Search: “llm”

LLMs are not (consistently) Bayesian: Quantifying internal (in)consistencies of LLMs' probabilistic beliefs

arXiv:2605.06915v1 Announce Type: new Abstract: Modern AI systems are being deployed in complex domains such as medicine, science, and law, where it is important that they not only produce correct ans

researcharxiv-cs-lg

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Agents

LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling

DGX agent

arXiv:2605.08083v1 Announce Type: new Abstract: Test-time scaling (TTS) has become an effective approach for improving large language model performance by allocating additional computation during infe

agentsarxiv-cs-cl

11 May 2026

NCL-UoR at SemEval-2026 Task 5: Embedding-Based Methods, Fine-Tuning, and LLMs for Word Sense Plausibility Rating

arXiv:2603.08256v2 Announce Type: replace Abstract: Word sense plausibility rating requires predicting the human-perceived plausibility of a given word sense on a 1-5 scale in the context of short nar

One of the most important properties of LLMs that we take for granted is that newer, bigger models are just better at everything. The AI Lab…

model-releasesethan-mollick--x

One of the most important properties of LLMs that we take for granted is that newer, bigger models are just better at everything. The AI Labs are pouring effort into economically valuable fields like

Star Elastic: Many-in-One Reasoning LLMs with Efficient Budget Control

arXiv:2605.07182v1 Announce Type: new Abstract: Training a family of large language models (LLMs), either from scratch or via iterative compression, is prohibitively expensive and inefficient, requiri

The Position Curse: LLMs Struggle to Locate the Last Few Items in a List

arXiv:2605.07127v1 Announce Type: cross Abstract: Modern large language models (LLMs) can find a needle in a haystack (locating a single relevant fact buried among hundreds of thousands of irrelevant

Open sourced an iOS app that runs LLMs on-device with llama.cpp, and lets you plug in your own Ollama for automatic health insights from HealthKit

An iOS application that enables large language models to run directly on-device using llama.cpp technology, allowing users to integrate their own Ollama instances for processing Apple HealthKit data t

model-releasesr-ollama

9 May 2026

The human brain🧠 is incredibly efficient because it only activates the specific neurons needed for a thought. Modern LLMs naturally try to …

The human brain🧠 is incredibly efficient because it only activates the specific neurons needed for a thought. Modern LLMs naturally try to do this too (> 95% of neurons in feedforward layers stay sile

safetydavid-ha--x

8 May 2026

Graph-Augmented LLMs for Swiss MP Ideology Prediction

arXiv:2605.04643v1 Announce Type: new Abstract: Approximating the ideological position of Members of Parliament (MPs) is a fundamental task in political science, helping researchers understand legisla

safetyarxiv-cs-cl

MedFabric and EtHER: A Data-Centric Framework for Word-Level Fabrication Generation and Detection in Medical LLMs

arXiv:2605.04180v1 Announce Type: new Abstract: Large Language Models exhibit strong reasoning and semantic understanding capabilities but often hallucinate in domains that require expert knowledge, a

safetyarxiv-cs-cl

Multi-Agent Strategic Games with LLMs

arXiv:2605.03604v1 Announce Type: cross Abstract: This paper asks whether large language models (LLMs) can be used to study the strategic foundations of conflict and cooperation. I introduce LLMs as e

agentsarxiv-cs-ai

Nsanku: Evaluating Zero-Shot Translation Performance of LLMs for Ghanaian Languages

arXiv:2605.04208v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated impressive multilingual capabilities for well-resourced languages, yet their performance on low-resource

Terminus-4B: Can a Smaller Model Replace Frontier LLMs at Agentic Execution Tasks?

arXiv:2605.03195v1 Announce Type: new Abstract: Modern coding agents increasingly delegate specialized subtasks to subagents, which are smaller, focused agentic loops that handle narrow responsibiliti

Are LLMs More Skeptical of Entertainment News?

arXiv:2605.01727v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for automated news credibility assessment, yet it remains unclear whether they apply even-handed stan

Local Ai

BIT.UA-AAUBS at ArchEHR-QA 2026: Evaluating Open-Source and Proprietary LLMs via Prompting in Low-Resource QA

arXiv:2605.03618v1 Announce Type: new Abstract: This paper presents the joint participation of the BIT.UA and AAUBS groups in the ArchEHR-QA 2026 shared task, which focuses on clinical question answer

local-aiarxiv-cs-cl

Tutorials

Give LLMs 1. A latent space diffusion-like reasoning. 2. A real recurrent state. 3. A world-model pre-pre-training. And we are done.

tutorialsjeremy-howard--x

Jeremy Howard proposes three key enhancements for large language models: incorporating latent space diffusion-like reasoning processes, adding a persistent recurrent state mechanism, and implementing

LLMs Should Not Yet Be Credited with Decision Explanation

arXiv:2605.01164v1 Announce Type: new Abstract: This position paper argues that LLMs should not yet be credited with decision explanation. This matters because recent work increasingly treats accurate

On the Privacy of LLMs: An Ablation Study

arXiv:2605.02255v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in interactive and retrieval-augmented settings, raising significant privacy concerns. While at

SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification

arXiv:2605.03701v1 Announce Type: new Abstract: Event Causality Identification (ECI) requires models to determine whether a given pair of events in a context exhibits a causal relationship. While Larg

safetyarxiv-cs-cl

Using LLMs in Software Design: An Empirical Study of GitHub and A Practitioner Survey

arXiv:2605.01392v1 Announce Type: cross Abstract: Recent advancements in Large Language Models (LLMs) have demonstrated significant potential across a wide range of software engineering tasks, includi

Tools

vLLM V0 to V1: Correctness Before Corrections in RL

This article from Hugging Face discusses an approach to reinforcement learning (RL) that prioritizes ensuring model correctness as a foundational step before applying corrections or refinements. The p

toolshugging-face

Activation Compression in LLMs: Theoretical Analysis and Efficient Algorithm

arXiv:2605.01255v1 Announce Type: new Abstract: Training large language models (LLMs) is highly memory-intensive, as training must store not only weights and optimizer states but also intermediate act

Causal2Vec: Improving Decoder-only LLMs as Embedding Models through a Contextual Token

arXiv:2507.23386v3 Announce Type: replace Abstract: Decoder-only large language models (LLMs) have been increasingly adopted to build embedding models for diverse tasks. To overcome the inherent limit

Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders

arXiv:2605.01372v1 Announce Type: new Abstract: Large language models (LLMs) have been widely explored for embedding generation. While recent studies show that in-context learning (ICL) effectively en

FeedbackLLM: Metadata driven Multi-Agentic Language Agnostic Test Case Generator with Evolving prompt and Coverage Feedback

arXiv:2605.01264v1 Announce Type: cross Abstract: Traditional approaches to test case generation often involve manual effort and incur significant computational overhead. Additionally, these approache

Lost in the Tower of Babel: The Adverse Effects of Incidental Multilingualism in LLMs

arXiv:2605.01224v1 Announce Type: new Abstract: This paper argues that contemporary multilingual NLP has converged on a fragile and misleading paradigm of incidental multilingualism. Today's LLMs appe

agentsarxiv-cs-cl

oMeBench: Towards Robust Benchmarking of LLMs in Organic Mechanism Elucidation and Reasoning

arXiv:2510.07731v3 Announce Type: replace-cross Abstract: Organic reaction mechanisms are the stepwise elementary reactions by which reactants form intermediates and products, and are fundamental to u

Teaching LLMs Brazilian Healthcare: Injecting Knowledge from Official Clinical Guidelines

arXiv:2605.01077v1 Announce Type: new Abstract: Brazil's Unified Health System (SUS) relies on official clinical guidelines that define diagnostic criteria, treatments, dosages, and monitoring procedu

Local Ai

Zero-Shot Confidence Estimation for Small LLMs: When Supervised Baselines Aren't Worth Training

arXiv:2605.02241v1 Announce Type: cross Abstract: How reliably can a small language model estimate its own correctness? The answer determines whether local-to-cloud routing-escalating queries a cheap

local-aiarxiv-cs-cl

Bring Your Own Prompts: Use-Case-Specific Bias and Fairness Evaluation for LLMs

arXiv:2407.10853v5 Announce Type: replace Abstract: Bias and fairness risks in Large Language Models (LLMs) vary substantially across deployment contexts, yet existing approaches lack systematic guida

Applications

BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs

arXiv:2605.00422v1 Announce Type: new Abstract: Large language models (LLMs) have driven major progress in NLP, yet their substantial memory and compute demands still hinder practical deployment. Bina

applicationsarxiv-cs-lg

Comparing Exploration-Exploitation Strategies of LLMs and Humans: Insights from Standard Multi-armed Bandit Experiments

arXiv:2505.09901v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to simulate or automate human behavior in complex sequential decision-making settings. A na

researcharxiv-cs-cl

Tutorials

Hilarious (and maybe a little bit scary) that even in 2026 Marc Andreessen still hasn’t learned that LLMs don’t know how to reliably follow …

Hilarious (and maybe a little bit scary) that even in 2026 Marc Andreessen still hasn’t learned that LLMs don’t know how to reliably follow system prompts. don't feel bad, guys, the techno-optimist ma

tutorialsgary-marcus--x

Statistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium

arXiv:2503.10990v2 Announce Type: replace-cross Abstract: Aligning large language models (LLMs) with diverse human preferences is critical for ensuring fairness and informed outcomes when deploying th

safetyarxiv-cs-lg

Trident: Improving Malware Detection with LLMs and Behavioral Features

arXiv:2605.00297v1 Announce Type: cross Abstract: Traditionally, machine learning methods for PE malware detection have relied on static features like byte histograms, string information, and PE heade

researcharxiv-cs-lg

Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs

arXiv:2506.07180v3 Announce Type: replace-cross Abstract: As video large language models (Video-LLMs) become increasingly integrated into real-world applications that demand grounded multimodal reason

1 May 2026

Perturbation Probing: A Two-Pass-per-Prompt Diagnostic for FFN Behavioral Circuits in Aligned LLMs

arXiv:2604.27401v1 Announce Type: new Abstract: Perturbation probing generates task-specific causal hypotheses for FFN neurons in large language models using two forward passes per prompt and no backp

1 May 2026

REBENCH: A Procedural, Fair-by-Construction Benchmark for LLMs on Stripped-Binary Types and Names (Extended Version)

arXiv:2604.27319v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable progress in recent years, driving their adoption across a wide range of domains, including compu

1 May 2026

CoQuant: Joint Weight-Activation Subspace Projection for Mixed-Precision LLMs

arXiv:2604.26378v1 Announce Type: new Abstract: Post-training quantization (PTQ) has become an important technique for reducing the inference cost of Large Language Models (LLMs). While recent mixed-p

Multimodal LLMs are not all you need for Pediatric Speech Language Pathology

arXiv:2604.26568v1 Announce Type: new Abstract: Speech Sound Disorders (SSD) affect roughly five percent of children, yet speech-language pathologists face severe staffing shortages and unmanageable c

ReGATE: Learning Faster and Better with Fewer Tokens in MLLMs

arXiv:2507.21420v3 Announce Type: replace-cross Abstract: The computational cost of training multimodal large language models (MLLMs) grows rapidly with the number of processed tokens. Existing effici

researcharxiv-cs-cl

Thank you AI Dev Day '26 @DeepLearningAI @jerryjliu0 shares why SOTA LLMs can build an app but can't read a PDF 🤯

Jerry Liu discusses a key limitation of state-of-the-art large language models at AI Dev Day '26, highlighting that while modern LLMs excel at building applications, they struggle with fundamental tas

agentsjerry-liu--x

FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments

arXiv:2604.25135v1 Announce Type: new Abstract: Large Language Models are being increasingly deployed as the decision-making core of autonomous agents capable of effecting change in external environme

29 Apr 2026

FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices

arXiv:2604.25421v1 Announce Type: new Abstract: Federated fine-tuning provides a practical route to adapt large language models (LLMs) on edge devices without centralizing private data, yet in mobile

29 Apr 2026

Tutorials

Beyond Experience Retrieval: Learning to Generate Utility-Optimized Structured Experience for Frozen LLMs

arXiv:2602.02556v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are largely static and often redo reasoning or repeat mistakes. Prior experience reuse typically relies on extern

tutorialsarxiv-cs-ai

Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMs

arXiv:2512.16378v4 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) expand beyond text, integrating speech as a native modality has given rise to SpeechLLMs, which directly proce

KARL: Mitigating Hallucinations in LLMs via Knowledge-Boundary-Aware Reinforcement Learning

arXiv:2604.22779v1 Announce Type: cross Abstract: Enabling large language models (LLMs) to appropriately abstain from answering questions beyond their knowledge is crucial for mitigating hallucination

agentsarxiv-cs-ai

Mechanistic Steering of LLMs Reveals Layer-wise Feature Vulnerabilities in Adversarial Settings

arXiv:2604.23130v1 Announce Type: cross Abstract: Large language models (LLMs) can still be jailbroken into producing harmful outputs despite safety alignment. Existing attacks show this vulnerability