arXiv AI June 26, 2026 By Jasmine Brazilek, Juliana Seawell

Helpfulness Hurts: Domain-Dependent Degradation of Mid-Trained Compassion Values Under Post-Training

arXiv:2606. 26102v1 Announce Type: cross Abstract: Standard post-training pipelines apply supervised fine-tuning (SFT) and reinforcement learning (RL) to make language models helpful, but these processes may inadvertently degrade values instilled during pre-training.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Aug 25

Alignment midtraining for animals

The paper studies how midtraining with synthetic documents can align AI models to the value of animal compassion. It introduces ANIMA, a 26‑question benchmark covering 13 ethical dimensions, and shows that training on 3,000 documents yields 77% accuracy versus 40% for instruction‑tuning. The improvement disappears after 5,000 samples, indicating that document‑based interventions may need explicit preservation strategies.

By Jasmine Brazilek, Miles Tidmarsh

benchmarks safety

arXiv AI

Jun 4

Do LLMs Hold Their Values? MANTA: A Multi-Turn Adversarial Benchmark for Animal Welfare Reasoning

arXiv:2605. 16301v2 Announce Type: replace-cross Abstract: Evaluating animal welfare reasoning in LLMs remains an open challenge despite rapid deployment in consumer and professional contexts where welfare considerations appear implicitly in everyday queries.

By Isabella Luong, Joyee Chen, Arturs Kanepajs, Jasmine Brazilek, Sankalpa Ghose, David Williams-King, Linh Le, Allen Lu

llms benchmarks safety

arXiv Machine Learning

Jun 5

Moral Sensitivity in LLMs: A Tiered Evaluation of Contextual Bias via Behavioral Profiling and Mechanistic Interpretability

arXiv:2605. 03217v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in settings that require nuanced ethical reasoning, yet existing bias evaluations treat model outputs as simply "biased" or "unbiased.

By Yash Aggarwal, Atmika Gorti, Vinija Jain, Aman Chadha, Krishnaprasad Thirunarayan, Manas Gaur

llms efficiency safety

arXiv Computation and Language

Sep 17

Does Moral Reasoning Training Help or Hurt? Red-Teaming RL-Trained Ethical Agents with Persona Attacks

arXiv:2609.17552v1 Announce Type: new Abstract: Moral-reward RL can make language-model agents more cooperative, but whether that alignment survives adversarial persona pressure is unknown. Such atta...

By Arth Singh

llms agents reinforcement-learning benchmarks safety

arXiv AI

Jul 15

Rethinking Reward Models for Multi-Domain Test-Time Scaling

arXiv:2510. 00492v3 Announce Type: replace Abstract: The reliability of large language models (LLMs) during test-time scaling is often assessed with \emph{external verifiers} or \emph{reward models} that distinguish correct reasoning from flawed logic.

By Dong Bok Lee, Seanie Lee, Sangwoo Park, Minki Kang, Jinheon Baek, Dongki Kim, Dominik Wagner, Jiongdao Jin, Heejun Lee, Tobias Bocklet, Jinyu Wang, Jingjing Fu, Sung Ju Hwang, Jiang Bian, Lei Song

llms reinforcement-learning

arXiv AI

Sep 1

Drop the Act: Probe-Filtered RL for Faithful Chain-of-Thought Reasoning

arXiv:2605.11467v2 Announce Type: replace-cross Abstract: Reasoning models post-hoc rationalize answers they have already committed to internally, producing chains of *reasoning theater*: deliberativ...

By Swapnil Parekh, Naman Goyal

llms reinforcement-learning benchmarks safety

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Related stories

Alignment midtraining for animals

Do LLMs Hold Their Values? MANTA: A Multi-Turn Adversarial Benchmark for Animal Welfare Reasoning

Moral Sensitivity in LLMs: A Tiered Evaluation of Contextual Bias via Behavioral Profiling and Mechanistic Interpretability

Does Moral Reasoning Training Help or Hurt? Red-Teaming RL-Trained Ethical Agents with Persona Attacks

Rethinking Reward Models for Multi-Domain Test-Time Scaling

Drop the Act: Probe-Filtered RL for Faithful Chain-of-Thought Reasoning