Understanding Reasoning from Pretraining to Post-Training
Paper • 2607.16097 • Published • 28
AI & ML interests
AI security & privacy, algorithmic bias, foundations of ML
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Flash-BoN: Instant Drafts for Inference-Time Scaling in Diffusion Models
Teaching Pretrained Language Models to Think Deeper with Retrofitted Recurrence
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Answer Matching Outperforms Multiple Choice for Language Model Evaluation
Paper • 2507.02856 • Published • 9
MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation
Paper • 2508.11032 • Published • 2
The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs
Paper • 2509.09677 • Published • 37
Training Dynamics Impact Post-Training Quantization Robustness
Paper • 2510.06213 • Published • 3
Adaptive Attacks on Trusted Monitors Subvert AI Control Protocols
Paper • 2510.09462 • Published • 6
Efficient Parallel Samplers for Recurrent-Depth Models and Their Connection to Diffusion Language Models
Paper • 2510.14961 • Published • 8
Rewiring Experts on the Fly:Continuous Rerouting for Better Online Adaptation in Mixture-of-Expert models
Paper • 2510.14853 • Published • 5
Teaching Pretrained Language Models to Think Deeper with Retrofitted Recurrence
Paper • 2511.07384 • Published • 21
Training AI Co-Scientists Using Rubric Rewards
Paper • 2512.23707 • Published • 21
Scaling Open-Ended Reasoning to Predict the Future
Paper • 2512.25070 • Published • 20
NESSiE: The Necessary Safety Benchmark -- Identifying Errors that should not Exist
Paper • 2602.16756 • Published • 4
Multi-Stream LLMs: Unblocking Language Models with Parallel Streams of Thoughts, Inputs and Outputs
Paper • 2605.12460 • Published • 17
Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs
Paper • 2603.24511 • Published