Search: “llm”

a common pattern for (a part of) memory that we see: Wiki Memory Examples: - DeepWiki (@cognition) - AutoWiki (@FactoryAI) - LLM Wiki (@karp…

This post describes a common architectural pattern for integrating memory components into AI systems, illustrated through examples like DeepWiki and AutoWiki that automatically generate and maintain k

agentsharrison-chase--x

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Agents

Benchmarking LLM Agents on Meta-Analysis Articles from Nature Portfolio

DGX agent

arXiv:2606.17041v4 Announce Type: replace Abstract: Meta-analysis is a demanding form of evidence synthesis that combines literature retrieval, PI/ECO-guided study selection, and statistical aggregati

agentsarxiv-cs-cl

30 Jun 2026

Budgeted Act-or-Defer Multi-Agent LLM Deliberation with Local Reliability Bounds

arXiv:2606.29654v1 Announce Type: new Abstract: Multi-agent deliberation among LLMs can improve reasoning, but deployment requires deciding when the current answer is reliable enough to act on and whe

Experience Augmented Policy Optimization for LLM Reasoning

model-releasesarxiv-cs-lg

arXiv:2606.30420v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful paradigm for improving the reasoning capabilities of large language models (LLMs). H

Four Types of LLM Reliance and Their Predictors Among Undergraduate Writers: A Mixed-Methods Study at a Minority-Serving R1 University

arXiv:2606.28749v1 Announce Type: cross Abstract: Although most undergraduates now use large language models (LLMs), a form of generative artificial intelligence (GenAI) for academic writing, no valid

Geometrically Principled Randomized Optimization for Efficient LLM Training

model-releasesarxiv-cs-lg

arXiv:2510.01878v2 Announce Type: replace Abstract: Low-rank gradient optimization for large language models is currently divided into two categories: structured methods that rigorously identify subsp

KernelSight-LM: A Kernel-Level LLM Inference Simulator

arXiv:2606.28565v1 Announce Type: cross Abstract: As large language models (LLMs) move into production serving, practitioners must rapidly evaluate inference performance across diverse hardware, model

local-aiarxiv-cs-ai

LLM Agents Are Latent Context Managers: Eliciting Self-Managed Context via a Proprioceptive Dashboard

arXiv:2606.30005v1 Announce Type: new Abstract: Long-horizon tool agents are bottlenecked by how their context grows toward the limits of the context window. Recent systems make context management age

Mitigating the Safety-utility Trade-off in LLM Alignment via Adaptive Safe Context Learning

arXiv:2602.13562v2 Announce Type: replace-cross Abstract: While reasoning models have achieved remarkable success in complex reasoning tasks, their increasing power necessitates stringent safety measu

MOPD: Multi-Teacher On-Policy Distillation for Capability Integration in LLM Post-Training

arXiv:2606.30406v1 Announce Type: new Abstract: Modern large language models (LLMs) rely on reinforcement learning during post-training to push specific capabilities, yet integrating multiple capabili

Neural Procedural Memory: Empowering LLM Agents with Implicit Activation Steering

arXiv:2606.29824v1 Announce Type: cross Abstract: While Large Language Models (LLMs) excel as static solvers, transforming them into autonomous agents remains challenging. This transition requires con

agentsarxiv-cs-ai

On the Nonlinearity of Learning Rate Scaling for LLM Training

arXiv:2606.29158v1 Announce Type: cross Abstract: Learning-rate transfer can reduce the cost of training large language models: instead of sweeping learning rates at target scale, practitioners extrap

Sparse Autoencoders are Capable LLM Jailbreak Mitigators

arXiv:2602.12418v2 Announce Type: replace-cross Abstract: Jailbreak attacks remain a persistent threat to large language model safety. We propose Context-Conditioned Delta Steering (CC-Delta), an SAE-

Structure-Preserving Document Translation via Multi-Stage LLM Pipeline: A Case Study in Marathi

arXiv:2606.28796v1 Announce Type: new Abstract: Government documents in India are predominantly issued in regional languages such as Marathi, creating substantial accessibility barriers for non-native

Tool Use Enables Undetectable Steganography in Multi-Agent LLM Systems

arXiv:2606.28425v1 Announce Type: cross Abstract: Increasingly autonomous agentic AI systems pose novel multi-agent risks, such as secret collusion via covert communication channels. The natural defen

Applications

Cluster, Route, Escalate: Cascaded Framework for Cost-Aware LLM Serving

arXiv:2606.27457v1 Announce Type: cross Abstract: Efficient deployment of large language models (LLMs) in production forces a trade-off between accuracy and cost. Operators often default to a single m

applicationsarxiv-cs-cl

EntMTP: Accelerating LLM Inference with Entropy Guided Multi Token Prediction

arXiv:2606.27550v1 Announce Type: new Abstract: Multi-token prediction has been shown to increase data density during training, improve downstream text-generation quality, and serves as the defacto ap

Learning to Refine Hidden States for Reliable LLM Reasoning

arXiv:2606.17524v2 Announce Type: replace Abstract: Large language models show strong reasoning ability, but their internal reasoning process can remain unstable in complex multi-step settings, where

safetyarxiv-cs-lg

Low-Agreeableness Persona Conditioning for Safe LLM Fine-Tuning

arXiv:2606.27709v1 Announce Type: cross Abstract: Recent work has shown that fine-tuning large language models (LLMs) for social warmth degrades factual reliability and increases sycophancy. We invest

Mechanism-Driven Monitors for Preemptive Detection of LLM Training Instability

arXiv:2606.28116v1 Announce Type: new Abstract: Frontier large language model training consumes massive accelerator fleets and long wall-clock computation, making stability failures costly when they o

Output-Space Allocation Costs for Calibration-Guided LLM Compression: An Empirical Study

arXiv:2606.27785v1 Announce Type: cross Abstract: Training-free compression methods for large language models (LLMs) often use calibration data to guide compression decisions. ROCKET, a recent method

ToolPrivacyBench: Benchmarking Purpose-Bound Privacy in Tool-Using LLM Agents

arXiv:2606.28061v1 Announce Type: cross Abstract: Large language models (LLMs) have increasingly moved from standalone text generation systems to agents that invoke external tools, access environments

When Does Personality Composition Matter for Multi-Agent LLM Teams?

arXiv:2606.27443v1 Announce Type: new Abstract: Personality prompting shapes how large language models communicate, yet whether these behavioral shifts affect objective task outcomes remains under-exp

agentsarxiv-cs-ai

Beyond Logical Forms: LLM-Extracted Patterns for Fallacy Classification

arXiv:2606.26698v1 Announce Type: cross Abstract: In today's fast-paced information era, logical fallacies, defined as defective patterns of reasoning, inevitably contribute to the growth of informati

Cascaded Multi-Granularity Pruning for On-Device LLM Inference in Industrial IoT

arXiv:2606.26861v1 Announce Type: new Abstract: Deploying large language models (LLMs) on Industrial Internet of Things (IIoT) edge devices demands extreme compression, yet existing structured pruning

Dynamic-dLLM: Dynamic Cache-Budget and Adaptive Parallel Decoding for Training-Free Acceleration of Diffusion LLM

arXiv:2606.26120v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) offer a promising alternative to autoregressive models, excelling in text generation tasks due to their bidirect

From Weights to Features: SAE-Guided Activation Regularization for LLM Continual Learning

arXiv:2606.26629v1 Announce Type: cross Abstract: Weight-space regularization methods such as Elastic Weight Consolidation (EWC) are the standard approach to catastrophic forgetting in continual learn

Paved with True Intents: Intent-Aware Training Improves LLM Safety Classification Across Training Regimes

arXiv:2606.27210v1 Announce Type: new Abstract: We argue that safety classifiers should model user intent as an explicit signal between the prompt and the final label. To study this, we introduce AIMS

ProfileFoundry: A Synthetic Person-Object Substrate for Privacy, Memory, and Tool-Use Evaluation in LLM Agent

arXiv:2606.26403v1 Announce Type: new Abstract: Foundation-model research increasingly needs data about people: user state, personal histories, relationships, contact-like fields, documents, and longi

local-aiarxiv-cs-cl

Vis-CoT: A Human-in-the-Loop Framework for Interactive Visualization and Intervention in LLM Chain-of-Thought Reasoning

arXiv:2509.01412v3 Announce Type: replace Abstract: Large language models (LLMs) show strong reasoning via chain-of-thought (CoT) prompting, but the process is opaque, which makes verification, debugg

What We are Missing in Multimodal LLM Evaluation?

arXiv:2606.26348v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can process diverse inputs, e.g., text, images, audio, and video, and generate textual responses. While their c

Where Do CoT Training Gains Land in LLM based Agents?

arXiv:2606.26935v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning is widely used in language-model agents, but prior work has shown that verbalized CoT is not always faithful and may in

An Approach for a Supporting Multi-LLM System for Automated Certification Based on the German IT-Grundschutz

arXiv:2606.25608v1 Announce Type: cross Abstract: This paper presents a novel approach to perform semi-automated BSI IT-Grundschutz certification using a MultiLarge Language Model system (MLS) with Hy

Cliff Tokens: Identifying Single-Token Failure Triggers in LLM Mathematical Reasoning

arXiv:2606.25524v1 Announce Type: cross Abstract: Large language models (LLMs) reach high accuracy in mathematical reasoning, but individual traces on the same problem diverge; some arrive at the corr

local-aiarxiv-cs-cl

Decoupling Reconnaissance and Exploitation: Measuring the Capability Boundaries of LLM-Based Web Penetration Testing

arXiv:2606.25332v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown promise for automated penetration testing, yet existing end-to-end black-box evaluations are highly susceptibl

agentsarxiv-cs-ai

Don't Go Breaking My LLM: The Impact of Pruning Attention Layers on Explanation Faithfulness and Confidence Calibration

arXiv:2606.24970v1 Announce Type: new Abstract: Pruning Large Language Models (LLMs) reduces memory and inference costs by removing parts of the network, producing smaller models that retain most of t

researcharxiv-cs-lg

Explainable Control Framework (XCF) based on Fuzzy Model-Agnostic Explanation and LLM Agent-Supported Interface

arXiv:2606.25941v1 Announce Type: cross Abstract: Increasing demand for precise and reliable control in complex scenarios has led to the development of increasingly sophisticated controllers, includin

local-aiarxiv-cs-ai

Forget to Improve: On-Device LLM-Agent Continual Learning via Budget-Curated Memory

arXiv:2606.25115v1 Announce Type: new Abstract: On-device language-model agents improve by accumulating experience in retrieved memory rather than by updating weights. This memory is hard-bounded and

local-aiarxiv-cs-lg

LLM Performance on a Real, Double-Marked GCSE Benchmark

arXiv:2606.24973v1 Announce Type: new Abstract: We introduce a dataset of 32,534 double-marked real student responses to GCSE mock exams (GCSEs are the UK's national exams, taken at age ~16), spanning

MINIF2F-DAFNY: LLM-Guided Mathematical Theorem Proving via Auto-Active Verification

model-releasesarxiv-cs-lg

arXiv:2512.10187v3 Announce Type: replace Abstract: LLMs excel at reasoning, but validating their steps remains challenging. Formal verification offers a solution through mechanically checkable proofs

Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents

arXiv:2606.26080v1 Announce Type: new Abstract: Process reward models enable fine-grained, step-level evaluation of LLMs, yet building them for agentic settings remains prohibitively difficult: long-h

safetyarxiv-cs-lg

Small edits, large models: How Wikipedia advocacy shapes LLM values

arXiv:2606.24890v1 Announce Type: new Abstract: Can a small group of volunteers shape how AI systems discuss animal welfare, just by editing Wikipedia? We show that they can. Wikipedia appears in near

AdversaBench: Automated LLM Red-Teaming with Multi-Judge Confirmation and Cross-Model Transferability

arXiv:2606.24589v1 Announce Type: new Abstract: Scaling adversarial evaluation of large language models requires both a method for generating hard inputs and a reliable way to confirm that resulting f

24 Jun 2026

An LLM-based Two-Stage Transformer Framework for Cross-Domain Bearing Fault Diagnosis with Limited Data

arXiv:2606.24459v1 Announce Type: cross Abstract: Bearing fault diagnosis faces critical challenges when dataset heterogeneity, operating condition variations, and limited labeled data occur simultane

24 Jun 2026

Beyond Trajectory Imitation: Strategy-Guided Policy Optimization for LLM Reasoning

arXiv:2606.24064v1 Announce Type: new Abstract: Distilling reasoning capabilities from strong to weak language models typically involves imitating specific solution trajectories, effectively transferr

24 Jun 2026

Applications

All Green, Still Broken: Real-Flow Verification Lessons from an LLM-Integrated, Multi-Market Web Application

arXiv:2606.22475v1 Announce Type: cross Abstract: Modern web applications increasingly combine three ingredients that are hard to test: output from large language models, multi-market internationaliza

applicationsarxiv-cs-lg

23 Jun 2026

Coherence Under Commitment: Probing Generalization and Vacuous Memorization in LLM Logical Reasoning

arXiv:2606.21083v1 Announce Type: cross Abstract: Large language models (LLMs) deployed for logical reasoning in knowledge-intensive domains exhibit a subtle but critical failure: coherence can be vac

researcharxiv-cs-lg

23 Jun 2026

Applications

Compress the Easy, Explore the Hard: Difficulty-Aware Entropy Regularization for Efficient LLM Reasoning