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新论文 · v1arXiv:2611.13162

Thermodynamically consistent neural constitutive models for elastoplasticity via eigen-representations

Harikrishnan Vijayakumaran, Shunyu Yin, Martin Abendroth, Miguel A. Bessa, Karl A. Kalina

原文摘要

Neural network material models can represent yield surfaces and hardening laws with far more flexibility than classical closed-form plasticity formulations, but training such a model end-to-end through an implicit return map introduces non-trivial challenges. This work presents a thermodynamically consistent neural constitutive model for rate-independent, isotropic elastoplasticity that jointly learns a flexible yield function together with its evolution laws. For non-associative flow, the yield function extends the convex flow potential by a jointly trained additive term, which renders the model thermodynamically consistent by construction. Isotropy and convexity are enforced simultaneously across all learnable components through a permutation-invariant, spectral (eigenvalue-based) representation. Training proceeds through a backward-Euler return map, with gradients propagated by the implicit function theorem together with an eigenvalue-degeneracy-aware scheme for safe differentiation. The robustness and generality of the approach are demonstrated by reproducing known constitutive models, including non-associative flow, learning material behavior from stress--strain data extracted from a full-field finite element simulation, and capturing unknown non-associative behavior from homogenized representative volume elements of a foam. Finally, the proposed neural plasticity model is evaluated within finite element simulations to solve boundary-value-problem under plane stress.

cs.CE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11751

GeoPilot: Diagnosis-Guided Interventions and Geometric Evaluation for Aerial Reconstruction

Pengyu Yin, Jie Pan, Junxiang Tan, Mengqi Fu, Kun Cheng, Yifan Liu

原文摘要

When a mesh reconstructed from aerial remote-sensing imagery disagrees with reference geometry, deciding what to change requires a diagnosis of the reconstruction process. We present GeoPilot, a workflow that turns diagnostics and prior observations into executable program or camera-configuration interventions. A large language model proposes the change, numerical tools reconstruct the mesh, and an external evaluator measures its geometry under a stated coordinate and reference protocol. The main case tests a shared-intrinsics hypothesis proposed before the comparison. On 224 Metashape-raster development images, shared intrinsics improved all six native-coordinate measures and reduced mean surface-to-reference distance by 7.04%. Reference-assisted alignment fitted and evaluated on separate within-scene checkerboard samples reversed the mean-distance ranking, limiting that gain to native-frame delivery. A separate matched comparison on publisher-undistorted images favored fixed published cameras on all six measures, reducing mean distance by 12.11%; the cameras retain upstream image and laser-scanning adjustment information. With one reconstruction per configuration, these comparisons provide conditional evidence for camera handling. Three end-to-end runs each delivered a mesh and six-metric evaluation from all 327 images of a second development scene.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10379

RefineSplat: Reconstruction-Guided Object-Centric Gaussian Splatting for Open-Vocabulary Querying

Yiren Lu, Disheng Liu, Sreya Srinidhi, Yunlai Zhou, Yiran Qiao, Chaoda Song, Tuo Liang, Jing Ma 等 10 位作者

原文摘要

Open-vocabulary 3D scene understanding aims to segment and retrieve arbitrary objects in 3D scenes using natural language queries. Most existing methods typically follow a splat-then-segment paradigm, where 3D Gaussian Splatting is first reconstructed and then augmented with labels or language features for subsequent segmentation. However, this pipeline often suffers from view inconsistency, ambiguous object boundaries, and weak object-level correspondence, especially in complex static and dynamic scenes. Building on our prior Segment-then-Splat framework, we present RefineSplat, a reconstruction-guided extension that retains its object-centric paradigm: it first tracks objects in 2D and then reconstructs each object with a dedicated set of 3D Gaussians, preserving explicit object-Gaussian correspondence for unified open-vocabulary segmentation and retrieval. To improve robustness, we further close the loop between 2D tracking and 3D reconstruction. Since off-the-shelf video trackers can produce unreliable masks that propagate errors into object reconstruction, we introduce a reconstruction-guided feedback mechanism that refines object tracking using rendered masks and multi-view consistency. Moreover, instead of assigning each object a single language embedding, we propose a viewpoint-aware embedding assignment strategy, where each object maintains multiple view-clustered embeddings to capture view-dependent semantic cues. We also introduce Blender-OVS, a synthetic benchmark of static and dynamic sequences with exact object meshes and point clouds, enabling direct evaluation of extracted geometry. Experiments on static and dynamic benchmarks show improved rendered-view segmentation and lower object-level Chamfer distances, with competitive scene reconstruction quality.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04331

Warpline: Binary-Level Tracing and Unified Evidence for Agentic GPU Kernel Optimization

Junyu Yin, Vinod Grover, Keren Zhou

原文摘要

LLM agents are becoming GPU kernel engineers, but they still tune kernels without seeing where the time goes. Hardware profilers report how busy each unit was, not where each warp's time went; intra-kernel tracers can show it, but only with hand-placed probes and a recompilation that alters the very kernel being measured. We present Warpline, which gives agents this view automatically while leaving the kernel essentially unchanged. Warpline analyzes the binary to decide which events to record and inserts probes into the binary itself without recompilation or any manual step. It then aligns the trace with hardware profiling data on one unified timeline. Because it needs only the binary, it can regenerate this evidence for every new version of the kernel at whatever level the agent edits it (source, IR, or PTX), closing the optimization loop without human intervention. On NVIDIA B200, across 35 kernels in Triton, CuTe DSL, and CUDA C++, Warpline adds a median overhead of 1.2% and leaves a median 99.9% of executed instructions unchanged, whereas existing tracers have median overheads of 1.6% to 13.2% and leave only 4.6% to 32.7% unchanged. Warpline also traces closed-source kernels, which these tracers cannot. On 10 SOL-ExecBench problems, agents using Warpline spend less time and fewer model requests than a bare coding agent and NVIDIA's Kernel Design Agents (KDA), yet lead both after every round in geometric mean and reach a 7.69$\times$ geometric-mean speedup, against 6.94$\times$ and 6.13$\times$. Two case studies further demonstrate that Warpline reveals hidden bottlenecks and provides actionable optimization insights.

cs.PF · cs.PL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02752

HPC-Bench: A Comprehensive Benchmark for High Performance Computing Codes

Bowen Cui, Junyu Yin, Tejas Ramesh, Leo Lim, Oscar Hernandez, Keren Zhou

原文摘要

Large Language Models (LLMs) have shown strong capabilities in code generation and reasoning, motivating extensive benchmarks for evaluating functional correctness. However, performance optimization in High-Performance Computing (HPC) poses fundamentally different challenges that are not captured by existing evaluations: HPC performance depends on low-level code, intricate memory access patterns, and computational kernels embedded within sophisticated execution contexts. We introduce HPC-Bench, a performance-grounded benchmark for evaluating LLMs as optimizers of computational kernels in realistic HPC workloads, including 114 workloads across 16 HPC computational motifs. HPC-Bench provides a fully automated, end-to-end evaluation pipeline that combines program-level correctness verification with two correctness-gated metrics, Fast@k and Speedup@k, to characterize reliability and speedup under a fixed sampling budget. Our evaluation of three representative LLMs across serial CPU, OpenMP, and CUDA settings shows that stricter speedup requirements substantially reduce the probability of finding a correct and fast optimization. HPC-Bench is available at https://github.com/Deep-Learning-Profiling-Tools/HPC-Bench.

cs.PF

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2512.17541

FLEG: Feed-Forward Language Embedded Gaussian Splatting from Any Views via Compact Semantic Representation

Qijian Tian, Jiayu Ying, Xuhong Wang, Yuan Xie, Lizhuang Ma, Xin Tan

原文摘要

We present FLEG, a feed-forward network that reconstructs language-embedded 3D Gaussians from multi-view images. Previous feed-forward language-embedded Gaussian reconstruction methods are restricted to a fixed number of input views and typically attach a language-aligned semantic embedding to each Gaussian, resulting in impractical input settings and semantic redundancy. In contrast, we introduce a geometry-semantic dual-branch distillation framework that supports a variable number of uncalibrated and unposed multi-view images as input. We also propose a novel-view-based distillation strategy during training that mitigates overfitting to input views. In addition, we observe that semantic representations are significantly sparser than geometric ones, and per-Gaussian language embedding is unnecessary. To exploit this sparsity, we design a decoupled language embedding strategy that represents language information with a sparse set of semantic Gaussians, rather than attaching embeddings to every Gaussian. Compared with dense pixel-aligned per-Gaussian embedding schemes, our method uses only 5% of the language embeddings while maintaining comparable semantic fidelity, effectively reducing storage costs. Extensive experiments demonstrate that FLEG outperforms state-of-the-art feed-forward reconstruction and language-embedded Gaussian methods in both reconstruction quality and language-aligned semantic representation.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.25808

Improving Sampling Efficiency in RLVR through Adaptive Rollout and Response Reuse

Yuheng Zhang, Wenlin Yao, Changlong Yu, Yao Liu, Qingyu Yin, Bing Yin, Hyokun Yun, Lihong Li

原文摘要

Group relative policy optimization (GRPO) trains large language models with verifiable rewards by normalizing rewards within response groups. When every response in a group receives the same reward, the advantages vanish and the group provides no policy gradient signal. We propose Adaptive Rollout and Response Reuse Policy Optimization (AR3PO) to obtain informative groups with fewer generations. Adaptive rollout samples responses to each prompt in stages and stops sampling once the model generates a correct response. If all sampled responses are incorrect, response reuse replaces one of them with a past correct response to the same prompt, when available, and recomputes the group advantages. Policy-recomputed reuse trains on the updated groups using probabilities recomputed under the rollout policy; advantage-only reuse uses historical responses only to compute advantages. Across four mathematical benchmarks with 7B and 8B models, AR3PO consistently improves over GRPO and achieves performance comparable to DAPO while requiring up to $4.2\times$ fewer generated responses. Experiments with a 32B model also show comparable performance to DAPO at a similar training stage with substantially fewer generated responses.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.18564

Observation Quality in DSM-Assisted Aerial Localization: A Benchmark and Base-Anchored Uncertainty Calibration

Pengyu Yin, Jie Pan, Junxiang Tan, Mengqi Fu, Kun Cheng, Yifan Liu

原文摘要

Aerial map matching provides position observations for navigation, but a position error alone does not determine how an observation should be weighted. We define five benchmark tasks: position availability and agreement, grouped error structure, predictive uncertainty, controlled updates, and temporal accumulation. We use digital-surface-model-assisted localization to test whether base-anchored uncertainty calibration improves the weighting of unchanged position estimates. The method combines propagated image and map sensitivities with the second moment of a compact configuration subset relative to the reported position. Scale is fitted using separate position references without local surface-height ground truth. Thirteen covariance models are evaluated on two held-out query sets from locations represented in the calibration data. Using reference residuals, we compare the method with a constant full-matrix baseline. With all localization failures included, the method reduces the normalized position-update risk, averaged equally across locations, by 7.70% on 728 conditions and 5.46% on a separate 142 conditions under an independent prior. On the primary set, the equal-location average negative log likelihood on reference residuals improves by 2.571 nat. At these seen locations, exploratory base-only quality calibration and conditional-addition controls leave the incremental update benefit of configuration disagreement unresolved. Navigation benefit and real temporal accumulation remain untested.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.22684HF 社区推荐

AlphaBet: Reinforcement Learning for LLM Event Forecasting Agents

Liqin Ye, Rongzhi Zhang, Xin Liu, Zheng Li, Yuan He, Qingyu Yin, Priyanka Nigam, Chao Zhang

原文摘要

Forecasting an unresolved event requires finding relevant evidence and deciding how strongly it supports each outcome. Yet outcome-based training of Large Language Model (LLM) forecasters often begins with preassembled evidence at a single prediction date, leaving evidence acquisition outside the learning loop. We introduce AlphaBet, an agentic reinforcement learning (RL) framework that trains a single LLM policy to search, reason, and report probabilities across multiple pre-resolution dates. Each dated forecast is scored by a proper scoring rule against the eventual outcome, allowing one resolved event to supervise decisions under different information states. We decompose forecasting regret into an evidence-acquisition gap and a probability-reporting gap, and show that Group Relative Policy Optimization (GRPO)'s standard-deviation normalization can distort the reporting target under multi-class proper scores. On GLM-4.5-Air, AlphaBet reduces the Brier score from 0.731 to 0.626 on Forecast-Dojo and from 0.612 to 0.528 on FutureX. Controlled ablations attribute these gains to multi-date training and show that they come from improvements in both information acquisition and probability reporting. Our empirical results validate that GRPO's std normalization degrades proper-scoring-rule performance while leaving top-outcome accuracy largely unchanged.

cs.LG · cs.AI

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1081ACL Anthology · ACL 2026

Instant Personalized Large Language Model Adaptation via Hypernetwork

Zhaoxuan Tan University of Notre Dame, Zixuan Zhang Amazon, Haoyang Wen Amazon, Zheng Li, Rongzhi Zhang Georgia Institute of Technology, Zhejiang University and Amazon, Pei Chen Amazon, Fengran Mo, Zheyuan Liu University of Notre Dame 等 11 位作者

原文摘要

Personalized large language models (LLMs) tailor content to individual preferences using user profiles or histories. However, existing parameter-efficient fine-tuning (PEFT) methods, such as the “One-PEFT-Per-User” (OPPU) paradigm, require training a separate adapter for each user, making them computationally expensive and impractical for real-time updates. We introduce Profile-to-PEFT, a scalable framework that employs a hypernetwork, trained end-to-end, to map a user’s encoded profile directly to a full set of adapter parameters (e.g., LoRA), eliminating per-user training at deployment. This design enables instant adaptation, generalization to unseen users, and privacy-preserving local deployment. Experimental results demonstrate that our method outperforms both prompt-based personalization and OPPU while using substantially fewer computational resources at deployment. The framework exhibits strong generalization to out-of-distribution users and maintains robustness across varying user activity levels and different embedding backbones. The proposed Profile-to-PEFT framework enables efficient, scalable, and adaptive LLM personalization suitable for large-scale applications.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表