Yibo Peng, James Song, Lei Li, Xinyu Yang, Mihai Christodorescu, Ravi Mangal, Corina S. Pasareanu, Haizhong Zheng, Beidi Chen. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Large Language Models (LLMs) inference increasingly require mechanisms that provide runtime visibility into what is actually executing, without exposing model weights or code. We present WAVE, a hardware-grounded monitoring framework that leverages GPU performance counters (PMCs) to observe LLM inference. WAVE is built on the insight that legitimate executions of a given model must satisfy hardware-constrained invariants, such as memory accesses, instruction mix, and tensor-core utilization, induced by the model's linear-algebraic structure. WAVE collects lightweight PMC traces and applies a two-stage pipeline: (1) inferring architectural properties (e.g., parameter count, layer depth, hidden dimension, batch size) from the observed traces; and (2) using an SMT-based consistency checker to assess whether the execution aligns with the provisioned compute and the claimed model's constraints. We evaluate WAVE on common open-source LLM architectures, such as LLaMA, GPT, and Qwen, across multiple GPU architectures, including NVIDIA Ada Lovelace, Hopper, and Blackwell. Results show that WAVE recovers key model parameters with an average error of 6.8% and identifies disguised executions under realistic perturbations. By grounding oversight in hardware invariants, WAVE provides a practical avenue for continuous, privacy-preserving runtime monitoring of LLM services.

Automatic, camera-free lifelogging offers new opportunities for memory rehabilitation, personal informatics, and assistive technologies. However, most existing approaches limit daily activities to isolated event labels, offering little context and lacking the narrative coherence essential for effective lifelogging. Recent advances in audio–language models combine foundation audio processing with language-based reasoning, enabling open-ended sound understanding. We introduce EchoScriptor, an end-to-end system that transforms raw in-home audio into context-aware natural-language descriptions, generating coherent narrative lifelogs of activities and acoustic contexts. In moment-level evaluation, EchoScriptor achieved 94.15% activity recognition and 89.25% background recognition accuracy, and at the summary level, achieved an F1 score of 0.92, outperforming the classifier+LLM baseline. In our user study with 20 participants across 10 household activity videos, EchoScriptor summaries were consistently rated highly, approaching the perceived utility of human-written ones. By advancing from event detection to narrative understanding, EchoScriptor establishes a significant step toward automated, unobtrusive, context-aware lifelogging technologies.

High-quality human-annotated data is crucial for modern deep learning pipelines, yet the human annotation process is both costly and time-consuming. Given a constrained human labeling budget, selecting an informative and representative data subset for labeling can significantly reduce human annotation effort. Well-performing state-of-the-art (SOTA) coreset selection methods require ground truth labels over the whole dataset, failing to reduce the human labeling burden. Meanwhile, SOTA label-free coreset selection methods deliver inferior performance due to poor geometry-based difficulty scores. In this paper, we introduce ELFS (Effective Label-Free Coreset Selection), a novel label-free coreset selection method. ELFS significantly improves label-free coreset selection by addressing two challenges: 1) ELFS utilizes deep clustering to estimate training dynamics-based data difficulty scores without ground truth labels; 2) Pseudo-labels introduce a distribution shift in the data difficulty scores, and we propose a simple but effective double-end pruning method to mitigate bias on calculated scores. We evaluate ELFS on four vision benchmarks and show that, given the same vision encoder, ELFS consistently outperforms SOTA label-free baselines. For instance, when using SwAV as the encoder, ELFS outperforms D2 by up to 10.2% in accuracy on ImageNet-1K. We make our code publicly available on GitHub.

We rethink test-time scaling laws from a practical efficiency perspective, revealing that the effectiveness of smaller models is significantly overestimated. Prior work, grounded in compute-optimality, overlooks critical memory access bottlenecks introduced by inference-time strategies (e.g., Best-of-N, long CoTs). Our holistic analysis, spanning models from 0.6B to 32B parameters, reveals a new Kinetics Scaling Law that better guides resource allocation by incorporating both computation and memory access costs. The Kinetics Scaling Law suggests that test-time compute is more effective when used on models above a threshold (14B) than on smaller ones. A key reason is that in test-time scaling, attention—rather than parameter count—emerges as the dominant cost factor. Motivated by this, we propose a new scaling paradigm centered on sparse attention, which lowers per-token cost and enables longer generations and more parallel samples within the same resource budget. Empirically, we show that sparse attention models consistently outperform dense counterparts, achieving over 60-point gains in low-cost regimes and over 5-point gains in high-cost regimes for problem-solving accuracy on AIME and LiveCodeBench. These results suggest that sparse attention is essential for realizing the full potential of test-time scaling because, unlike training where parameter scaling saturates, test-time accuracy continues to improve through increased generation.

Reinforcement learning, such as PPO and GRPO, has powered recent breakthroughs in LLM reasoning. Scaling rollout to sample more prompts enables models to selectively use higher-quality data for training, which can stabilize RL training and improve model performance, but at the cost of significant computational overhead. In this paper, we first show that a substantial portion of this overhead can be avoided by skipping uninformative prompts before rollout. Our analysis of reward dynamics reveals a strong temporal consistency in prompt value: prompts that are uninformative in one epoch of training are likely to remain uninformative in near future epochs. Based on these insights, we propose GRESO (GRPO with Efficient Selective Rollout), an online, lightweight pre-rollout filtering algorithm that predicts and skips uninformative prompts using reward training dynamics. By evaluating GRESO on a broad range of math reasoning benchmarks and models, like Qwen2.5-Math-1.5B, DeepSeek-R1-Distill-Qwen-1.5B, Qwen2.5-Math-7B, Qwen2.5-14B, and Qwen2.5-32B, we show that GRESO achieves up to 2.4x wall-clock time speedup in rollout and up to 2.0x speedup in total training time without accuracy degradation. We make our code publicly available at https://github.com/Infini-AI-Lab/GRESO/.

Terms and conditions for online shopping websites often contain terms that can have significant financial consequences for customers. Despite their impact, there is currently no comprehensive understanding of the types and potential risks associated with unfavorable financial terms. Furthermore, there are no publicly available detection systems or datasets to systematically identify or mitigate these terms. In this paper, we take the first steps toward solving this problem with three key contributions.

Perception is crucial in the realm of autonomous driving systems, where bird's eye view (BEV)-based architectures have recently reached state-of-the-art performance. The desirability of self-supervised representation learning stems from the expensive and laborious process of annotating 2D and 3D data. Although previous research has investigated pretraining methods for both LiDAR and camera-based 3D object detection, a unified pretraining framework for multimodal BEV perception is missing. In this study, we introduce CALICO, a novel framework that applies contrastive objectives to both LiDAR and camera backbones. Specifically, CALICO incorporates two stages: point-region contrast (PRC) and region-aware distillation (RAD). PRC better balances the region- and scene-level representation learning on the LiDAR modality and offers significant performance improvement compared to existing methods. RAD effectively achieves contrastive distillation on our self-trained teacher model. CALICO's efficacy is substantiated by extensive evaluations on 3D object detection and BEV map segmentation tasks, where it delivers significant performance improvements. Notably, CALICO outperforms the baseline method by 10.5\% and 8.6\% on NDS and mAP. Moreover, CALICO boosts the robustness of multimodal 3D object detection against adversarial attacks and corruption. Additionally, our framework can be tailored to different backbones and heads, positioning it as a promising approach for multimodal BEV perception.

Large Language Models (LLMs) have achieved remarkable success with their billion-level parameters, yet they incur high inference overheads. The emergence of activation sparsity in LLMs provides a natural approach to reduce this cost by involving only parts of the parameters for inference. However, existing methods only focus on utilizing this naturally formed activation sparsity in a post-training setting, overlooking the potential for further amplifying this inherent sparsity. In this paper, we hypothesize that LLMs can learn to be efficient by achieving more structured activation sparsity. To achieve this, we introduce a novel training algorithm, Learn-To-be-Efficient (LTE), designed to train efficiency-aware LLMs to learn to activate fewer neurons and achieve a better trade-off between sparsity and performance. Furthermore, unlike SOTA MoEfication methods, which mainly focus on ReLU-based models, LTE can also be applied to LLMs like LLaMA using non-ReLU activations. Extensive evaluation on language understanding, language generation, and instruction tuning tasks show that LTE consistently outperforms SOTA baselines. Along with our hardware-aware custom kernel implementation, LTE reduces LLaMA2-7B inference latency by 25% at 50% sparsity.

Adversarial training is an effective defense method to protect classification models against adversarial attacks. However, one limitation of this approach is that it can require orders of magnitude additional training time due to high cost of generating strong adversarial examples during training. In this paper, we first show that there is high transferability between models from neighboring epochs in the same training process, i.e., adversarial examples from one epoch continue to be adversarial in subsequent epochs. Leveraging this property, we propose a novel method, Adversarial Training with Transferable Adversarial Examples (ATTA), that can enhance the robustness of trained models and greatly improve the training efficiency by accumulating adversarial perturbations through epochs. Compared to state-of-the-art adversarial training methods, ATTA enhances adversarial accuracy by up to 7.2% on CIFAR10 and requires 12~14x less training time on MNIST and CIFAR10 datasets with comparable model robustness.

Online Social Networks (OSNs) are facing an increasing threat of sybil attacks. Sybil detection is regarded as one of major challenges for OSN security. The existing sybil detection proposals that leverage graph theory or exploit the unique clickstream patterns are either based on unrealistic assumptions or limited to the service providers. In this study, we introduce a novel sybil detection approach by exploiting the fundamental mobility patterns that separate real users from sybil ones. The proposed approach is motivated as follows. On the one hand, OSNs including Yelp and Dianping allow us to obtain the users' mobility trajectories based on their online reviews and the locations of their visited shops/restaurants. On the other side, a real user's mobility is generally predictable and confined to a limited neighborhood while the sybils' mobility is forged based on the paid review missions. To exploit the mobility differences between the real and sybil users, we introduce an entropy based definition to capture users' mobility patterns. Then we design a new sybil detection model by incorporating the newly defined location entropy based metrics into other traditional feature sets. The proposed sybil detection model can significantly improve the performance of sybil detections, which is well demonstrated by extensive evaluations based on the data set from Dianping.