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DELE-w0.5: Inferring Action from Future Latent State for Robotic Manipulation
von Fenghao Lei, Zhixiong Huang, Long Yang, Jiabao Chen, Peilin Huang, Han Fu, Zhuo Li, Xiaoxue Ren am September 1, 2026 um 4:00 a.m.
arXiv:2608.22067v4 Announce Type: replace-cross Abstract: World-Action Models (WAMs) build robot control on video-generation backbones, which jointly predict dense future visual trajectories and robot actions. We argue that video generation is an unnecessary intermediate objective for world-action modeling. For robotic manipulation, the goal of a world model is not to reproduce how the world looks at every intermediate moment, but to predict the state that the world will reach after an action is executed. The intermediate frames only describe the visual […]
CoJEPA: Combining Contrastive Learning and JEPA for Global-Local Music Representations
von Gabriel Meseguer-Brocal, Yuexuan Kong, Romain Hennequin am September 1, 2026 um 4:00 a.m.
arXiv:2608.30974v1 Announce Type: cross Abstract: Joint-Embedding Predictive Architecture (JEPA) has shown strong performance in learning rich representations through self-supervised prediction in latent space. However, it typically relies on teacher–student architecture with an EMA to stabilise training, and can tend to yield uninformative representations. Contrastive learning is stable to train and produces strong global representations, but remains limited on local tasks by the global nature of its objective. In this work, we combine both into CoJEPA: a […]
Learning Materials Properties from Scarce Labels and Unlabeled Crystals
von Wentao Li, Yizhe Chen, Jiangjie Qiu, Yijun Li, Leyi Zhao, Xiaonan Wang am September 1, 2026 um 4:00 a.m.
arXiv:2608.30682v1 Announce Type: cross Abstract: Learning materials properties from scarce labels and unlabeled crystals is a central challenge for data-driven materials discovery. We present SemiMat, a controlled benchmark for semi-supervised materials property regression, and MatRank, a reliability-weighted objective for continuous pseudo-label uncertainty. SemiMat fixes labeled and unlabeled crystal inputs, graph-backbone interfaces, validation-only checkpoint selection, held-out test reporting, normalized MAE (NMAE), and method-rank summaries across six […]
ABLE: Representing and Mapping LLMs via Attribution-Based Large-model Embedding
von Zirui Wang, Yusen Hou, Shaofeng Liang, Bowen Tian, Yanlin Zhang, Wenshuo Chen, Yutao Yue am September 1, 2026 um 4:00 a.m.
arXiv:2606.07524v2 Announce Type: replace-cross Abstract: The explosive growth of large language models (LLMs) has created a heterogeneous and poorly documented ecosystem, making systematic model comparison increasingly important for provenance auditing, security analysis, and model selection. Existing representation methods struggle to address this setting efficiently. Approaches analyzing internal parameters are powerful when architectures are compatible, but face scalability barriers under structural heterogeneity, while methods relying on external outputs […]
Correctness Forensics for Batch Speculative Decoding: Diagnosing the Ragged Tensor Problem
von Ranran Haoran Zhang, Soumik Dey, Ashirbad Mishra, Hansi Wu, Binbin Li, Rui Zhang am September 1, 2026 um 4:00 a.m.
arXiv:2510.22876v4 Announce Type: replace-cross Abstract: Inference optimizations are routinely evaluated by throughput alone, without verifying output correctness. We conduct a forensic analysis of batch speculative decoding and find that several widely-used implementations silently produce corrupted outputs (repetitive tokens, symbols) while reporting competitive speed; failures invisible to metrics like ROUGE. We trace the root cause to the ragged tensor problem: variable token acceptance desynchronizes position IDs, attention masks, and KV-cache across a […]
Variable-Length Audio Fingerprinting
von Hongjie Chen, Hanyu Meng, Huimin Zeng, Ryan A. Rossi, Lie Lu, Josh Kimball am September 1, 2026 um 4:00 a.m.
arXiv:2603.23947v2 Announce Type: replace-cross Abstract: Audio fingerprinting converts audio to much lower-dimensional representations, allowing distorted recordings to still be recognized as their originals through similar fingerprints. Existing deep learning approaches rigidly fingerprint fixed-length audio segments, thereby neglecting temporal dynamics during segmentation. To address limitations due to this rigidity, we propose Variable-Length Audio FingerPrinting (VLAFP), a novel method that supports variable-length fingerprinting. To the best of our […]
BiG-SURE – Bipartite Graph for Semantic Uncertainty and Reliability Estimation of LLMs
von Debarpan Bhattacharya, Malay Phadke, Sriram Ganapathy am September 1, 2026 um 4:00 a.m.
arXiv:2608.30646v1 Announce Type: cross Abstract: Reliable uncertainty estimation is a crucial requirement for deploying large language models (LLMs) and vision-language models (VLMs) in safety-critical settings, especially when the model parameters are not accessible (black-box). We propose BiG-SURE, an uncertainty estimator based on cross-temperature semantic agreement. The method samples low-temperature responses as stable semantic anchors and high-temperature responses as probes under meaning-preserving input transformations. It then constructs an […]
RTPO: Reverse-Turn Policy Optimization for Stabilizing Agentic RL Training
von Yugu Li, Zehong Cao, Jianglin Qiao, Siyi Hu am September 1, 2026 um 4:00 a.m.
arXiv:2608.18682v3 Announce Type: replace Abstract: Training multi-turn agentic workflows with reinforcement learning (RL) enables large language models to perform complex reasoning, use external tools, and conduct iterative search beyond single-turn settings. Yet multi-turn RL training remains highly unstable, often causing severe performance degradation as the number of turns increases. Through theoretical analysis, we identify three tightly coupled sources of instability: rollout-training context mismatch, weak turn-level credit assignment under sparse […]
von Haihang Xia, Xinyu Zhao, Xuecheng Wang, John Goodenough, Charith Abhayaratne, Panagiotis A. Panagiotou, Chunyi Song, Tiantai Deng am September 1, 2026 um 4:00 a.m.
arXiv:2606.06159v2 Announce Type: replace-cross Abstract: Spiking neural networks (SNNs) have the potential to emerge as the third generation of neural networks and have attracted increasing attention across a wide range of applications. However, the large number of synaptic connections in SNNs leads to intensive weight-update computation by on-chip learning algorithms during training, resulting in substantial hardware resource utilization and energy consumption. Among existing SNN learning algorithms, spike-timing-dependent plasticity (STDP) is one of the most […]
Divide-and-Conquer Modeling for the CTF-4-Science Lorenz Benchmark
von Shundong Li am September 1, 2026 um 4:00 a.m.
arXiv:2606.10084v2 Announce Type: replace-cross Abstract: This submission documents the divide-and-conquer modeling strategy developed for the CTF-4-Science Lorenz Chaotic Systems Challenge at AI-DEEDS 2026. The challenge uses the CTF-4-Science Lorenz benchmark to evaluate chaotic-system prediction across twelve hidden scores and five scenario families: clean forecasting, noisy reconstruction, noisy-input forecasting, few-shot learning, and parametric generalization. Rather than forcing one model class to handle all regimes, the final system matched each […]
Gurukul AI: An Interactive AI-Driven Educational Platform for Indian Education System
von Isha Narang, Sneh Gosai, Mayank Singh am September 1, 2026 um 4:00 a.m.
arXiv:2608.28611v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) like ChatGPT and LLaMA have transformed AI-driven education, but these systems are predominantly trained on Western-centric data, making them ill-suited for regional curricula like India’s. The Indian education system is linguistically diverse, exam-oriented, and structured around standardized syllabi, not addressed by existing datasets or tools. In this work, we curate a syllabus-aligned QA dataset based on NCERT (National Council of Educational Research and […]
Memory-Native Non-Terrestrial Networks for Embodied Intelligence
von Chengyang Li, Yikun Wang, Jiahui He, Yujie Wan, Shuai Wang, Yuan Wu, Yik-Chung Wu, Chengzhong Xu, Huseyin Arslan am September 1, 2026 um 4:00 a.m.
arXiv:2607.00029v2 Announce Type: replace-cross Abstract: Non-terrestrial networks (NTN) provide ubiquitous connectivity for embodied intelligence (EI), enabling robots in the wilderness to leverage cloud resources or report critical information to remote centers. However, the synergy is nontrivial due to the highly dynamic, resource-constrained, topology-varying, and task-oriented environment. Existing memoryless NTN protocols become inefficient, since the decisions are driven by local channel conditions and instantaneous service demands. To address these […]
Learning to Trace Seiberg Dualities
von Jonathan J. Heckman, Shani Meynet, Alessandro Mininno, Gary Shiu am September 1, 2026 um 4:00 a.m.
arXiv:2607.28628v2 Announce Type: replace-cross Abstract: Dualities play an important role in establishing both microscopic and emergent phenomena in a wide range of physical systems. In practice, though, it can often be computationally challenging to establish when two systems are dual, even when all of the “rules of the game” are well-known. Said differently, when confronted with two systems, how can one efficiently establish that they are in fact dual? In this paper we use machine learning methods to address this question for Seiberg dualities of […]
von Hehai Lin, Yu Yan, Zixuan Wang, Bo Xu, Sudong Wang, Weiquan Huang, Ruochen Zhao, Minzhi Li, Chengwei Qin am September 1, 2026 um 4:00 a.m.
arXiv:2603.21475v2 Announce Type: replace Abstract: Automatic Multi-Agent Systems (MAS) generation has emerged as a promising paradigm for solving complex reasoning tasks. However, existing frameworks are fundamentally bottlenecked when applied to knowledge-intensive domains (e.g., healthcare and law). They either rely on a static library of general nodes like Chain-of-Thought, which lack specialized expertise, or attempt to generate nodes on the fly. In the latter case, the orchestrator is not only bound by its internal knowledge limits but must also […]
Token-Efficient Data Reasoning Agents via Adaptive Structuring of Unstructured Data
von Milad Rezaei Hajidehi, Qitong Wang, Stratos Idreos am September 1, 2026 um 4:00 a.m.
arXiv:2608.31082v1 Announce Type: new Abstract: Valuable data remains embedded in unstructured sources: web pages, reports, contracts, filings, earnings calls, and PDFs. The big bet in enterprise AI is deploying LLM agents that reason over this data to answer complex questions for every knowledge worker. Agents can do this today, but at prohibitive cost. Each question repeatedly opens large documents to recover scattered evidence, consuming up to a million tokens. However, if the data were already structured, the same question would reduce to a cheap database […]
von Tim Genewein, Matija Franklin, Alexander Lerchner, Laurent Orseau, Samuel Albanie, Adam Bales, Cole Wyeth, Stephanie Chan, Iason Gabriel, Joel Z. Leibo, Allan Dafoe, Marcus Hutter, Thore Graepel, Shane Legg am September 1, 2026 um 4:00 a.m.
arXiv:2606.12683v2 Announce Type: replace Abstract: Over the last decade, building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations. Achieving this goal would have profound and far-reaching impacts on human society, which raises many complex questions for the decade ahead. This report investigates how AI itself might continue to develop in a post-AGI world along the continuum of machine intelligence. The endpoint of this continuum, Universal AI, is […]
Single Canonical Prompts Underestimate LLM Safety’s Surface-Form Sensitivity
von Yongxi Zhou, Junwei Yao, Yuanzhe Liu, Zihan Dong, Wenbo Ye, Jiaxi Wen, Lai Yun Choi am September 1, 2026 um 4:00 a.m.
arXiv:2608.02665v2 Announce Type: replace-cross Abstract: A benchmark score is a measurement instrument, yet most benchmarks read each item at a single canonical surface form. We ask whether that reading is faithful: when an item’s intent is held fixed and only its meaning-preserving surface form varies, does the canonical-form score estimate model behavior well, and how much of any variation is decoding/judge noise rather than signal? We instantiate this in safety, a high-stakes setting with no gold label to average toward. To avoid prior confounds, we […]
CLIPure: Purification in Latent Space via CLIP for Adversarially Robust Zero-Shot Classification
von Mingkun Zhang, Keping Bi, Wei Chen, Jiafeng Guo, Xueqi Cheng am September 1, 2026 um 4:00 a.m.
arXiv:2502.18176v3 Announce Type: replace-cross Abstract: In this paper, we aim to build an adversarially robust zero-shot image classifier. We ground our work on CLIP, a vision-language pre-trained encoder model that can perform zero-shot classification by matching an image with text prompts “a photo of a .”. Purification is the path we choose since it does not require adversarial training on specific attack types and thus can cope with any foreseen attacks. We then formulate purification risk as the KL divergence between the joint distributions of the […]
Governing Well in the Algorithmic Age: The Foundations of Digital Statecraft
von Zeynep Engin, Tim Gordon, Viviana Bastidas, Tom Crick, Jon Crowcroft, Jean-Martin Denis, David J. Hand, Ed Humpherson, Lauren Maffeo, Jakob M\"okander, Irene Ng, Anastasija Nikiforova, Giulio Quaggiotto, David Uriel Socol de la Osa, Rhonda Syler, Philip Treleaven, Stefaan Verhulst am September 1, 2026 um 4:00 a.m.
arXiv:2607.18483v3 Announce Type: replace-cross Abstract: The digital substrate – data, algorithms, infrastructure, platforms, applications – is being governed without adequate conceptual foundations. The ability and legitimacy required to govern this substrate, and to govern with it, are simultaneously misaligned, contested, and structurally absent. We introduce digital statecraft as the organising concept for this emerging field, arguing that ‘digital’ reconstitutes the statecraft question rather than merely extending its domain. The concept operates on two […]
Beyond Factual QA: Mentorship-Oriented Question Answering over Long-Form Multilingual Content
von Parth Bhalerao, Ruiwen Guan, Diola Dsouza, Oana Ignat am September 1, 2026 um 4:00 a.m.
arXiv:2601.17173v3 Announce Type: replace-cross Abstract: Question answering systems are typically evaluated on factual correctness, yet many real-world applications-such as education and career guidance-require mentorship: responses that provide reflection and guidance. Existing QA benchmarks rarely capture this distinction, particularly in multilingual and long-form settings. We introduce MentorQA, the first multilingual dataset and evaluation framework for mentorship-focused question answering from long-form videos, comprising nearly 9,000 QA pairs from 180 […]