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版本更新 · v3arXiv:2608.10805

Fast and Memory-Efficient Wavelet Convolutions via I/O-Aware Reformulation

Amit Aflalo, Shahaf E. Finder, Roy Amoyal, Eran Treister, Oren Freifeld

原文摘要

Wavelet convolution (WTConv) has emerged as an increasingly popular drop-in replacement for standard convolutions, expanding a network's receptive field exponentially with the number of decomposition levels while keeping the parameter count linear. However, its reference implementation is severely memory-bound due to excessive data movement through high-bandwidth memory (HBM). We develop an I/O model of WTConv to characterize this bottleneck and use it to guide three algebraic reformulations: (1) recomputing the inexpensive Haar analysis butterfly on chip, (2) collapsing the multi-level synthesis cascade into a single closed-form pass indexed by output-coordinate bits, and (3) folding learned per-channel scales into the convolution weights. Together, these reformulations enable an I/O-aware fused implementation that substantially reduces HBM traffic. We evaluate the WTConvNeXt configuration across decomposition levels and a broad range of tensor shapes. Despite performing comparable arithmetic, the reference WTConv is substantially slower than the depthwise convolution it replaces. Our reformulation reduces modeled HBM traffic by approximately $2.55\times$, yielding up to a $4.35\times$ training speedup over the reference while roughly halving peak memory usage. Thus, our reformulation preserves the benefits of WTConv while substantially reducing its execution time and memory footprint, removing the systems overhead that previously limited its practical efficiency.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.22215

FutureLift: Lifting Trajectory Proposals into Bidirectional Future Graphs for Motion Forecasting

Daniel Grimm, J. Marius Zöllner

原文摘要

Accurate motion prediction is critical for safe autonomous driving, yet even strong forecasters leave residual errors in their multimodal proposals. Existing refiners compress complete proposals or update their segments sequentially, preventing the full proposed future from serving as explicit relational context for correction. We introduce FutureLift, a backbone-agnostic refiner that represents each multimodal proposal as a heterogeneous future graph, with spatially grounded temporal nodes connected to agent histories, map elements, alternative modes, and, for joint prediction, other agents future trajectories. Bidirectional temporal edges allow later evidence to revise earlier segments in a single pass, and the updated nodes predict residual coordinate and confidence corrections. Requiring only proposals, scores, and scene context, FutureLift provides a common refinement interface for structurally different forecasting backbones. On Argoverse2 and the Waymo Open Motion Dataset, it consistently improves frozen proposals and outperforms native and specialized refiners across marginal and joint forecasting. On the WOMD motion-prediction leaderboard, FutureLift ranks first in minADE (0.5049m) and minFDE (0.9942m) among the compared standard-input methods, without LiDAR or ensembling, while achieving a competitive 0.4874 Soft mAP.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06160

DSRS: Teaching MLLMs to Perceive, Reason, and Segment in Complex Driving Scenes

Sicen Guo, Zhengfei Song, Kuanhsun Chen, Xu Huang, Ping Zhong, Wei Ye, Shaoyi Du, Rui Fan

原文摘要

Existing reasoning segmentation frameworks typically employ multimodal large language models (MLLMs) with broad world knowledge to extract semantics in a human-like manner and generate corresponding segmentation masks. Nevertheless, these frameworks lack the necessary scene-specific awareness. This paper introduces driving scene reasoning segmentation (DSRS), a scene-specific task that requires models to interpret implicit instructions, infer spatial relations, detect potential risks, handle challenging conditions, and comprehend underlying traffic rules, ultimately generating accurate segmentation masks. Meanwhile, we establish a benchmark dataset to facilitate future research on the DSRS task. To pioneer this task, we propose DSRS-Net, a novel architecture with advanced reasoning segmentation capability for automated driving. Specifically, DSRS-Net seamlessly infuses geometric features for deeper reasoning segmentation, without compromising the original multimodal fusion mechanism. This strategy effectively mitigates the modality representation mismatch in MLLMs caused by RGB-only pretraining, while enhancing the integration of geometric, contextual, and linguistic features. Extensive experiments demonstrate the superior performance of DSRS-Net in both driving scene reasoning segmentation and other language-driven segmentation tasks across various public datasets. The source code and dataset will be released upon publication.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06151

CARM: Conformal Adaptive Risk Masking for Reliable Scientific Super-Resolution

Guangtao Zhang, Haonan Wu, Junxuan Gao, Zhengyang Liu, Zhaoxi Zeng, Zhihao Li, Jianguo Huang, Zhiqiang Liu

原文摘要

Deep learning-based super-resolution (SR) can reconstruct fine-scale Earth-observation fields from coarse observations, but reconstruction accuracy alone does not provide a calibrated rule for identifying reliable spatial regions. Conformal Mask Calibration (CMC), built on conformal risk control, converts a spatial uncertainty score into a trust mask and controls the expected worst-case local distortion under exchangeability. However, its fixed threshold can become miscalibrated in Earth-observation streams affected by seasonality and episodic events. We propose $\textit{CARM}$ (Conformal Adaptive Risk Masking), a statistical framework for adaptive spatial risk control under temporal distribution shift. CARM maintains experts with heterogeneous update rates and aggregates their threshold proposals online using an asymmetric surrogate loss, balancing rapid adaptation to distribution changes against stability during relatively stationary periods. All updates are causal and use only mature feedback available after a fixed delay, allowing CARM to operate with any SR model that provides a spatial uncertainty score. We prove that the time-averaged mask-conditioned risk converges to at most $α$ at an $O(1/T)$ rate, without distributional assumptions and for any choice of aggregation weights across experts. On a daily South China Sea chlorophyll-$\textit{a}$ SR stream, CARM reduces the empirical violation rate from $43.49\%$ to $9.38\%$ and the moving-average exceedance frequency from $47.29\%$ to $1.22\%$ at $α=0.10$, while retaining a non-trivial trusted region. Experiments on ERA5 wind and OSTIA sea-surface temperature further show consistent relative improvements across multiple SR backbones.

cs.AI · cs.LG · math.PR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.20482

ConStruM: A Structure-Guided LLM Framework for Context-Aware Schema Matching

Houming Chen, Zhe Zhang, H. V. Jagadish

原文摘要

Column matching is a central task in reconciling schemas for data integration. Column names and descriptions are valuable for this task. LLMs can leverage such natural-language schema metadata. However, in many datasets, correct matching requires additional evidence beyond the column itself. Because providing complete schema metadata remains impractical for most LLMs and can obscure the relevant evidence even when it fits, the core challenge becomes to select and organize the most useful contextual information. We present ConStruM, a structure-guided framework for budgeted evidence packing in schema matching. ConStruM constructs a lightweight, reusable structure in which, at query time, it assembles a small context pack emphasizing the most discriminative evidence. ConStruM is designed as an add-on: given a shortlist of candidate targets produced by an upstream matcher, it augments the matcher's final LLM prompt with structured, query-specific evidence so that the final selection is better grounded. For this purpose, we develop a context tree for budgeted multi-level context retrieval and a global similarity hypergraph for grouping and differentiating confusable columns. Across HRS-B and PSID-B, ConStruM achieves a mean combined accuracy of 95.4%, compared with 51.6% for the no-context LLM, and ranks first among all eleven evaluated methods on both benchmarks. On MIMIC--OMOP and Synthea--OMOP, attaching it to our Matchmaker reconstruction produces no statistically significant accuracy change. Together, these results show that ConStruM can deliver large gains when matching depends on context while remaining compatible with a strong matcher in the other tested settings.

cs.DB

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06147

SafeStart: Mitigating the Overlooked Safety Cost of Active Expert Reduction in MoE LLM Serving

Erfan Baghaei Potraghloo, Saeid Shokoufa, Seyedarmin Azizi, Arya Fayyazi, Souvik Kundu, Mehdi Kamal, Massoud Pedram

原文摘要

Mixture-of-Experts (MoE) language models send each token through a few of many expert subnetworks. Serving methods make these models faster by running even fewer experts per token, and they are validated on accuracy benchmarks. We show that these methods can weaken the model's refusal of harmful requests, and we introduce SafeStart, which largely restores refusal while keeping most of the speedup. In vLLM on one RTX A6000, keeping about two thirds of the selected experts speeds up offline decoding by 15 to 28% at batch size 8. We find that, at this setting, under a jailbreak that tells the model not to refuse, the share of harmful requests that the model answers rises by 18.4 percentage points on Qwen1.5-MoE, 9.5 on OLMoE, and 6.6 on DeepSeek-V2-Lite, the three of nine models where it rises significantly, while MMLU accuracy drops by less than two points. We discover that most of this increase arises while the model processes the prompt and generates its first few tokens. SafeStart therefore runs all experts for the prompt and the first eight generated tokens, and the reduced set of experts afterwards, through a fused kernel in vLLM. With experts removed, SafeStart keeps at least 85% of the offline decode speedup at batch sizes 8 and 32 and brings the change under this jailbreak to between -2.1 and +1.3 points for three expert-reduction methods on these three models. On 889 held-out requests, in an analysis fixed before generation, truncation raises harmful compliance by 5.8 to 21.4 points, and SafeStart leaves a rise of 0.6 to 2.6 points, non-inferior to the original model on two of the three models. Our results show that faster MoE serving need not cost safety: a serving system can keep most of the gain from running fewer experts by running all of them at the start of each response, and it should test refusal, not only accuracy, before it enables such a method.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06962

Formalizing inverse problems in the age of AI: possibilities, pitfalls, and an invitation

Christian Clason, Tapio Helin, Medet Nursultanov, Lauri Oksanen

原文摘要

Proof assistants such as Lean check mathematical proofs down to the axioms, and AI agents have recently made writing such proofs cheap. We describe AutoIP, a Lean library in which classical linear regularization theory, together with the functional analysis it rests on, has been autoformalized by AI agents. A machine-checked proof certifies only the formal statement it proves, so the remaining human task is to confirm that each formal statement says what was intended. We organize the library around this task: a reviewer compares the formal statements with their natural-language counterparts and never needs to read a proof. Our experience is that the risks lie in the statements rather than in the proofs, and that simple automated tests on the statements can expose problems even in the established literature. We illustrate this with a textbook theorem whose hypothesis is more restrictive than asserted. We invite the inverse problems community to extend and build on the library.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06143

PTQ4SAM3: Semantics-Preserving Post-Training Quantization for Segment Anything Model 3

Xi Xiao, Yunbei Zhang, Lin Zhao, Janet Wang, Xinru Jiang, Yanshu Li, Chenrui Ma, Pan Wang 等 10 位作者

原文摘要

Segment Anything Model 3 (SAM 3) can segment objects from text prompts and track them across video, but its memory and computation costs make deployment difficult. Post-training quantization (PTQ) reduces these costs by using fewer bits. However, a quantized model can select the wrong object or lose track of its target across frames. We call this prompt-alignment drift. Controlled tests show that perturbing low-variance feature directions strongly affects target selection. We propose Semantics-Preserving Quantization (SPQ), which gives more calibration weight to concept-sensitive parameters, preserves residual features, and corrects the mean error in video memory. Fixed text concepts guide calibration without task annotations. All three components operate offline and add no inference operators. With a W4A8 image encoder, SPQ improves RefCOCO Pr@0.5 from 42.1 to 72.5 over PTQ4SAM while retaining 54.8 COCO mask AP. Experiments on LVIS, RefCOCO variants, and SA-V show consistent gains. A CLIP study also shows improved image-text retrieval.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2304.07407

Learning to Incentivize Perfect-Knowledge Agents with Unobserved Rewards

Ilgin Dogan, Zuo-Jun Max Shen, Anil Aswani

原文摘要

Motivated by a number of real-world applications from domains like healthcare and sustainable transportation, in this paper we study a scenario of repeated principal-agent games within a multi-armed bandit (MAB) framework, where: the principal gives a different incentive for each bandit arm, the agent picks a bandit arm to maximize its own expected reward plus incentive, and the principal observes which arm is chosen and receives a reward (different than that of the agent) for the chosen arm. Designing policies for the principal is challenging because the principal cannot directly observe the reward that the agent receives for their chosen actions, and so the principal cannot directly learn the expected reward using existing estimation techniques. As a result, the problem of designing policies for this scenario, as well as similar ones, remains mostly unexplored. In this paper, we construct a policy that achieves a low regret (i.e., square-root regret up to a log factor) in this scenario for the case where the agent has perfect-knowledge about its own expected rewards for each bandit arm. We design our policy by first constructing an estimator for the agent's expected reward for each bandit arm. Since our estimator uses as data the sequence of incentives offered and subsequently chosen arms, the principal's estimation can be regarded as an analogy of online inverse optimization in MAB's. Next, we construct a policy that we prove achieves a low regret by deriving finite-sample concentration bounds for our estimator. We conclude with numerical simulations demonstrating the applicability of our framework to personalized adherence-improving incentive design for long-term statin treatments.

cs.LG · cs.AI · cs.GT · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06141

RDT-3D: Resolution-Decoupled Training for High-Fidelity 3D Geometry Generation

Jiale Zhang, Yang Liu, Chengyang Li, Jin Yuan, Qianxi Jia, Taicheng Huang

原文摘要

High-fidelity 3D geometry generation requires maintaining global structural coherence alongside fine-grained surface details. While recent coarse-to-fine frameworks leverage Rotary Position Embeddings (RoPE) to condition local queries, we reveal a critical training-inference resolution discrepancy: coordinate guidance and geometric supervision are aligned during training, whereas inference decodes high-resolution geometry from coarse spatial coordinates. This mismatch introduces a severe distribution shift that degrades fine details. To overcome this, we propose Resolution-Decoupled Training (RDT), which anchors RoPE guidance at 512 resolution while supervising geometry at 1536, enabling robust detail extrapolation. Additionally, to eliminate high-resolution data bottlenecks, we present Dual3D-300K, a large-scale paired dataset comprising 300K watertight 3D point clouds explicitly aligned across 512 and 1536 resolutions. Extensive experiments demonstrate that RDT substantially improves sharp geometric regions, while Dual3D-300K offers a scalable benchmark for high-resolution 3D generation.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.29711

Decoupling Knowledge and Privacy: Post-Task Self-Distillation Replay for LLM Continual Learning

Shengtao Wen, Yunying Yang, Xiang Chen, Lingbing Guo, Yu Tian, Sheng-Jun Huang

原文摘要

Privacy-preserving continual learning (PPCL) must reduce the reproduction of sensitive content while retaining useful knowledge across sequential tasks. Formal privacy guarantees characterize randomized mechanisms, whereas operational output control concerns whether a trained model selectively reduces the likelihood of sensitive content in its outputs. In this work, we investigate the latter together with continual-learning utility under realistic task evolution. Retention and privacy correction operate at different granularities: task acquisition requires broad preservation of current- and old-task behavior, whereas privacy correction targets sparse annotated positions. Joint optimization leaves the current-task preservation target continually changing. We propose SPARK, a retention-correction decomposition that first freezes the learned post-task distribution and then applies selective correction around this stable reference. Self-Distillation Replay learns the current task while distilling behavior from previous tasks, and Post-Task Privacy Correction reduces annotated-PII likelihood while anchoring current- and old-task non-PII behavior to the resulting checkpoint. Extensive evaluations demonstrate that SPARK achieves effective selective PII suppression while preserving strong continual-learning utility and knowledge retention across diverse settings. Code and data will be released upon publication.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06138

KVGuard: A KV Cache Safety Checker for LLM Serving Systems

Jonguk Cheong, Myungbin Hwang, Eulgyu Kim, Byoungyoung Lee

原文摘要

Modern LLM serving systems manage the KV cache through PagedAttention, a custom GPU allocator that partitions a single CUDA-allocated region into fixed-size physical blocks and maps each request to those blocks through a CPU-built block table. This block-based design exposes a previously unstudied class of memory safety bugs in the KV cache, including double-free, use-after-free, out-of-bounds access, uninitialized read, and permission violation, which silently corrupt outputs or break request isolation without ever crashing the system. Existing memory sanitizers cannot detect them, because they reason about CUDA-level addresses rather than KV cache blocks, requests, and block tables, and their redzone- and quarantine-based designs do not fit the scarce GPU memory budget of LLM serving. This paper presents KVGuard, the first systematic memory-safety abstraction for KV cache management in LLM serving systems. KVGuard maintains a compact shadow block for each physical KV block, recording per-request permission and per-entry initialization, and uses this metadata to perform five safety checks across the PagedAttention workflow, spanning CPU allocation, block table construction and translation, and GPU-side block table lookup and KV entry access. Each check fires at the boundary that introduced the violation, enabling early detection during development rather than reporting downstream output corruption many requests later. A prototype of KVGuard on vLLM and SGLang covers all 29 historical real-world KV cache safety bugs collected from the two systems' patch histories. KVGuard further found 8 previously unknown safety violations while reporting no false positives. It adds 3.0% and 7.4% geomean TPOT overhead on Qwen 2.5 14B on vLLM and SGLang and at most 0.11% of the KV cache in memory overhead, two orders of magnitude below NVIDIA Compute Sanitizer on the same workloads.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06137

Essential Unitarity for Higher-Order Quantum Computation (Extended Abstract)

Samson Abramsky, Radha Jagadeesan

原文摘要

We develop a boundary-centric semantic framework for higher-order quantum computation, building on the Kelly-Laplaza description of compact closure and Abramsky's execution account. In the semantic carrier Perm(C), morphisms are complex-linear combinations of polarized boundary linkings, composed by execution. Finite-family addresses provide coherent control over finite-level quantum registers (qudits) while retaining the multiplicative boundary structure. We identify essential unitarity, a boundary condition extending ordinary unitarity to higher-order interfaces. On positive qudit registers it coincides with ordinary matrix unitarity; at higher order it expresses preservation of information across the full polarized boundary. We define a unit-free coherent quantum core generated bymultiplicative wiring, unitary gates on positive qudit registers, and contextual coherent control, and prove that every one of its morphisms is essentially unitary. The framework realizes an applied coherent quantum switch and the unitary stages of equal-ratio one-slot supermap dilations with explicit memory. Complete proofs appear in the full version of this paper.

cs.LO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.29628

Kriging and Neural Network models for pressure losses across perforated plates

Shuai Li

原文摘要

In this paper, two novel data-driven models based on Kriging and neural networks (NN) are proposed to predict pressure losses across perforated plates with circular perforations in turbulent flows. The models are developed using two sets of experimental data available in the literature. The predictive performance of the proposed models is assessed and compared against widely used empirical formulae. It is found that the proposed models consistently outperform existing empirical models for most perforated plate configurations contained in the experimental datasets. Besides, the predicted pressure losses generally show good agreement with experimental measurements, demonstrating that data-driven approaches based on Kriging and NN provide a feasible framework for modelling pressure losses across perforated plates. Overall, both approaches are promising, despite being trained on a relatively limited amount of experimental data, owing to the scarcity of measurements reported in the literature. To demonstrate the applicability of the proposed models in numerical simulations, two-dimensional channel flows are simulated using the Reynolds-averaged Navier-Stokes (RANS) equations, in which the new pressure-loss models are implemented as a source term in the momentum equations. The RANS predictions are insensitive to mesh resolution and the number of cells used to represent the perforated plate, and they agree well with the model predictions, confirming the applicability of the proposed approaches to practical computational fluid dynamics (CFD) simulations.

physics.flu-dyn · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06133

Accelerating Long-Context Decoding in Large-Scale Hybrid LLMs Without Retraining

João Eduardo Batista, Emil Vatai, Mohamed Wahib

原文摘要

The full-attention layers of large language models (LLMs) make every generated token more expensive than the previous one, slowing models down on long-context tasks. Hybrid models reduce this cost by replacing most full-attention layers with linear-attention layers, but the first still dominate decoding at long contexts. We accelerate these remaining layers without any training or weight changes using combined sparse attention: each layer attends to a sliding window of recent tokens, a pinned prefix, and a fixed budget of most relevant tokens in the context. The window size and budget are found on a per-layer basis using a multi-objective search algorithm. Across six hybrid models from three families, from 4B to 397B parameters, the selected configurations preserve their baseline model's accuracy on 21 long-context datasets. Decoding 64k/256k tokens becomes up to 1.26x/2.22x faster on Qwen3.5-4B, 1.20x/2.23x on Qwen3.5-122B-A10B, and 1.16x/2.06x on Ornith-1.0-397B.

cs.CL · cs.PF

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.19183

A Mode-Unified, Continuous, and Biomimetic Control Strategy of a Robotic Lower Limb Prosthesis

Hanjun Kim, Sixu Zhou, John Shim, Xinyuan Guo, Kinsey R. Herrin, Aaron J. Young

原文摘要

Existing biomimetic lower-limb prosthesis controllers are commonly designed for discrete locomotion modes, requiring separate parameterizations for level walking, ramps, and stairs. We present a mode-unified prosthesis control framework that represents knee and ankle biomechanics continuously as functions of terrain slope and gait phase across those locomotion conditions. Locally optimized ascent and descent polynomial models were unified using radial basis function (RBF) interpolation, preserving the accuracy of the partitioned models while outperforming a global polynomial representation. The resulting biomechanics were transformed into continuous and slope-dependent impedance parameters and implemented within a single finite-state-machine (FSM) controller using bilateral sensing for gait-state estimation. Experiments with 10 individuals with transfemoral amputation demonstrated terrain-adaptive locomotion from level walking through ramps and stairs up to 35 degrees, including 13 degree ramp and 15 degree stair conditions absent from the underlying able-bodied (AB) dataset. Experimental results reproduced the principal slope-dependent biomechanical trends predicted by the unified model, with high agreement for knee-related measures (r-square = 0.82 - 0.99) and ankle kinematics (r-square= 0.78 - 0.96). These findings demonstrated that unified biomechanical parameterization enables a prosthesis control architecture across diverse terrain conditions while preserving key features of AB biomechanics without requiring independently tuned impedance parameter sets for each locomotion mode. To facilitate reproducibility and further development, the biomechanics modeling and impedance parameterization code is publicly available at https://github.com/kim2576.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06131

ModalSearcher: Weaving Visual Evidence into Autonomous Cross-Modal Deep Search

Zhihan Yin, Ruyi Li, Chengyu Fang, Yifeng Yao, Nonghai Zhang, Pengfei Xiong, Terry Zhang, Huishuai Zhang 等 9 位作者

原文摘要

Static and incomplete parametric knowledge limits the performance of multimodal large language models on novel-knowledge and knowledge-intensive tasks, especially those requiring multi-hop evidence acquisition. Existing multimodal agentic search methods often fix visual evidence at the input or a predefined trajectory position, making it difficult for agents to select search modalities as their information needs evolve and to handle complex real-world queries. We define this capability as autonomous cross-modal search: an agent chooses among direct reasoning, text-to-text search, text-to-image search, and reverse-image search, actively introduces visual evidence when appropriate, and switches between and integrates modalities across multiple hops. We propose ModalSearch-Pipeline, a scalable procedure for constructing an interleaved multimodal knowledge graph. It samples paths with diverse depths and modality compositions and uses backward question construction to obscure entity names and image targets that would expose reasoning shortcuts while retaining necessary disambiguating clues, yielding text-only, image-initialized, and arbitrarily interleaved multi-hop problems. We train the ModalSearcher Agent with supervised fine-tuning and reinforcement learning in a live Web environment, and construct ModalSearch-Bench. Experiments show that ModalSearcher-8B improves average accuracy by 16.2 points over Qwen3-VL-8B under the same agentic workflow across five multimodal knowledge-intensive QA benchmarks, with consistent gains on independently constructed active visual-search benchmarks.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.06648

Final Checkpoints Are Not Enough: Analyzing Latent Reasoning Faithfulness Along Training Trajectories

Hengyu Jin, Shu Yang, Di Wang

原文摘要

Latent reasoning performs multi-step inference in continuous hidden states, promising more compact and efficient reasoning. However, these opaque states raise a question of faithfulness: whether the latent reasoning steps drive the final answer. Prior work reports unfaithful behaviors at selected checkpoints, leaving their development during training unexamined. We track behavioral and activation-based evidence across training using verified counterfactual edits and interventions on the latent reasoning states. We find that accuracy can improve while models become less responsive to edits that change the required reasoning path and the correct answer. This counterfactual responsiveness follows distinct trajectories across latent reasoning approaches. On the graph reachability task ProsQA, output sensitivity to noise replacement declines alongside counterfactual responsiveness. Across separately trained binary-choice and open-ended versions of GSM8K math problems, interventions on all latent states show opposite sensitivity trajectories. Guided by these trajectories, we intervene on the training procedure and improve counterfactual responsiveness. These results show that evaluating only a final checkpoint can obscure both when counterfactual responsiveness changes and what the latent states contribute.

cs.LG · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06130

Short-Term Forecasting of Heterogeneous Wind Power Fleets: Global Temporal Fusion Transformer Leveraging Static Covariates

Viktor Walter, Andreas Wagner

原文摘要

Accurate short-term wind power forecasting is critical for grid stability and electricity trading. While transformer-based deep learning is state of the art, industry practice relies mostly on local models trained for individual assets. This study investigates whether a global temporal fusion transformer (TFT), trained jointly on a heterogeneous portfolio of wind farms across Germany, outperforms site-specific local models in 48-hour-ahead forecasting. Using numerical weather prediction (NWP) data from the regional ICON-D2 and the global ECMWF HRES models, we compare the global TFT against local models and a linear regression baseline. The global model leverages static covariates, such as farm age, altitude above sea level, hub height, rotor diameter and power curve parameters, to learn shared representations across sites. We evaluate on 100 synthetic wind farm profiles and a real portfolio of 11 German wind farms, across two seasonal test periods with intermediate retraining. The global TFT outperforms the local models for 87% of the synthetic and 77% of the real wind farms and clearly surpasses the linear baseline. Wilcoxon signed-rank tests confirm that these gains are significant across both seasons and datasets. Crucially, this superiority depends on static covariates: without asset-specific context, the global model fails to generalize. The TFT's variable selection network (VSN) reveals the learned feature selection. Altitude emerges as a relevant static discriminator on both datasets, while hub height is decisive on the real portfolio. For dynamic inputs, the encoder prioritizes historical power, whereas the decoder combines upper-level wind speeds from both NWP models, consistent with averaging out model-specific errors. These findings support a shift from single-site to scalable multi-site forecasting, reducing operational complexity while improving accuracy in large portfolios.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.26853

From Representations to Behaviors: Exploring the Person-Situation-Behavior Triad in LLMs

Ruikang Zhang, Shuo Wang, Qi Su

原文摘要

Human personality theories characterize traits not as isolated attributes captured by a single score, but as stable individual tendencies expressed through the interplay among persons, situations, and behaviors. Existing studies of personality-related behavior in LLMs have primarily focused on outputs elicited under personality conditioning, characterizing observable trait-related expressions while lacking mechanistic evidence for the existence of internal personality-related representations, their cross-situational expression, and how these representations shape specific behaviors. Building on Funder's personality triad framework, we adapt its three components for LLM analysis: Person as personality-related internal representations, Situation as contexts that afford trait-relevant responses, and Behavior as response patterns on broader social tasks. We introduce a framework for discovering, controlling, and validating trait-like representations in LLMs. First, using contrastive behavior pairs grounded in shared situations, we identify sparse internal features associated with opposing poles of personality traits through SAE decomposition. We validate their trait relevance through effects on behavior to situation, token-level activation patterns, and robustness to paraphrasing. Second, feature-level interventions induce bidirectional trait-related shifts across a separate, diverse set of situations while preserving response validity, demonstrating consistent expression across contexts. Third, applying the same interventions to social intelligence tasks reveals behavioral changes with benefit-tradeoff patterns consistent with findings from human personality research, providing behavioral-level validation beyond personality scores. Our findings provide evidence that LLMs contain controllable trait-like representations linking internal states, situational expression, and behavioral outcomes.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2504.15669

UINO-FSS: Unifying Representation Learning and Few-shot Segmentation via Hierarchical Distillation and Mamba-HyperCorrelation

Wei Zhuo, Zhiyue Tang, Wufeng Xue, Hao Ding, Junkai Ji, Linlin Shen

原文摘要

Few-shot semantic segmentation has attracted growing interest for its ability to generalize to novel object categories using only a few annotated samples. To address data scarcity, recent methods incorporate multiple foundation models to improve feature transferability and segmentation performance. However, they often rely on dual-branch architectures that combine pre-trained encoders to leverage complementary strengths, a design that limits flexibility and efficiency. This raises a fundamental question: can we build a unified model that integrates knowledge from different foundation architectures? Achieving this is, however, challenging due to the misalignment between class-agnostic segmentation capabilities and fine-grained discriminative representations. To this end, we present UINO-FSS, a novel framework built on the key observation that early-stage DINOv2 features exhibit distribution consistency with SAM's output embeddings. This consistency enables the integration of both models' knowledge into a single-encoder architecture via coarse-to-fine multimodal distillation. In particular, our segmenter consists of three core components: a bottleneck adapter for embedding alignment, a meta-visual prompt generator that leverages dense similarity volumes and semantic embeddings, and a mask decoder. Using hierarchical cross-model distillation, we effectively transfer SAM's knowledge into the segmenter, further enhanced by Mamba-based 4D correlation mining on support-query pairs. Extensive experiments on PASCAL-5$^i$ and COCO-20$^i$ show that UINO-FSS achieves new state-of-the-art results under the 1-shot setting, with mIoU of 80.6 (+3.8%) on PASCAL-5$^i$ and 64.5 (+4.1%) on COCO-20$^i$, demonstrating the effectiveness of our unified approach.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06127

NN-Based FeFET Compact Modeling for SPICE Simulation

Rodion Novkin, Simon Thomann, Hussam Amrouch

原文摘要

Prototyping and early-stage circuit evaluation for emerging transistor technologies require fast and accurate compact models for SPICE simulations. Standard physics-based compact models, such as BSIM, offer high fidelity but demand extensive domain expertise and time-consuming equation formulation for every new physical effect, significantly slowing technology development. Neural network (NN)-based models offer a scalable alternative approach, but they rely heavily on smooth numerical solver data with explicit high-order derivative information. However, measurement data suffers from complex noise patterns, making derivative extraction challenging and complicating NN-based transistor modeling. This work presents a derivative-free NN training framework employing spectral normalization to naturally smooth derivative behavior and filter measurement noise. Using drain current measurements from a fabricated 28 nm ferroelectric device, we design an NN-based compact model that captures the full spectrum of polarization amplitudes and supports dynamic polarization switching in transient simulations. To demonstrate its capability, we deploy the model in a commercial SPICE software to simulate write and read operations for large-scale memory arrays with up to 512x512 size, including parasitic effects. Our approach bridges raw measurements with NN-based SPICE-compatible transistor modeling, significantly reducing model development time and enabling rapid circuit-level prototyping for emerging devices.

cs.ET

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2509.26468

fev-bench: A Realistic Benchmark for Time Series Forecasting

Oleksandr Shchur, Abdul Fatir Ansari, Caner Turkmen, Lorenzo Stella, Nick Erickson, Pablo Guerron, Michael Bohlke-Schneider, Yuyang Wang

原文摘要

Benchmark quality is critical for meaningful evaluation and sustained progress in time series forecasting, particularly with the rise of pretrained models. Existing benchmarks often have limited domain coverage or overlook real-world settings such as tasks with covariates. Their aggregation procedures frequently lack statistical rigor, making it unclear whether observed performance differences reflect true improvements or random variation. Many benchmarks lack consistent evaluation infrastructure or are too rigid for integration into existing pipelines. To address these gaps, we propose fev-bench, a benchmark of 100 forecasting tasks across seven domains, including 46 with covariates. Supporting the benchmark, we introduce fev, a lightweight Python library for forecasting evaluation emphasizing reproducibility and integration with existing workflows. Using fev, fev-bench employs principled aggregation with bootstrapped confidence intervals to report performance along two dimensions: win rates and skill scores. We report results on fev-bench for pretrained, statistical, and baseline models and identify promising future research directions.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06124

A Post-Quantum Interactive Hashing Theorem

Céline Chevalier, Laurent Holin, Quoc-Huy Vu

原文摘要

Interactive hashing, introduced by Ostrovsky, Venkatesan, and Yung [OVY93], is a building block in many cryptographic protocols, including perfectly hiding commitments from one-way permutations [NOVY92]. Its multi-output generalization is a key ingredient in constructions of statistical zero-knowledge arguments for NP and statistically hiding commitments from arbitrary one-way functions [NOV06,HR07]. In this note, we give a post-quantum security proof for this multi-output interactive hashing protocol with arbitrary relations.

quant-ph · cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06123

From Handcrafted to Generative: RLVR-Optimized Icon Generation for Alexa+

Rohan Sequeira, Matthew Rosenberg, Amin Mahmoudabadbozchelou, Oleg Poliannikov, Jonathan Rubin

原文摘要

Alexa+ devices with screens display animated icons during conversations to visually reinforce what is being said. The original set of these icons was hand-drawn by designers one at a time, a process that produces polished results but is time-consuming and cannot scale to the range of topics encountered in open-ended conversation. We describe an automated pipeline, deployed in production, that generates these vectorised animated icons from a text description alone and surfaces relevant animations to customers based on their ongoing conversation. The pipeline decomposes icon creation into a sequence of stages (image generation, semantic decomposition, vectorisation, styling, and animation). Its quality is determined at the very first of these, image generation, on which every downstream stage depends. We therefore focus on improving this first and most critical stage. Because the icons are rendered on small display canvases under strict design guidelines, generations are often unsuitable for production. Supervised fine-tuning is not viable, as no dataset of line-art icons exists under these constraints. Instead, we design objective verifiers that automatically flag generations violating the design guidelines, and use them to fine-tune a flow matching image generation model via reinforcement learning from verifiable rewards (RLVR). The fine-tuned model raises the pass rate from 15.7% to 58.7% (3.7x) under a VLM judge that is independent of the reward model. Both the trained model and the verifier are deployed in the production pipeline, where the verifier also serves as the automated quality gate before final design review.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06122

Ab Initio Accuracy Beyond the Cutoff: Distributed Message-Passing Equivariant Attention at Tens-of-Millions-of-Atom Scale

Allex Kan, Ji Zhang, Hao Yin, Hao Li, Owen Fu, Xunxing Chen, Lin Huang, Zion Wang 等 13 位作者

原文摘要

Molecular dynamics predicts molecular motion from atomic forces and trajectory integration. Density functional theory (DFT) provides first-principles forces, but its $O(N^3)$ cost limits simulations to thousands of atoms; empirical force fields scale to millions but miss bond breaking and charge transfer. Equivariant message-passing Transformers learn the ab initio potential at linear cost. Stacked layers propagate information beyond a single cutoff, giving four layers a receptive field up to 23Å. At this frontier, scale is limited by communication: layers exchange boundary features across devices, force backpropagation repeats the exchange, and moving atoms change neighbor graphs and tensor shapes. We present Tessera, a distributed runtime that fixes workload shapes between rebuilds. It sorts atoms along a space-filling curve into equal-count shards, ensuring identical atom counts and tensor shapes per rank. Peer sets scale as $K^{0.5}$, not $K$. A conservative neighbor skin preserves topology across updates, enabling each rebuild interval to replay as one CUDA Graph. Fused kernels, three-level neighbor search, and overlapped All-to-All complete it. On 512 GPUs, Tessera sustains 30.7M atoms at 89.7% weak-scaling efficiency (77--90% across three per-GPU loads); per-step time rises 1.11--1.29x as machine and problem grow 16x. Graph reuse cuts full-step time $1.87\times$. Across an $8\times$ device increase, neighbor-search cost grows $1.7\times$, versus $3.0\times$ for Morton order. In a live $10.24$M-atom salt solution, NVE energy drift is $0.42\,μ$eV/atom/ps over $10$ ps, and structure matches a serial reference. A 10.06M-atom Norovirus capsid sustains 100 ps of NVT at $\approx 8$ ps/day on 256 A100 GPUs. Over 200,000 steps, it has no broken bonds or anomalously close pairs and uses three compiled variants, with no graph breaks or eager fallbacks.

cs.DC · cs.AI · physics.bio-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06120

ReactWorld: Structuring Interaction Responses for Embodied Video Generation

Hanwei Liu, Huijia Zhang, Hanzhang Wang, Guangyu Liu, Hanbei Zhan, Linjie Hu, Bin Xiao, Xuan Zhou 等 9 位作者

原文摘要

Embodied video world models predict scene changes caused by robot actions, yet generated object responses may remain inconsistent with task intent and contact dynamics. Critical responses in embodied scenes often concentrate in local regions and specific time intervals. Inspired by chain-of-thought reasoning, we introduce ReactWorld, an embodied video generation model guided by structured interaction anticipation. In the generation backbone's feature space, an interaction response field predictor anticipates how the interaction will unfold, explicitly describing participant roles, future motion response intensity, and local motion changes. A response feedback adapter forms increments to object features from the difference between the object branch's outputs with predicted and zeroed local dynamics inputs. It also converts predicted displacements into positional biases for temporal attention. These two mechanisms guide object feature updates and information retrieval across frames, respectively. Through interaction anticipation and generative feedback, ReactWorld achieves an average score of 77.51 on RBench, outperforming PhysisForcing by 3.29 points with improvements in all nine categories. On EZS-Bench, it achieves a Domain score of 86.98 and an average score of 81.87.

cs.CV · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.05176

Extending the MM-AR Metamodeling Platform with Process Mining Capabilities

Geraldine Hürzeler, Hans-Georg Fill, Daniel Borcard

原文摘要

Process mining derives process models from event data recorded by information systems, but its results are usually presented as two-dimensional visualizations that are separate from the environments in which enterprise models are created and maintained. Metamodeling platforms for extended reality (XR), such as MM-AR, allow users to define modeling languages and to display models in 2D and 3D, but they do not provide access to event data or mining algorithms so far. In this paper we therefore present a first proof-of-concept for showing how an XR-based metamodeling platform can be extended with process discovery capabilities without changes to the platform core through a separate Python service based on the open source PM4Py process mining library. We demonstrate the approach using three common discovery algorithms for obtaining Petri nets and BPMN models based on CSV or XES event logs. A technical evaluation using four public event logs shows that the element counts of the created model instances match those of the PM4Py models in all 16 configurations. The integration adds between about 8 and 53 seconds per run, and this overhead grows with the size of the discovered model rather than the size of the event log. Although the prototype does not yet use the third dimension to encode process information, it established the foundations for studying such approaches.

cs.SE · cs.DB · cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.06764

Efficient Classical Simulation of Low-Rank-Width Quantum Circuits Using ZX-Calculus

Fedor Kuyanov, Aleks Kissinger

原文摘要

In this paper, we introduce an algorithm for contracting (i.e. numerically evaluating) ZX-diagrams whose computational complexity is governed by rank-width, a structural graph parameter that behaves nicely under ZX rewrite rules. Our method evaluates a graph-like ZX-diagram in ~O(4^R) time, given its rank-decomposition of width R. By constructing suitable rank-decompositions, we provide a classical simulation algorithm for quantum circuits whose runtime is bounded by both ~O(2^n) and ~O(2^(t/2)), where n is the number of qubits and t is the number of non-Clifford phase gates. Thus, our method is no slower than either the naive state-vector simulation or the elementary stabiliser decomposition baseline, and in practice can be even faster when the reduced ZX-diagram has low rank-width. We benchmark our simulation algorithm against Quimb, a popular tensor contraction library, and observe substantial reductions in floating-point operations, often by several orders of magnitude, for random and structured non-Clifford circuits and for random ZX-diagrams.

quant-ph · cs.CC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06118

Online Adaptation to Latent Physical Properties for Learning from Demonstration

Kotaro Kajitsuka, Masaki Murooka, Fumio Kanehiro

原文摘要

Manipulating visually identical objects with different physical properties requires adapting actions based on information acquired through interaction. We address the problem of learning from demonstrations how to adapt manipulation actions, such as regrasping and changing contact locations, based on physical-property information acquired during interaction. To this end, we propose a learning-from-demonstration method that learns latent representations of physical properties needed to predict object responses and conditions a policy on online estimates of these representations. A dynamics model and object-specific latent representations are jointly learned from demonstrations that include behaviors adapting to the objects' physical properties, and the learned representations are then used to condition an imitation policy. During execution, the dynamics model and policy weights remain fixed, while the latent representation is updated based on the discrepancy between predicted and actual observations, allowing interaction with the object to inform action generation. Real-world and simulation experiments with objects whose centers of mass differ suggest that online adaptation through latent representations of physical properties can improve task success rates. However, in simulation, success rates on center-of-mass locations absent from the demonstrations remained around 30%, indicating limited adaptation to unseen physical properties. Project page: https://isri-aist.github.io/latent-dynamics-project-page/

cs.RO

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arXiv ↗PDF ↗