Prépublication
· 2026 arXiv
EN
Reproducible and analytically robust research findings are essential to scientific progress, but evaluating these properties typically requires costly manual effort. We investigate whether LLM agents can help automate reproduction and robustness analysis, usin...
Shicheng Liu, Krista Opsahl-Ong et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Self-driving laboratories (SDLs) accelerate scientific discovery by automating experimental execution and optimization. AI agents can increasingly propose new experiments, but existing SDLs can only run the procedures they were built for. Adapting them to a ne...
Jiaru Bai, Yuchi Zhao et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Sparse autoencoders (SAEs) explain foundation model representations by decomposing activations into sparse combinations of learned dictionary atoms. However, the learned dictionaries can vary across training runs, limiting the reliability and reproducibility o...
Jinho Choi, Hyesu Lim et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Transformer prefill reuses weights across tokens and is dominated by matrix multiplication and activation data movement; single-token decode has limited reuse and is dominated by weight and KV-cache reads. We present FUTRANSFORMER, a CUDA implementation of a L...
Wei Wang
#cs.AI
Prépublication
· 2026 arXiv
EN
Computational drug discovery requires agents to compose scientific operations and adapt to changing evidence and constraints. Here we introduce DrugHarness, an evolvable agent harness coupling composable scientific execution with conditional skill induction. I...
Yuxing Chen, Zhisheng Zhang et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Existing agent benchmarks typically evaluate agents on well-specified tasks defined by issue reports, specifications, or task tickets. Real-world customer delivery is different: forward-deployed engineers (FDEs) must clarify ambiguous requirements, work in liv...
Dongxu Yang, Hongda Yan et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Large language models (LLMs) are expanding from assisting scientific research and engineering to autonomously developing and improving AI systems, advancing AI for AI (AI4AI) as a pathway toward recursive self-improvement (RSI). Workflow-driven machine learnin...
Shangheng Du, Jinxin Shi et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Large language models (LLMs) can often tell when they are being tested. This ability, known as evaluation awareness, can lead models to act more cautiously during safety evaluations than in real use. Existing work has studied evaluation awareness almost entire...
Trizal Garg, Sibayan Mitra et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Deep search agents solve knowledge-intensive tasks through long-horizon, multi-turn interactions, where assigning credit to individual search decisions is fundamentally difficult. Outcome-based reinforcement learning provides only a single terminal reward shar...
Siqi Chen, Zhengxi Lu et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Continuous diffusion language models (CDLMs) offer an alternative approach to parallel language generation; however, most existing studies directly use transformer architectures from either causal large language models (LLMs) or Diffusion Transformers (DiTs) f...
Weijian Luo, Yujian Chen et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Training language agents is bottlenecked by the high cost of long-horizon rollouts, yet standard agentic reinforcement learning (RL) learns only from a terminal scalar reward, leaving the supervision in intermediate steps largely unused. Prior work exploits th...
Haofei Yu, Yixing Chen et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Automated research with LLM agents has shown promising results in various domains. Through iterative refinement and scored verifiers, LLM agents are capable of discovering new ideas. There are different ways to scale automated research: let one agent iterate f...
Shangyin Tan, Ben Zhou et al.
#cs.AI