Tootfinder

Crosslisted article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/2]:
- The Geometry of Harmful Intent: Training-Free Anomaly Detection via Angular Deviation in LLM Resi...
Isaac Llorente-Saguer
https://arxiv.org/abs/2603.27412 https://mastoxiv.page/@arXiv_csLG_bot/116323180390164201
- LongCat-Next: Lexicalizing Modalities as Discrete Tokens
Meituan LongCat Team, et al.
https://arxiv.org/abs/2603.27538 https://mastoxiv.page/@arXiv_csCV_bot/116323299668026852
- Emergent Social Intelligence Risks in Generative Multi-Agent Systems
Huang, Jiang, Wang, Zhuang, Luo, Ma, Xu, Chen, Moniz, Lin, Chen, Chawla, Dziri, Sun, Zhang
https://arxiv.org/abs/2603.27771 https://mastoxiv.page/@arXiv_csMA_bot/116322908437739020
- KVSculpt: KV Cache Compression as Distillation
Bo Jiang, Sian Jin
https://arxiv.org/abs/2603.27819 https://mastoxiv.page/@arXiv_csLG_bot/116323241993833314
- Q-Bridge: Code Translation for Quantum Machine Learning via LLMs
Runjia Zeng, Priyabrata Senapati, Ruixiang Tang, Dongfang Liu, Qiang Guan
https://arxiv.org/abs/2603.27836 https://mastoxiv.page/@arXiv_quantph_bot/116323164660887506
- EffiSkill: Agent Skill Based Automated Code Efficiency Optimization
Zimu Wang, Yuling Shi, Mengfan Li, Zijun Liu, Jie M. Zhang, Chengcheng Wan, Xiaodong Gu
https://arxiv.org/abs/2603.27850 https://mastoxiv.page/@arXiv_csSE_bot/116322989347928729
- Efficient Inference of Large Vision Language Models
Surendra Pathak
https://arxiv.org/abs/2603.27960 https://mastoxiv.page/@arXiv_csLG_bot/116323256085918152
- CDH-Bench: A Commonsense-Driven Hallucination Benchmark for Evaluating Visual Fidelity in Vision-...
Kesheng Chen, Yamin Hu, Qi Zhou, Zhenqian Zhu, Wenjian Luo
https://arxiv.org/abs/2603.27982 https://mastoxiv.page/@arXiv_csCV_bot/116323319000206060
- MOSS-VoiceGenerator: Create Realistic Voices with Natural Language Descriptions
Huang, Fan, Jiang, Jiang, Tu, Zhu, Zhang, Zhao, Yang, Fei, Li, Yang, Cheng, Qiu
https://arxiv.org/abs/2603.28086 https://mastoxiv.page/@arXiv_csSD_bot/116322971980743316
- Does Claude's Constitution Have a Culture?
Parham Pourdavood
https://arxiv.org/abs/2603.28123 https://mastoxiv.page/@arXiv_csCY_bot/116322911684465443
- MiroEval: Benchmarking Multimodal Deep Research Agents in Process and Outcome
Fangda Ye, et al.
https://arxiv.org/abs/2603.28407 https://mastoxiv.page/@arXiv_csAI_bot/116323220038984883
- IsoQuant: Hardware-Aligned SO(4) Isoclinic Rotations for LLM KV Cache Compression
Zhongping Ji
https://arxiv.org/abs/2603.28430 https://mastoxiv.page/@arXiv_csLG_bot/116323286231537351
- Entropic Claim Resolution: Uncertainty-Driven Evidence Selection for RAG
Davide Di Gioia
https://arxiv.org/abs/2603.28444 https://mastoxiv.page/@arXiv_csAI_bot/116323220366355511
- Moving Beyond Review: Applying Language Models to Planning and Translation in Reflection
Seyed Parsa Neshaei, Richard Lee Davis, Tanja K\"aser
https://arxiv.org/abs/2603.28596 https://mastoxiv.page/@arXiv_csHC_bot/116323161382060848
- ResAdapt: Adaptive Resolution for Efficient Multimodal Reasoning
Huanxuan Liao, Zhongtao Jiang, Yupu Hao, Yuqiao Tan, Shizhu He, Jun Zhao, Kun Xu, Kang Liu
https://arxiv.org/abs/2603.28610 https://mastoxiv.page/@arXiv_csCV_bot/116323344559859277
- The Ultimate Tutorial for AI-driven Scale Development in Generative Psychometrics: Releasing AIGE...
Lara Russell-Lasalandra, Hudson Golino, Luis Eduardo Garrido, Alexander P. Christensen
https://arxiv.org/abs/2603.28643 https://mastoxiv.page/@arXiv_csAI_bot/116323236095523987
- SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning
Philip Schroeder, Thomas Weng, Karl Schmeckpeper, Eric Rosen, Stephen Hart, Ondrej Biza
https://arxiv.org/abs/2603.28730 https://mastoxiv.page/@arXiv_csRO_bot/116323253135037252
- ParaSpeechCLAP: A Dual-Encoder Speech-Text Model for Rich Stylistic Language-Audio Pretraining
Anuj Diwan, Eunsol Choi, David Harwath
https://arxiv.org/abs/2603.28737 https://mastoxiv.page/@arXiv_eessAS_bot/116322903493463665
toXiv_bot_toot

Bridging Distant Ideas: the Impact of AI on R&D and Recombinant Innovation
Emanuele Bazzichi, Massimo Riccaboni, Fulvio Castellacci
https://arxiv.org/abs/2604.02189 https://arxiv.org/pdf/2604.02189 https://arxiv.org/html/2604.02189
arXiv:2604.02189v1 Announce Type: new
Abstract: We study how artificial intelligence (AI) affects firms' incentives to pursue incremental versus radical knowledge recombinations. We develop a model of recombinant innovation embedded in a Schumpeterian quality-ladder framework, in which innovation arises from recombining ideas across varying distances in a knowledge space. R&D consists of multiple tasks, a fraction of which can be performed by AI. AI facilitates access to distant knowledge domains, but at the same time it also increases the aggregate rate of creative destruction, shortening the monopoly duration that rewards radical innovations. Moreover, excessive reliance on AI may reduce the originality of research and lead to duplication of research efforts. We obtain three main results. First, higher AI productivity encourages more distant recombinations, if the direct facilitation effect is stronger than the indirect effect due to intensified competition from rivals. Second, the effect of increasing the share of AI-automated R&D tasks is non-monotonic: firms initially target more radical innovations, but beyond a threshold of human-AI complementarity, they shift the focus toward incremental innovations. Third, in the limiting case of full automation, the model predicts that optimal recombination distance collapses to zero, suggesting that fully AI-driven research would undermine the very knowledge creation that it seeks to accelerate.
toXiv_bot_toot