Self-Evolving Software Agents

Disclosure in the era of generative artificial intelligence

Generative artificial intelligence (AI) has rapidly become embedded in academic writing, assisting with tasks ranging from language editing to drafting text and producing evidence. Despite

Debiasing Reward Models via Causally Motivated Inference-Time Intervention

arXiv:2604.27495v1 Announce Type: cross Abstract: Reward models (RMs) play a central role in aligning large language models (LLMs) with human preferences. However, RMs are often

Why Self-Supervised Encoders Want to Be Normal

arXiv:2604.27743v1 Announce Type: cross Abstract: We develop a geometric and information-theoretic framework for encoder-decoder learning built on the Information Bottleneck (IB) principle. Recasting IB as

PiCSAR: Probabilistic Confidence Selection And Ranking for Reasoning Chains

arXiv:2508.21787v2 Announce Type: replace-cross Abstract: Best-of-n sampling improves the accuracy of large language models (LLMs) and large reasoning models (LRMs) by generating multiple candidate solutions

CausalCompass: Evaluating the Robustness of Time-Series Causal Discovery in Misspecified Scenarios

arXiv:2602.07915v2 Announce Type: replace-cross Abstract: Causal discovery from time series is a fundamental task in machine learning. However, its widespread adoption is hindered by a

Disclosure in the era of generative artificial intelligence

Debiasing Reward Models via Causally Motivated Inference-Time Intervention

Why Self-Supervised Encoders Want to Be Normal

PiCSAR: Probabilistic Confidence Selection And Ranking for Reasoning Chains

CausalCompass: Evaluating the Robustness of Time-Series Causal Discovery in Misspecified Scenarios

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