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新论文 · v1arXiv:2610.36561

Governed Sovereign RAG for Public Administration: Tripartite Epistemic Routing, Facet-Guided Clarification, and Deterministic Numeric Verification in Citizen Services

Andres Cruz Degante

原文摘要

Deploying conversational assistants at government service desks faces a tension between regulatory certainty (zero tolerance for invented fees), data sovereignty prohibiting commercial clouds, and high rates of underspecified or out-of-jurisdiction citizen queries. We present and evaluate a governed, sovereign Retrieval-Augmented Generation (RAG) architecture deployed on the official portal of the State of Tlaxcala, Mexico (tlaxcaladigital.gob.mx), covering 1,143 procedures and a decoupled directory of 924 offices. The system governs interactions via three pillars: (1) a two-stage tripartite epistemic gate (clarify_policy and a Boltzmann distribution over AnyJev) arbitrating among answering (SPECIFIC), facet-guided clarification (CLARIFY), and abstaining (ABSTAIN); (2) a post-generation deterministic numeric verifier (verify_numeric_guardrail) with SHA-256 cache invalidation ensuring every generated fee or validity figure belongs to the retrieved official record; and (3) an empirical evaluation across 380 organic production traces (8 weeks) and a 200-query stratified benchmark (5-fold CV: 86.41% +/- 7.50%). In production, 89.47% of traffic is resolved at Level 0 via deterministic templates without free generation (global p50 = 0.665 s; direct L0 p50 = 0.445 s; slow-tail p95 = 30.412 s under CPU queuing), with 0/40 fiscal amount hallucinations observed in Level 1 (95% bound <= 8.81%). On the benchmark, the system achieves 86.50% strict Top-1 accuracy (+10.00 pp over a binary neural gate, p = 0.00453, p_Holm = 0.01172), pairing a 93.00% conditional safe upper bound with a 70.87% dropout-adjusted effective resolution after accounting for 53.33% citizen drop-off in clarification menus.

cs.CY · cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36559

CCP-DP: Cross-cluster context-guided phase dynamic programming for DNA storage sequence reconstruction

Zixin Zhang, Guanjin Qu, Huaming Wu

原文摘要

Motivation: DNA data storage offers high information density and long-term stability, but accurate sequence reconstruction remains challenging under limited sequencing coverage and strong insertion, deletion, and substitution (IDS) noise. Existing reconstruction methods mainly rely on local evidence within individual read clusters. When sequencing coverage is low or indel-induced phase shifts become pronounced, such local evidence may be insufficient to distinguish closely competing candidate sequences. Results: We present Cross-Cluster Context-Guided Phase Dynamic Programming (CCP-DP), which uses dataset-level cross-cluster information to complement cluster-local evidence and selectively refines ambiguous provisional consensuses. Under the main benchmark settings, CCP-DP achieved exact reconstruction rates of 95.36%-99.98% and outperformed existing methods on multiple datasets. At $5\times$ coverage, CCP-DP improved exact reconstruction by 6.2-17.5 percentage points over the stronger of BBS and CPL across the tested IDS conditions, while maintaining low reconstruction error and computational overhead. Overall, CCP-DP introduces cross-cluster shared information as a complementary source of evidence for DNA sequence reconstruction under challenging conditions. Availability: Source code is available at https://github.com/ZixinZ3899/CCP-DP.

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36558

Holomorphic Cauchy Networks for Efficient Stationary Tail Computation in High-Dimensional Reflected Brownian Motions

Kehan Wang

原文摘要

Stationary tail computation for high-dimensional reflected Brownian motions (RBMs) remains computationally challenging. Building on NN4MGF's shared-coordinate basic-adjoint-relationship (BAR) framework, we develop Holomorphic Cauchy Networks (HCNs). Native complex-valued rational Cauchy subnetworks enforce holomorphicity on pole-free domains, removing sampled Cauchy-Riemann penalties and their input-derivative graphs. Exact origin anchoring and boundary-coordinate exclusion enforce structural identities, while anchored interactions represent dependent transforms. Under holomorphic-log assumptions, we establish simultaneous value and first-derivative approximation for the constrained architectures and a mixed-derivative lower bound for coordinate-only representations under nonzero cross-covariance. Closed-form real-axis derivatives and sequential backpropagation further control memory. On the same NVIDIA H100 NVL, the tested 30D HCN configuration uses approximately tenfold less peak allocated memory and has 9.7-fold lower mean update time than NN4MGF; extrapolation to the respective scheduled update counts gives a projected full-schedule update-time ratio of 58.2. With 64 million sampled base inputs per high-dimensional run, HCN reaches 50 dimensions on a laptop GPU with 6.367% mean relative error over thresholds 1 through 8. Degree-matched comparisons yield lower reported mean relative errors over the primary evaluation ranges, including 1.422% versus 6.684% on the 2D Harrison non-product benchmark. An exploratory 10D correlated example illustrates the interaction extension.

cs.LG · math.NA · math.PR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36557

QESChunker: A Single Objective Unifies Overlapping and Non-Overlapping Chunking for RAG

Yifan Zhao, Qianyang Zhao, Xinyu Guan, kai wei, Yuming Deng

原文摘要

Chunking is a fundamental preprocessing step in retrieval-augmented generation (RAG), yet most existing chunkers rely primarily on heuristics. We introduce the Question--Evidence-Supervised Chunker (QESChunker), to our knowledge the first method for constructing static chunk indices from question--evidence supervision. QESChunker constructs document-specific chunk indices by optimizing over human-annotated or LLM-generated question--evidence pairs, without access to held-out test queries. We formulate non-overlapping and overlapping index construction through a unified question--evidence utility-maximization objective, turning chunking into a supervised combinatorial optimization problem. Under the non-overlap constraint, the objective decomposes over chunks and can be optimized exactly in polynomial time by dynamic programming. When overlap is allowed, the objective becomes monotone submodular; exact optimization is NP-hard, but our greedy achieves a $(1-1/\sqrt{e})$-approximation. Across two self-constructed and three public datasets, both variants outperform existing chunking methods in complete-evidence recall in nearly all evaluated settings, under the same overlap regime and a fixed hybrid retriever. These results recast chunking as supervised, budget-aware index construction for evidence retrieval.

cs.IR · cs.CL · math.OC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.08555

FAWAM: Force-Aware World Action Models for Closed-Loop Contact-Rich Manipulation

Haotian He, Zeyu Yan, Qipeng Liu, Ning Guo, Wenzhao Lian

原文摘要

Force signals provide critical interaction cues for contact-rich robotic manipulation. However, existing methods mostly use force as an additional observation modality, without fully exploiting its role in modeling future interaction dynamics or guiding execution-time feedback correction. In this paper, we propose FAWAM, a force-aware world action model that incorporates force information at three levels: perception, prediction, and closed-loop execution. FAWAM first encodes historical 6-axis force/torque signals to modulate action generation, then jointly predicts future actions and end-effector wrenches to explicitly model contact evolution. It further introduces a residual correction module that uses the predicted wrench trajectory as an execution-time reference to refine actions online based on real-time force feedback. Real-world experiments across multiple contact-rich tasks show that FAWAM improves the average success rate by 36.25% over vision-only baselines and 21.25% over existing force-aware baselines, demonstrating the effectiveness of our force-aware framework for robust contact-rich manipulation.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.28862

TextCloak: Safeguarding Data from Unauthorized LLM Fine-Tuning via RL-Driven Unlearnable Examples

Chengshuai Zhao, Pingchuan Ma, Dawei Li, Bohan Jiang, Zhiyuan Yu, Zhen Tan, Huan Liu

原文摘要

The development of Large Language Models (LLMs) has led to significant advances across a wide range of language tasks, while simultaneously raising growing concerns about unauthorized online data exploitation and privacy leakage. Unlearnable examples (UEs) emerge as a promising defense by injecting crafted perturbations into data such that models trained on them exhibit degraded utility. However, existing methods for text protection are primarily designed for classification tasks and discriminative language models, which limits their broad applicability (e.g., instruction following or reasoning) in LLMs. In this work, we propose **TextCloak**, an RL-driven data synthesis framework that safeguards textual data against unauthorized LLM fine-tuning. TextCloak employs a *UE generative policy* that transforms clean text into unlearnable examples while preserving semantic fidelity and linguistic naturalness. To optimize the policy, we introduce **GRPO-UE**, which simulates the attacker's LLM fine-tuning and rewards downstream degradation via group-relative policy optimization. This overall formulates a bi-level optimization loop, allowing the generator to discover effective protective patterns. Comprehensive experiments on six publicly available datasets and nine state-of-the-art LLMs demonstrate that TextCloak consistently impairs unauthorized LLM fine-tuning while maintaining text utility for legitimate use. Further analyses confirm its transferability and robustness across model architectures, training configurations, and adaptive attacks, highlighting its broad applicability as a practical defense for online content.

cs.CL · cs.AI · cs.CR · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36553

NextEval: An Agent-Based Solver and Benchmark for Derivative-Free Optimization

Shuo Chen, Li Cui, Cunxin Huang, Ya-Feng Liu, Minghang Xie, Meng Xu, Zaikun Zhang

原文摘要

Derivative-free optimization (DFO) addresses problems where derivatives are unavailable or costly to obtain. This setting often arises in simulation-based and experimental applications, where evaluating the black-box objective can itself be costly. Thus, it remains a central challenge in DFO to effectively exploit accumulated observations to select informative evaluation points under a limited budget. In this light, we introduce the NextEval solver, an agent-based DFO solver, which integrates the capabilities of a large language model (LLM) to guide the sequential selection of evaluation points. Specifically, we establish a system that combines an LLM agent with a numerical environment. The environment provides tools for analyzing past evaluations and executing numerical methods, while maintaining a shared evaluation history and method-specific state to support continuation across method switches. Using observations and tool feedback, the LLM agent selects tools, configures their parameters, and allocates the evaluation budget, while numerical methods compute and evaluate candidate points. Building on this system, we also introduce NextEval Bench, a benchmark protocol for comparing agent configurations on shared DFO tasks using the same numerical tools under matched evaluation budgets and information constraints. Numerical results on low-dimensional DFO problems demonstrate that the NextEval solver outperforms the state-of-the-art DFO baselines in function-evaluation efficiency.

cs.LG · cs.AI · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36552

A Fibrational Framework For Multi-Conclusion Logics

Oualid Merzouga

原文摘要

The fibrational framework of Licata, Shulman, and Riley gives a uniform account of substructural and modal logics for single-conclusion judgments. This thesis extends it to a multiple-output setting in which cartesian resources may be used freely, while linear resources occur exactly once on either side of the turnstile. Judgments are indexed by descriptors: directed acyclic graphs generalizing the tree-shaped expressions of the original framework. Descriptor $2$-cells encode structural transformations, and explicit gates provide weakening, contraction, coweakening, and cocontraction at linear modes. We define a sequent calculus generated by two connectives, $F$ and $U$. We prove identity, invertibility, and cut admissibility. The mode and type theories present linear-non-linear $2$-polycategories, and the projection is a local discrete fibration and a fibration, with opcartesian lifts for one-output base morphisms. Two adequacy results validate the framework: its cartesian fragment recovers the Licata--Shulman--Riley calculus, and its purely linear fragment presents classical multiplicative linear logic. Finally, we relate indexed and fibrational semantics. After developing representability correspondences for multicategories and polycategories, we establish an equivalence between multi-indexed categories over a fixed representable base and multicategorical opfibrations over it, together with its biclosed bifibrational version. We also give a one-way construction from planar poly-indexed data to a planar polycategory.

cs.LO · math.LO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.07253

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling

Haewon Jeon, Si-Hyeon Lee

原文摘要

Distilled diffusion models accelerate image generation by reducing the number of denoising steps, but often suffer from degraded image quality. To mitigate this trade-off, test-time optimization methods improve quality, yet their iterative nature incurs substantial computational overhead and leads to slow inference, limiting practical usability. Recent hypernetwork-based approaches amortize this process during training, but still require costly noise modulation in high-dimensional latent spaces. In this work, we propose LENS (Low-frequency Eigen Noise Shaping), an efficient noise modulation framework that operates in a low-dimensional subspace. Our approach is motivated by the observation that low-frequency components of the noise largely determine the global structure and visual fidelity of generated images. Based on this observation, we provide a theoretical justification for restricting modulation to the low-frequency subspace and derive a principled training objective. Building on this, LENS employs a lightweight, standalone network to selectively modulate these components, enabling efficient and targeted noise modulation. Extensive experiments demonstrate that LENS achieves competitive image quality while reducing FLOPs by 400-700$\times$, model parameters by 25-75$\times$, and inference-time overhead by 10-20$\times$ compared to prior methods.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36549

From Ripple to Boundary: Deterministic Impact Boundaries for LLM-Based Software Evolution

Jiameng Bai, Zhitao Wang, Long Wang, Keyue Qian, Sai Wu

原文摘要

The landscape of software evolution is undergoing a fundamental paradigm shift in the era of Large Language Models. As industrial software engineering increasingly transitions toward design-driven code generation, the bottleneck of evolution has shifted from writing code to maintaining deterministic consistency between multi-source design assets and the generated code. When a design asset changes, LLM agents currently lack a deterministic impact boundary to identify which code must be evolved, leading to two failure modes: hallucinating missing changes or breaking unrelated code. Probabilistic approaches, such as semantic retrieval or text-based diffing, fail to capture the structural ripple effects across highly interconnected design dependencies. We propose a paradigm shift from probabilistic semantic retrieval to deterministic structural constraints. We formalize the design assets as a Knowledge Graph and organize them into a content-addressed Merkle Directed Acyclic Graph. Our key insight reveals a topological isomorphism between the logical ripple effect of a design change and the content hash propagation within the DAG. By leveraging this, we transform ambiguous impact analysis into verifiable structural operations. Through a two-phase diffing process, we extract a precise causal chain that defines the exact modification boundary. This boundary enables deterministic context injection, providing the LLM agent with strictly scoped code and design updates, thereby fundamentally guaranteeing evolution safety. As a by-product, the KG serves as an agent-queryable knowledge base for natural language design retrieval. Early deployment on a large-scale industrial payment platform demonstrates that our mechanism successfully computes the exact impact boundary and generates incremental code modifications that meet the expected design changes.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.34717

ReMCTS: Reflection-Enhanced Monte Carlo Tree Search for Code Generation

Huifei Wang, Xinying Huang, Yiheng Sun, Yifan Yuan

原文摘要

Open-weight large language models (LLMs) can generate function-level programs from natural-language prompts, but plausible candidates still fail on hidden semantics and repeat mistakes across repair attempts. We present ReMCTS, an execution-grounded, memory-augmented, LLM-guided MCTS-style search framework. It organizes program candidates as tree states, retains branch-local debugging context, retrieves failure experience across branches, and distinguishes failed checks from unavailable evidence. On HumanEval and MBPP-Sanitized, visible-test ReMCTS improves over direct generation in 8 of 10 model-dataset pairs under held-out evaluation, whereas proxy-only search is less stable. Controlled tree-search, sampling, repair, and memory ablations characterize the source and limits of these gains. A 30-task HumanEval-X C++ pilot further demonstrates compatibility with compiler-backed execution, but does not constitute a broad multilingual evaluation.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.09711

Decoupling and Regulating Modality Heads for Fake News Detection under Missing Modality

Kai Qian, Dongjie Xu, Weijie Shi, Yezhao Xu, Jiaqi Wang, Hanghui Guo, Mengze Li, Hao Chen 等 10 位作者

原文摘要

Multimodal news often loses its image or text as it spreads across Web platforms. Existing methods treat this as information loss, assuming that the original veracity label remains valid. This assumption fails for fake news reusing authentic material, whose remainder may be genuine. Detectors should therefore decouple modality cues and adapt their contributions to modality availability. However, coarse news labels provide little guidance for modality decoupling, while training on complete inputs can induce modality bias. To address these challenges, we propose Head-wise Modality Decoupling and Regulation (HMDR) for multimodal large language models. First, Modality Head Decoupling decouples modality representations through soft head grouping by attention preference. Second, Modality Head Regulation regulates head contributions through compensation for imbalanced modality attention. Finally, Modality Invariance Learning promotes invariance by aligning representations of each modality under replacement of the other. Experiments on two benchmarks show that HMDR improves detection under missing modalities while maintaining performance on complete inputs.

cs.CV · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36546

REACH: An RL-based Hierarchical Humanoid Crawling Framework for Adaptable Confined Space Traversal

Shengzhang Xu, Yuning Guo, Weiyi Hu, Yifeng Xu, Zhongyu Li, He Kong

原文摘要

Humanoid locomotion in confined spaces requires continuous height adaptation across multiple locomotion modes, yet most existing approaches remain limited to height-adjustable bipedal walking. We present REACH, an RL-based hierarchical framework that enables a Unitree G1 humanoid to traverse height-constrained environments through variable-height bipedal walking and crawling. The learned policies regulate body height over complementary command ranges of 1.27--0.50m in bipedal locomotion and 0.50--0.20m in crawling, extending traversal beyond the clearance limits of crouched walking. To address contact-rich crawling dynamics, we employ a history-conditioned latent representation that captures terrain and interaction context from proprioceptive observations. A bidirectional transition policy further connects crouched bipedal locomotion and crawling across large kinematic and contact discontinuities. These skills are integrated with a LiDAR-based planner that converts point clouds into collision-free three-dimensional paths and locomotion commands, including proactive height targets. Real-world experiments demonstrate accurate height and velocity tracking, robust terrain traversal, reliable mode transitions and confined-space traversal with the proposed pipeline. Code and videos are available at https://aislab-sustech.github.io/REACH/.

cs.RO · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36545

FrameForge: Harnessing Priors of Video Foundation Models for Unified Image Generation

Yijing Lin, Zhendong Mao, Mengqi Huang, Yuxin Wang, Yongdong Zhang

原文摘要

Unified conditional image generation requires a single model to preserve different aspects of a visual input while producing a target image that follows textual guidance. Pretrained video models provide a promising foundation by encoding visual transformations across time, yet existing approaches typically assign the condition and target to fixed temporal positions. We observe that condition-target relationships vary substantially across generation tasks, while video models naturally encode visual transformations at different temporal scales. This motivates us to model conditional generation through input-adaptive virtual temporal relations. We propose FrameForge, which captures two complementary aspects of such relations. Mixture of Temporal Experts (MoTE) models how far the target lies from the condition by dynamically selecting temporal RoPE experts with different virtual intervals. Chain of Temporal Reasoning (CoTR) models how the condition is transformed into the target through a latent intermediate state, with a reasoning-bottleneck attention mask encouraging information propagation through this pathway. Experiments on DreamBench, COCO2017, and GEdit-Bench show that FrameForge achieves superior performance in subject-driven generation, canny-to-image generation, and image editing among the compared methods.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36544

Monitor Accuracy Isn't the Answer: A Decision-Value Framework for Evaluating AI Agent Restart Monitors

Siddharth Jain, Venkat N. Vedam

原文摘要

Classification accuracy and true-positive and false-positive rates alone do not determine whether restarting an AI agent improves success. Using an exact alarm-to-action identity for an independent clean restart with zero monitoring overhead, we show analytically that a monitor with fixed 90% accuracy can swing from a five-percentage-point loss to a 35-point gain in success rate, depending solely on the replacement attempt's residual-budget success probability. We introduce two techniques for evaluating monitors and progress signals in decision terms. First, a decision-sufficiency criterion, a binary-action specialization of Blackwell's comparison of experiments, identifies which progress representations or context compressions preserve the information needed to choose optimally between the specified actions, independent of trace reconstruction fidelity. Second, a finite-sample, model-free decision audit uses simultaneous binomial intervals over existing agent traces, without additional model calls, to certify a preference for "restart" or "continue" under stated sampling assumptions, or return "abstain" when evidence is insufficient. In a study of 194 externally evaluated model executions on 19 selected SymPy issues from SWE-bench Verified, with a retrospective audit of 130 of these traces, we find a 2.29-point apparent restart gain traced to a single surviving failed attempt, a 42.29-point loss on a second model, no positive replication of the initial gain on prospective task cohorts, and zero of 103 live comparisons meeting the specified certification bar. These results motivate evaluating agent monitors through recovery opportunity, decision value, and statistical support, rather than classification performance alone.

cs.AI · cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.05900

Tri-Directional Sub-Voxel Displacement and Ipsilateral Territory Decoupling for 3D Bilateral Coronary Artery Tree Reconstruction in Cardiac CT Angiography

Zeyu Jia, Xin Ming

原文摘要

Three-dimensional (3D) reconstruction of bilateral coronary trees (LCA and RCA) from cardiac computed tomography angiography (CCTA) is limited by $64^3$ quantization, territory ambiguity, and sinotubular junction (STJ) ostium drift. We present a canonical $SO(3)$ $(Z_{\text{IS}}, Y_{\text{PA}}, X_{\text{LR}})$ multi-scale representation and Riemannian arborescence framework. First, we map 3D CCTA volumes, centerlines, and thick-stem ostia into $(Z_{\text{IS}}, Y_{\text{PA}}, X_{\text{LR}})$ space, extending a 3D pyramid and tubular decoder with a $128^3 \to 64^3$ boundary skip, a stop-gradient $128^3$ residual branch, a sub-voxel displacement head $Δ\hat{\mathbf{u}}(\mathbf{x})$, a dual-channel ipsilateral head $(\mathbf{P}_{\text{LCA}}, \mathbf{P}_{\text{RCA}})$, and a $32^3$ ostium detector. Second, all $327,385$ active parameters ($398,843$ total) are trained in one 30-epoch AdamW loop ($ε= 10^{-8}$) under decoupled L2-SP and aortic-wall consistency. Third, our Riemannian solver couples cylindrical Valsalva-STJ calibration, $128^3$ orifice contact rooting, contralateral inhibition, centripetal pruning, and 4-stage curvature-adaptive normal-plane pullback. On 14 unseen ImageCAS development volumes (28 trees; cases 201--250 isolated), the framework achieves $2.1652 \pm 0.4686\text{ mm}$ $\text{CD}_{\text{CL}}$ ($\text{CD}_{\text{CL}}^{\text{arc1mm}} = 2.2697 \pm 0.4863\text{ mm}$, $p \le 0.00024$; $57.40\% / 78.20\%$ $\text{F1}@1/2\text{mm}$; $4.6880 / 4.9298\text{ mm}$ ostia). Zero-shot evaluation on 50 unseen volumes (Cases 251--300, 100 trees) yields $2.9389 \pm 1.2785\text{ mm}$ $\text{CD}_{\text{CL}}$ ($p \le 7.24 \times 10^{-7}$; ostia $5.1815 / 7.5442\text{ mm}$), with $2.7696 \pm 1.1919\text{ mm}$ pooled $N=64$ $\text{CD}_{\text{CL}}$ and $128/128$ trees ($β_0 = 1, β_1 = 0$, $0.0000\text{ mm}$ detachment).

cs.CV · q-bio.TO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36540

Frugal Handwritten Text Recognition for Sub-Saharan Civil-Status Registers: A Smartphone-to-Records Pipeline with CPU-Only Inference

Ousmane Hamadou, Jean Louis Kedieng Ebongue Fendji

原文摘要

Handwritten civil-status registers across Sub-Saharan Africa are being photographed, but rarely transcribed, which leaves their content unsearchable. Local administrations that hold these records typically lack GPUs, reliable connectivity, and the budget for external digitization services. We study whether a frugal pipeline, with consumer-grade capture and CPU-only inference, can make such registers machine-readable. We introduce a corpus of 1,568 smartphone images of handwritten birth, marriage, and death records from a municipal registry in Chad, with line-level transcriptions for 185 records. Our pipeline combines a fully classical preprocessing stage, running at about 2.6 images per second on a laptop CPU, with a Kraken recognizer fine-tuned from a pretrained model in about four hours on a free-tier GPU. On manually corrected line segmentation and a single-registry test set whose writers also appear in training, fine-tuning reduces the character error rate from 14.53% to 0.50% and the word error rate from 56.93% to 3.37%. On a six-core laptop CPU, the recognizer weighs 61 MiB, peaks below 2.6 GiB of RAM, and processes an image in under 6 s on average, excluding line segmentation. Finally, a downstream information-extraction task shows that residual recognition errors cost about 9 F1 points on role resolution, compared with a 36.7-point gap between the fine-tuned and pretrained recognizers. These results are evidence of feasibility within one registry, not of performance on unseen writers or sites; we discuss these limitations in detail.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36539

CARD: Contact-Anchored Retargeting for Dexterous Manipulation

Cunxi Dai, Botao He, Francois R Hogan, Wei Pan, Rao Fu, Wen-Han Hsieh, Ruoshi Liu, Jitendra Malik 等 10 位作者

原文摘要

Humans are dexterous, but traditional data collection paradigms such as teleoperation, kinesthetic teaching, and wearable exoskeletons force humans to act through the robot embodiment, bottlenecking their dexterity. Retargeting can avoid this. However, existing methods purely translate kinematics and ignore the real-world contact dynamics essential for task success. We present CARD: Contact-Anchored Retargeting for Dexterous Manipulation, a simulator and object-free framework that retargets human demonstrations to robot actions by explicitly preserving contact force targets. CARD unifies human data sources including video, motion capture, and tactile gloves via a shared hand--contact representation and generates explicit contact force targets in addition to kinematics. To maintain these contact targets during hardware open-loop replay, CARD also introduces a tactile tracking controller. For video which does not contain an explicit tactile source, we present Vid-HACO, our video-to-contact model that sets a new state of the art. Across our video, glove, motion-capture data and the GRAB dataset, CARD maintains contact on $43$--$78\%$ of contact frames, while kinematic and object-aware retargeting methods maintain contact on $<10\%$ and $22.5\%$ of the frames respectively. Further, integrating our formulation into physics-based retargeting improves strict task success from 12\% to 32\% on GRAB dataset. Finally, we show that our tactile tracker is critical to reproduce demonstrated forces during hardware replay. These results establish that contact preservation via CARD is a scalable interface for transferring human dexterity to robots.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.01535

Can LLMs Design Video Coding Tools? A Case Study on Planar Mode

Yingwen Zhang, Meng Wang, Liqiang He, Shiqi Wang

原文摘要

This paper explores whether large language models (LLMs) can design video coding tools, a highly challenging task due to the intricate algorithmic coupling of tool modifications. In particular, we present an empirical case study on the Planar mode, a long-standing intra prediction tool in video coding standards. Our experiments operate within a generation-and-evaluation loop, with the LLM generating new Planar predictors, encoder trials evaluating their coding performance, and the LLM re-generating refined implementations based on the evaluation feedback. We first examine directly replacing the default Planar mode in the Fraunhofer Versatile Video Encoder (VVenC) under its faster preset. Experimental results demonstrate that the LLM-generated mode can outperform the conventional Planar mode on this lightweight toolset, achieving 0.18% bitrate savings with 0.4% complexity overhead on the standard benchmark. We further extend our evaluation to the Enhanced Compression Model (ECM). Leveraging newly introduced directional Planar modes, we investigate two integration strategies: directly replacing them, and introducing the LLM-generated predictor as an additional prediction mode with new syntax elements. The empirical results suggest that both strategies can yield coding gains under a constrained low-resolution setting. Overall, this study offers preliminary evidence and practical insights, highlighting both the potential and open challenges of LLM-based coding tool design.

cs.MM · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36538

Agentic Data Sampling Framework for Multilingual ASR via Linguistic-Aware Error-Driven Optimization

Zhiheng Qian, Junhao Du, Jing Peng, Hanqi Li, Kunyang Peng, Feng Xue, Lu Chen, Shuai Wang 等 9 位作者

原文摘要

Multilingual automatic speech recognition (ASR) has achieved rapid progress with large-scale pretrained models and multilingual speech corpora. Previous studies have proved the pivotal role of data sampling in multilingual ASR performance. However, existing sampling strategies largely rely on time-consuming manual, heuristic-based designs. To address this, we propose a linguistic-aware agentic data sampling framework that introduces a data sampling LLM agent into the multilingual ASR training loop. The agent is driven by a linguistically grounded rubric that analyzes ASR errors from phonological, morphological, and syntactic perspectives, and uses the resulting feedback together with training statistics, budget constraints, and algorithm profiles to select domain-level, sample-level, and token-level sampling strategies. This design turns evaluation results into adaptive data-selection decisions rather than treating WER/CER as final endpoints only. We conduct experiments on twelve Common Voice languages using Whisper-medium as the ASR backbone. The results show that our method achieves performance better than full-data training while using less training data and lower computational resources, and substantially accelerates training convergence compared to conventional sampling strategies.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36537

A Trainable CPG Framework for Quadruped Locomotion

Xiyan Liu, Yide Liu, Guoyong Mao, Shaoxing Qu

原文摘要

We present CPG-ENN, a trainable quadruped locomotion framework that combines explicit neural coordination with learned motion realization. A symmetry-based eight-neuron central pattern generator (CPG) supplies gait rhythms, while a pretrained, leg-shared execution neural network (ENN) maps rhythmic signals and trajectory parameters to joint-angle references without online inverse kinematics. A single feedback policy modulates six physically interpretable trajectory variables. The controller tracks planar velocity and yaw-rate commands across walk, trot, pace, bound, and pronk without gait-specific learned controllers or transitionspecific rewards. Simulation yields 99.39% transition success across all 20 directed gait pairs and all source-target speed combinations from {0.5, 1.0, 1.5, 2.0} m/s. Ablations show high cross-gait transition feasibility without transition exposure; exposure mainly refines gait acquisition and velocity transients. Comparisons with AllGaits and Walk These Ways demonstrate accurate gait-speed regulation, and Unitree Go2 experiments validate five gaits and their transitions.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36536

ATEMPO: Amortized Transfer Entropy Model Per-lag Over-Time

Emma Andrews, Konstantinos Kontras, Keane Ong, Maarten De Vos, Gianmarco Mengaldo

原文摘要

When one time series informs another, the delay at which that information arrives determines if models can utilize this relationship effectively. A source may be strongly predictive at one delay but uninformative at neighboring lags. Therefore, fixed input windows may obscure this structure by bundling informative and irrelevant history together. Transfer entropy (TE) can recover these delays by measuring how much a source's past tells us about a target beyond the target's own history. However, existing TE estimators require separate fits across source and conditioning depths, with further estimation on local windows to track delays over time. We introduce ATEMPO, a diffusion-based transfer entropy estimator amortized over lags and time. ATEMPO samples source and conditioning depths during training and conditions explicitly on temporal position, allowing a single score network to estimate the full TE surface without refitting. Differencing this surface recovers when information arrives. Across synthetic datasets with ground-truth TE, ATEMPO achieves the lowest mean relative error among baselines, and on datasets with known lags, ATEMPO achieves $0.98\pm0.04$ lag-detection accuracy. ATEMPO requires a single fit across source depths $K=6$ and target depths $L=3$, whereas existing estimators require 18 separate fits to evaluate the same $K \times L$ grid. On three systems with time-varying lag, ATEMPO is the only estimator to maintain perfect lag-detection accuracy across all evaluated window lengths. These recovered delays improve downstream forecasting: ATEMPO-selected inputs reduce MSE on WEATHER-5K by 7.25%, while 120 selected inputs outperform the benchmark's 240-feature default by 4.6%. Across three brain-computer interface tasks, using ATEMPO-selected lags achieves the highest or equal-best $R^2$, improving mean $R^2$ by 4.1% relative to the strongest competing baseline.

cs.AI · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36535

A generalist policy for muscle-driven control across individual musculoskeletal models

Bozhao Wang, Yinglei Zhu, Zhengqing Li, Zhiyuan Zhang, Weiming Kang, Yiheng He, XinJun Liu, Senhao Hou 等 10 位作者

原文摘要

Differences in musculoskeletal morphology challenge policy reuse across individual models. Here we present MyoAcross, a framework that combines individual musculoskeletal model construction, morphology-conditioned policy learning and virtual model augmentation to enable muscle-driven reference-motion tracking without individual fine-tuning. Across 31 held-out individual models, MyoAcross achieves 99.24% completion and reduces tracking error by 22.60% relative to an unconditioned multilayer perceptron. Policy reuse extends to models of 22 held-out individuals constructed from single-image shape estimates, measured height and body mass, and, with asymmetry augmentation, to models with unilateral lower-limb shortening. Comparisons with measurements from 120 additional individuals show agreement in vertical ground reaction force waveforms and channel-dependent agreement between simulated muscle activations and electromyography. These results establish a common control framework for comparative simulations of movement across individual musculoskeletal models.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36534

Resolvent Components of Completely Normal Elements and the Morgan-Mullen Conjecture

Florian Hellwig

原文摘要

We study completely normal elements of $\mathbb{F}_{q^n}$ over $\mathbb{F}_q$, that is, elements that are normal over every intermediate field, through partial Lagrange resolvents. In these coordinates, complete normality splits into independent components, in a way closely related to Hachenberger's decomposition theory. Our main tool is a structure theorem for the components; together with a finite computation, it shows that every component has weight (the sum of the probabilities that its conditions fail) at most $3/4$, uniformly in $n$. Combined with the classical character-sum method, this gives an explicit sufficient criterion for the existence of a primitive completely normal element. We verify the criterion for all $n>q$ except $70$ pairs $(q,n)$, which we settle with explicit elements found by computer. Since the case $n\le q$ is due to Garefalakis and Kapetanakis, this confirms the conjecture of Morgan and Mullen (1996) that every $\mathbb{F}_{q^n}$ contains a primitive element that is completely normal over $\mathbb{F}_q$. The same estimates give two further results. First, Hachenberger's conjectured lower bound $(q-1)^{n'}q^{(p^a-1)n'}$ for the number of completely normal elements, where $p$ is the characteristic and $n=p^an'$ with $p\nmid n'$, holds for all pairs $(q,n)$; it was known for regular pairs. Second, primitivity and complete normality are asymptotically independent: as $q^n\to\infty$, the number of primitive completely normal elements is $(1+o(1))\,\varphi(q^n-1)/(q^n-1)$ times the number of completely normal elements (for fixed $n$ this was known). Our arguments for $n>q$ and for both further results, including every computer-assisted step, are formalised in Lean 4 with Mathlib; the formalisation takes the case $n\le q$ and an explicit bound of Rosser and Schoenfeld as axioms and relies on Lean's compiler and interpreter for the large computations.

math.NT · cs.LO

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arXiv ↗PDF ↗
版本更新 · v2arXiv: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 which architectural components drive this acceleration and whether transferred circuits remain stable under continued optimization remain unclear. Across a scale-matched 108-run factorial battery ($N=12$ paired seeds), transferring internal attention/MLP matrices ($B$) alongside representations ($E+U$) lifts early accuracy by $5.46$ percentage points and cuts validation confirmation latency by $617$ steps (Holm $p=0.0039$). Whereas readout plus internal blocks ($U+B$) matches full transfer within $\pm 500$ steps in 1-layer models (TOST $p=2.91\times 10^{-13}$), a 2-layer replication confirms internal-block acceleration ($+704.67$ units, $p=4.88\times 10^{-4}$; $8.1\times$ faster trigger) while exposing an inter-block interface where omitting embeddings incurs a $-4{,}475.6$-unit deficit (TOST $p=1.00$). Under continued optimization, unshielded runs suffer severe post-grokking relapse. Freezing carriers ($E+U$) suppresses 1-layer drawdown ($22.06\% \to 0.60\%$ on $2a+b$; $28.56\% \to 5.09\%$ on $3a+b$; $11.09\% \to 0.15\%$ on $a^2+b^2$), but leaves internal blocks vulnerable at high step sizes. Dissecting 12 post-trigger schedules shows that Curvature-Preconditioned Bounded Step-Budget Consolidation ($S_H \approx 0.02, λ=1.0$, preserved $v_t$)---via 201-step micro-stepping ($86.6\%$ lower active compute) or exponential decay ($τ=200$)---compresses drawdown to $0.08\%$--$0.17\%$ on 1-layer $2a+b/3a+b$, $0.13\%$--$0.20\%$ on 2-layer $2a+b$, $0.02\%$ ($66.81\% \to 0.02\%$, Holm $p=0.0020$) on unseen pure-FP32 2-layer subtraction ($a-b$), and $0.09\%$--$0.18\%$ (Holm $p=0.031$) on non-abelian $S_5$ where carrier freezing fails ($10.73\%$ drawdown, $p=0.273$).

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36533

RoboAuto: Autonomous Robot Program Search through Multi-Level Physical Understanding

Buzhao Liu, Jianing Ye, Xiu Yuan, Yangtong Feng, Chengsong Huang, Tyler Dinh, Yevgeniy Vorobeychik, Ning Zhang 等 9 位作者

原文摘要

Recently, LLM-based autonomous agents have shown strong capabilities in iteratively generating, evaluating, and refining executable solutions. In robotic manipulation, Code-as-Policy lets these agents search over modular and editable robot programs. However, effective program search requires physical understanding: agents must understand how objects can be manipulated, how scene geometry constrains actions, and why interactions succeed or fail. Existing methods rely mainly on execution feedback for program refinement, often leaving these physical questions unresolved. We introduce RoboAuto, a framework for robot program search that explicitly organizes physical understanding at the object, scene, and interaction levels through three components. First, a hierarchical knowledge base combines executable skills with object-centric knowledge of affordances, spatial relations, and action effects, learned from embodied data and online task solving. Second, scene grounding connects this knowledge to the current environment by identifying relevant objects and resolving action-critical quantities. Third, multi-stage planning with self-designed experiments checks whether each stage produces its expected physical effects and actively tests competing hypotheses to resolve physical uncertainties. RoboAuto achieves superior performance across five benchmark families, attaining an 89.1% success rate on short-horizon LIBERO-PRO tasks and more than twice the mean success rate of ASPIRE on LIBERO-PRO Long and LIBERO-Plus. It also outperforms ASPIRE and $π_{0.5}$ on three challenging long-horizon real-robot tasks requiring precise manipulation. We further show that the knowledge base transfers across simulators and embodiments, and that program search performance scales with its size. Project page: https://roboauto-ai.github.io/

cs.RO · cs.AI · cs.CL · cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2607.11976

LiteTopK: Exploiting the Curse of Dimensionality for a Fused Indexer-TopK Kernel in Long-Context Sparse Attention

Ziqi Yin, Jianyang Gao, Peiqi Yin, Jiangneng Li, Gao Cong

原文摘要

Indexer-TopK, which computes scores and selects the top-$k$ candidates, is a key primitive in long-context sparse attention for large language models and vector retrieval for recommendation systems and vector databases. However, existing GPU-based Indexer-TopK kernels like DeepSeek Sparse Attention (DSA) typically separate scoring from selection and materialize the full score matrix in high-bandwidth memory. This strategy is inefficient for large score matrices, such as those encountered in long-context sparse attention, due to excessive global-memory traffic, costly synchronization, and prohibitive memory overhead. In this study, motivated by the curse of dimensionality, we observe that sparse attention scores exhibit a concentration phenomenon, with scores falling within a narrow range. Based on this observation, we propose LiteTopK, an efficient fused Indexer-TopK kernel. LiteTopK samples a small subset of data to estimate query-data score ranges, then partitions candidates into bins accordingly. This design allows LiteTopK to maintain a slightly relaxed threshold online and write back only promising candidates, reducing unnecessary I/O and memory overhead while preserving exact top-$k$ correctness. Building on LiteTopK, we further propose LiteDSA, which packs the overlapping candidates of neighboring query tokens into a shared key/value list for joint attention and uses per-query masks to preserve each token's original top-$k$ membership while reducing memory traffic. We integrate LiteTopK into three widely used LLM training and inference systems: vLLM, SGLang, and Megatron-LM. Experiments in a production environment with eight NVIDIA B200 GPUs demonstrate prefill speedups of up to $1.49\times$ across GLM-5.2, DeepSeek-V4-Flash, and Hy-4-Preview at a context length of 1M tokens, without compromising model capabilities.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.15773

TS-DP: Reinforcement Speculative Decoding For Temporal Adaptive Diffusion Policy Acceleration

Ye Li, Jiahe Feng, Yuan Meng, Kangye Ji, Chen Tang, Jiajun Fan, Xinwan Wen, Jiawei Li 等 12 位作者

原文摘要

Diffusion Policy (DP) excels in embodied control but suffers from high inference latency and computational cost due to multiple iterative denoising steps. Existing diffusion model (DMs) acceleration methods improve inference speed by reducing computational overhead, as DMs for image generation are computation-bound. In this work, we reveal that DP are I/O bound, and thus propose using speculative decoding, a parallel computing framework, to accelerate DP for the first time. However, it is non-trivial to address the following challenges: (1) How to preserve the denoising quality of the base model at reduced computational cost under time-varying task difficulty in embodied settings; and (2) How to dynamically allocate computation according to task difficulty in such environments. Therefore, we propose Temporal-aware Reinforcement-based Speculative Diffusion Policy (TS-DP), the first framework that enables speculative decoding for DP with temporal adaptivity. First, to handle dynamic environments where task difficulty varies over time, we distill a Transformer-based drafter to imitate the base model and replace its costly denoising calls. Second, an RL-based scheduler further adapts to the time-varying task difficulty by adjusting speculative parameters to maintain accuracy while improving efficiency. Extensive experiments show that TS-DP achieves up to a 4.17x NFE reduction and a 3.2x wall-clock speedup with a draft acceptance rate of over 85% in simulation. Real-world experiments further demonstrate a 3.8x NFE reduction while improving success rates on real-time tasks (e.g., a 23% gain on the conveyor-belt task), reaching an inference frequency of 26 Hz and enabling real-time control while maintaining comparable task performance. The code is available at https://github.com/ChildTang/TS-DP.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2610.03827

The Score Is Not the Structure: Brain Alignment and Cross-Lingual Transfer

Saman Rahbar

原文摘要

Similarity scores are often offered as evidence that a model shares structure with the brain or across languages. We ask what such a score reads when that structure is removed, or when the instrument measuring it does not work, in two settings. Across seventeen languages, a grammaticality probe transfers worse between more distant languages (r = -0.66). But the probe's own accuracy falls along the same axis and is at chance in four languages, four of the five most distant. Dropping them halves the explained variance, and because it also narrows the range of distances, the design cannot say how much of the gradient is the instrument. Counting the 272 language pairs as independent gives p = 0.0006 for a steering effect that is null when the seventeen languages are the unit (p = 0.155). In brain alignment, a training objective raises a language model's similarity to fMRI responses from 0.10 to 0.34 (reliability ceiling 0.54), yet targets with the brain correspondence destroyed still reach 0.31. A shuffled target scores about k/n against the real one, for rank k and n sentences, a baseline that can be computed before any model is trained. Both interventions work, yet we detect no distance-graded steering effect and no syntactic benefit from alignment. Before reading a correspondence score, measure how much of it survives without the correspondence.

cs.CL · cs.AI · cs.LG · q-bio.NC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.01271

Choosing Coding-Agent Benchmarks for Practice: Lessons from Task Demands and Agent Behaviour

Radin Shayanfar, Keheliya Gallaba, Ahmed E. Hassan

原文摘要

Software teams evaluating coding agents need evidence that reflects the changes they expect those agents to make. Benchmark scores summarize how often an agent resolves tasks under broad labels such as bug fixing and feature implementation, but provide little guidance on whether the evaluated work resembles a team's workload. To support this decision, we characterize 2,487 tasks from five public benchmarks and analyze 14,922 runs of six agent configurations. We introduce the Spread-Novelty-Centrality (SNC) profile to describe how the reference solution is distributed across files and directories, how much it adds relative to what it removes, and the dependencies, history, and complexity of the code it touches. We also compare the scope of agent patches and their file exploration. Every pair of benchmarks differs on at least two SNC axes, including benchmarks with the same task label. Among tasks resolved by Claude Opus 4.6, patches are larger than the reference on FeatBench and smaller on FeatureBench. Across the six configurations, resolved runs tend to involve less distributed changes and code with lower values on the Centrality indicators, while the patch sizes associated with resolution differ between the Claude and Qwen configurations. These observations suggest that practitioners should select evaluation tasks based on their change characteristics, inspect the information provided in task descriptions, and assess patch scope alongside test outcomes. We explain how teams can apply this analysis to completed changes and identify the additional evidence needed before using it for task assignment or review automation.

cs.SE · cs.CL

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