Search: “llm”

SynthPert: Enhancing LLM Biological Reasoning via Synthetic Reasoning Traces for Cellular Perturbation Prediction

arXiv:2509.25346v2 Announce Type: replace Abstract: Predicting cellular responses to genetic perturbations represents a fundamental challenge in systems biology, critical for advancing therapeutic dis

28 Apr 2026

Human

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The Price of Agreement: Measuring LLM Sycophancy in Agentic Financial Applications

DGX agent

arXiv:2604.24668v1 Announce Type: new Abstract: Given the increased use of LLMs in financial systems today, it becomes important to evaluate the safety and robustness of such systems. One failure mode

model-releasesarxiv-cs-ai

28 Apr 2026

Controllable Spoken Dialogue Generation: An LLM-Driven Grading System for K-12 Non-Native English Learners

arXiv:2604.22542v1 Announce Type: cross Abstract: Large language models (LLMs) often fail to meet the pedagogical needs of K-12 English learners in non-native contexts due to a proficiency mismatch. T

safetyarxiv-cs-ai

How Learning Rate Decay Wastes Your Best Data in Curriculum-Based LLM Pretraining

arXiv:2511.18903v2 Announce Type: replace-cross Abstract: Due to the scarcity of high-quality data, large language models (LLMs) are often trained on mixtures of data with varying quality levels, even

LLM+Graph@VLDB'2025 Workshop Summary

arXiv:2604.02861v2 Announce Type: replace-cross Abstract: The integration of large language models (LLMs) with graph-structured data has become a pivotal and fast evolving research frontier, drawing s

researcharxiv-cs-ai

TreeCoder: Systematic Exploration and Optimisation of Decoding and Constraints for LLM Code Generation

model-releasesarxiv-cs-lg

arXiv:2511.22277v2 Announce Type: replace Abstract: Large language models (LLMs) have shown remarkable ability to generate code, yet their outputs often violate syntactic or semantic constraints when

TRINITY: An Evolved LLM Coordinator https://arxiv.org/abs/2512.04695

TRINITY is a large language model coordinator system that evolves and improves its approach to orchestrating multiple LLMs or reasoning processes. The system likely uses evolutionary or adaptive mecha

researchdavid-ha--x

26 Apr 2026

Dialect vs Demographics: Quantifying LLM Bias from Implicit Linguistic Signals vs. Explicit User Profiles

arXiv:2604.21152v1 Announce Type: cross Abstract: As state-of-the-art Large Language Models (LLMs) have become ubiquitous, ensuring equitable performance across diverse demographics is critical. Howev

HWE-Bench: Benchmarking LLM Agents on Real-World Hardware Bug Repair Tasks

arXiv:2604.14709v2 Announce Type: replace Abstract: Existing benchmarks for hardware design primarily evaluate Large Language Models (LLMs) on isolated, component-level tasks such as generating HDL mo

Logic Jailbreak: Efficiently Unlocking LLM Safety Restrictions Through Formal Logical Expression

arXiv:2505.13527v3 Announce Type: replace-cross Abstract: Despite substantial advancements in aligning large language models (LLMs) with human values, current safety mechanisms remain susceptible to j

safetyarxiv-cs-ai

Secure LLM Fine-Tuning via Safety-Aware Probing

arXiv:2505.16737v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved remarkable success across many applications, but their ability to generate harmful content raises s

Serialisation Strategy Matters: How FHIR Data Format Affects LLM Medication Reconciliation

arXiv:2604.21076v1 Announce Type: cross Abstract: Medication reconciliation at clinical handoffs is a high-stakes, error-prone process. Large language models are increasingly proposed to assist with t

Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation

arXiv:2510.11423v3 Announce Type: replace-cross Abstract: Community Notes, the crowd-sourced misinformation governance system on X (formerly Twitter), allows users to flag misleading posts, attach con

Cooperative Profiles Predict Multi-Agent LLM Team Performance in AI for Science Workflows

arXiv:2604.20658v1 Announce Type: new Abstract: Multi-agent systems built from teams of large language models (LLMs) are increasingly deployed for collaborative scientific reasoning and problem-solvin

Duluth at SemEval-2026 Task 6: DeBERTa with LLM-Augmented Data for Unmasking Political Question Evasions

arXiv:2604.20168v1 Announce Type: new Abstract: This paper presents the Duluth approach to SemEval-2026 Task 6 on CLARITY: Unmasking Political Question Evasions. We address Task 1 (clarity-level class

ItemRAG: Item-Based Retrieval-Augmented Generation for LLM-Based Recommendation

arXiv:2511.15141v2 Announce Type: replace-cross Abstract: Recently, large language models (LLMs) have been widely used as recommender systems, owing to their reasoning capability and effectiveness in

researcharxiv-cs-ai

LLM-Guided Safety Agent for Edge Robotics with an ISO-Compliant Perception-Compute-Control Architecture

arXiv:2604.20193v1 Announce Type: new Abstract: Ensuring functional safety in human-robot interaction is challenging because AI perception is inherently probabilistic, whereas industrial standards req

safetyarxiv-cs-ro

Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization

arXiv:2601.12078v2 Announce Type: replace Abstract: Large language models (LLMs) excel at general-purpose tasks, yet adapting their responses to individual users remains challenging. Retrieval augment

R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling

arXiv:2604.20316v1 Announce Type: new Abstract: Function calling empowers large language models (LLMs) to interface with external tools, yet existing RL-based approaches suffer from misalignment betwe

researcharxiv-cs-lg

SolidCoder: Bridging the Mental-Reality Gap in LLM Code Generation through Concrete Execution

arXiv:2604.19825v1 Announce Type: cross Abstract: State-of-the-art code generation frameworks rely on mental simulation, where LLMs internally trace execution to verify correctness. We expose a fundam

researcharxiv-cs-ai

Hardware

Advancing Emerging Optimizers for Accelerated LLM Training with NVIDIA Megatron

This blog post discusses higher-order optimization algorithms such as Shampoo that have been applied in neural network training for at least a decade. The article explores how these emerging optimizer

hardwarenvidia-developer

Local Ai

Distillation Traps and Guards: A Calibration Knob for LLM Distillability

arXiv:2604.18963v1 Announce Type: cross Abstract: Knowledge distillation (KD) transfers capabilities from large language models (LLMs) to smaller students, yet it can fail unpredictably and also under

local-aiarxiv-cs-ai

Hardware

Efficient Mixture-of-Experts LLM Inference with Apple Silicon NPUs

arXiv:2604.18788v1 Announce Type: new Abstract: Apple Neural Engine (ANE) is a dedicated neural processing unit (NPU) present in every Apple Silicon chip. Mixture-of-Experts (MoE) LLMs improve inferen

hardwarearxiv-cs-lg

Evaluating LLM-Driven Summarisation of Parliamentary Debates with Computational Argumentation

arXiv:2604.19331v1 Announce Type: new Abstract: Understanding how policy is debated and justified in parliament is a fundamental aspect of the democratic process. However, the volume and complexity of

Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection

arXiv:2604.19526v1 Announce Type: cross Abstract: Cross-site scripting (XSS) remains a persistent web security vulnerability, especially because obfuscation can change the surface form of a malicious

researcharxiv-cs-lg

HarDBench: A Benchmark for Draft-Based Co-Authoring Jailbreak Attacks for Safe Human-LLM Collaborative Writing

arXiv:2604.19274v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as co-authors in collaborative writing, where users begin with rough drafts and rely on LLMs to compl

One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalization

arXiv:2601.18572v2 Announce Type: replace Abstract: Personalization of LLMs by sociodemographic subgroup often improves user experience, but can also introduce or amplify biases and unfair outcomes ac

STAR-Teaming: A Strategy-Response Multiplex Network Approach to Automated LLM Red Teaming

arXiv:2604.18976v1 Announce Type: new Abstract: While Large Language Models (LLMs) are widely used, they remain susceptible to jailbreak prompts that can elicit harmful or inappropriate responses. Thi

Take Out Your Calculators: Estimating the Real Difficulty of Question Items with LLM Student Simulations

arXiv:2601.09953v2 Announce Type: replace Abstract: Standardized math assessments require expensive human pilot studies to establish the difficulty of test items. We investigate the predictive value o

Applications

UAF: A Unified Audio Front-end LLM for Full-Duplex Speech Interaction

arXiv:2604.19221v1 Announce Type: new Abstract: Full-duplex speech interaction, as the most natural and intuitive mode of human communication, is driving artificial intelligence toward more human-like

applicationsarxiv-cs-ai

Aligning Backchannel and Dialogue Context Representations via Contrastive LLM Fine-Tuning

arXiv:2604.16622v1 Announce Type: new Abstract: Backchannels (e.g., `yeah', `mhm', and `right') are short, non-interruptive feedback signals whose lexical form and prosody jointly convey pragmatic mea

BIASEDTALES-ML: A Multilingual Dataset for Analyzing Narrative Attribute Distributions in LLM-Generated Stories

arXiv:2604.17008v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used to generate narrative content, including children's stories, which play an important role in social a

Budget-Aware Anytime Reasoning with LLM-Synthesized Preference Data

arXiv:2601.11038v2 Announce Type: replace Abstract: We study the reasoning behavior of large language models (LLMs) under limited computation budgets. In such settings, producing useful partial soluti

Applications

Calibrated? Not for Everyone: How Sexual Orientation and Religious Markers Distort LLM Accuracy and Confidence in Medical QA

arXiv:2604.17316v1 Announce Type: new Abstract: Safe clinical deployment of Large Language Models (LLMs) requires not only high accuracy but also robust uncertainty calibration to ensure models defer

applicationsarxiv-cs-cl

Can LLM-Generated Text Empower Surgical Vision-Language Pre-training?

model-releasesarxiv-cs-cv

arXiv:2604.18134v1 Announce Type: new Abstract: Recent advancements in self-supervised learning have led to powerful surgical vision encoders capable of spatiotemporal understanding. However, extendin

Applications

Common Corpus: The Largest Collection of Ethical Data for LLM Pre-Training

arXiv:2506.01732v2 Announce Type: replace Abstract: Large Language Models (LLMs) are pre-trained on large data from different sources and domains. These datasets often contain trillions of tokens, inc

applicationsarxiv-cs-cl

Data Compressibility Quantifies LLM Memorization

arXiv:2507.06056v4 Announce Type: replace Abstract: Large Language Models (LLMs) are known to memorize portions of their training data, sometimes even reproduce content verbatim when prompted appropri

Do LLM-derived graph priors improve multi-agent coordination?

model-releasesarxiv-cs-lg

arXiv:2604.17191v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) is crucial for AI systems that operate collaboratively in distributed and adversarial settings, particularly i

From Domains to Instances: Dual-Granularity Data Synthesis for LLM Unlearning

arXiv:2601.04278v2 Announce Type: replace Abstract: Although machine unlearning is essential for removing private, harmful, or copyrighted content from LLMs, current benchmarks often fail to faithfull

MASPO: Unifying Gradient Utilization, Probability Mass, and Signal Reliability for Robust and Sample-Efficient LLM Reasoning

arXiv:2602.17550v3 Announce Type: replace Abstract: Existing Reinforcement Learning with Verifiable Rewards (RLVR) algorithms, such as GRPO, rely on rigid, uniform, and symmetric trust region mechanis

safetyarxiv-cs-lg

NL2SQLBench: A Modular Benchmarking Framework for LLM-Enabled NL2SQL Solutions

arXiv:2604.16493v1 Announce Type: cross Abstract: Natural Language to SQL (NL2SQL) technology empowers non-expert users to query relational databases without requiring SQL expertise. While large langu

Predicting LLM Compression Degradation from Spectral Statistics

model-releasesarxiv-cs-lg

arXiv:2604.18085v1 Announce Type: new Abstract: Matrix-level low-rank compression is a promising way to reduce the cost of large language models, but running compression and evaluating the resulting m

Representation-Guided Parameter-Efficient LLM Unlearning

arXiv:2604.17396v1 Announce Type: new Abstract: Large Language Models (LLMs) often memorize sensitive or harmful information, necessitating effective machine unlearning techniques. While existing para

Semantic Density Effect (SDE): Maximizing Information Per Token Improves LLM Accuracy

arXiv:2604.17659v1 Announce Type: new Abstract: We introduce the Semantic Density Effect (SDE): the empirical finding that prompts carrying higher semantic information per token consistently produce m

When Helpers Become Hazards: A Benchmark for Analyzing Multimodal LLM-Powered Safety in Daily Life

arXiv:2601.04043v2 Announce Type: replace Abstract: As Multimodal Large Language Models (MLLMs) become an indispensable assistant in human life, the unsafe content generated by MLLMs poses a danger to

Characterising LLM-Generated Competency Questions: a Cross-Domain Empirical Study using Open and Closed Models

arXiv:2604.16258v1 Announce Type: new Abstract: Competency Questions (CQs) are a cornerstone of requirement elicitation in ontology engineering. CQs represent requirements as a set of natural language

20 Apr 2026

Agents

EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis

arXiv:2601.05808v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are expected to be trained to act as agents in various real-world environments, but this process relies on rich a

agentsarxiv-cs-ai

20 Apr 2026

'Excuse me, may I say something...' CoLabScience, A Proactive AI Assistant for Biomedical Discovery and LLM-Expert Collaborations

arXiv:2604.15588v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) into scientific workflows presents exciting opportunities to accelerate biomedical discovery. However,