Search: “llm”

SecureForge: Finding and Preventing Vulnerabilities in LLM-Generated Code via Prompt Optimization

arXiv:2605.08382v1 Announce Type: cross Abstract: LLM coding agents now generate code at an unprecedented scale, yet LLM-generated code introduces cybersecurity vulnerabilities into codebases without

agentsarxiv-cs-cl

12 May 2026

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Model Releases

Two Ways to De-Bias an LLM-as-a-Judge: A Continuous-Score Comparison of Hierarchical Bayesian Calibration and Neural-ODE Score Transport

DGX agent

arXiv:2605.09227v1 Announce Type: new Abstract: [Abridged] Using a Large Language Model (LLM) as an automatic rater (LLM-as-a-judge) is cheap but potentially biased: some judges run lenient, others st

model-releasesarxiv-cs-cl

12 May 2026

This works really well btw, at the end of your query ask your LLM to 'structure your response as HTML', then view the generated file in your…

This works really well btw, at the end of your query ask your LLM to 'structure your response as HTML', then view the generated file in your browser. I've also had some success asking the LLM to prese

researchkarpathy--x

11 May 2026

BenCSSmark: Making the Social Sciences Count in LLM Research

arXiv:2605.04886v1 Announce Type: new Abstract: This position paper argues that the under-representation of social science tasks in contemporary LLM benchmarks limits advances in both LLM evaluation a

7 May 2026

RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization

arXiv:2605.04539v1 Announce Type: new Abstract: Direct Preference Optimization (DPO), the efficient alternative to PPO-based RLHF, falls short on knowledge-intensive generation: standard preference si

7 May 2026

A Low-Latency Fraud Detection Layer for Detecting Adversarial Interaction Patterns in LLM-Powered Agents

arXiv:2605.01143v1 Announce Type: new Abstract: Large Language Model (LLM)-powered agents demonstrate strong capabilities in autonomous task execution, tool use, and multi-step reasoning. However, the

arXiv Papers → LLM Artifacts This is how I keep up with AI research now. It's like having access to the most personalized arXiv feed. Automa…

arXiv Papers → LLM Artifacts This is how I keep up with AI research now. It's like having access to the most personalized arXiv feed. Automations run everyday to curate papers based a set of rules and

agentsdair-ai--x

LLM-Powered AI Agent Systems and Their Applications in Industry

arXiv:2505.16120v2 Announce Type: replace Abstract: The emergence of Large Language Models (LLMs) has reshaped agent systems. Unlike traditional rule-based agents with limited task scope, LLM-powered

Local Ai

Segmenting Human-LLM Co-authored Text via Change Point Detection

arXiv:2605.03723v1 Announce Type: new Abstract: The rise of large language models (LLMs) has created an urgent need to distinguish between human-written and LLM-generated text to ensure authenticity a

local-aiarxiv-cs-cl

Tutorials

ValueBlindBench: Agreement-Gated Stress Testing of LLM-Judged Investment Rationales Before Returns Are Observable

arXiv:2604.25224v2 Announce Type: replace Abstract: LLM-based financial agents increasingly produce investment rationales before the outcomes needed to evaluate them are observable. This creates a del

tutorialsarxiv-cs-ai

LLM-Foraging: Large Language Models for Decentralized Swarm Robot Foraging

model-releasesarxiv-cs-ro

arXiv:2605.01461v1 Announce Type: new Abstract: Swarm foraging algorithms, such as the central-place foraging algorithm (CPFA), typically rely on offline parameter optimization using genetic algorithm

5 May 2026

MultiBreak: A Scalable and Diverse Multi-turn Jailbreak Benchmark for Evaluating LLM Safety

arXiv:2605.01687v1 Announce Type: new Abstract: We present MultiBreak, a scalable and diverse multi-turn jailbreak benchmark to evaluate large language model (LLM) safety. Multi-turn jailbreaks mimic

5 May 2026

Watermarking LLM Agent Trajectories

arXiv:2602.18700v2 Announce Type: replace-cross Abstract: LLM agents rely heavily on high-quality trajectory data to guide their problem-solving behaviors, yet producing such data requires substantial

5 May 2026

ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineering

arXiv:2505.23723v2 Announce Type: replace Abstract: The emergence of large language model (LLM)-based agents has significantly advanced the development of autonomous machine learning (ML) engineering.

4 May 2026

LLMs Capture Emotion Labels, Not Emotion Uncertainty: Distributional Analysis and Calibration of Human--LLM Judgment Gaps

arXiv:2604.27345v1 Announce Type: new Abstract: Human annotators frequently disagree on emotion labels, yet most evaluations of Large Language Model (LLM) emotion annotation collapse these judgments i

researcharxiv-cs-cl

1 May 2026

Tools

llm 0.32a0

LLM 0.32a0 is an alpha release of Simon Willison's LLM Python library and CLI tool for accessing large language models, featuring consequential architectural changes. The release includes two key chan

toolssimon-willison

29 Apr 2026

Analytica: Soft Propositional Reasoning for Robust and Scalable LLM-Driven Analysis

arXiv:2604.23072v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly tasked with complex real-world analysis (e.g., in financial forecasting, scientific discovery), yet t

Judging the Judges: A Systematic Evaluation of Bias Mitigation Strategies in LLM-as-a-Judge Pipelines

arXiv:2604.23178v1 Announce Type: new Abstract: LLM-as-a-Judge has become the dominant paradigm for evaluating language model outputs, yet LLM judges exhibit systematic biases that compromise evaluati

Revisable by Design: A Theory of Streaming LLM Agent Execution

arXiv:2604.23283v1 Announce Type: new Abstract: Current LLM agents operate under an implicit but universal assumption: execution is a transaction -- the user submits a request, the agent works in isol

agentsarxiv-cs-lg

Uncertainty Propagation in LLM-Based Systems

arXiv:2604.23505v1 Announce Type: cross Abstract: Uncertainty in large language model (LLM)-based systems is often studied at the level of a single model output, yet deployed LLM applications are comp

researcharxiv-cs-ai

Do LLM Decoders Listen Fairly? Benchmarking How Language Model Priors Shape Bias in Speech Recognition

arXiv:2604.21276v1 Announce Type: cross Abstract: As pretrained large language models replace task-specific decoders in speech recognition, a critical question arises: do their text-derived priors mak

24 Apr 2026

ActuBench: A Multi-Agent LLM Pipeline for Generation and Evaluation of Actuarial Reasoning Tasks

arXiv:2604.20273v1 Announce Type: new Abstract: We present ActuBench, a multi-agent LLM pipeline for the automated generation and evaluation of advanced actuarial assessment items aligned with the Int

Bias in the Tails: How Name-conditioned Evaluative Framing in Resume Summaries Destabilizes LLM-based Hiring

arXiv:2604.19984v1 Announce Type: cross Abstract: Research has documented LLMs' name-based bias in hiring and salary recommendations. In this paper, we instead consider a setting where LLMs generate c

LLM Agents Predict Social Media Reactions but Do Not Outperform Text Classifiers: Benchmarking Simulation Accuracy Using 120K+ Personas of 1511 Humans

arXiv:2604.19787v1 Announce Type: cross Abstract: Social media platforms mediate how billions form opinions and engage with public discourse. As autonomous AI agents increasingly participate in these

LLM-guided phase diagram construction through high-throughput experimentation

arXiv:2604.20304v1 Announce Type: cross Abstract: Constructing phase diagrams for multicomponent alloys requires extensive experimental measurements and is a time-consuming task. Here we investigate w

LLM StructCore: Schema-Guided Reasoning Condensation and Deterministic Compilation

arXiv:2604.20560v1 Announce Type: new Abstract: Automatically filling Case Report Forms (CRFs) from clinical notes is challenging due to noisy language, strict output contracts, and the high cost of f

researcharxiv-cs-cl

Separable Pathways for Causal Reasoning: How Architectural Scaffolding Enables Hypothesis-Space Restructuring in LLM Agents

arXiv:2604.20039v1 Announce Type: new Abstract: Causal discovery through experimentation and intervention is fundamental to robust problem solving. It requires not just updating beliefs within a fixed

researcharxiv-cs-ai

Mesh Memory Protocol: Semantic Infrastructure for Multi-Agent LLM Systems

arXiv:2604.19540v1 Announce Type: cross Abstract: Teams of LLM agents increasingly collaborate on tasks spanning days or weeks: multi-day data-generation sprints where generator, reviewer, and auditor

22 Apr 2026

Applications

Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption

arXiv:2510.18333v2 Announce Type: replace-cross Abstract: Despite progress in watermarking algorithms for large language models (LLMs), real-world deployment remains limited. We argue that this gap st

applicationsarxiv-cs-cl

22 Apr 2026

Semantic Needles in Document Haystacks: Sensitivity Testing of LLM-as-a-Judge Similarity Scoring

arXiv:2604.18835v1 Announce Type: cross Abstract: We propose a scalable, multifactorial experimental framework that systematically probes LLM sensitivity to subtle semantic changes in pairwise documen

22 Apr 2026

Clinical Note Bloat Reduction for Efficient LLM Use

arXiv:2604.16364v1 Announce Type: cross Abstract: Health systems are rapidly deploying large language models (LLMs) that use clinical notes for clinical decision support applications. However, modern

researcharxiv-cs-cl

21 Apr 2026

Downgrade to Upgrade: Optimizer Simplification Enhances Robustness in LLM Unlearning

arXiv:2510.00761v5 Announce Type: replace Abstract: Large language model (LLM) unlearning aims to surgically remove the influence of undesired data or knowledge from an existing model while preserving

safetyarxiv-cs-lg

21 Apr 2026

LLM-AUG: Robust Wireless Data Augmentation with In-Context Learning in Large Language Models

arXiv:2604.17770v1 Announce Type: new Abstract: Data scarcity remains a fundamental bottleneck in applying deep learning to wireless communication problems, particularly in scenarios where collecting

researcharxiv-cs-lg

21 Apr 2026

📊 The latest from the LangSmith Signal: We looked at API call volume and developer adoption trends across LLM providers using LangChain ope…

The LangSmith Signal report analyzes API call volume and developer adoption trends across major LLM providers, leveraging LangChain's open-source data and observability platform to track usage pattern

agentsharrison-chase--x

20 Apr 2026

Rethinking LLM-Driven Heuristic Design: Generating Efficient and Specialized Solvers via Dynamics-Aware Optimization

arXiv:2601.20868v2 Announce Type: replace Abstract: Large Language Models (LLMs) have advanced the field of Combinatorial Optimization through automated heuristic generation. Instead of relying on man

researcharxiv-cs-lg

17 Apr 2026

The Autocorrelation Blind Spot: Why 42% of Turn-Level Findings in LLM Conversation Analysis May Be Spurious

arXiv:2604.14414v1 Announce Type: new Abstract: Turn-level metrics are widely used to evaluate properties of multi-turn human-LLM conversations, from safety and sycophancy to dialogue quality. However

safetyarxiv-cs-cl

17 Apr 2026

The cognitive companion: a lightweight parallel monitoring architecture for detecting and recovering from reasoning degradation in LLM agents

model-releasesarxiv-cs-lg

arXiv:2604.13759v1 Announce Type: cross Abstract: Large language model (LLM) agents on multi-step tasks suffer reasoning degradation, looping, drift, stuck states, at rates up to 30% on hard tasks. Cu

16 Apr 2026

Adaptive Budget Allocation in LLM-Augmented Surveys

arXiv:2604.12497v1 Announce Type: new Abstract: Large language models (LLMs) can generate survey responses at low cost, but their reliability varies substantially across questions and is unknown befor

researcharxiv-cs-lg

Local Ai

GigaCheck: Detecting LLM-generated Content via Object-Centric Span Localization

arXiv:2410.23728v3 Announce Type: replace Abstract: With the increasing quality and spread of LLM assistants, the amount of generated content is growing rapidly. In many cases and tasks, such texts ar

local-aiarxiv-cs-cl

How memory can affect collective and cooperative behaviors in an LLM-Based Social Particle Swarm

arXiv:2604.12250v1 Announce Type: new Abstract: This study examines how model-specific characteristics of Large Language Model (LLM) agents, including internal alignment, shape the effect of memory on

One of the fastest ways to lose trust in a self-hosted LLM: prompt injection compliance [P]

researchr-machinelearning

This r/MachineLearning post discusses how self-hosted LLMs that comply with prompt injection attempts — effectively following malicious or overriding instructions embedded in user input — represent a

PrivacyReasoner: Can LLM Emulate a Human-like Privacy Mind?

arXiv:2601.09152v2 Announce Type: replace Abstract: Prior work on LLM-based privacy focuses on norm judgment over synthetic vignettes, rather than how people think about a specific data practice and f

Back to the Barn with LLAMAs: Evolving Pretrained LLM Backbones in Finetuning Vision Language Models

arXiv:2604.10985v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have rapidly advanced by leveraging powerful pre-trained Large Language Models (LLMs) as core reasoning backbones. As new

Local Ai

ClawGuard: A Runtime Security Framework for Tool-Augmented LLM Agents Against Indirect Prompt Injection

arXiv:2604.11790v1 Announce Type: cross Abstract: Tool-augmented Large Language Model (LLM) agents have demonstrated impressive capabilities in automating complex, multi-step real-world tasks, yet rem

local-aiarxiv-cs-ai

ConfigSpec: Profiling-Based Configuration Selection for Distributed Edge--Cloud Speculative LLM Serving

arXiv:2604.09722v1 Announce Type: cross Abstract: Speculative decoding enables collaborative Large Language Model (LLM) inference across cloud and edge by separating lightweight token drafting from he

CONSCIENTIA: Can LLM Agents Learn to Strategize? Emergent Deception and Trust in a Multi-Agent NYC Simulation

arXiv:2604.09746v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed as autonomous agents, understanding how strategic behavior emerges in multi-agent environmen

Diagnosing Retrieval vs. Utilization Bottlenecks in LLM Agent Memory

arXiv:2603.02473v2 Announce Type: replace Abstract: Memory-augmented LLM agents store and retrieve information from prior interactions, yet the relative importance of how memories are written versus h

From Agent Loops to Structured Graphs:A Scheduler-Theoretic Framework for LLM Agent Execution

arXiv:2604.11378v1 Announce Type: new Abstract: The dominant paradigm for building LLM based agents is the Agent Loop, an iterative cycle where a single language model decides what to do next by readi