{"total":10,"items":[{"citing_arxiv_id":"2605.11125","ref_index":63,"ref_count":2,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Language Modeling with Hyperspherical Flows","primary_cat":"cs.LG","submitted_at":"2026-05-11T18:32:32+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"15266, 2025. [61] Shen Nie, Fengqi Zhu, Chao Du, Tianyu Pang, Qian Liu, Guangtao Zeng, Min Lin, and Chongxuan Li. Scaling up masked diffusion models on text, 2025. [62] Hunter Nisonoff, Junhao Xiong, Stephan Allenspach, and Jennifer Listgarten. Unlocking guidance for discrete state-space diffusion and flow models.arXiv preprint arXiv:2406.01572, 2024. [63] OpenAI. Gpt-4 technical report, 2024. [64] OpenAI. Gpt-oss: open-weight language models by openai. https://github.com/openai/ gpt-oss, 2024. GitHub repository. [65] Vassilis Papadopoulos, Jérémie Wenger, and Clément Hongler. Arrows of time for large language models, 2024. [66] William Peebles and Saining Xie. Scalable diffusion models with transformers, 2023."},{"citing_arxiv_id":"2605.08802","ref_index":16,"ref_count":2,"confidence":0.55,"is_internal_anchor":false,"paper_title":"CoLVR: Enhancing Exploratory Latent Visual Reasoning via Contrastive Optimization","primary_cat":"cs.CV","submitted_at":"2026-05-09T08:47:00+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"CoLVR uses latent contrastive objectives with angle-based perturbation and RL trajectory rewards to increase exploratory visual reasoning in MLLMs, delivering 5-8% gains on VSP, Jigsaw, and MMStar benchmarks.","context_count":1,"top_context_role":"baseline","top_context_polarity":"baseline","context_text":"InThe F ourteenth International Conference on Learning Representations, 2026. [14] Jizheng Ma, Xiaofei Zhou, Yanlong Song, and Han Yan. Cocova: Chain of continuous vision-language thought for latent space reasoning.arXiv e-prints, pages arXiv-2511, 2025. [15] Leland McInnes, John Healy, and James Melville. Umap: Uniform manifold approximation and projection for dimension reduction, 2020. [16] OpenAI. Gpt-4 technical report, 2024. [17] OpenAI. Gpt-4o system card, 2024. [18] Yiming Qin, Bomin Wei, Jiaxin Ge, Konstantinos Kallidromitis, Stephanie Fu, Trevor Darrell, and XuDong Wang. Chain-of-visual-thought: Teaching vlms to see and think better with continuous visual tokens.arXiv preprint arXiv:2511.19418, 2025. [19] Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan"},{"citing_arxiv_id":"2604.18003","ref_index":1,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"SELF-EMO: Emotional Self-Evolution from Recognition to Consistent Expression","primary_cat":"cs.AI","submitted_at":"2026-04-20T09:27:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"SELF-EMO lets LLMs bootstrap better emotion recognition and expression via self-play, data flywheel filtering with smoothed IoU rewards, and SELF-GRPO reinforcement learning, yielding SOTA gains on IEMOCAP, MELD, and EmoryNLP.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.04496","ref_index":60,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"The Indra Representation Hypothesis for Multimodal 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science, and math while transferring across domains and models.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"superior performance even when transferred across domains and models, demon- strating their robustness and generality. Provided we develop it safely, our work illustrates the potential of an exciting new research direction toward automatically designing ever-more powerful agentic systems to benefit humanity. All code is open-sourced at https://github.com/ShengranHu/ADAS. 1 I NTRODUCTION Foundation Models (FMs) such as GPT (OpenAI, 2024; 2022) and Claude (Anthropic, 2024b) are quickly being adopted as powerful general-purpose agents for agentic tasks that need flexible rea- soning and planning (Wang et al., 2024). Despite recent advancements in FMs, solving problems re- liably often requires an agent to be a compound agentic system with multiple components instead of a monolithic model query (Zaharia et al."},{"citing_arxiv_id":"2408.03314","ref_index":25,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters","primary_cat":"cs.LG","submitted_at":"2024-08-06T17:35:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"An adaptive compute-optimal strategy for scaling LLM test-time compute achieves over 4x efficiency gains versus best-of-N and lets smaller models outperform 14x larger ones on some problems.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Hallinan, L. Gao, S. Wiegreffe, U. Alon, N. Dziri, S. Prabhumoye, Y. Yang, S. Gupta, B. P. Majumder, K. Hermann, S. Welleck, A. Yazdanbakhsh, and P. Clark. Self- refine: Iterative refinement with self-feedback, 2023. [24] N. McAleese, R. Pokorny, J. F. Cerón Uribe, E. Nitishinskaya, M. Trębacz, and J. Leike. Llm critics help catch llm bugs.OpenAI, 2024. [25] OpenAI. Gpt-4 technical report, 2024. [26] Y. Qin, S. Liang, Y. Ye, K. Zhu, L. Yan, Y. Lu, Y. Lin, X. Cong, X. Tang, B. Qian, S. Zhao, L. Hong, R. Tian, R. Xie, J. Zhou, M. Gerstein, D. Li, Z. Liu, and M. Sun. Toolllm: Facilitating large language models to master 16000+ real-world apis, 2023. URLhttps://arxiv.org/abs/2307.16789. [27] C. Qu, S. Dai, X."}],"limit":50,"offset":0}