Search: “llm”

Amortizing Sequential Experimental Design in Reasoning LLMs

arXiv:2610.20832v1 Announce Type: cross Abstract: Despite their extensive pretraining knowledge and strong reasoning capabilities, large language models (LLMs) struggle to adaptively gather informatio

16 Oct 2026

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Librarian Agents: Training LLMs to Curate Long-Context Workspaces that Anticipate Future Queries

DGX agent

arXiv:2610.22273v1 Announce Type: cross Abstract: Reasoning over long document contexts remains a challenge for large language models (LLMs). Standard retrieval methods are inherently reactive, bottle

model-releasesarxiv-cs-ai

16 Oct 2026

Memorilla: Latent Semantic Memory for LLMs

arXiv:2610.21375v1 Announce Type: cross Abstract: Memory enables large language models (LLMs) to use information beyond their parameters, such as past user conversations, long documents, or an agent's

16 Oct 2026

Safety

Beyond Accuracy: Counterfactual Fragility and Demographic Bias in Clinical Evaluation of LLMs

arXiv:2609.32807v2 Announce Type: replace Abstract: Clinical LLM evaluation often emphasizes answer accuracy; however, accuracy alone does not test counterfactual consistency or demographic robustness

safetyarxiv-cs-ai

Can LLMs Access their World Knowledge for Event Prediction?

arXiv:2610.19021v1 Announce Type: new Abstract: Recent work has questioned whether large language models (LLMs) can be useful for temporal event prediction. Despite vast pretrained world knowledge, LL

How Well Do LLMs Perform in Peer Review? Evidence from Finance and Accounting

arXiv:2610.18435v1 Announce Type: cross Abstract: Referees increasingly use large language models (LLMs), while finance and accounting journals disagree about whether to allow it. We ask which part of

Measuring the Inference Energy Efficiency of Open-Weight LLMs across Benchmarks, Reasoning Modes, and Hardware

arXiv:2610.14389v1 Announce Type: new Abstract: Benchmark scores tell us what an LLM can do, but not how much energy is required to deliver that capability. We directly measure this trade-off for 86 o

PerSo: Investigating Facets of LLMs' Social Intelligence in Real-World Persuasion Scenarios

arXiv:2610.20548v1 Announce Type: new Abstract: Understanding the social intelligence of large language models (LLMs) is increasingly important for applications involving human-AI interaction and mult

SAGE: Pushing 2-Bit Vector-Quantized LLMs toward Real-World Usability

arXiv:2610.14745v1 Announce Type: new Abstract: Extreme low-bit weight-only quantization is an effective approach to reducing the deployment costs of large language models. Below 3 bits, scalar quanti

Applications

Simulating Student-Tutor Interactions with LLMs for Multilingual Math Feedback

arXiv:2506.04920v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated the ability to generate formative feedback and instructional hints in English, making them incr

applicationsarxiv-cs-ai

Safety

Rethinking Projector Training in Multimodal LLMs

arXiv:2610.18598v1 Announce Type: new Abstract: Multimodal LLMs are typically trained by first aligning a vision encoder with a language model via projector training on large image-caption datasets, f

safetyarxiv-cs-cv

14 Oct 2026

SWE-DeceptionBench: Benchmarking Deceptive Behaviors of LLMs Across the Software Development Lifecycle

arXiv:2610.13626v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used throughout the software development lifecycle. This growing reliance on LLMs raises concerns about

12 Oct 2026

Amortized Off-Policy Evaluation for LLMs

arXiv:2610.10848v1 Announce Type: new Abstract: Accurate evaluation is central to selecting which LLM to deploy, yet testing a candidate on live traffic exposes real users to an unvetted model. Teams

9 Oct 2026

Safety

Marrying Pricing and Advertising with LLMs

arXiv:2610.09985v1 Announce Type: cross Abstract: We study a sequential pricing problem in which a seller jointly posts a price and an advertisement generated by a large language model (LLM). The sell

safetyarxiv-cs-lg

8 Oct 2026

zkLLMPoT: Efficient Zero Knowledge Proof of Training for Large Language Models

arXiv:2610.08258v1 Announce Type: new Abstract: Auditing the claimed outcomes of large language model (LLM) training is challenging when model weights and training data are private, while cryptographi

7 Oct 2026

Hidden in the Comments: A Context-Injection Attack Surface in Code LLMs

arXiv:2610.05139v1 Announce Type: new Abstract: Code large language model (Code LLM) assistants generate code from heterogeneous development contexts, including open files, imported modules, pasted sn

researcharxiv-cs-lg

6 Oct 2026

Applications

Programming Manufacturing Robots with Imperfect AI: LLMs as Tuning Experts for FDM Print Configuration Selection

arXiv:2603.22118v2 Announce Type: replace Abstract: We use fused deposition modeling (FDM) 3D printing as a case study of how manufacturing robots can use imperfect AI. In FDM, print configuration str

applicationsarxiv-cs-ro

6 Oct 2026

Tutorials

Multi-Task Evolution for Zero-Shot Cross-Problem Generalization using LLMs

arXiv:2610.03316v1 Announce Type: new Abstract: Designing effective heuristics for diverse combinatorial optimization problems requires substantial expertise and repeated search. Large language models

tutorialsarxiv-cs-ai

5 Oct 2026

When Numbers Start Talking: Numerical Signalling and Strategic Behaviour Among LLMs

arXiv:2610.03033v1 Announce Type: new Abstract: Large language model (LLM)-based agents increasingly operate in multi-agent systems (MAS) characterised by strategic interaction. However, little is kno

agentsarxiv-cs-ai

5 Oct 2026

Tutorials

Backdoor Containment via Expert Quarantine and Shutdown in LLMs

arXiv:2610.00663v1 Announce Type: new Abstract: Backdoored large language models (LLMs) can behave normally on benign inputs while producing attacker-specified outputs under hidden triggers. Existing

tutorialsarxiv-cs-ai

Can LLMs Reliably Annotate Bioassay Metadata to Improve Data Readiness?

arXiv:2610.01616v1 Announce Type: cross Abstract: The emergence of foundation models for molecular property prediction requires a high degree of AI data readiness, including reliable metadata annotati

researcharxiv-cs-ai

Federated Learning for LLMs over Mobile Networks: Issues and Solutions in the RAN Transport

arXiv:2610.01304v1 Announce Type: cross Abstract: Federated LLM fine-tuning enables large models to be adapted using private and geographically distributed data at the network edge, creating recurring

SeedFlood: A Step Toward Scalable Decentralized Fine-Tuning of LLMs

arXiv:2602.18181v2 Announce Type: replace Abstract: This work presents SeedFlood, a new approach to decentralized LLM fine-tuning designed to scale across large models, large collaborations, and compl

SyntheticHLS: Building Diverse Synthetic High-Level Synthesis Datasets using LLMs

arXiv:2610.00106v1 Announce Type: cross Abstract: Deep learning and large language models (LLMs) are rapidly gaining adoption in semiconductor design, driving demand for training datasets. Most effort

CARAT: Do Materials LLMs Reason or Recite?

arXiv:2609.38340v1 Announce Type: new Abstract: When a materials LLM answers a question about crystal structure, does it reason from the structure or copy an answer already printed in its input? Accur

1 Oct 2026

QATFactory: A Versatile, Deployment-Aligned Framework for Quantization-aware Training and Distillation of LLMs

arXiv:2609.39223v1 Announce Type: new Abstract: Large language model (LLM) inference is increasingly moving toward lower precision to realize the throughput of hardware accelerators, but aggressive po

1 Oct 2026

AgentPerfBench: A Benchmarking and Evaluation Suite for Inference Performance of Agentic LLMs

arXiv:2609.34683v1 Announce Type: new Abstract: The optimization of LLM serving engines, such as vLLM and SGLang, is largely benchmark-driven: optimizations, scheduling policies, hardware and system d

29 Sept 2026

Prospective Interpretation Risk: Principled Communication Control Between LLMs

arXiv:2609.33885v1 Announce Type: cross Abstract: Large language model (LLM) agentic systems increasingly rely on models communicating with one another, yet existing uncertainty and multi-agent method

agentsarxiv-cs-lg

29 Sept 2026

Do LLMs Understand Context? A Knowledge Graph-Based Evaluation Framework

arXiv:2609.30484v1 Announce Type: new Abstract: While large language models (LLMs) have achieved remarkable linguistic capabilities, a profound question lingers at their core: do these models truly co

researcharxiv-cs-ai

28 Sept 2026

Energy Efficiency of Locally Deployed LLMs: A Preliminary Quantitative GPU Power Benchmark on Consumer Hardware

arXiv:2608.00008v2 Announce Type: replace Abstract: The local deployment of large language models (LLMs) is gaining traction due to privacy concerns and the desire for on-premise inference. However, t

28 Sept 2026

Also please feel free to fight in the comments about 'pure LLMs' versus multimodal LLMs versus multimodal LLMs with tool use or whatever. I …

Ethan Mollick invited readers to discuss the distinctions between **pure large language models (LLMs)**, **multimodal LLMs** that process multiple data types (e.g., text, images), and **multimodal LLM

agentsethan-mollick--x

27 Sept 2026

Provably Complete Generalized Planning with LLMs

arXiv:2609.27105v1 Announce Type: new Abstract: Generalized planning aims to compute a plan that solves all instances of a planning domain. Recent work has used LLMs to automatically generate and debu

24 Sept 2026

CivBench: Progress-Based Evaluation for LLMs' Strategic Decision-Making in Civilization V

arXiv:2604.07733v2 Announce Type: replace Abstract: Evaluating strategic decision-making in LLM-based agents requires generative, competitive, and longitudinal environments, yet few benchmarks provide

23 Sept 2026

Federated Multilingual Speech-LLMs: Architecture and Aggregation Strategy Benchmarking

arXiv:2609.23825v1 Announce Type: new Abstract: We present a comprehensive benchmark of Federated Learning (FL) for multilingual Automatic Speech Recognition (ASR), evaluating four Speech-LLM architec

22 Sept 2026

LLMs as Linguistic Chameleons: Decoupling Semantics and Structure for Privacy-Preserving Communication

arXiv:2609.23193v1 Announce Type: cross Abstract: As Large Language Model (LLM) APIs become increasingly integrated into privacy-sensitive workflows, ensuring inference-time privacy without compromisi

researcharxiv-cs-cl

22 Sept 2026

From Discharge Notes to Patient Understanding: Persona-Grounded, Open-Ended Simulation of LLMs as Discharge Educators

arXiv:2609.20827v1 Announce Type: new Abstract: Hospital discharge education is an interactive teaching task: a clinician adapts a discharge plan to a patient's literacy, recall, and personality. Exis

agentsarxiv-cs-cl

21 Sept 2026

Is It Still Worth Training a Classical Model in the Era of LLMs? A Crossover Benchmark on Tabular Data

arXiv:2609.20218v1 Announce Type: cross Abstract: Large language models can label a tabular row from a plain-English description with no training - a capability now shipping in mainstream spreadsheet

18 Sept 2026

Align, Integrate, and Fire: Efficient Token-Level Alignment for Zero-Shot SpeechLLMs

arXiv:2609.18516v1 Announce Type: new Abstract: While Large Language Models excel in natural language processing, efficiently extending their capabilities to spoken input remains a significant challen

17 Sept 2026

ABSOL: Aggregated Bayesian Subsampling Orchestrated with LLMs

arXiv:2609.15007v1 Announce Type: new Abstract: Large language models are increasingly used as natural-language interfaces to structured data, yet they remain unreliable when answers require consisten

Disentangling Topology and Diversity in Multi-Agent LLMs for Multilingual Low-Resource Emotion Detection

arXiv:2609.14570v1 Announce Type: cross Abstract: Multi-agent LLM systems combine multiple inference calls, but prior work often confounds how calls are connected with how they are diversified. We stu

Issue Bias in Generative AI Writing Assistance: Political Issues and LLMs in the Swedish 2026 Election

arXiv:2609.15207v1 Announce Type: new Abstract: Generative AI writing assistants and the Large Language Models (LLMs) that power them are increasingly part of how voters gather information before elec

Merging the Knowledge of LLMs for Automatic Speech Recognition

arXiv:2609.15743v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems, trained on paired speech-text data, have been improved by leveraging language models (LMs) trained on text-o

researcharxiv-cs-cl

RFCLLM: Evaluating LLMs' Reasoning Ability of Network Protocol State Machines

arXiv:2609.13389v1 Announce Type: new Abstract: Mapping textual specifications into formal representations is essential for ensuring the correctness of protocol designs and implementations. LLM-genera

researcharxiv-cs-cl

Fortunate Recall: Ontology-Driven Memory Lifecycle Management for Persistent Coherence in LLMs

arXiv:2609.10413v1 Announce Type: new Abstract: Current LLM memory systems treat all personal facts identically, so stores grow without bound while retrieval precision degrades. The core challenge is

11 Sept 2026

Towards Reliable Medical LLMs: Benchmarking and Enhancing Confidence Estimation of Large Language Models in Medical Consultation

arXiv:2601.15645v2 Announce Type: replace Abstract: Large-scale language models (LLMs) often offer clinical judgments based on incomplete information, increasing the risk of misdiagnosis. Existing stu

11 Sept 2026

Frontier LLMs are effective batch optimizers: Assessing reasoning models in continuous and discrete settings

arXiv:2609.03177v1 Announce Type: new Abstract: Frontier large language models (LLMs) have become attractive priors for optimization due to their large-scale pretraining that enables them to navigate

researcharxiv-cs-lg

4 Sept 2026

Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications

arXiv:2508.00669v2 Announce Type: replace-cross Abstract: The proliferation of Large Language Models (LLMs) in medicine has enabled impressive capabilities, yet a critical gap remains in their ability

agentsarxiv-cs-ai

4 Sept 2026

Speak for Me: Giving LLMs the Situational Awareness to Participate in a Meeting