Cross-dataset analysis shows that semantic chunking improves extraction datasets with explicit relation cues, such as GM-CIHT and DDI, while fixed chunking remains competitive or stronger for dense biochemical extraction and binary classification settings such as ChemProt and ADE.
Riya Ahuja, Tim Kacprowski, Roya Shiasi Sardoabi Institute of Data Science in Biomedicine et al.· 0 citations
DIASENTINEL demonstrates a practical framework for reliable, auditable, and privacy-preserving LLM-based clinical decision support for type 2 diabetes mellitus risk screening and guideline-grounded report generation from electronic health records (EHRs).
This work introduces PaperGym, a unified framework that turns each research paper into a complete training environment, and releases the pipeline, the 20,000-instance corpus PaperGym-20k, and the benchmarks PaperGym-Innov and PaperGym-Design.
Yu-Han Wang, Zhengxi Lu, Yuchen Yan et al.· 0 citations
This work introduces ASPIRE, a benchmark for vague-goal-driven self-evolution and shows that vague goals redirect search effort toward goal interpretation, and evaluates the resulting systems on a hidden, expert-authored set of 520 items spanning six goals.
Yu-Hao Wu, Jingyuan Zhang, Jia-Jun Shi et al.· 0 citations
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These findings show that recognizing successful actions is insufficient; agents must also transform feedback into executable and transferable policies, and provide a unified framework for diagnosing this process and identifying the bottlenecks that prevent agents from translating interaction experience into reliable self-improvement.
Jia-Jun Shi, Siyang Tao, Yu-Hao Wu et al.· 0 citations
Results show that first-token clues can guide multi-token concept recovery, while subsequent hidden states provide vectors for readout and intervention.
Across four reasoning benchmarks and four models, \textsc{MIST} consistently outperforms baseline methods, suggesting that model-internal saliency provides an effective proxy for reasoning-token importance.
Tianyi Zhao, Yinhan He, Wendy Zheng et al.· 0 citations
This paper shows that another part of the pipeline matters at least as much: the queries used to elicit information extraction, and introduces List of Questions (LoQ), which generates document-specific question sets, and FeedQ, a feedback-driven optimization method that iteratively refines questions against extraction outcomes.
Omar Sharif, S. Vosoughi, Nikhil Singh· 0 citations
This analysis shows that, in new sentences, the contextual associations of tokens representing old entity types exhibit a significantly stronger bias towards new entity types compared to their contexts in old sentences, which intensifies the degradation of old knowledge while promoting the overfitting of new knowledge.
Duzhen Zhang, Yahan Yu, Xiuyi Chen et al.· IEEE Transactions on Artific...· 0 citations
This paper analyzes the token statistics of 13 NACs spanning multi-codebook residual vector quantization, single-codebook VQ, and non-VQ designs, evaluated on three corpora under clean, white-noise, and real-world DEMAND-noise conditions to provide architecture-conditioned conventions for applying language-statistical analysis to NAC tokens.
Joonyong Park, Shinnosuke Takamichi, David M. Chan et al.· 1 citation
The results support analyzing subjective speech rewards as predictor-axis-base tuples as predictor-axis-base tuples and provide practical diagnostics for selecting rewards before multi-reward speech post-training.
Audited three commercial AI scribes on the same 142 consultations: 565 notes from recorded UK primary-care and US ambulatory encounters plus authored scenarios, with one failure mode drawn from published scribe-error taxonomies.
Sebastian Fox, L. Markham, Ryan Lail 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.
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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