arXiv AI

A Transdiagnostic Space of Disorder Like Phenotypes in Reinforcement Learning Agents

arXiv:2607. 07753v1 Announce Type: cross Abstract: Modelling psychological disorders in artificial agents offers both a testbed for computational psychiatry and a lens on the failure modes of affective control.

arXiv Machine Learning

Aug 6

Reward Structure Shapes the Interaction Between Episodic Exploration and Neural Memory in Reinforcement Learning

arXiv:2608. 05111v1 Announce Type: new Abstract: In partially observable reinforcement learning, agents face a dual bottleneck: they must explore to encounter rewarding states and retain that experience in memory to optimize their policies.

By Jai Malegaonkar, Rohan Patil, Henrik I. Christensen

agents reinforcement-learning efficiency

arXiv AI

Jun 3

When RLHF Fails: A Mechanistic Taxonomy of Reward Hacking, Collapse, and Evaluator Gaming

arXiv:2606. 03238v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) makes large-scale post-training possible by replacing an underspecified human objective with learned and scalable proxies.

By Zelalem Abahana

llms reinforcement-learning safety

arXiv AI

Jul 15

Human-AI Agent Interaction as a Neuroplastic Training Environment

arXiv:2607. 12823v1 Announce Type: new Abstract: Interaction with AI agents has become one of the most frequent activities of everyday digital life.

By Eranga Bandara, Ross Gore, Asanga Gunaratna, Ravi Mukkamala, Nihal Siriwardanagea, Gihan Siriwardanagea, Sachini Rajapakse, Isurunima Kularathna, Pramoda Karunarathna, Chalani Rajapakse, Sachin Shetty, Christopher K. Rhea, Ng Wee Keong, Kasun De Zoysa, Amin Hass, Shaifali Kaushik, Wathsala Herath, Preston Samuel, Anita H. Clayton, Atmaram Yarlagadd

llms agents

arXiv Machine Learning

Jun 18

Online Reward-Punishment Learning from Fixed-Channel Perceptual Event Streams without Environment Rewards

arXiv:2606. 18963v1 Announce Type: new Abstract: We study online reward-punishment learning when the environment provides no scalar reward or evaluative label.

By Zirong Li

agents

arXiv Machine Learning

Jun 16

Faithful Action-unit Causal Reasoning for Counterfactually Faithful Emotion Explanations

arXiv:2606. 15779v1 Announce Type: cross Abstract: Multimodal models can name the action units (AUs) behind a facial emotion, but their AU->emotion rationales are typically plausible rather than faithful: nothing forces the AUs a model invokes to be the AUs that actually drive its prediction.

By Van Thong Huynh, Hong Hai Nguyen, Thuy Pham, Trong Nghia Nguyen, Soo-Hyung Kim

multimodal benchmarks

arXiv Machine Learning

Jul 31

Psych-ECA: A Reproducible Semi-Synthetic Benchmark for Synthetic Control Arms in Longitudinal Psychiatry

arXiv:2607. 27224v1 Announce Type: cross Abstract: External and synthetic control arms (ECAs) are entering psychiatric drug development, but the field lacks a benchmark that evaluates the properties regulators care about: not only how accurately a method reconstructs untreated trajectories, but whether its uncertainty is calibrated, whether it is robust to the informative observation times common in mental-health records (sicker patients are seen more often), and what false-positive rate it induces in go/no-go trial decisions.

By Aakash Bhagat, Shashank Choudhary

benchmarks safety

arXiv AI

Jun 2

GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective Testing

arXiv:2606. 00860v1 Announce Type: cross Abstract: Self-report questionnaires remain the prevailing tool for probing the psychological states of persona-conditioned agents (PC-Agents).

By Ming Wang, Shuang Wu, Bixuan Wang, Lu Lin, Yuxin Chen, Xiaocui Yang, Daling Wang, Shi Feng, Yifei Zhang, Yufan Sun

llms agents benchmarks safety

arXiv Machine Learning

Jun 30

Predictive Objectives Discard Exogenous Control-Relevant Features: A Controlled Mechanistic Study

arXiv:2606. 30068v1 Announce Type: new Abstract: Joint-embedding predictive (JEPA-style) objectives learn representations by predicting future latents.

By Ayan Pendharkar

rag agents

arXiv Machine Learning

Jul 9

Reward Valuation in Vision Language Models: Causal Mechanisms Underlying Anhedonia

arXiv:2607. 06626v1 Announce Type: new Abstract: Recent Vision-Language Models capture increasingly complex aspects of human cognition.

By Melika Honarmand, Samin Mahdipour Aghabagher, Martin Schrimpf

llms multimodal safety

arXiv Machine Learning

Aug 5

Sensitivity, Causality, and Repair Dissociate: A Layer-Wise Analysis of Perturbation Robustness and Its Scaling

arXiv:2608. 03842v1 Announce Type: cross Abstract: When a language model fails on surface-perturbed input (typos, OCR noise, homophones), "which layer is responsible" has three natural operationalizations: where representations diverge most (sensitivity), where restoring clean activations recovers the prediction (causality), and where a small adapter can repair the damage (compensatory capacity) - and we show these three layer maps dissociate.

By Nathan Labiosa, David Buff, Ena Nayak, Erica Donno

llms computer-vision

arXiv Machine Learning

Jun 5

A Pre-Registered Causal Partition of Self-Consistency Elicitation and Reward Design in RLVR

arXiv:2606. 05932v1 Announce Type: cross Abstract: Reinforcement learning from verifiable rewards (RLVR) improves reasoning even when the reward signal is spurious -- assigning credit to the group-plurality answer rather than a ground-truth verifier.

By Yuze Gao

reinforcement-learning safety

arXiv AI

Aug 6

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning

arXiv:2608. 04663v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning often adds social terms to individual rewards, yet the scale of those terms is usually chosen by hand.

By Aaditya Mehta, Arya Shah

agents reinforcement-learning

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