Continuous diffusion language models offer an alternative to autoregressive generation, but their generations may suffer from repetition. We find that unconditional generations from ELF, a recent family of continuous diffusion language models, are more repetitive than human text, while Gen-PPL, a common likelihood-base...
Shuai Zhang, Zijie Chen, Huachuan Qiu et al.· 0 citations
Computer use agents are expanding from single-device operation toward cross-device systems that coordinate tasks across heterogeneous environments. Execution conditions are often only partially known at planning time and revealed through interaction. Failures may require intra-device modality switching or inter-device...
This paper focuses on automatically generating informative ad descriptions in sponsored search. Unlike ad titles which are usually optimized to attract user click feedbacks, ad descriptions have a longer text span and possess the potential of incorporating world knowledge to address user search intents while presenting...
Penghui Wei, Jiayu Wu, Chao Ye et al.· 0 citations
Sign language translation has made substantial progress between sign and spoken languages, while translation across sign languages remains less explored. Translating directly between sign languages could support communication across signing communities without requiring a shared written language. A cascade of sign-to-t...
Zetian Wu, Bowen Xie, Wuyang Meng et al.· 0 citations
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When an LLM judge only has to assign a three-way support label to a candidate answer given a reference, does asking it to decompose the answer into atomic claims help, and at what cost? We compare four single-call designs that share the judge model, the inputs, and the level of instruction detail: candidate-side atomic...
What is known about the values of national populations rests almost entirely on questionnaires, which prompt respondents to rate researcher-supplied descriptions of each motivation. Social media instead records values as they are invoked spontaneously, in communication addressed to a real audience. We measure value exp...
Hallucinations in Large Language Models (LLMs), i.e., plausible but non-factual generations, pose a significant challenge to reliable deployment in high-stakes environments. However, many existing hallucination detectors require expensive repeated sampling for consistency checks or access to model-internal states unava...
Yitong Qiao, Licheng Pan, Yu Mi et al.· 0 citations
Large language models (LLMs) have shown promise for recommendation reranking, but their use introduces an important tradeoff between recommendation quality and serving efficiency. We investigate whether a decision-oriented model provides a useful alternative when the reranking task is fundamentally a structured choice...
Large vision-language models (LVLMs) exhibit strong multimodal in-context learning (ICL) capabilities, yet this ability degrades substantially as model size decreases. Knowledge distillation offers a natural way to bridge this gap, but existing methods primarily align output distributions or hidden representations dire...
Yanshu Li, Jia-Qian Li, Can-Ran Xiao et al.· 0 citations
Test-time scaling has recently emerged as a powerful approach for improving LLM reasoning by allocating additional computation during inference, substantially enhancing accuracy on challenging tasks such as mathematics and coding. To accelerate the exploration of reasoning paths, recent studies proposed speculative exe...
LLM watermarking has become an effective approach to distinguishing AI-generated text from human-written text by embedding detectable patterns during generation. However, a small post-generation edit may change the meaning of the text without removing its overall watermark signal, creating a risk that the modified cont...
Ze-Wei Deng, M. Siddeek, Li-Yan Xie et al.· 0 citations
TTS systems with autoregressive semantic modeling have demonstrated strong zero-shot voice cloning performance and rich expressive variation, but their sequential decoding incurs substantial latency. Non-autoregressive alternatives offer much faster generation, yet often rely on more restrictive reference conditioning,...
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.
