AI PAPERS

Optimized supervised fine-tuning data composition enables reasoning models to consistently process and respond in diverse non-English languages without requiring reasoning supervision in each target language.

A proxy layer isolates critical ROS 2 subscribers from degraded ones via topic splitting and dynamic rate control to eliminate DDS backpressure.

Neurosymbolic reasoning is vulnerable to incorrect but verdict-matching formal translations, which are addressed by a generative verification method that scores reference equivalence without an oracle and improves downstream accuracy.

ActReview is a rebuttal-guided post-training framework that generates diagnostic claims and concrete revision suggestions for peer review by leveraging author responses as latent supervision.

The study introduces a benchmark to evaluate whether research methods are specified clearly enough for implementation, finding that identifying missing details is the primary challenge for language models.

MaP-WAM improves non-Markovian robotic manipulation by separating memory-grounded planning from plan-conditioned execution, using compact episodic segment records and progress-calibrated action chunks to maintain fixed inference latency.

Negative Self-Distillation improves large language model reasoning by pushing models away from self-generated flawed reasoning via a dynamic gating mechanism that protects linguistic capabilities.

FreeFlow is a hierarchical transformer for optical flow that eliminates task-specific inductive biases and achieves state-of-the-art accuracy using window, shifted-window, and global attention.