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natural language processing

9,568 papers

#artificial intelligence Preprint Oct 2026

Strong Helps Weak: Directional Cross-Modal Alignment Transfer in Multi-modal LLMs

Multi-modal large language models (MLLMs) achieve strong modality understanding by pairing a large language model (LLM) with an encoder for a target modality such as vision, video, or audio. However, improving an MLLM's capability for a given modality typically requires additional training on large modality-specific da...

H. Seo, Byung Hyun Lee, Minjun Kim et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Autonomous Structuring of Radiology Reports Across Modalities at Archive Scale Using an Open-Weight Large Language Model

Purpose: To develop and evaluate an open-weight large language model (LLM) pipeline that converts an entire archive of free-text radiology reports into structured reports without human oversight. Materials and Methods: In this retrospective study, a pipeline with 150 hierarchically organized templates was developed at...

Friedrich Puttkammer, F. Drexel, Marlene Fritzsche et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

ShadowMiner v1 - An Experience Report on Implementing and Measuring a Problem-and-Hypothesis Discovery Engine

ShadowMiner v1 is a system that automatically discovers research problems and generates hypotheses from AI papers. It is a nine-stage pipeline. It structures documents into a knowledge graph and finds graph gaps in it - structural blind spots in research. These graph gaps are included in the LLM generation prompt. Each...

Jinhyuk Choi · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Suppressing Pressure, Amplifying Evidence: Self-Guided Attention Steering to Mitigate Sycophancy and Stubbornness

Reliable language models should resist unsupported user pressure while effectively using objective contextual information. However, models may exhibit sycophancy by yielding to unsupported user pressure or contextual stubbornness by failing to update their answers when relevant contextual information warrants revision....

Yinghao He, Mengyu Xu, Haixiang Sun et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Bidirectional Preference Synthesis: Learning Prompt-Conditioned Preferences from Boundary Failures

Correction-based offline preference pipelines commonly treat model failures only as rejected responses under the original prompt. This supervision is incomplete for boundary failures: responses that violate the given instruction yet coherently satisfy a nearby intent or constraint setting. We introduce Bidirectional Pr...

Jun-Bo Wang, Li-Dong Lu, Zhuo-Qun Li et al. · 0 citations
#artificial intelligence Preprint Oct 2026

From Latent Space to Jacobian Space: Measuring, Evading, and Training Against Safety-Content Accessibility

Output-only safety monitoring sees only the end of a model's computation, yet the model computes its answer before emitting it: what it is internally poised to say is safety-critical. Jacobian-space (J-space) readouts, linear maps from hidden states to the output vocabulary via the model's input-output Jacobian, have b...

Mohammad Mosafer · 0 citations
#artificial intelligence Review Oct 2026

Language-Conditioned Token and Reasoning Efficiency in Large Language Models: A Paired Cross-Lingual Study Protocol

Large language models incur language-dependent representation and inference costs, but existing comparisons often conflate input language, assigned observable-trace language, and answer realization. We specify a prospective paired study that separates these interfaces while holding the semantic item, checkpoint, and an...

Gen-Liang Zhu, Chu Wang · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Language Model Activations Inhabit Privileged Error-Correcting Basins

Language models exhibit remarkable robustness, continuing to produce coherent text even when their activations are perturbed by interventions like linear steering. We hypothesize that this robustness is a result of passive dynamics, i.e., constraining mechanisms in the forward pass that funnel activations toward "good"...

Matthew Finlayson, Francisco Pernice, Eric Todd et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

InvestigationWorlds: An Agentic Environment for Legal Investigation

We introduce InvestigationWorlds, an agentic environment for legal investigation. We build on an underused artifact of U.S. civil litigation: the summary judgment motion. This motion relies upon a record composed of real evidence exhibits, and results in a court-adopted hypothesis that is treated as ground truth for th...

Albert Yu Sun, Andrew Benard, Sil Hamilton et al. · 0 citations
#artificial intelligence Review Oct 2026

Behavioral History Outperforms Descriptions of the Person for LLM Synthetic Personas

Large language models (LLMs) are increasingly used as synthetic personas representing survey respondents. Their validity as substitutes for particular respondents depends on whether they reproduce individuals'decisions. We examine what information helps synthetic respondents predict each individual's later choices, usi...

Khashayar Pourtaheri, Ahmad Zareei · 1 citation
#artificial intelligence Review Oct 2026

Proxy Confidence: Auditing Black-Box LLM Agents with a Surrogate's Log-Probabilities

A deployed LLM agent emits tool calls, queries, and code that can be silently wrong -- by the time the error surfaces, the action has run. Frontier chat APIs hide the model's token probabilities; the agent's stated confidence barely beats chance on the mistakes that matter; and resampling does not help, since frontier...

Yi-Kai Zhao, Saurabh Pandey, Pradeep Kumar Misra · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Training Numerical Intelligence via Auto-Diagnosis and Skill Discovery

AI agents are becoming increasingly capable of generating scientific code, but generating code is not the same as improving the algorithms behind it. For numerical solvers, execution feedback can expose poor performance, but rarely reveals its underlying cause and how to address it. We introduce Auto-Diagnosis and Skil...

Peter Chen, Wotao Yin · 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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