Category
8,685 papers
A byte crossover is derived, the context length at which the two savings are equal, together with ideal speedup bounds for each branch and for their composition, from model dimensions and keep ratios alone and is derived at https://github.com/js-lee-AI/ByteCross.
It is found and estimated that different words carry different amounts of information, and less-informative words tend to absorb more contextual information, and finer-grained selectivity enables more precise routing to promote information transmission between matched words.
Examining task affinities from supervised fine-tuning gradients and solving a constrained mixed-integer program(MIP) to construct partially overlapping specialist groups highlights specialization and capability integration as connected but distinct design problems in chemical reasoning.
RareTrap provides model developers a principled approach for evaluating language models under a common distribution, and prioritizing alignment effort to improve safety and mitigate risks.
Reach audiences
Advertise in front of researchers, engineers, and readers.
Temperature-Grouped Reinforcement Learning (TGRL), which turns temperature-induced diversity into an explicit training signal, and reaches equivalent accuracy up to 36% faster than strong RLVR baselines without expanding the rollout budget.
SMAT (Simple MAT) is introduced, which jointly optimizes expert loss and expected loss at simulated merged parameters generated by sampling scaling coefficients, masks, and additive noise, and improves the mean score across five merging methods.
CalibHyper is proposed, a chance-corrected relational hypergraph method based on the joint label distribution that achieves ROC-AUC competitive with the strongest reported results.
Training-free KV-cache compression methods such as SnapKV, H2O, and PyramidKV evict tokens at the end of prefill, aiming to preserve the attention mass that future queries are expected to use -optimizing what to keep. We show that this objective fails in two distinct ways. (1) Compensation: restoring the evicted attent...
On-Policy Attention Self-Distillation (OPASD), which complements token-level supervision with solution-conditioned attention distillation, shows that solution-conditioned attention provides a complementary supervision signal that makes on-policy self-distillation more accurate, stable, and compute-efficient.
This work studies language-model synthesis of Clifford circuits, which prepare the stabilizer states used in quantum error correction and admit exact classical verification, in a target-conditioned framework.
Group-relative reinforcement learning (RL) relies on reward variation among sampled responses to estimate informative relative advantages. As language models become increasingly capable, existing training data can become reward-saturated: all sampled responses to the same problem might receive equally high rewards, whe...
Tory of Scene (ToS), a training-free reasoning schema in which each agent reads its public role, the only difference between the agents, and the task context they all observe, is proposed, which outperforms all six baselines on every benchmark, and each baseline falls far behind it in at least one setting.
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
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.
A new language-processing tool could help identify the highest-risk individuals from natural language, enabling swifter interventions.
A Blog post by Liquid AI on Hugging Face
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
