Proof-of-concept evidence that strong single-turn safety does not necessarily persist during sustained adversarial interaction is provided, highlighting the need for long-horizon evaluations and conversation-level safeguards that account for risk accumulating across turns.
The shared task is a shared task for evaluating hallucination detection and factual verification in Arabic question answering under challenging generalization settings, based on two Arabic datasets: HalluScore and HalluTruthQA.
Aisha Alansari, Abdessalam Bouchekif, A. Hasanaath et al.· 0 citations
Large language models (LLMs) are increasingly used to answer questions about politically contentious issues, yet evaluations typically treat a model's stance as a relatively stable property. Real users, however, communicate political signals through their terminology, assumptions, and personal context. We investigate w...
Olivia Macmillan-Scott, Michael Jacobs, Nils W. Metternich et al.· 0 citations
Long-running tools can dominate coding-agent latency: compilers, test suites, and repository commands take seconds to minutes while the agent idles. This observation stall presents the same tension that drove out-of-order processors -- asequential interface hides work that can be predicted and started early, but a spec...
Jiang-Nan Yu, Ce-Yu Xu, Meng-Ming Li et al.· 0 citations
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Survprompt is introduced, a framework that converts structured patient covariates into free-text clinical vignettes and prompts pre-trained LLMs to predict survival zero-shot, and feature ablations revealed that LLMs prioritized clinical variables similarly to specialized survival models.
Juan M. Zambrano-Chaves, Peniel N. Argaw, Risa Ueno et al.· 0 citations
Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments. World-model methods address this by training agents to predict future observations, at the cost of additional training and errors that compound when predictions are used for plann...
Yu-Han Guo, Jin-Ming Liu, Liang Xu et al.· 0 citations
Reusable Latent Correction (RLC) is proposed, which converts one-off natural-language guidance from a black-box LLM into persistent corrective experiences in the hidden space of an SLM, enabling the SLM to reuse LLM-derived corrections during inference without any online LLM calls.
Bo-Han Zhang, Li-Nan Yue, Weibo Gao et al.· 0 citations
Comparing empirical cross-lingual transfer with typology-based similarity, it is found that transfer BLEU identifies closely interacting language pairs better than URIEL similarity, though neither predicts which varieties benefit from joint training.
Frank Lawrence Nii Adoquaye Acquaye, Eric George Parakal, Jesse Johnson et al.· 0 citations
NAMOH, an architecture-native sparse attention mechanism that activates only its assigned tokens and performs causal attention within this subsequence, is introduced, and it is hoped this work offers a new path for scaling attention, with parameter scaling directly enabling context scaling.
The rapid development of AI-driven tools, particularly large language models (LLMs), is reshaping professional writing. Still, key aspects of their adoption such as language support, ethics, and long-term impact on writers' voice and creativity remain underexplored. In this work, we carried out a questionnaire (N = 301...
Large language models (LLMs) are increasingly deployed as automated judges for AI-generated content, yet a single judge is unreliable and even a panel of judges leaves a hard residue: when judges disagree, majority voting discards the conflict instead of resolving it. We present JuryFlow, a disagreement-guided, human-i...
Existing full-duplex speech benchmarks cover only subsets of real-time interaction behaviors, often under limited contextual conditions. We introduce DuplexAct-Bench, a bilingual benchmark that systematically covers six complementary behaviors, from interruption and yielding to proactive initiation, active silence, and...
Keyue Xing, Wentao Ding, Mengmeng Wang et al.· 0 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.