from 0.122 to 0.111 eV. Under a frozen dev/test protocol, the deployable configuration further improves errors from 0.123 to 0.117 eV (electronic bandgap) and from 0.181 to 0.173 eV (optical bandgap). Beyond this primary task, we validate transferability across additional materials datasets, including 3D perovskite solar cell efficiencies and Matbench benchmarks with structure-based splits. Across domains, MOTIF consistently improves robustness under distribution shift, outperforming expert-defined groupings, rule-based heuristics, and clustering baselines, while remaining competitive with strong classical selection methods. These results demonstrate that LLMs are most effective not as predictors, but as tools for discovering structure in data.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.08506

Structure is in the zoom: probing neural symmetry through dimensionality scaling

Adil Amin

原文摘要

Scaling exponents of neural dimensionality are read as properties of the code, and used to compare representations and benchmark models against cortex. Yet one recording of 11,311 mouse V1 neurons gives 0.25, 0.30 or 0.35, depending only on how it is subsampled. We show that such an exponent measures the population and the probe together. We split it exactly into a floor, which random subsets of the same sizes give, and a shift, which a declared axis of stimulus classes adds. The usual random-subsample exponent is almost all floor. Stimulus direction lifts the exponent in every grating recording by reading both symmetry sectors of the code: orientation, which repeats every half turn, and direction, which does not. The lift lives in trial-to-trial fluctuations that differ between directions, while the class means, which linear decoders read, pull it down. Two measured terms, how much the classes' fluctuations overlap and how the means sit against them, account for the shift. They give its sign where it changes: in Allen Neuropixels recordings, whose class means mostly raise the exponent, and in macaque V1. In an XY lattice whose classes stay on one orbit, temperature flips the sign as the two terms say, and in GPT-2 the shift ranks the indirect-object circuit's name-mover and backup heads. Reported with its path, its floor and its shift, an exponent says how much structure a probe finds and what carries it. Our open tool, rung, reports all three for any labelled neural recording or language-model layer: https://github.com/adilamin89/structure-in-the-zoom

q-bio.NC · cond-mat.dis-nn · cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11842

A Benchmark and Layered Evaluation Protocol for Evidence-Grounded Shared Belief Updating in Connected Robotics

Ranulfo Bezerra, Kimitoshi Yamazaki

原文摘要

Connected physical systems fuse observations from robots, IoT sensors, and edge-AI modules into a shared account of the world, yet most evaluations score only the final answer or action and cannot tell whether a method attributed the evidence, updated its belief, handled contradictions, and justified its decision. We argue that shared reasoning in Connected Robotics should be evaluated as an evidence-to-belief trajectory, and introduce the Connected Robotics Evidence Graph (CREG) Benchmark to make that trajectory measurable. CREG comprises 350 deterministically generated scenarios across seven regimes (justified revision, belief preservation, relation association, contradiction onset and resolution, stale/delayed evidence, and source reliability), each exposing a normative posterior reference at every evidence prefix, validated by an independent log-odds checker and screened by a blinded expert audit. Nine representation conditions separate relation content from serialization, ordering, and metadata. Evaluating four open-weight instruction models and six transparent fusion baselines yields three findings. First, entity grounding accounts for a substantial part of the weak-model deficit. Second, deleting semantic relations costs 15.3 points of strict belief-state accuracy on relation-dependent scenarios (Holm-corrected p<0.001), but an information-matched structured list outperforms the full graph and shuffling the graph does not hurt: relation content matters where required, while graph serialization confers no consistent advantage. Third, belief accuracy is strongly non-monotonic along the evidence sequence, with mid-trajectory failures that endpoint-only evaluation cannot detect. All scenarios, references, prompts, metrics, and analysis code are released for offline, hardware-free reproduction.

cs.RO · cs.AI · cs.CL · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11841

AI and Mathematical Work on Mastodon: Attention, Topics, and Target-Specific Evaluations

Takayuki Uchiba, Kunihiro Miyazaki

原文摘要

How does a mathematical community discuss AI on a federated social platform? We study 24,920 public posts from January--September 2026 by 272 accounts in a fixed roster of 1,002 Mastodon accounts whose profiles indicate mathematical research or study. LLM-assisted extraction identifies 1,191 AI-for-mathematics posts by 101 accounts. Their share of all posts rises from 1.1\% in January to 11.5\% in September, with contributing accounts increasing from 10 to 59. We represent discussion through linked target--aspect--stance triples, separating what is discussed from the property assessed and the author's evaluation. From July to September, capability and usefulness decline as a share of related discussion, while social and institutional concerns increase; changing contributors account for much of the latter shift. Among 38 accounts evaluating both technical quality or utility and communication, credit, or verification procedures, the procedural negative-component fraction is 19.4 percentage points higher on average (95\% account-bootstrap interval: 3.8--35.1). Matching targets within posts gives a similar direction. Repeated extraction varies substantially at the level of individual triples, although replacing the repeated sample preserves the aggregate contrast. Around OpenAI's October 6 mathematics release, related-post share rises from 9.6\% to 13.6\%, and contributing accounts increase from 13 to 28. Post-release discussion includes appreciation of the results' potential mathematical value and applications alongside criticism of promotion centered on result counts, limited methodological disclosure, and verification arrangements.

cs.CY · math.HO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11839

MOOD: Benchmarking Post-Hoc OOD Detection for Materials Property Prediction

Shirley X. Yu, Amey P. Pasarkar, Adji Bousso Dieng

原文摘要

Out-of-distribution (OOD) detection is essential for deploying machine learning models in high stakes scientific applications, yet methods that flag OOD inputs without retraining the model have been validated almost exclusively on image classification. We introduce MOOD (Materials Out-of-Distribution Detection), a benchmark for evaluating whether these post-hoc OOD detectors transfer to graph neural networks (GNNs) on materials property prediction. MOOD evaluates 15 post-hoc detectors and 2 supervised diagnostic probes across 4 GNN architectures, 5 MatBench tasks, and 15 physically motivated compositional and structural shifts, yielding 264 evaluation settings and over 13,000 AUROC measurements across three encoder training seeds. Our evaluation reveals three key findings. First, current post-hoc detectors perform near chance; all 15 detectors achieve an AUROC around 0.5. Second, detector failures decompose into two regimes: method bottlenecks and encoder bottlenecks. Method bottlenecks arise when supervised probes can separate in-distribution (ID) from OOD samples but unsupervised detectors fail, meaning that the embeddings contain OOD signal that current detectors do not extract. Encoder bottlenecks arise when even supervised probes fail to separate ID from OOD samples, indicating that the relevant shift is not accessible in the frozen GNN representation. Third, when OOD prediction error exceeds ID error, the AUROC of distance-based and density-based detectors correlates with the size of that gap. Collectively, these findings indicate that reliable OOD detection in scientific domains requires representation learning strategies that explicitly preserve OOD relevant signal, particularly for tasks where current GNN architectures encode no extractable signal at all.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11830

ROS2MR: Connecting ROS 2 Motion Planning to Mixed-Reality Trajectory Visualization

Santiago Berrezueta-Guzman, Habiba-Loai Khalil, Andrei Koshelev, Stefan Wagner

原文摘要

Mixed reality (MR) allows users to inspect planned robot motion within their physical surroundings, but displaying trajectories from robotics software requires converting their joint configurations and timing into a format the MR application can play back. This paper presents ROS2MR, an extension of the MR-Robotics LAB platform that converts YAML-serialized ROS 2 joint trajectories, including MoveIt motion plans, into the platform's existing JSON playback format. The converter validates the input, maps arm joints by name, and resamples the trajectory at a nominal rate of 30 Hz, using cubic Hermite interpolation when joint velocities are supplied and linear interpolation otherwise, while binary gripper commands are sampled separately using a zero-order hold. A Python export helper combines sequences of motion plans and gripper states into a single trajectory and checks positional agreement at segment boundaries. The MR application was also adapted for Apple Vision Pro in addition to Meta Quest 3. A technical evaluation showed that the converter preserved the normalized duration and endpoint configurations for dense, sparse, and irregularly timed inputs. With supplied velocities, the position RMSE was approximately 2.91x10^-5 degrees for all sampling patterns; without them, sparse input increased it to 0.85 degrees, and close positional agreement did not guarantee accurate velocity profiles. A pick-and-place sequence retained its open--closed--open gripper order, although off-grid transitions were shifted to the output sampling grid. The resulting file-based pathway enables inspection of ROS-generated trajectories in MR without a live ROS connection.

cs.HC · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11826

How Path Integral Diffusion Decides a Finite Spin Glass

Michael Chertkov

原文摘要

When an AI diffusion-type generator samples a frustrated spin glass, which macroscopic alternatives does it decide first, and when? We study this for bridge-diffusion models of the Path Integral Diffusion (PID) type started from a point source. We show that every isotropic PID is a time-changed Gaussian stochastic localization, and conversely every signal-to-noise schedule is realized by a PID protocol. What a continuous-time generator decides therefore does not depend on the protocol, which only sets when and at what cost. We describe the generator's state by its partial commitments: the probabilities it assigns, at each moment, to nested classes of configurations fixed by the target. The partial commitments are martingales that move only along class contrasts. Their ensemble average gives the information about each level at an exact rate set by squared magnetization contrasts. Each split is decided, approximately, like a binary Gaussian channel at signal-to-noise ratio 1.04/λ_P, with λ_P the leading eigenvalue of the parent's correlation matrix. The theory is applied to Bethe-lattice and Sherrington--Kirkpatrick (SK) glasses with up to 64 spins. It is observed generically that a trajectory first shifts its weight to one of the two mirror halves of the zero-field measure and then to the glassy classes inside it. These classes appear near the glass transition and keep their identity on cooling. Nested glassy levels have similar λ_P and are decided together, so a trajectory commits in one or two distinct events---the mirror choice and a compound glassy decision---which separate further with coupling and size, as the spectra predict. For a generator that takes finitely many steps, the protocol matters through the late, local stage rather than the macroscopic decisions.

cond-mat.dis-nn · cond-mat.stat-mech · cs.AI · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11823

AscSpMM: Efficient SpMM on Ascend NPUs via Pipelined Vector-Cube Collaboration

Fei Li, Haoxin Li, Kehong Liu, Yanzi Zhou, Weiguo Wu, Shiqiang Nie, Jinyu Wang, Song Liu

原文摘要

Neural processing units (NPUs) provide high compute density and energy efficiency through specialized matrix engines and software-managed on-chip memory. However, their libraries primarily target dense computation. Sparse matrix-dense matrix multiplication (SpMM), a key kernel in graph analytics, sparse neural networks, and scientific computing, requires index-driven operand selection, indirect addressing, scattered memory accesses, and load balancing. These requirements conflict with the fixed shapes and contiguous layouts preferred by NPU matrix engines. GPU Tensor Core SpMM methods cannot be directly transferred to NPUs with decoupled vector and matrix engines. We present AscSpMM, which separates irregular operand preparation from regular matrix computation through a parameterized Vector-CSR representation. The vector engine decodes indices and gathers and reorganizes dense operands, while the Cube engine performs matrix multiply-accumulate operations. A double-slot ring buffer enables dependency-aware asynchronous pipelining, while operand reuse and runtime-adaptive tiling reduce cross-engine stalls. On an Ascend 910B3, AscSpMM achieves geometric-mean speedups of 2.36x over GEMM, 19.92x over an existing SpMM implementation, and 5.82x over cuSPARSE on an A100. It reduces matrix-input storage and peak HBM footprint by 48.24% and 22.98% on average, while remaining competitive with specialized GPU Tensor Core SpMM kernels. The code is available at https://github.com/LfLab-AI/Ascend-NPU-SpMM.

cs.AR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11820

Exploiting Native Hardware Heterogeneity: Custom GPSIMD Operators for Accelerating Emerging LLM Memory on AWS Trainium

Zifan He, Jason Cong

原文摘要

Emerging memory modules of large language models, such as Engram in DeepSeek-V4.1-Flash, mix irregular integer work (a 64-bit n-gram hash and a sparse table lookup) with dense computation (a GEMM and a gate). When deploying on a GPU-FPGA system, we can disaggregate operations to different devices for Engram to speed up by 5.9$\times$. AWS Trainium provides a similar heterogeneity natively: each Trainium NeuronCore pairs dense engines with a customizable GPSIMD Engine, and all engines share the on-chip SBUF. However, the Neuron Kernel Interface (NKI) exposes the GPSIMD cores only through fixed built-in instructions, and the official GPSIMD custom-operator toolchain does not support Trainium 2 or 3 and cannot be used with NKI. In this work, we build a toolchain that compiles C++ custom operators for the GPSIMD cores and injects them into the compiled NEFF from the NKI kernel on Trainium 3, expanding the customizability of GPSIMD with the current software stack. Over a hand-optimized NKI baseline of DeepSeek-V4.1-Flash, two GPSIMD operators speed up the n-gram hashing by 1.79$\times$ at decode, the table retrieval by up to 4.95$\times$, and the whole Engram computation by up to 1.14$\times$.

cs.DC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.08811

Learning Theory of Transformers: Local-to-Global Approximation via Softmax Partition of Unity

Zhongjie Shi, Wenjing Liao

原文摘要

This paper investigates the learning theory of Transformer networks for regression tasks on the compact Euclidean domain $[0,1]^d$ and $d$-dimensional compact Riemannian manifolds. We propose a novel constructive approximation framework for Transformers that builds local approximations of the target function and aggregates them into a global approximation via softmax partition of unity (POU). This approach leverages the attention mechanism to achieve spatial localization through affine transformations of the input. The softmax activation plays a crucial role in aggregating local approximations to a global output. From an approximation perspective, we prove that a dense Transformer equipped with only two encoder blocks and standard single-hidden-layer point-wise feed-forward networks can achieve a uniform $\varepsilon$-approximation error for $α$-Hölder continuous functions with $α\in (0,1]$ using $\mathcal{O}(\varepsilon^{-d/α})$ total parameters. Building upon this approximation guarantee, we establish a near minimax-optimal generalization error bound of order $\mathcal{O}\big(n^{-\frac{2α}{2α+d}} \log n\big)$ for the empirical risk minimizer, where $n$ is the training data size. The Transformer architecture studied in this paper uses native softmax attention and sinusoidal positional encodings, with a fixed depth of two encoder blocks. Finally, numerical experiments reveal spatially localized, Gaussian-like attention weights that emerge during training, resembling our Softmax POU weights.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11812

CaseLaw: A Policy-Decoupled Framework for Training-Free Content Moderation

Qi Lu, Sen Li, Likun Shi, Xiaolong Jiang

原文摘要

Content moderation is inherently policy-dependent: moderation boundaries are often subtle, subjective, and frequently updated, while conventional rule based or fine-tuning based systems require costly rule rewriting, model retraining, or system redeployment. We propose CaseLaw, a policy-decoupled and training-free framework for adaptive content moderation. CaseLaw reformulates moderation as precedent-based retrieval and voting, where multimodal content is transformed into evidence-preserved structured semantics, embedded into a stable semantic space, and classified by thresholded voting over retrieved precedent labels. We train NoteCap on a large-scale corpus of structurally annotated notes to ensure consistent semantic extraction and freeze it during subsequent policy adaptation, which requires no additional training. Policy adaptation is achieved by updating the semantic case library, including case labels and mappings, without retraining models or modifying prompts. To evaluate CaseLaw, we construct MFO-RedNotes, a benchmark for the male-female opposition risk domain. We additionally provide a five category CoPro comparison. Experiments show that CaseLaw consistently outperforms competing baselines in both precision and recall across policy-dependent risk levels and broader safety categories, with category-wise precision/recall reaching up to 86.8%/90.1% on MFO-RedNotes across both policy settings and 94.5%/92.3% on CoPro. Our work highlights the potential of precedent-based reasoning for policy adaptive content moderation and offers a practical direction toward more stable, transparent, and training-free moderation systems.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11811

Rethinking Adaptive Routing on Dragonfly

Ziyu Wang, Dezun Dong, Fei Lei

原文摘要

The Dragonfly topology is widely deployed in leading supercomputers, where adaptive routing is crucial for high-performance interconnection. Despite years of research, a dilemma remains: state-of-the-art adaptive routing algorithms achieve excellent performance but demand hardware budgets far beyond what real router chips can afford -- prohibitive buffers, logically dependent floating-point operations, or exhaustive path-set traversals -- whereas realizable algorithms deliver unsatisfactory performance. Furthermore, existing algorithms fail to exploit the abundant almost-minimal paths in Dragonfly: when only the local channels of a minimal path are congested, they reroute packets to significantly longer global non-minimal paths, wasting global bandwidth and increasing latency. To address both limitations, we analyze intra-group path contention and propose the Decoupled Incremental Adaptive Routing (DIAR) algorithm, which decouples routing into global path selection at the source and incremental local detours onto almost-minimal paths, guaranteeing deadlock freedom without additional virtual channels, escape rings, or turn models. Requiring only a few comparators, shifters, adders, and multiplexers, DIAR reduces buffer overhead by up to four orders of magnitude compared with state-of-the-art algorithms, while achieving comparable or superior performance across all evaluated scenarios: relative to the strongest realizable adaptive routing algorithm in each scenario, DIAR improves saturation throughput by up to 30.0% across synthetic traffic patterns and improves performance by up to 34.9% (13.2% on average) across HPC workloads.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11809

Budget-aware Correspondence-driven View Selection for Real-time 3D Perception of Mobile Robots

Vaishnav Ramesh, Jacob Carter, Shivali J. Patel, Sanjeev J. Koppal, Md Jahidul Islam

原文摘要

3D visual perception and scene reconstruction are critical for autonomous robots and intelligent transportation, yet real-world deployments often generate long, redundant image sequences that hinder downstream processing. This report investigates BCVS (budget-aware correspondence-driven view sampling), a pose-free view selection algorithm that offers 22+ FPS on a CPU, enabling online filtering for efficient 3D perception by mobile robots. Unlike existing approaches that rely on full structure-from-motion (SfM) pipelines or camera pose estimation, BCVS employs a purely correspondence-based scoring strategy that prioritizes view novelty while maintaining sufficient overlap for geometric stability. Given a user-specified budget, BCVS iteratively selects the most distinct frames and suppresses near-duplicates, producing compact subsets that preserve connectivity and minimize reconstruction loss. Extensive experiments on benchmark show that BCVS outperforms CPU-friendly baselines at tight budgets and remains competitive as budgets scale. Field trials with large aerial and marine robotics data further validate its practicality and scalability: BCVS reduces redundancy up to 16% while sustaining robust SfM registration and high-quality 3D reconstructions.

cs.RO · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13011

Compile-Gated Lean Proof Refactoring with Enterprise Agent Practices: A Model per Step under a US\$3 Cap, Followed by Programmatic Rewrite Rules

Bobber Cheng

原文摘要

Our closed-track entry (Arena username Vexorium-Agent) scored 86.31 points on the final board (L = 77.07, H = 81.86, Z = 100) over the 60 final problems, against 33.33 for the reference proofs, 8th at the deadline (top: 92.57), for US\$166.33 at list prices with every call counted (per problem: mean US\$2.772, at most US\$2.874; US\$3 cap). Three practices carry the entry. First, each paid stage runs the model that measured best per dollar for it among the 9 we tested in our own harness: GPT-6.1 Sol the early sessions, GPT-6.1 Terra the later ones and the tail at 70% of Sol's cost per turn, and Gemini 4 Argon the review and repair; Sol also runs the style rewrite. Second, what our development and research runs learned ships as compiler-checked programmatic rewrite rules rather than prompt text (+0.93 points from rules fixed before the release; +0.26 and +0.30, in sample, from passes built or switched on after the release). Third, every call is recorded and reviewed before each change (all 9 board scores came within -0.063 to +0.015 points of our meter's prediction). The harness, gate and many of these rules also serve our open-track entry. We report the full bill and the spread of three runs.

cs.SE · cs.AI · cs.LO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.13804

Attention in Constant Time: Vashista Sparse Attention for Long-Context Decoding with Exponential Guarantees

Vashista Nobaub

原文摘要

Large language models spend most of their inference cost on attention over long contexts, yet empirical behavior suggests that only a small subset of tokens meaningfully contributes to each query. We formalize this phenomenon by modeling attention as a projection onto the convex hull of key vectors and analyzing its entropic (softmax-like) relaxation. Our main theoretical contribution is a face-stability theorem showing that, under a strict complementarity margin (a support gap (Δ) certified by KKT multipliers), entropic attention concentrates on a constant-size active face: the total mass assigned to inactive tokens decays exponentially as (\exp(-Ω(Δ/\varepsilon))), while the error on the active face scales linearly in the temperature/regularization parameter (\varepsilon). This yields a practical criterion for when sparse long-context decoding is safe and provides a principled knob to trade accuracy for compute. Building on these guarantees, we introduce Vashista Sparse Attention, a drop-in mechanism that maintains a small candidate set per query through a paging-style context selection strategy compatible with modern inference stacks. Across long-context evaluations, we observe stable constant-size effective support, strong wall-clock speedups, and minimal quality degradation in the regimes predicted by the support-gap diagnostics. Finally, we discuss deployment implications for privacy-sensitive and air-gapped settings, where interchangeable attention modules enable predictable latency and cost without external retrieval dependencies.

cs.AI · cs.LG · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11801

Proactive Dimension-Adaptive Denoising for Graph Collaborative Filtering via Representation Coherence

Wenjun Li, Dong Zhao, Jiaying Shi, Juan Zhang, Zhaoming Pan, Zhimin Zhang

原文摘要

Implicit feedback in recommender systems contains noisy interactions (e.g., accidental clicks, impulse purchases) that propagate through user-item graphs and contaminate learned representations. Existing explicit denoising methods uniformly rely on training loss to estimate interaction reliability. However, loss is a fundamentally passive signal: it detects noise only after it has caused prediction errors, and it is heavily confounded by training stage, negative sampling, and hard positives. We propose Noise-aware Dimension Learning (NDL), a proactive dimension-adaptive denoising framework. NDL introduces Representation Coherence Detection (RCD), which evaluates the coherence between an interaction representation and the user's historical preference centroid in embedding space, providing an active noise signal independent of loss. RCD and the loss signal are fused via activity-aware weighting (active users rely more on coherence while cold-start users automatically fall back to loss), and the fused estimate drives an interaction-level dimension-adaptive mask that limits the effective representation capacity of unreliable interactions. A lightweight contrastive regularizer further improves embedding uniformity, stabilizing the geometry on which RCD discriminates. On three public benchmarks NDL consistently outperforms state-of-the-art denoising methods, lifting Recall@5 by +17.95% on Amazon-book, +12.33% on Yelp, and +5.63% on MovieLens-1M over the strongest baseline DAR; the core variant without contrastive learning already surpasses DAR on sparser datasets, confirming representation coherence as the primary driver of these gains. NDL is confined to the training loop and is serving-compatible with LightGCN-based production pipelines, adding no online latency or parameters at inference time. Code: https://github.com/GordonDongZHAO/NDL

cs.IR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.37161HF 社区推荐

WorkFactory: Turning Professional Intents into Executable Work for Scalable Agent Training

Ziyang Zeng, Tingyang Chen, Jia Deng, Qiyao Wang, Shuo Tang, Yike Yang, Tao Ren, Wayne Xin Zhao 等 13 位作者

原文摘要

Scaling supervised fine-tuning for working agents requires not only broad coverage of professional tasks, but also expert demonstrations that faithfully carry out professional workflows to completion. However, missing or inconsistent task information can disrupt expert demonstrations, weakening the resulting supervision. We introduce WorkFactory, a scalable task-construction framework that turns professional intents into executable task environments through hybrid intent sourcing, semantic workflow planning, and execution-based repair. Starting from these intents, WorkFactory constructs a Semantic Workflow Graph that explicitly represents information requirements, dependencies, and locations within the task environment. The graph guides the instantiation of task environments, while an adversarial execution-refinement loop exposes missing or inconsistent information and steers targeted repairs that preserve the original intent. Using WorkFactory, we construct 112,686 tasks spanning 612 occupations and train the WorkPilot family at 4B, 27B, and 35B-A3B scales on the resulting teacher trajectories. All three models improve over their respective base models across four professional-work benchmarks, with WorkPilot-35B-A3B rising from 879 to 1565 Elo on GDPval-AA v2.1. Scaling experiments with up to 110k tasks further show that larger models benefit more from additional task data but exhibit diminishing returns at smaller data scales, while WorkPilot's long-horizon execution evolves from verbose trajectories toward more efficient task completion. Controlled component studies quantify the negative effects of task-environment defects on downstream performance and show how semantic workflow planning and execution-based repair yield downstream gains through improved task-environment quality. The subset of data are available at https://github.com/IQuestLab/WorkFactory.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11792

APO2CS: Advantage-Aware Preference Optimization for Two-Level Constructive Search in Combinatorial Optimization

Kaixi Yang, Linbo Qiao, Wei Shang, Yanqi Shi, Dongsheng Li

原文摘要

Preference optimization has emerged as a promising alternative to reward-based training for neural combinatorial optimization. However, existing methods typically rely on binary pairwise labels, which discard the magnitude of solution-quality differences and provide supervision only at the complete-solution level. We propose Advantage-aware Preference Optimization for Two-level Constructive Search (APO2CS), a preference-based training objective for constructive neural solvers that integrates both macro-level and micro-level supervision. At the macro level, APO2CS enriches pairwise preferences with an advantage term derived from objective-value differences. At the micro level, it compares partial trajectories, thereby providing denser supervision throughout the sequential construction process. The resulting objective can be used either to train a policy from scratch or to fine-tune a pretrained one. Experimental results show that APO2CS improves the expressiveness and search capability of preference learning for combinatorial optimization, while demonstrating strong effectiveness and adaptability across multiple combinatorial optimization tasks.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11790

FMA-Net: Flexible Modality Adaptation for Robust Multimodal Face Presentation Attack Detection

Yuhao Gao, Yichen Shi, Zitong Yu, Hongyang Wang, Hongrui Li, Ruixin Zhang, Shouhong Ding, Jun Feng

原文摘要

Multimodal face presentation attack detection (fPAD) improves robustness using complementary RGB, depth, and infrared (IR) cues, but deployment remains challenging when modalities are unavailable or incomplete. Existing flexible-modality methods mainly use zero-filling or modality omission, potentially discarding cross-modal information and introducing modality-dependent bias. We propose the Flexible Modality Adaptation Network (FMA-Net), a unified framework that compensates for missing modalities through instruction-guided latent-space translation. The Face Modality Instruct Translator (FMIT) estimates target modality observations from RGB inputs, while context-aware category enhancement uses learnable text prompts as modality-invariant semantic anchors. A Facial Feature Adapter (F2A) integrates global facial structure and local spoof-relevant cues to enhance discriminative representations. Synthetic-to-Real Face Domain Alignment (SR-FDA) reduces feature discrepancies between translated and real modalities, with benefits depending on the missing-modality pattern and data domain. Experiments on WMCA and PADISI-Face validate the framework. On WMCA with a 70% modality missing ratio, FMA-Net achieves a mean ACER of 3.22% across four intra-domain missing-modality protocols, compared with 4.24% for the strongest baseline by the same criterion. When both depth and IR are entirely unavailable, it achieves 5.23% ACER, compared with 5.99% for the best competing result. Cross-domain experiments also reveal limitations of synthetic-to-real alignment under dataset shifts. These results support FMA-Net as a practical approach to fPAD with flexible and incomplete modalities.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.34716

From Pixel Generation to Topological Inference: Structural Dual Super-Resolution for Trustworthy Cross-Physical-Domain Trabecular Morphology Learning

Fan Zhang, Yi Zhang, Yuhui Dong, Ling Wang, Xin Xu

原文摘要

Clinical CT and UHRCT cannot resolve individual trabeculae, whereas synchrotron radiation microCT (SRuCT) provides high-resolution references but is not applicable for in vivo imaging. The two domains differ by a 32x resolution gap, are only coarsely paired, and exhibit severe physical differences including partial volume effects, noise, and artifacts. Existing super-resolution networks and pretrained-prior methods (GLEAN/StyleGAN2, Stable SR/LDM) underperform because they target pixel generation---diverse details and SSIM/PSNR---and do not model these physical differences. Pixel generation for a 32x resolution gap is intrinsically ill-posed. We propose a paradigm shift from pixel generation to topological inference: deterministically predicting invariant microstructures from macro-scale low-resolution inputs, evaluated by morphological parameters. The core of our 2D morphology learning lies in training on 2D slices while evaluating on 3D morphological parameters, ensuring that the learned representations capture true three-dimensional trabecular topology rather than 2D pixel statistics. We realize this paradigm via structural dual super-resolution, coupling forward physical degradation (micro-to-macro) with inverse structural inference (macro-to-micro) through structural duality constraints. The method is an end-to-end, few-shot, compact structural dual network (SDN), comprising a bidirectional modeling network, a multi-scale structural consistency discriminator, and four structural duality constraints. On the test set, SDN achieves morphological parameters largely consistent with SRuCT across six metrics, with SSIM reaching 0.8. Trained on 3.2 um SSRF data, the model generalizes well to 3.25 um isotropic BSRF data from an independent source, validating cross-source generalization and confirming trustworthy structural inference rather than pixel generation.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2507.04380

Transferring Visual Explainability from Self-Explaining to Prediction-Only Vision Transformers via Task Arithmetic

Yuya Yoshikawa, Ryotaro Shimizu, Takahiro Kawashima, Yuki Saito

原文摘要

In image classification scenarios where both prediction and explanation efficiency are required, self-explaining models that perform both tasks in a single inference are effective. However, for users who already have prediction-only models, training a new self-explaining model from scratch is costly in both labeling and computation. This study proposes a method to transfer the visual explanation capability of self-explaining Vision Transformer (ViT) models learned in a source domain to prediction-only, zero-shot-capable ViT models based on vision-language models (VLMs) in a target domain via task arithmetic, without target-side explanation supervision. The proposed method endows target models with explanation capability by adding an explainability vector induced by explanation supervision in the source domain based on the task arithmetic framework. Experiments on 10 diverse target datasets show that a single explainability vector learned on ImageNet-1k augmented with patch-level explanation supervision transfers consistently, improving explanation quality while largely preserving classification accuracy. Within-model faithfulness evaluation and comparisons with control vectors (e.g., random vectors of the same norm) show that the transferred explanations are faithful to the model's own predictions and that the effect is specific to the explainability vector. Beyond the transfer itself, we further investigate whether transfer success can be anticipated a priori from model-internal features alone, and find that, for a fixed source, a small set of such features carries non-trivial signal about the transfer outcome on held-out targets. Code is available at https://github.com/yuyay/explainability_vector.

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.13625

How to Interpret Agent Behavior

Jie Gao, Kaiser Sun, Jen-tse Huang, Katherine Van Koevering, Sijie Ji, Heyuan Huang, Weiyan Shi, Zhuoran Lu 等 11 位作者

原文摘要

Autonomous agents such as Claude Code and Codex now operate for hours or even days. Understanding their runtime behavior has become critical for downstream tasks such as diagnosing inefficiencies, fixing bugs, and ensuring better oversight. A primary way to gain this understanding is analyzing the reasoning trajectories and execution traces these agents generate. Yet such data remains in unstructured natural-language form, making it difficult for humans to interpret at scale. We introduce Act*ONOMY, a taxonomy for describing and analyzing agent behavior at runtime. Act*ONOMY has two components: (1) the taxonomy itself, developed through Grounded Theory and structured as a three-level hierarchy of 10 actions, 46 subactions, and 120 leaf categories; and (2) an open repository that hosts the living taxonomy, provides an automated analysis pipeline that applies it to agent trajectory analysis, and defines an extension protocol for customization and growth. Our experiments show that Act*ONOMY can be used to compare behavioral profiles across agents and characterize a single agent's behavior across diverse trajectories, surfacing patterns indicative of failure modes. By providing a shared vocabulary, Act*ONOMY aims to help agent researchers, designers, and deployment engineers interpret agent behavior more effectively and consistently, supporting further study of oversight and control.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2412.19439

Paleoinspired Vision: From Exploring Colour Vision Evolution to Inspiring Camera Design

Yijie Lu, Zhimin Zong, Junjie Zhang, Shenghan Su, Zeyun Zhao, Ziteng Cui, Yan Pu, Jing Lu 等 11 位作者

原文摘要

The evolution of colour vision is captivating, as it reveals the adaptive strategies of extinct species while simultaneously inspiring innovations in modern imaging technology. In this study, we present a simplified model of visual transduction in the retina, introducing a novel opsin layer. We quantify evolutionary pressures by measuring machine vision recognition accuracy on colour images shaped by specific opsins. Building on this, we develop an evolutionary conservation optimisation algorithm to reconstruct the spectral sensitivity of opsins, enabling mutation-driven adaptations to more effectively spot fruits or predators. This model condenses millions of years of evolution into seconds using GPU, providing an experimental framework to test long-standing hypotheses in evolutionary biology, such as vision of early mammals, primate trichromacy from gene duplication, retention of colour blindness, blue-shift of fish rod and multiple rod opsins with bioluminescence. Beyond validating existing paleontological hypotheses, our model further enables speculative explorations of hypothetical species, such as organisms with visual systems adapted to Martian conditions. These constitute our contributions to AI for life science and neuroscience. Besides, the principles derived from these biological studies can translate into practical engineering solutions. In practice, our findings suggest a minimalist yet effective approach to task-specific camera filter design, optimising the spectral response function to meet application-driven demands. Code repository: https://github.com/luyijie-research/PaleVision.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.37364HF 社区推荐

EmbeddingGemma 2: Unified and Efficient Multimodal Representations

Henrique Schechter Vera, Sahil Dua, Jovan Andonov, Sara Smoot, Sebastian Russo, Ivan Llanos, Max Matveev, Jae Hyeon Yoo 等 77 位作者

原文摘要

We present EmbeddingGemma 2, an open multimodal embedding model which natively represents text, images, audio, video, and combinations thereof in a unified space. Our approach introduces improvements such as simplified per-layer embeddings and a multi-stage curriculum with modality and objective progression which ensure strong quality across unimodal, cross-modal, and mixed-modal evaluations. Notably, in comprehensive embedding benchmarks across all modalities, EmbeddingGemma 2 establishes a new Pareto frontier, outperforming both specialist models and models two to twelve times larger. Furthermore, our design offers flexibility for resource-constrained environments, supporting selective modality encoder loading, adjustable vision token budgets, truncatable embedding dimensions, and quantized weights. We present ablations analyzing key decisions and release EmbeddingGemma 2 to promote further research.

cs.LG · cs.CL · cs.CV · cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11745

Reproducible quantum clustering for single-cell transcriptome atlases

Henry Han

原文摘要

Current single-cell clustering pipelines discard most genes and can yield inconsistent clusters across random seeds. We introduce KQE-2, a training-free hybrid quantum-classical framework for reproducible whole-transcriptome clustering. KQE-2 maps cells into entanglement-free quantum states, enabling exact feature evaluation on classical hardware and direct checks on a quantum processor. Theoretically, we prove that removing entanglement is necessary and sufficient to bound single-qubit feature variance locally. Practically, while retraining scVI shifted 14% of cells and altered rare-type markers and drug targets, KQE-2 delivered identical biology across runs. On standard highly variable genes, the quantum encoding alone raised accuracy on a developing cortex from 0.61 to 0.79. KQE-2 scales to 11.4 million nuclei on one server, outperforming the compared methods on eight of ten complete atlases (and within the best method's run-to-run range on two), and leads on six even under standard downstream clustering.

cs.AI · q-bio.GN · q-bio.QM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11744

Breaking the $\sqrt{d}$ Communication Barrier in Federated Sampling with Adaptive Hamiltonian Monte Carlo

Jiajun Liang, Linxuan Wang, Guang Lin, Qifan Song

原文摘要

This work proposes the Adaptive Federated Hamiltonian Monte Carlo (AFHMC) for Bayesian federated learning (FL) tasks. The prior work on HMC applications for FL achieves $O(\sqrt{d}/ε)$ communication cost under log-convex distributions, already demonstrating its advantage over the Langevin dynamic-based counterpart. Based on a refined analysis, this paper further reveals the benefit of HMC for the FL regime by incorporating a control mechanism for local node shifts. We prove that AFHMC at most requires $O(\{d/ε^2\}^{1/3})$ communication cost up to a logarithmic term, which is significantly better than the existing results. Moreover, the proposed method adapts to client heterogeneity. Under low-heterogeneity settings (i.e., the target sampling precision $ε^2$ is higher than the heterogeneity level), the communication rate of AFHMC can be as good as $O(\log(d/ε^2))$, resembling the communication costs for FL optimization tools. To the best of our knowledge, this provides the first logarithmic communication complexity result for federated sampling, even though it requires low heterogeneity. Numerically, we also justify AFHMC's advantage under the low-heterogeneity regime. Our code is available at https://github.com/laaaarrywang/Adaptive-Federated-HMC

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11733

Can the Remaining Answers Change the Prediction? Worst-Case Stability Checks for Personalized Rating Models

Priyanka Siddappa, Muntaqim Ahmed Raju, Shifat Haider Al Amin, Ruizhe Ma

原文摘要

Missing ratings can overturn a personalized model's current prediction. For completed models with additive response scores, exact worst case bounds check all missing assignments without changing the current predictor. We evaluate this audit retrospectively in two self-triage cohorts, 108 clinicians rating three simulated psychiatric interviews, and 126 counseling demonstrations annotated by ten experts. Under shared random half histories, Fisher adaptation based on observed ratings reduces log loss relative to response frequency by 0.1071 nats in the clinician audit (95% CI [0.0867,0.1261]) and 0.0223 in counseling (95% CI[0.0121,0.0321]). The counseling test excludes target experts and source videos from training. Certification covers 5.4% and 10.5% of targets; fixed width acquisition raises coverage to 13.7% and 28.6% at the same budgets. With weights and responses held fixed, the observed-rating update also has lower loss than an affine partial update while certifying exactly the same evaluated targets. The certificate establishes agreement with the completed model, not correctness or stability at intermediate updates. These rating tasks test the method; clinical usefulness remains untested.

cs.AI · stat.AP

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arXiv ↗PDF ↗