IndicParam, a human-curated benchmark of over 13,000 multiple-choice questions covering 11 Indic languages plus Sanskrit-English code-mixed set, provides insights into limitations of cross-lingual transfer and establishes a challenging benchmark for Indic languages.
Analysis of reasoning errors in vision language models and how they impact user trust and the ability to detect errors highlights how CoT explanations can simultaneously clarify and mislead, underscoring the need for NLP systems to provide explanations that encourage scrutiny and critical thinking rather than blind tru...
Eunkyu Park, Wesley Hanwen Deng, Vasudha Varadarajan et al.· arXiv.org· 6 citations
Parallel tokenizers are introduced, a new framework that first trains tokenizers monolingually and then aligns their vocabularies exhaustively using bilingual dictionaries or word-to-word translation to enforce a shared semantic space across languages while naturally improving fertility balance.
Muhammad Dehan Al Kautsar, Fajri Koto· 0 citations
This work proposes a new paradigm: Test-Time Policy Adaptation for Multi-Turn Interactions (T2PAM), which utilizes user feedback from the ongoing interaction as a reward signal to estimate a latent optimal policy aligned with user preferences, then updates a small subset of parameters to steer the model toward this pol...
Chen-Xing Wei, Hong Wang, Ying He et al.· arXiv.org· 4 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
Stronger learning signals do not necessarily produce a stronger small reasoning model. Off-policy supervised fine-tuning (SFT) supplies stronger traces that may be incompatible with the student. On-policy post-training instead remains constrained by the quality of the student's own trajectories: reinforcement learning...
Jaehoon Kim, Kwangwook Seo, Dongha Lee· 0 citations
Demographic analysis across 20 Jigsaw and 13 ToxiGen minority groups reveals structurally unequal adversarial vulnerability, exposing mechanistically traceable fairness gaps in current toxicity classifiers.
Shaz Furniturewala, A. Zubiaga· arXiv.org· 1 citation
PID-steering is described, a training-free, decoding-time method that modulates the steering strength with a PID controller driven by a lightweight chunk-level redundancy classifier.
Applying ALAS to four open-source Speech-LLMs across emotion recognition, open-ended SQA, and multi-choice audio understanding, it is found that the depth and strength of alignment reflect each model's audio-encoder design and the acoustic-versus-semantic demands of the task, and that ALAS tracks but does not duplicate...
Pooneh Mousavi, Yingzhi Wang, M. Ravanelli et al.· 4 citations
Reliable evaluation of large language models (LLMs) is critical as their deployment rapidly expands, particularly in high-stakes domains such as business and finance. The LLM-as-a-Judge framework, which uses prompted LLMs to evaluate response quality, is appealing due to its scalability, low cost, and strong correlatio...
Michael Krumdick, Charles Lovering, Varshini Reddy et al.· 0 citations
This work presents, to their knowledge, the first simultaneous sign-to-sign (S2S) translation system, with two wait-k regimes: test-time wait-k inference applied directly to a full-sentence model, and a trained wait-k model via stochastic multi-path supervision.
Ze-Tian Wu, Bo-Wen Xie, Stefan Lee et al.· 0 citations
ColNanoVDR is presented, to the authors' knowledge the first framework to bring this document-free distillation to multi-vector VDR, and it is proved that the resulting alignment cost bounds the MaxSim score difference on every page.
Zhu Liu, Zi-Yi Wang, Yao Zhang et al.· 0 citations
A small Office edit creates two obligations: propagate every required update and leave protected state untouched. Updating too little leaves dependencies inconsistent; updating too much changes content the user did not authorize. We introduce OfficeEditBench, a 170-task benchmark for change-scoped maintenance of spread...
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.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
