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natural language processing

9,568 papers

#artificial intelligence Preprint Sep 2026

Logical subspace in LLMs

The minimal viable subspace (MVS) method, which searches for the lowest-rank activation subspace at a layer that preserves task performance when everything outside that subspace is ablated, suggests a functionally localizable core machinery for logic akin to that in the human brain.

Hope H. Kean, Enric Boix-Adserà · 0 citations
#artificial intelligence Preprint Sep 2026

The Decomposition Tax: LLM Pipelines Lose Up to 40 Accuracy Points at Their Own Interfaces

A four-stage LLM pipeline gives up as much as 40.5 accuracy points at its own interfaces (gemma-3-12B on MATH-500, Holm-corrected p = 1.66e-19; the largest tax in the primary family). We hold model, problem, stages, stage prompts and completion budget fixed, vary only whether each stage can still see the original probl...

Tian-Qi Bu, Yu-Dong Peng, Jun-Teng Tu et al. · 0 citations

Overwhelmed by Choice: Studying LLM Decision Making at Scale

It is shown that strong small-option performance does not necessarily imply robust large-scale candidate comparison and that hierarchical partitioning and permutation-based inference improve accuracy by roughly 20 percentage points at $N=160$ on both HotpotQA and MIMIC.

Yu-Chieh Lin, Aryan Seth, Anshul Aravind et al. · 1 citation
#artificial intelligence Preprint Sep 2026

Re-derivability Decides What a Staged Agent Pipeline Recovers After an Upstream Fault

One variable sets what an upstream fault costs a staged pipeline of language-model agents: re-derivability, how much of what a stage needs it can rebuild from the original problem. Grounding an inspector agent in that problem is worth +0.608 [+0.517, +0.700] to +0.358 over a blind one on four open-weight backbones serv...

Tian-Qi Bu, Yu-Dong Peng, Jun-Teng Tu et al. · 0 citations
#artificial intelligence Review Sep 2026

CLAIRE: A Schema-Grounded Hybrid Workflow for Healthcare Administrative Form Completion

CLAIRE (Clinical Language and Agentic Intelligence for Reasoning and Entry), a hybrid workflow that separates field-state discovery, source-to-field mapping, deterministic validation, bounded correction, escalation, and audit tracing, is developed and evaluated.

Garapati Keerthana, Manik Gupta · 0 citations

Adaptive Consistency Graph for Long-Horizon Agents

The Adaptive Consistency Graph (ACG) is introduced, which incrementally organizes execution evidence and its provenance in a persistent graph, then constructs a temporary requirement-centered view for each decision under a bounded context budget.

Jie-Cong Wang, Hao Peng, Zhan-Yi Wang · 0 citations
#artificial intelligence Preprint Sep 2026

Retrospective Distillation Attribution via Normalized Response Similarity

Auditing publicly released descendants of distilled models spanning diverse post-training objectives, SCOUT consistently identifies the distillation source and tracing teacher-associated *syntactic signatures* along training trajectories reveals that they emerge during distillation and persist through subsequent prefer...

Minwoo Jang, Jaechang Kim, Minhyeon Oh et al. · 0 citations
#artificial intelligence Preprint Sep 2026

MassAlloc Attention: Let Attention Allocate Its Own Compute

MALA, a fused attention primitive that preserves score access to every legal causal interaction and uses normalized contribution to allocate post-score computation, indicates that allocating post-score computation according to normalized attention contributions can retain the evaluated capabilities of FullAttn while re...

Jing-Ze Shi, Zhang-Yang Peng, Xian-Duo Li et al. · 0 citations
#artificial intelligence Preprint Sep 2026

IGSD: Environment-Verified Hindsight Self-Distillation for Search Agents

Information-Gain-Gated Self-Distillation (IGSD) is proposed, which verifies on-policy token proposals with environment feedback before distillation, and supports environment-verified hindsight as an effective approach to reliable action-level supervision for search agents.

Ang-Qing Jiang, Gao-Ming Zhang, Chao-Qun Zhang 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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