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natural language processing

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#machine learning Preprint Oct 2026

Target-Token Asymmetry Can Masquerade As Depth Specialization

Layer-ablation studies often perturb early or late layers of a language model, compare the damage to prediction on two corpora, and read a larger early-layer effect on one corpus as early-layer specialization for its task. Such a comparison averages over the tokens that each corpus asks the model to predict, and these...

Dan-Lin Li, Miao Li, Xu Han et al. · 0 citations
#machine learning Open access Oct 2026

TopicMerge: A Divide and Conquer Approach to Topic Modeling

Recent advancements in topic modeling have split into two main paths: efficient embedding-based methods that offer scalability but are often outperformed on standard clustering metrics, and Large Language Model (LLM) approaches that achieve state-of-the-art clustering performance but at the cost of prohibitive computat...

Matthew Raffel, Li-Zhong Chen, Sai Munikoti et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Into the Unknown: Benchmarking AI agents on open scientific research problems

Can AI agents carry out research on open scientific problems? We introduce Into the Unknown, a benchmark that provides an agent with a science paper along with a research direction to pursue and tasks it to write a follow-up paper. We work with scientists at US Department of Energy (DOE) national laboratories, who each...

Seungone Kim, Jia-Yi Geng, Young-Jun Lee et al. · 3 citations · ⚡1
#artificial intelligence Preprint Oct 2026

From Copying to Composition: How Language Models Develop In-Context Learning

In-context learning (ICL) allows language models to adapt their behavior from instructions and demonstrations in a prompt, but accuracy alone does not reveal how contextual information controls their predictions. We study the acquisition and trainability of ICL with a controlled diagnostic that separates copying a near...

Minglai Yang, Xin-Yu Guo, Ren-Xiong Wang et al. · 0 citations
#machine learning Preprint Oct 2026

Similar Models, Broken Odds: Where Luce's Axiom Fails in Chatbot Arena

Chatbot Arena ranks language models with the Bradley-Terry model, which gives each model one score and so assumes Luce's choice axiom. Choice theory predicts where this fails: near-copies are told apart more decisively than their scores imply. On 1.8 million Arena votes, votes between versions of one model are 1.37 tim...

S. Bakshi, Apurba Bose, Aishwarya Bose et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Smart Alone, Blind Together: Why Accuracy Fails to CertifyTheory of Mind in Multi-Agent Teams

Coordinators that act on what they infer about teammates are usually certified by the accuracy of those inferences. We argue that accuracy is the wrong certificate, for two reasons. First, the accuracy needed to act depends on what a wrong action costs, so the bar belongs to the deployment, not the module. Second, a mo...

Will Beaumaster, Paul Schrater · 0 citations
#artificial intelligence Preprint Oct 2026

The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery

Modern coding agents are fundamentally reshaping the role of human input for computational research: they write code, launch in silico experiments, analyze their results, and autonomously operate with ever-increasing time horizons. Despite these developments, fully autonomous research agents suffer from a limited ideat...

R. Lange, Luca Grillotti, Yutaro Yamada et al. · 3 citations
#artificial intelligence Preprint Open access Oct 2026

Following the Flow the Law Requires: Training Voice Agents to Transfer to Unseen Regulated Procedures in Debt Collection

Voice agents built on large language models (LLMs) now handle customer calls, but in regulated applications completing the task is not enough: the agent must follow a conversational flow set by law. In debt collection, it must confirm it is speaking to the debtor before mentioning the debt, identify itself and the cred...

Petmongkon Pornpichitsuwan, Krittin Pachtrachai, Touchapon Kraisingkorn · 0 citations
#artificial intelligence Preprint Open access Oct 2026

One Task, Many Interfaces: Simple Cross-Interface Consistency Training Improves LLM Agent Robustness

Post-training binds the competence of an LLM agent to the interface it was trained on. Semantics-preserving perturbations of that interface, such as renaming an action or changing the notation, leave the underlying task unchanged yet make the agent worse at it. Scoring each sample independently means interface randomiz...

Artin Tajdini, Manpreet Kaur, Peng Qi · 0 citations
#machine learning Preprint Oct 2026

RM-OPD: On-Policy Distillation for Generative Reward Models

Reinforcement Learning on Mixed-domain preference data (Mix-RL) has become the mainstream approach for training general-purpose Generative Reward Models (GRMs). However, Mix-RL suffers from two critical bottlenecks: the reward sparsity induced by outcome rewards, and the cross-domain interference arising from jointly o...

Qing-Lin Su, Li-Cheng Pan, Zhen Fang et al. · 0 citations
#machine learning Preprint Open access Oct 2026

MolHop: Scaffold-Hopping Molecular Design with Agentic Reinforcement Learning

Molecular design is often formulated as an optimization problem over existing molecular spaces. However, molecular optimization and the discovery of structurally distinct replacement molecules represent two fundamentally different search problems. LLM-based molecular design agents typically perform the former, conducti...

Yanyi Su, Chenyan Wang, Yulei Gong et al. · 0 citations

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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.

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