Search: “llm”

GSAR: Typed Grounding for Hallucination Detection and Recovery in Multi-Agent LLMs

arXiv:2604.23366v1 Announce Type: new Abstract: Autonomous multi-agent LLM systems are increasingly deployed to investigate operational incidents and produce structured diagnostic reports. Their trust

28 Apr 2026

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LLM4SCREENLIT: Recommendations on Assessing the Performance of Large Language Models for Screening Literature in Systematic Reviews

DGX agent

arXiv:2511.12635v2 Announce Type: replace-cross Abstract: Context: Large language models (LLMs) are increasingly used to screen literature for systematic reviews (SRs), but the standard confusion-matr

model-releasesarxiv-cs-ai

28 Apr 2026

LLMs Reading the Rhythms of Daily Life: Aligned Understanding for Behavior Prediction and Generation

arXiv:2604.23578v1 Announce Type: cross Abstract: Human daily behavior unfolds as complex sequences shaped by intentions, preferences, and context. Effectively modeling these behaviors is crucial for

28 Apr 2026

LayerBoost: Layer-Aware Attention Reduction for Efficient LLMs

arXiv:2604.22050v1 Announce Type: cross Abstract: Transformers are mostly relying on softmax attention, which introduces quadratic complexity with respect to sequence length and remains a major bottle

researcharxiv-cs-cl

27 Apr 2026

AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning

arXiv:2604.05846v2 Announce Type: replace Abstract: Large Language Models (LLMs) increasingly rely on agentic capabilities-iterative retrieval, tool use, and decision-making-to overcome the limits of

safetyarxiv-cs-cl

24 Apr 2026

Why are all LLMs Obsessed with Japanese Culture? On the Hidden Cultural and Regional Biases of LLMs

arXiv:2604.21751v1 Announce Type: cross Abstract: LLMs have been showing limitations when it comes to cultural coverage and competence, and in some cases show regional biases such as amplifying Wester

24 Apr 2026

Explainable AML Triage with LLMs: Evidence Retrieval and Counterfactual Checks

arXiv:2604.19755v1 Announce Type: new Abstract: Anti-money laundering (AML) transaction monitoring generates large volumes of alerts that must be rapidly triaged by investigators under strict audit an

23 Apr 2026

From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context

arXiv:2508.07117v3 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have emerged as powerful tools for learning over structured data, including text-attributed graphs (TAGs), which are co

applicationsarxiv-cs-lg

23 Apr 2026

Surrogate modeling for interpreting black-box LLMs in medical predictions

arXiv:2604.20331v1 Announce Type: cross Abstract: Large language models (LLMs), trained on vast datasets, encode extensive real-world knowledge within their parameters, yet their black-box nature obsc

applicationsarxiv-cs-ai

23 Apr 2026

Local Ai

Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control

arXiv:2604.19018v1 Announce Type: cross Abstract: Inference-time LLM alignment methods, particularly activation steering, offer an alternative to fine-tuning by directly modifying activations during g

local-aiarxiv-cs-ai

22 Apr 2026

LLMAR: A Tuning-Free Recommendation Framework for Sparse and Text-Rich Industrial Domains

arXiv:2604.16379v1 Announce Type: cross Abstract: Industrial B2B applications (e.g., construction site risk prediction, material procurement) face extreme data sparsity yet feature rich textual intera

researcharxiv-cs-cl

21 Apr 2026

Local Ai

The Consensus Trap: Rescuing Multi-Agent LLMs from Adversarial Majorities via Token-Level Collaboration

arXiv:2604.17139v1 Announce Type: new Abstract: Multi-agent large language model (LLM) architectures increasingly rely on response-level aggregation, such as Majority Voting (MAJ), to raise reasoning

local-aiarxiv-cs-cl

21 Apr 2026

LLMs Corrupt Your Documents When You Delegate

model-releasesarxiv-cs-cl

arXiv:2604.15597v1 Announce Type: new Abstract: Large Language Models (LLMs) are poised to disrupt knowledge work, with the emergence of delegated work as a new interaction paradigm (e.g., vibe coding

20 Apr 2026

GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback

arXiv:2604.08553v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic understanding ability on te

13 Apr 2026

Automating Database-Native Function Code Synthesis with LLMs

arXiv:2604.06231v1 Announce Type: cross Abstract: Database systems incorporate an ever-growing number of functions in their kernels (a.k.a., database native functions) for scenarios like new applicati

Improved Evidence Extraction and Metrics for Document Inconsistency Detection with LLMs

arXiv:2601.02627v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are becoming useful in many domains due to their impressive abilities that arise from large training datasets and

Learning to Negotiate: Multi-Agent Deliberation for Collective Value Alignment in LLMs

arXiv:2603.10476v2 Announce Type: replace Abstract: LLM alignment has progressed in single-agent settings through paradigms such as RL with human feedback (RLHF), while recent work explores scalable a

safetyarxiv-cs-cl

Scientific Knowledge-driven Decoding Constraints Improving the Reliability of LLMs

arXiv:2604.06603v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong knowledge reserves and task-solving capabilities, but still face the challenge of severe hallucination,

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

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

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

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

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

Can LLMs Access their World Knowledge for Event Prediction?

model-releasesarxiv-cs-cl

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

model-releasesarxiv-cs-cl

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

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

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

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

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

Agents

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

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

Agents

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

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