Skip to content

Category

natural language processing

9,568 papers

#artificial intelligence Preprint Oct 2026

From Outcomes to Components: A Counterfactual Evaluation Framework for LLM-Proposed Repairs

A counterfactual evaluation framework that separates selected records (targets) from the applied change (an update) is introduced and four executions combine proposed and reference targets and updates to measure the effects of correcting or retaining each component and their interaction.

Heng-Yu Ye, Song-Hua Hu · 0 citations
#artificial intelligence Preprint Open access Oct 2026

VibeTraining-Bench: Data-Trainer-Harness Co-Design for Iterative System Improvement

Recursive self-improvement (RSI) requires agents to diagnose system bottlenecks and coordinate improvements across training and inference. We refer to the agents' capability to jointly optimize training data, the trainer, and the inference harness as cross-component co-design. We introduce VibeTraining-Bench, a benchma...

Ruotian Peng, Yuanyi Ren, Shuaiqi Liu et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

The Shape of Wisdom: How Language Models Converge on an Answer

When a language model answers a factual question, where does its internal state go? We asked four open models (Llama-3.1-8B, Qwen2.5-7B, Gemma-2-9B and Llama-3.2-1B) 2,256 factual questions whose answers recur, such as the country a person is a citizen of. At every layer, we recorded the state from which the answer is...

Shailesh Rana · 0 citations
#artificial intelligence Preprint Oct 2026

Benchmarking Arabic Dialects for Math Problems

Mathematical reasoning remains underexplored for low-resource languages, including Dialectal Arabic. To bridge this gap, we introduce the DAR-MGSM (Dialectal Arabic Multilingual Grade School Math) Benchmark, the first manually curated benchmark for evaluating mathematical reasoning in Dialectal Arabic. DAR-MGSM consist...

Noor Abo Mokh, Teresa Lynn, N. Habash et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Capable Teacher, Myopic Scorer: Reward-Free Teacher Repair for Agentic On-Policy Distillation

Agentic on-policy distillation (OPD) trains a student agent on its own trajectories while a teacher scores every token. A natural explanation of where it goes wrong is compounding error: the student's mistakes carry its trajectories beyond the region where the teacher is reliable, so recent methods keep training inside...

Shu Zhao, Tan Yu · 0 citations
#artificial intelligence Preprint Oct 2026

Large Flow Language Models

Continuous flow language models offer a path to parallel generation with tools for control and acceleration unavailable to discrete diffusion, yet effective techniques for building them at the billion-parameter scale remain less established. We present Lafa (Large flow language model), a fast, high-performing flow lang...

Arnav Chavan, Chanhyuk Lee, Udbhav Bamba et al. · 1 citation
#artificial intelligence Preprint Open access Oct 2026

Binary Discrete Diffusion: Candidate-Set Diffusion for Logic and Planning Tasks

Discrete diffusion models iteratively denoise a noisy sequence of categorical data such as text, enabling any-order generation with self-correction and in-filling capabilities. This makes them promising candidates for reasoning, planning, and constrained-generation tasks like coding and math. Yet masked and uniform dif...

Rodrigo Casado Noguerales, Dimitri von R\"utte, Bernhard Sch\"olkopf et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

BitMTP: When Multi-Token Prediction Meets Low-Bit Large Language Models

For efficient deployment of large language models (LLMs), methods of ultra-low-bit backbones reduce weight storage, memory traffic, and linear-layer execution cost, while multi-token prediction (MTP) methods reduce sequential decoding steps by predicting multiple future tokens in parallel. Although complementary, the d...

Ning Zhang, Shihao Wang, Jinrui Zhang et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Every Model Leaves a Trace: Multi-Resolution LLM Fingerprinting

Large language models leave model-specific traces in their generated text, but it remains unclear how finely these fingerprints can distinguish related models and which signals remain informative as obvious cues are reduced. We study this through fingerprint resolution, considering both model resolution, from different...

Mohammad Reza Modarres, Armin Tourajmehr, Artin Tavasoli et al. · 0 citations
#artificial intelligence Preprint Oct 2026

The Instruction Tax on Factual Recall: Base Models Can Beat Their Instruction-Tuned Counterparts

It is shown that the prompting methodology is as important as the model architecture: while the instruction-tuned model version achieves 0.5681 with its chat interface, the base model achieves 0.6836 when prompted using text completion, which decreases factual recall.

Christophe Khalil, Aya Mourad · 0 citations
#artificial intelligence Preprint Open access Oct 2026

MAS-Guard: Unified Guardrails from Individual Agents to Multi-Agent LLM Systems

Agentic AI extends large language models (LLMs) from generating responses to planning and executing complex workflows, substantially expanding their utility while exposing applications to unsafe actions, information leakage, and adversarial manipulation. Multi-agent systems further expand these capabilities through spe...

Mohammad Kachuee, Phu Mon Htut, Sheng Li et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Beyond a Single Sentiment Score: Topical and Affective Dimensions of Emoji Meaning on Twitter

It is shown that emojis used in tightly clustered topical contexts also exhibit more polarised crowd-sourced sentiment, not less, and that topical coherence and affective dispersion are distinct dimensions of emoji meaning.

Rohan James, Ioannis Christofilogiannis, Yuuv Jauhari et al. · 0 citations

From tech blogs

See all →
Microsoft Research Blog Oct 22, 2026

Flint-interactive: A language for creating interactive data visualizations

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 use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.