Self-Evaluation Elicitation (SEE) is introduced, a method that surfaces a latent ability to predict how a judge will score its own output through a short cycle comprising a calibration-coupled reinforcement learning phase that improves the answer and predicts the judge, followed by a masked distillation phase that sharpens the prediction while leaving the answer untouched.
RealClawBench is introduced, a live benchmark framework built from real OpenClaw sessions to capture the distribution, diversity, and real-world difficulty of deployed agent use and provides a practical path toward benchmarks that better measure agent capability in actual use.
Zongwei Lv, Zhewen Tan, Yao-Ming Li et al.· arXiv.org· 1 citation· ⚡1
By isolating task-specific patterns into independent modules, CRAM mitigates catastrophic forgetting across tasks and boost parameter efficiency, and utilizes adaptive-rank instantiation to identify the capability gap between existing expert capability and new task demands, and dynamically allocate only the necessary parameters.
Jun Tang, Zhen Xie, Yucheng Shi et al.· arXiv.org· 0 citations
This work introduces LongJudgeBench, a comprehensive benchmark for evaluating LLM judges on long-form outputs across diverse real-world scenarios and judging protocols, and systematically evaluates a broad range of LLM judges, covering multiple base models and judging settings.
Junjie Chen, Yuxin Dong, Haitao Li et al.· arXiv.org· 0 citations
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An Item Response Theory-based indicator is introduced that surfaces likely mislabels at 95% precision in the top 200 examples across seven preference and multiple-choice benchmarks using responses from 114 models, outperforming a supervised classifier.
Overall, the results show that HLV can be learned as annotator-specific label-explanation behavior, suggesting a path toward scalable explanation-based annotation grounded in annotator histories rather than labels alone.
This work examines whether large language models exhibit similar behaviors when assigned high or low status personas, and shows that LLMs show key socio-cognitive effects of power, albeit with nuances and variability, linking simulated interactions to both desirable and unsafe behaviors.
Anvesh Rao Vijjini, S. Manjunath, Snigdha Chaturvedi· Annual Meeting of the Associ...· 0 citations
A controlled pilot shows that LLM-generated Xiaohongshu-style posts can shift perceived standing and comparison-related affect even when prompt-based detection of the same construct remains fragile.
It is concluded that current transformer models do not explain human morphosyntactic processing, and that evaluations of transformers as cognitive models must adopt rigorous, comprehensive experimental designs to avoid spurious generalizations from isolated syntactic configurations or individual models.
Titus von der Malsburg, Sebastian Padó· arXiv.org· 2 citations
This work examines how LLMs respond to user prompts expressing varying degrees of Dark Triad traits (Machiavellianism, Narcissism, and Psychopathy) using a curated dataset, revealing systematic differences across models.
Zeyi Lu, A. Henestrosa, Pavel Chizhov et al.· arXiv.org· 1 citation
Large-scale computational research on conspiracy theories has focused exclusively on believers'online behavior, leaving the harm experienced by those closest to them under-examined. This paper bridges this gap by analyzing 12747 stories from r/QAnonCasualties, an online support group for people who have ``lost''someone to conspiracy beliefs. We design a computational pipeline to extract fine-grained thematic traits from personal narratives and cluster them into six coherent radicalization personas, which we then link to the emotional toll reported by narrators via LLM-assisted emotion detection and regression modeling. We find that personas are meaningful predictors of specific emotional harms: radicalization perceived as a deliberate ideological choice is associated with anger and disgust, while personas marked by personal and cognitive collapse correspond to fear and sadness. This work provides an empirically grounded computational framework for understanding the relational harms of radicalization, opening new avenues for research into its wider social consequences.
Ngoc Bich Doan, Giuseppe Russo, Gianmarco De Francisci Morales et al.· arXiv.org· 0 citations
We explore intrinsic dimension (ID) of LLM representations as a marker of linguistic complexity. Specifically, we test whether ID differences across model layers reflect well-known complexity contrasts established in (psycho)linguistics: coordination vs. subordination, right-branching vs. center-embedding, and unambiguous vs. ambiguous attachment. Our results on six different LLMs show that these contrasts are consistently reflected in ID differences, with more complex phenomena eliciting higher ID profiles. Notably, ID differences emerge at different points across layers for different contrasts, also reaching their peaks at different stages. Further experiments using representational similarity and layer pruning confirm the trends. We conclude that ID is a useful marker of linguistic complexity in LLMs, that it points to similar linguistic processing steps across disparate LLMs, and that it has the potential to differentiate between different types of complexity.
Marco Baroni, Emily Cheng, Iria deDios-Flores et al.· arXiv.org· 3 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
MIT News · Artificial Intelligence· news.mit.eduAug 31, 2026
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
New MIT research could lead to better materials for a fossil-fuel-free process for making the chemical that's essential to fertilizer and other products.
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