Many of the qualities that matter most in how people learn and grow, how someone regulates their emotions, reflects on a setback, or stays aware of others during a difficult conversation, are not directly observable. They have to be inferred from how someone speaks, moves, and sounds over time, and they resist the kind...
Siddhant Jain, Dimitra Tsovaltzi· Companion Publication of the...· 0 citations
By reframing speech BCIs as adaptive, user-centred systems, this work outlines the interdisciplinary priorities spanning speech neuroscience, neural engineering, machine learning, clinical practice, and neuroethics needed to move from proof-of-concept decoding toward reliable, expressive, and controllable communication...
Moein Khajehnejad, Forough Habibollahi, T. Boccato et al.· 0 citations
Surprisingly, like humans but to a greater extent, LLMs also demonstrated the same hypocrisy in condemning others who, like them, had deployed Bayes'rule.
The results indicate that any immediate effects on opinions of AI are modest, although they leave open the possibility that anthropomorphic language could have an effect in naturalistic settings, or over gradual, continued exposure.
Betty Li Hou, Sophie Hao, Sunoo Park et al.· 0 citations
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It is found that agents are less effective at resolving tasks when building on agent code compared to human code, with task resolve rate drops of up to 13.1%.
Shaswat Patel, Betty Hou, A. Purohit et al.· arXiv.org· 1 citation
CHERRL is introduced, a Controllable Hacking Environment for Rubric-based RL that enables stable reproduction of reward hacking, explicit observation of reward divergence, and identification of hacking onset and analyzes different judge biases from the perspectives of discoverability and exploitability.
An algorithm is introduced that uses the base model's next-token entropy as a proxy to identify key decision points and resample from those positions and it is proved that the method's mixing time scales with the number of decisions in a trace rather than with the number of tokens, which can be much larger.
Felix Zhou, Anay Mehrotra, Quanquan C. Liu· arXiv.org· 3 citations
This work introduces a temporal-graph-learning (TGL) controller that learns these predictions jointly and supplies both outputs in one forward pass, and shows that a small graph model can handle both decisions and improve the language agents it controls.
Xiaoze Liu, Ruowang Zhang, Amir H. Abdi et al.· 2 citations
The framework and EvalAwareBench provide the tools to measure, attribute, and mitigate evaluation awareness, building the foundation for future solutions to ground evaluation awareness in social psychology.
Changling Li, Terry Jingchen Zhang, Jie M. Zhang et al.· arXiv.org· 1 citation
PRISM, a closed-form upper bound on the cross-entropy risk gap between a target model and a proxy variant, is derived and it is proved that it splits exactly into three measurable axes: scale, shape, and head.
Adaptive optimizers such as Adam are standard for training Transformers, but storing gradient first and second moments incurs substantial memory overhead. We introduce PowerStep, a memory-efficient optimizer that achieves coordinate-wise adaptivity without storing second-moment statistics. Motivated by $\ell_p$-norm st...
Yao Lu, Dengdong Fan, Shixun Zhang et al.· 0 citations
GRAVITY (Generation-time Relational Anchoring Via Injected Topological MemorY), a host-independent auxiliary memory layer, is introduced and shows that generation-time structure is a portable complement to existing memory retrieval, while its interaction with host evidence depends on the benchmark and host.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduSep 24, 2026
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
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