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2,172 papers
A forced-choice political instrument of 32 author-written propositions, arranged as 16 mirrored pairs, administered to a panel of frontier and open-weight language models under a bare baseline, a balance instruction, a content-free placebo and a commitment condition. Instruments of this class report where a model sits...
Abstract This study introduces FILMIP-AI, an operationalization of the Filmic Metaphor Identification Procedure (FILMIP) for multimodal large language models (MLLMs) by converting its structured interpretive steps into chain-of-thought (CoT) prompts. We evaluate FILMIP-AI using a sample of 30 TV commercials across thre...
Artifact for the CASES/ESWEEK paper 'HELM: Compiler-Guided Heterogeneous Execution for Large Language Models on Memory-Constrained Systems'. Contains the HELM compiler and runtime (roofline-based CPU-GPU partition search, static per-device FX graphs, AVX2+F16C GEMV kernel, paged KV cache with streaming attention), benc...
code and data from the paper From Text to Structure: A Scalable Pipeline for Bibliographic Information Extraction and Connectivity Pathways Mapping Using Large Language Models
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This version corrects a wrong arXiv identifier. Version 2 cited the theoretical paper on chaotic regularization in recurrent neural networks, "Discrete signaling mediates chaotic regularization in recurrent neural networks", as arXiv:2606.04428 in six places, including item 3 of the abstract. That identifier belongs to...
This version corrects a citation error. Five passages on chaotic regularization in recurrent spiking networks cited the identifier 2606.04428, which belongs to an unrelated astrophysics paper on fast-spinning black holes; the passages describe arXiv:2606.04426 (Discrete signaling mediates chaotic regularization in recu...
Version 3 (2026-09-26). Version 3 corrects a misattributed citation and several overstated readings, found by an independent audit of version 2 and by a second review of the corrected draft. (1) The figures for context-based adversarial attacks on code generators (10.7x, from 3.5% to 37.4%; 100% on GPT-3.5-Turbo; 60-10...
The Key-Value (KV) cache is the dominant memory bottleneck in autoregressive large language model (LLM) inference, growing linearly with context length. This paper presents a systematic empirical study of three KV cache optimization strategies applied to Mistral-7B-Instruct-v0.2 on a single NVIDIA A100 GPU: (1) a full-...
This version (2026-09-26) corrects a citation error found by an automated check and confirmed by hand against the arXiv abstracts. Version 2 cited the identifier 2605.30998 for the ClawTrojan multi-step trojan benchmark and its DASGuard defense; that identifier is a security analysis of x402 payments, and ClawTrojan is...
Anonymized data and reproduction code for the study. Includes the 40-question cross-sectional pass, the four deep repeated questions, the twenty-question repeated set for both general-purpose models, and a self-contained, standard-library-only script (score_reproduce.py) that recomputes every reported figure: price-ran...
This version corrects the synthesis after one of its sources was withdrawn. arXiv:2605.29612 (CONCAT) was withdrawn by its authors on 2026-09-22 because a potential issue in its repeated-run evaluation "may have caused unintended prompt overlap across runs and affected the reported results". The previous version used t...
This version corrects a citation error found by an automated check and confirmed by hand. Section 4.5 cited arXiv:2605.29963 (Honeyval, an evaluation framework for LLM-powered HTTP honeypots) for its evidence on inference-system fingerprinting; the passage describes arXiv:2605.29979, "Fingerprinting Inference Systems o...
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
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