The paper introduces little m, an AI agent that helps formulate industrial process control models by combining a domain-specific knowledge repository with LLM-driven interaction. It tackles the challenge of converting messy real-world specifications, including natural language and spatial diagrams, into rigorous mathematical optimization models. The authors also present IPC-Bench, a multimodal dataset of 50 canonical scenarios, and show through automated and human evaluations that little m outperforms state‑of‑the‑art LLMs in generating semantically correct models.
By Yongchao Ye, Xinyu He, Dutliff Boshoff, Way Kuo, Lishuai Li
TARC (Time‑Adaptive Robotic Control) is a reinforcement‑learning framework that lets a policy predict both a control action and how long it should be applied, thereby learning temporally extended actions. By optimizing task performance under constraints on the number of control switches, TARC can adapt its control rate online, using high‑frequency feedback only when necessary. Experiments on a high‑speed RC car, a Unitree Go1 quadruped, and a vision‑language action model show that TARC matches the performance of high‑frequency discrete‑time controllers while operating at less than half their control frequency.
By Arnav Sukhija, Lenart Treven, Jin Cheng, Florian D\"orfler, Stelian Coros, Andreas Krause
TecoPrompt introduces a closed‑loop robust prompt‑learning framework that mitigates label noise in vision‑language models. It uses optimal transport in the CLIP semantic space to generate globally consistent pseudo‑labels, then verifies their reliability by checking trajectory stability over a K‑epoch window and an EMA‑based confidence gate. The verified labels are incorporated into prompt training via a tri‑group objective, yielding significant accuracy gains across multiple noisy datasets, such as a 0.843 accuracy on OxfordPets with 50% asymmetric noise.
By Zeyi Shao, Haowen Hua, Jiaxin Zhang, John See, Zeyd Boukhers, Cong Yang
FLAT (Flexible‑Length Aligned Transmodal representations) is a joint multimodal pre‑training framework that learns a shared encoder for images and text, producing 1‑D continuous embeddings that can be directly used by downstream generative decoders. By combining contrastive alignment with bidirectional cross‑modal generative objectives, FLAT yields representations that are both discriminative and generative, enabling cross‑modal retrieval and generation with a single pre‑training stage. The model achieves strong performance on T2I generation (GenEval 71.1), image captioning (BLEU‑4 40.5, CIDEr 138.6), and retrieval tasks (Recall@5 86.8/75.8 on MS‑COCO, 98.3/93.6 on Flickr30K), and supports linear interpolation, latent space arithmetic, and zero‑shot composed retrieval.
By Guangyu Sun, Shlok Kumar Mishra, Wentao Bao, Robert Zhenheng Yang, Xiao Wang, Xiyuan Wang, Yujunrong Ma, Chen Yuan, Max Xiangjun Fan, Jun Xiao, Jianpeng Cheng
arXiv:2609.16070v1 Announce Type: cross
Abstract: Generative recommendation reformulates item prediction as semantic identifier generation, yet episodic content introduces a fundamentally different s...
By Chenxing Wang, Nantao Zheng, Hao Miao, Juyuan Wang, Xinke Jiang, Yuchen Fang, Aolin Li, Haijun Wu
arXiv:2609.17443v1 Announce Type: new
Abstract: Vision-language models (VLMs) achieve strong visual question answering (VQA) performance, but processing large cluttered images is computationally expe...
By Yihui Peng, Guorui Lu, Qinyu Chen
arXiv:2609.17269v1 Announce Type: new
Abstract: Multimodal large language models generate natural-language responses from visual inputs, yet may mention objects absent from an image. In medication as...
By Ziheng Ren, Qian Gao, Jun Fan, Guohui Ding, Zhenyu Yang, Yuteng Xiao
EMODY Flow is a lightweight flow‑matching framework that generates synchronized full‑body motion and facial expressions conditioned on speech and emotion. It attaches to a frozen Qwen‑3 Omni model, reusing its audio codecs to drive two parallel DiT generators for SMPL‑X body pose and FLAME facial expressions. An auxiliary emotion classifier at training time restores emotion sensitivity, enabling EMODY Flow to achieve state‑of‑the‑art gesture quality on BEAT2 and zero‑shot facial animation on TFHP, with significant improvements in FGD, Beat Correlation, and Diversity metrics.
By Harsh Kumar Agarwal, Xavier Alameda-Pineda, Olivier Perrotin
arXiv:2609.16689v1 Announce Type: new
Abstract: Large-scale vision-language models (VLM) such as CLIP enable strong open-vocabulary reasoning, yet deploying these capabilities on resource-constrained...
By Jinwoo Jeon, GyuYeop Do, Yubin Lim, Nam-Joon Kim, Hyun Gon Ryu, Hyuk-Jae Lee, Byung-Jun Lee
arXiv:2609.17509v1 Announce Type: cross
Abstract: Neural audio codecs are a key component in speech language modeling. However, their high frame rates lead to long sequence lengths, increasing comput...
By Thanapat Trachu, Samuele Cornell, William Chen, Shinji Watanabe
arXiv:2609.16464v1 Announce Type: cross
Abstract: The clinical management of autism spectrum disorder (ASD) faces a bottleneck in early screening, mainly because trained specialists are scarce and co...
By Jun Chen, Qi Zhao, Yunliang Jiang, Shuqin Cao, Yunqiang Lin, Chenglong Jia, Qiang Guo, Guang Dai, Xiongtao Zhang, Mengmeng Wang, Xiaoyue Ma
The paper evaluates audio‑visual predictive turn‑taking models trained on clean data when applied to a noisy cocktail‑party scenario derived from the AVCocktail dataset. Results show a consistent performance drop—up to 38% relative in weighted F1—across both audio and visual modalities, with fine‑tuning improving robustness but varying by modality and pre‑training data size. The study highlights differing generalisation and adaptation abilities of audio versus visual inputs and underscores the need for robust modelling strategies in noisy human interactions.
By Long-Vu Hoang, Naomi Harte
Multi-encoder fusion extends Large Audio-Language Models (LALMs) beyond speech-centric recognition, but selecting encoders via intuition or exhaustive search often introduces redundant representations...
Speech Large Language Models (Speech-LLMs), typically built from a pre-trained speech encoder, a modality projector, and an LLM fine-tuned with Low-Rank Adapters (LoRA), have shown strong Automatic Sp...
Face--voice association models may rely on language or gender cues in the voice rather than on speaker-specific voice characteristics, which can lead to a performance deterioration when the model has...
Neural audio codecs are a key component in speech language modeling. However, their high frame rates lead to long sequence lengths, increasing computational costs. Dynamic frame rate codecs mitigate t...
Uncertainty estimation for Vision-Language-Navigation (VLN) models is a critical task since it can help identify ambiguous and unreliable predictions, enabling agents to make safer navigation decision...
This thesis develops robust and efficient AI frameworks for accelerating crystalline materials discovery by addressing both major stages of the materials-design pipeline: crystal property prediction a...
Multimodal Large Language Models have demonstrated impressive video understanding, yet their ability to reason over long-form narratives is often masked by visual-centric evaluations and inefficient c...
Multimodal representation learning seeks shared representations for cross-modal retrieval and knowledge transfer. Hub-based binding reduces pairwise supervision costs, but separate hub connections can...