Search: “llm”

From Solo to Social Learning: Characterizing Recursive Social Improvement in LLMs

arXiv:2609.38516v1 Announce Type: cross Abstract: Large language models (LLMs) can now improve themselves by revising the instructions they follow, and LLM agents are increasingly orchestrated to work

agentsarxiv-cs-ai

1 Oct 2026

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Koa-action: Fast and Consistent Structured Decision Making with Generative LLMs

DGX agent

arXiv:2609.36115v1 Announce Type: new Abstract: Industry applications often demand low-latency classification, yet current large language model (LLM) approaches remain poorly suited for latency-critic

model-releasesarxiv-cs-lg

30 Sept 2026

Population Fidelity: Evaluating Population Representativeness in LLMs

arXiv:2609.36253v1 Announce Type: cross Abstract: Large language models (LLMs) show considerable potential in simulating human attitudes and preferences. Prior work finds that LLM-generated responses

30 Sept 2026

Audio LLMs Know When They Can't Hear You

arXiv:2609.30625v1 Announce Type: new Abstract: Audio large language models allow users to interact with the model through speech. When an input recording is too degraded, the model may misinterpret t

28 Sept 2026

From ASR to ASP: Evaluating Prompt Attack Vulnerabilities Against Open-Source LLMs

arXiv:2505.14368v3 Announce Type: replace-cross Abstract: Recent studies demonstrate that Large Language Models (LLMs) are vulnerable to attacks that generate harmful or sensitive outputs. As open-sou

28 Sept 2026

Tutorials

Therapy as an NLP Task: Comparing LLMs and Human Peers Behaviors in CBT Sessions

arXiv:2409.02244v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly being used as ad hoc therapists. While prior research has found that LLMs outperform human couns

tutorialsarxiv-cs-cl

23 Sept 2026

What is the best way to use LLMS's in 2026? I feel like a caveman with the way I use them?

I'm still using LLM's the 2022/23 way of doing everything manually from the chat box. I copy and paste everything manually into the box and ask the AI to explain or do something, such as pasting code

agentsr-chatgpt

19 Sept 2026

Behavior2Value: Benchmarking and Empowering LLMs for Consumer Value Measurement from E-commerce Behaviors

arXiv:2609.18203v1 Announce Type: new Abstract: Human values are deep motivational orientations that shape human behaviors. In e-commerce, they reveal the stable drivers behind users' purchase decisio

17 Sept 2026

BENCHCOMPASS: From Scores to Signals for Training and Harness Decisions in Payment-Domain LLMs

arXiv:2609.18270v1 Announce Type: new Abstract: Payment operations are a critical financial infrastructure, but the value of large language models in this domain remains unclear because payment rules

17 Sept 2026

Scaling Articulated Rationales for MLLM-based Recommendation

arXiv:2609.17639v1 Announce Type: cross Abstract: Modern recommendation systems largely infer user preferences from implicit behaviors such as clicks, watch time, and negative feedback, but these sign

17 Sept 2026

Benchmarking Factual Robustness of LLMs via Multi-conversation Persuasion

arXiv:2609.16777v1 Announce Type: new Abstract: As Large Language Models (LLMs) increasingly serve as primary knowledge retrieval interfaces, their robustness against extit{persuasion attacks}---attem

safetyarxiv-cs-cl

16 Sept 2026

Breaking the 1.58-bit Barrier for Ternary LLMs

arXiv:2609.16338v1 Announce Type: new Abstract: Ternary Large Language Models (LLM) store every weight as one of three symbols {-1,0,+1}, so the cost of a ternary model is conventionally referenced to

16 Sept 2026

Tutorials

LLMs as Master Forgers: Generating Synthetic Time Series Data for Manufacturing

arXiv:2609.16155v1 Announce Type: cross Abstract: This paper presents a novel framework leveraging Large Language Models (LLMs) to generate synthetic time series data for manufacturing processes. Moti

tutorialsarxiv-cs-ai

16 Sept 2026

CHAI for LLMs: Improving Code-Mixed Translation in Large Language Models through Reinforcement Learning with AI Feedback

arXiv:2411.09073v4 Announce Type: replace-cross Abstract: Large language models (LLMs) show strong performance across many tasks but remain weak at understanding code-mixed (CM) language. Despite this

15 Sept 2026

LLMs or Naive Bayes? Old Gems or New Ways

arXiv:2609.13185v1 Announce Type: cross Abstract: Large language models (LLMs) prompt a recurring question in research computing: should classical methods like Naive Bayes (NB) be retired? We benchmar

15 Sept 2026

Making Alternative Data Work: Context-Augmented LLMs for Financial Forecasting

arXiv:2609.11607v1 Announce Type: new Abstract: When forecasting a firm's future financial performance, alternative data - data collected from non-traditional sources such as consumer transactions, we

12 Sept 2026

Hardware

Building py-kvcache: A Performance Characterization of External KV Caching for vLLM with NVMe SSDs

arXiv:2609.11744v1 Announce Type: cross Abstract: Prefix caching can reduce the time to first token (TTFT) of long-context LLM requests by reusing previously computed key-value (KV) states, but for sh

hardwarearxiv-cs-lg

11 Sept 2026

Leveraging LLMs for Context-Aware Implicit Textual and Multimodal Hate Speech Detection

arXiv:2510.15685v2 Announce Type: replace Abstract: This paper investigates the use of an LLM to generate auxiliary background context for social media posts, and explores four methods to incorporate

researcharxiv-cs-cl

11 Sept 2026

FreqBLiMP: Frequency-Controlled Minimal Pairs Reveal Robustness and Fragility of LLMs Under Lexical Rarity

arXiv:2609.07153v1 Announce Type: cross Abstract: Minimal-pair benchmarks such as BLiMP evaluate linguistic knowledge by testing whether language models (LMs) prefer acceptable sentences over minimall

10 Sept 2026

LLMs for Social Network Modeling: From Network Generation to Dynamic Processes

arXiv:2609.08049v1 Announce Type: cross Abstract: Large language models (LLMs) are rapidly emerging as a new paradigm for modeling social networks by representing users and their relationships and int

10 Sept 2026

Activation- and Influence-Aware Ranks (AIR): Function-Preserving SVD Compression for LLMs

model-releasesarxiv-cs-lg

arXiv:2606.19993v2 Announce Type: replace Abstract: We present Activation- and Influence-Aware Ranks (AIR), an SVD-based LLM compression framework that guides each weight matrix's low-rank approximati

9 Sept 2026

Local Ai

How Do Prompt Variations Affect Energy Consumption in On-Device LLMs?

arXiv:2609.01798v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed on mobile devices, making energy efficiency a key deployment constraint, yet the energy impact of

local-aiarxiv-cs-cl

3 Sept 2026

Can LLMs Design Video Coding Tools? A Case Study on Planar Mode

arXiv:2609.01535v1 Announce Type: cross Abstract: This paper explores whether large language models (LLMs) can design video coding tools, a highly challenging task due to the intricate algorithmic cou

Evaluating Second-Order Bias of LLMs Through Epistemic Entitlement

arXiv:2606.17506v2 Announce Type: replace Abstract: Evaluations of social bias in LLMs largely focus on whether models generate or imply biased content. However, as LLMs are increasingly used as judge

safetyarxiv-cs-cl

From Detection to Refusal: Safer LLMs via Circuit-Guided Weight Scaling

arXiv:2609.00051v1 Announce Type: cross Abstract: Despite extensive alignment efforts, Large Language Models (LLMs) remain vulnerable to generating unsafe content under adversarial prompting, yet the

Long-Horizon State Tracking in LLMs: Executing MD5 through a Deep Sequence of Dependent Tool Calls

arXiv:2609.00012v1 Announce Type: new Abstract: Long-horizon tasks remain uncommon in large language model (LLM) evaluation, and for a reason: when each step depends on the last, per-step accuracy tha

agentsarxiv-cs-ai

Predicting Program Exit Code with LLMs and Programming Language Semantics

arXiv:2609.00579v1 Announce Type: cross Abstract: Large language models (LLMs) have shown proficiency in various software engineering tasks, such as code generation and translation. However, a key lim

Compression-Aware Abstention: Teaching LLMs to Refuse When KV-Compression Masks Remove Answer Evidence

arXiv:2608.29934v1 Announce Type: new Abstract: KV-cache compression reduces LLM inference memory by evicting context tokens, but when the evicted tokens contain answer-bearing evidence, the model may

Generating Workflow DAGs from Natural Language with Non-Reasoning LLMs

arXiv:2608.30250v1 Announce Type: new Abstract: This paper addresses the problem of translating natural-language routing rules written by business administrators into executable workflow graphs for en

PAC: Progress-Augmented Advantage Curriculum for Multi-Task Reinforcement Learning of LLMs

arXiv:2608.30528v1 Announce Type: new Abstract: Reinforcement learning (RL) is used to improve the reasoning abilities of LLMs, while training data span heterogeneous tasks. However, most RL post-trai

safetyarxiv-cs-lg

Risk-Adjusted Harm Scoring for Automated Red Teaming for LLMs in Financial Services

arXiv:2603.10807v2 Announce Type: replace-cross Abstract: Existing LLM safety evaluations rely on binary attack-success rates and domain-agnostic taxonomies, leaving regulated Banking, Financial Servi

Who Flips? Self- and Cross-Model Counterarguments Reveal Answer Instability in LLMs

arXiv:2606.16011v2 Announce Type: replace Abstract: Standard accuracy benchmarks evaluate whether large language models (LLMs) reach correct answers. However, they do not test whether models maintain

How LLMs Distort Our Written Language

arXiv:2603.18161v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are used by over a billion people globally, most often to assist with writing. In this work, we demonstrate that

28 Aug 2026

LLMs for Survey Text Analysis - A Performance Comparison Between Humans and GPT-5 on Inductive Content Analysis

arXiv:2608.22417v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to support text analysis in qualitative research, yet evidence on their performance in inductive cont

25 Aug 2026

Evaluating and Explaining Prompt Sensitivity of LLMs Using Interactions

arXiv:2608.18539v1 Announce Type: cross Abstract: The remarkable capabilities of large language models (LLMs) are often undermined by their instability. Even subtle and semantically irrelevant changes

20 Aug 2026

Local Ai

Redakto - The Incognito Tab for LLMs

arXiv:2608.18260v1 Announce Type: new Abstract: Large Language Models (LLMs) are being increasingly used in everyday applications. A major challenge in the context of LLMs or Artificial Intelligence (

local-aiarxiv-cs-ai

20 Aug 2026

When Do LLMs Actually Help? Evaluating LLMs as Data Quality Annotators

arXiv:2608.18158v1 Announce Type: cross Abstract: LLMs have been increasingly used to catch data quality issues automatically, but we know very little about how consistent these judgments actually are

20 Aug 2026

A Framework for Using and Evaluating LLMs as Surrogate Experts in Security Surveys: Reliability, Bias, and Implications

arXiv:2608.16893v1 Announce Type: cross Abstract: Expert surveys are widely used in security research to study practitioner workows and decision-making, yet recruiting domain experts - especially in S

19 Aug 2026

Can LLMs Reason in a Legally Meaningful Manner? A Small-scale Study on European Court of Human Rights Cases

arXiv:2608.17168v1 Announce Type: cross Abstract: Reasoning has become a standard technique and feature for contemporary LLMs; however, its application and quality in the context of demanding legal-or

19 Aug 2026

Prompting is not enough: supervised baselines and leakage control for measuring shared decision-making with LLMs in pediatric encounters

arXiv:2608.14792v1 Announce Type: cross Abstract: Objectives: To determine whether zero-shot prompting of a large language model (LLM) is sufficient to detect shared decision-making (SDM) behaviors in

18 Aug 2026

AaLLM: An End-to-End Analog Circuit Design Framework from Topology Generation to Sizing Using Large Language Models

arXiv:2608.13472v1 Announce Type: cross Abstract: Analog circuit design is a time-consuming, iterative process in a nonlinear and high-dimensional design space that relies heavily on expert intuition.

agentsarxiv-cs-ai

14 Aug 2026

A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination

arXiv:2608.04872v1 Announce Type: cross Abstract: Symbolic regression aims to discover closed-form equations from data, but existing LLM-guided methods often rely on a unified proposal loop that compr

6 Aug 2026

LLMs struggle to simulate human belief updates in controlled environments

arXiv:2607.28347v1 Announce Type: new Abstract: LLMs are increasingly deployed as proxies for human study participants in social science experiments, yet the fidelity of this practice has rarely been

31 Jul 2026

STEREODISCO: Discovering Stereotypicality in LLMs

model-releasesarxiv-cs-lg

arXiv:2607.27824v1 Announce Type: cross Abstract: LLMs encode, convey, and perpetuate stereotypes. Prior computational research focuses on a small set of semantic axes investigated in social psycholog

31 Jul 2026

Auditing Alignment Controllability in LLMs via Political Axes

arXiv:2607.23519v1 Announce Type: cross Abstract: Political audits of large language models (LLMs) usually reduce each to one point on a political compass. But that resting point barely matters in dep

28 Jul 2026

Speech Signals Complement LLMs for Predicting Interpersonal Attraction in Speed Dating

arXiv:2607.23037v1 Announce Type: new Abstract: Large language models (LLMs) can predict interpersonal attraction from conversation transcripts, but it remains unclear what a speech predictor can add

researcharxiv-cs-cl

28 Jul 2026

Task-Conditional Faithfulness Auditing of Multimodal LLMs for Grid Diagnosis

arXiv:2607.24539v1 Announce Type: new Abstract: Multimodal large language models (LLMs) can combine topology, measurements, and incident text for grid diagnosis, yet answer accuracy does not establish

28 Jul 2026

LatentLens: Revealing Highly Interpretable Visual Tokens in LLMs

arXiv:2602.00462v5 Announce Type: replace-cross Abstract: Transforming a large language model (LLM) into a vision-language model (VLM) can be achieved by mapping the visual tokens from a vision encode