Language models are now routinely graded by other language models. If post-training makes a model's own writing more predictable, it may also teach the same model, acting as a judge, to reward predictable writing, so that progress on creativity would be invisible to automated evaluation. We follow two open model famili...
Gate thresholds are implemented as couplings to a constant the network builds for the distribution it is reading, with the bias parameters contributing little; how much of that constant is carried in the stream and how much in parameters depends on the architecture.
Only on MathNet-Retrieve could an inversion, benchmark score up and real retention down, to one edit of a training file be pined, and three trained models are released.
A. Habibullah, M. Alshiekh, Yazan Alshoibi et al.· 0 citations
Retrieval-Augmented Generation (RAG) introduces a specific failure mode in discrete diffusion language models: when retrieved context contradicts parametric knowledge, the iterative denoising process becomes a visible battleground between competing knowledge sources. We identify temporal semantic divergence as an obser...
Sravan Karthick T, Pranav Darshan, Pranav A et al.· 0 citations
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This study links sentiment extraction, bounded response modeling, and anomaly detection in a unified framework for consumer complaint assessment and finds that negative sentiment intensity is not a sharp classifier of outcomes on its own; combined with monetary and categorical attributes, it isolates unusually severe c...
This work uses empirical studies to reveal the existence of a massive, untapped performance headroom for personalized generation through test-time alignment, and proposes a parameter-efficient framework utilizing million-parameter scale multi-layer perceptron (MLP) ranking models.
Attention often concentrates on a small subset of tokens in the context, but which subset matters changes from one query to the next. To exploit this changing structure, we introduce SANTA++, a training-free stochastic attention method that uses representative keys for memory-efficient selection without scanning the en...
Kyle Lee, Christian Z. Pratt, Ruo-Yu Fang et al.· 0 citations
This work develops an asynchronous LLM framework that lets users (or the agents themselves) define inference coroutines with overlapping memory states and showcases that Qwen 3.x models are capable of asynchronous operation for streaming video understanding, videogames, and monitoring, without task-specific training.
George Yakushev, Denis Mazur, В. Д. Бартенев et al.· 0 citations
TANGO is presented, a watermark for masked-diffusion language models that keys each new token to a nearby token that is already unmasked, and TANGO biases the new token toward a color determined by the key and the nearby token's color.
Kasra Arabi, Nir Weinberger, Micah Goldblum et al.· 0 citations
Objective-wise Reconciled Policy Gradient (ORPG), which constructs a separate clipped policy objective for each reward and reconciles the resulting gradients into one policy update, achieves the highest average full-budget accuracy and three-budget hypervolume among the compared methods.
Shi-Cheng Fang, Yi-Wen Zhao, Wen-Bo Tian et al.· 0 citations
Safety-aligned LLMs can exhibit emergent misalignment (EM): narrow domain adaptation unexpectedly triggers catastrophic safety failures across unrelated domains. Prior static analyses leave training dynamics unmapped, while existing defenses rely on heuristics that degrade utility. We present a dynamic, second-order ge...
Wei-Qiao Que, Rui-Zhe Li, Cheng-Yu Wang et al.· 0 citations
This work takes a fine-grained look at memorization by decomposing the loss trajectory of memorized sequences over training and model parameters, finding that memorization in both cases is characterized by sequence-level gradient alignment.
Florian Eichin, Philipp Mondorf, Andrei Mircea et al.· 0 citations
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.
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
