Search: “llm”

arXiv:2607.26181v1 Announce Type: new Abstract: Functional verification dominates integrated circuit (IC) front-end engineering effort, and a single missed bug that escapes to silicon can trigger a co

hardwarearxiv-cs-ai

31 Jul 2026

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IDP AutoOpt: Agent-Driven Optimization of Document Processing Pipeline Configurations

DGX agent

arXiv:2607.26075v1 Announce Type: cross Abstract: We present IDP AutoOpt, an autonomous LLM agent that discovers high-performing configurations for intelligent document processing (IDP) pipelines. Tun

model-releasesarxiv-cs-ai

31 Jul 2026

MemTxn: A Transaction Boundary for Source-Supported Updates and Complete-State Recovery in Agent Memory

arXiv:2607.27834v1 Announce Type: cross Abstract: Persistent memory lets long-running large language model agents reuse information across sessions and tasks. Yet errors in writable memory can persist

agentsarxiv-cs-cl

31 Jul 2026

StealthBench: Measuring Operational Stealth in Autonomous Offensive-Security Agents

arXiv:2607.26314v1 Announce Type: cross Abstract: Stealth, the discipline of achieving an objective without revealing your presence, capabilities, or collected intelligence, is what separates sophisti

31 Jul 2026

Choosing Where and How to Moderate: End-to-End Trade-offs in Filter Placement and Response Rewriting

arXiv:2607.26200v1 Announce Type: new Abstract: Content-moderation classifiers are usually evaluated in isolation, but deployment requires choosing where to intervene and what follows a flag. We evalu

HoF-Bench: Rediscovering Real AI-Discovered CVEs Without Frontier Models

model-releasesarxiv-cs-lg

arXiv:2607.27030v1 Announce Type: cross Abstract: LLM-based analyzers have begun finding real vulnerabilities in mature open-source projects: AISLE's analyzer is credited with more than 280 CVEs acros

Keyless Attention: Value-Space Routing and Value-Only Caching for Efficient Transformers

arXiv:2606.21848v2 Announce Type: replace Abstract: Transformer architectures form the foundation of modern natural language processing, making it crucial to address the efficiency and scalability lim

researcharxiv-cs-cl

Probing the Origins of Reasoning Performance: Representational Quality for Mathematical Problem-Solving in RL vs. SFT Fine-Tuned Models

arXiv:2607.26119v1 Announce Type: cross Abstract: Large reasoning models trained via reinforcement learning (RL) have been increasingly shown to outperform their supervised fine-tuned (SFT) counterpar

safetyarxiv-cs-cl

Local Ai

TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM

arXiv:2607.27205v1 Announce Type: new Abstract: Vision-language-action (VLA) models commonly adopt an LLM-centric V o L o A pathway, where visual observations are projected into the representation spa

local-aiarxiv-cs-cv

Detecting Knowledge Inconsistencies Across Text, Tables, and Knowledge Graphs

arXiv:2607.25959v1 Announce Type: cross Abstract: Wikipedia and Wikidata are widely used for information access, LLM pre-training, and retrieval-augmented generation. Their knowledge is deeply connect

29 Jul 2026

Stable FP4 Training via Transposition-Invariant Block Quantization

arXiv:2607.24953v1 Announce Type: cross Abstract: Reducing training precision is a key lever for improving the e ciency of large language model (LLM) training, but pushing beyond FP8 to 4-bit oating p

29 Jul 2026

A Computational Ethical Framework for Financial Digital Phenotyping for Mental Health

arXiv:2607.24275v1 Announce Type: cross Abstract: Ethical governance of AI-driven systems is often expressed through high-level principles and static documentation, creating a gap between regulatory r

Agent Team Work Zone: An Automated, Persistent Workspace for Long-Lived Coding Agent Teams

arXiv:2607.22917v1 Announce Type: new Abstract: Large Language Model (LLM) agents have significantly improved coding and programming workflows. Claude Code, in particular, is one of the most powerful

An Unofficial FastLAS Tutorial: A Programmer's Guide

arXiv:2607.23557v1 Announce Type: cross Abstract: FastLAS is a scalable system for Inductive Logic Programming (ILP): you give it some background knowledge, a language bias, and a set of examples, and

Beyond Success Rate: Cost-Aware Evaluation of Offensive and Defensive Security Agents

arXiv:2607.15263v3 Announce Type: replace-cross Abstract: Security-agent evaluations commonly measure peak offensive capability under generous inference budgets, emphasizing vulnerability discovery, e

CallBench: A Benchmark for Dual-Goal Coordination in Phone Call Assistants

arXiv:2607.22635v1 Announce Type: new Abstract: Target-oriented dialogue systems have demonstrated strong capabilities in completing user goals through interactive conversations. However, existing stu

CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning

arXiv:2505.18334v2 Announce Type: replace-cross Abstract: Past work has demonstrated that autonomous vehicles can drive more safely if they communicate with each other. However, this communication is

CRAFT: Learn the Schema, Execute the Plan

arXiv:2607.22642v1 Announce Type: new Abstract: Enterprise coding agents translate natural-language analytical requests into executable code over proprietary APIs, schemas, and metric definitions. Yet

CuraWeb: Joint Optimization of Quality, Redundancy, and Diversity for Web-Scale Pretraining Data

arXiv:2607.22662v1 Announce Type: new Abstract: Open-web corpora curated via highly selective filters, such as FineWeb-Edu and DCLM, constitute the core of LLM pretraining data and have significantly

Local Ai

Falsifiable Commitment Planning for Self-Correcting Web Agents

arXiv:2607.24167v1 Announce Type: new Abstract: Long-horizon web agents often go off track before final failure: a trajectory can remain locally plausible even after the current state, reused skill, o

local-aiarxiv-cs-ai

From Vibe to Code -- and Back: Lexical Oscillation in the Formation of Design Intent with Generative AI

arXiv:2607.23126v1 Announce Type: cross Abstract: Generative AI design tools make natural-language prompts a starting point for design, placing new articulation demands on designers. Rather than treat

How Context Attribution Handles What the Model Already Knows

arXiv:2607.23804v1 Announce Type: cross Abstract: Context attribution methods for large language models (LLMs) identify which input context contributes to the model response. Recent works show the ini

In-Context Learning as Implicit Policy Gradient

arXiv:2607.23153v1 Announce Type: cross Abstract: Recent work has shown that large language models (LLMs) can iteratively improve their outputs by incorporating generated samples and their correspondi

Applications

Let Me Look at You: Advanced Facial Expression Modeling for Conversational Speech Synthesis

arXiv:2607.24430v1 Announce Type: cross Abstract: Conversational Speech Synthesis is a fundamental component of human-computer interaction, aiming to generate contextually appropriate, expressive, and

applicationsarxiv-cs-cl

Lexical discovery in unknown environments orchestrated by Large Language Models

arXiv:2607.22591v1 Announce Type: new Abstract: Populations of autonomous agents deployed in unknown environments (e.g. planetary or deep-sea exploration) must develop shared vocabularies to refer to

Applications

Low-Latency Turn-Taking via Context-Aware Preface Generation in a Real-World Dialogue Robot

arXiv:2607.23204v1 Announce Type: cross Abstract: Large language model (LLM)-based dialogue systems suffer response delays because generation begins only after final speech recognition. While fixed fi

applicationsarxiv-cs-cl

Multi-agent DRL-based Lane Change Decision Model for Cooperative Platooning in Mixed Traffic

arXiv:2601.11809v2 Announce Type: replace Abstract: Connected automated vehicles (CAVs) possess the ability to communicate and coordinate with one another, enabling cooperative platooning that enhance

Reconstructing Item Characteristic Curves using Fine-Tuned Large Language Models

arXiv:2601.02580v2 Announce Type: replace-cross Abstract: Traditional methods for determining assessment item parameters, such as difficulty and discrimination, rely heavily on expensive field testing

SAGE: Safety-First Defense-in-Depth Guardrails for Verified Lifecycle Control of High-Impact Generative AI

arXiv:2607.22926v1 Announce Type: new Abstract: High-impact generative AI makes catastrophic misuse a lifecycle-control problem, not merely a prompt-filtering problem. SAGE is a safety-first, authoriz

Seesaw: Accelerating Training by Balancing Learning Rate and Batch Size Scheduling

arXiv:2510.14717v2 Announce Type: replace-cross Abstract: Increasing the batch size during training -- a ''batch ramp'' -- is a promising strategy to accelerate large language model pretraining. While

Systematic Analysis of Large Language Models and Transformer-Based Machine Translation for English-Tamil and Tamil-English Across Diverse Datasets

arXiv:2607.24515v1 Announce Type: new Abstract: The challenge of Machine Translation for low resource languages such as Tamil is primarily caused by the restricted amount of parallel data for these la

safetyarxiv-cs-cl

TableMind: An Autonomous Programmatic Agent for Tool-Augmented Table Reasoning

arXiv:2509.06278v4 Announce Type: replace Abstract: Table reasoning requires models to jointly perform comprehensive semantic understanding and precise numerical operations. Although recent large lang

The Few-shot Dilemma: Over-prompting Large Language Models

arXiv:2509.13196v2 Announce Type: replace Abstract: Over-prompting, a phenomenon where excessive examples in prompts lead to diminished performance in Large Language Models (LLMs), challenges the conv

Toward Automated Detection of Documentation Inconsistencies in Electronic Health Records

arXiv:2607.22954v1 Announce Type: new Abstract: Objective: To characterize the kinds of internal documentation inconsistencies a general-domain large language model (LLM) can surface from real-world d

TRE: Training-Free Hallucination Detection for Diffusion Language Models

arXiv:2607.22661v1 Announce Type: new Abstract: Diffusion large language models (D-LLMs) have recently gained increasing attention, yet their reliability is significantly hindered by the hallucination

Local Ai

UltraViT: Latency-Optimized On-device Vision Encoder for Large Vision-Language Models

arXiv:2607.23373v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) remain bottlenecked by massive computational footprints, precluding their deployment on resource-constrained edge d

local-aiarxiv-cs-cv

Understanding Tone-Dependent Inference Cost in Large Language Models

arXiv:2607.23915v1 Announce Type: cross Abstract: We examine how prompt tone affects both accuracy of the LLM answers and inference cost as reflected in output-token consumption. Experiments were perf

Verbalized Particle Posterior: Bayesian Inference over Natural Language Hypotheses

arXiv:2607.22961v1 Announce Type: cross Abstract: Verbalized Machine Learning (VML) parameterizes a model as a natural-language prompt that an LLM evaluates as f(x; theta). The framework is interpreta

Applications

Token-Operations-Oriented Inference Optimization Techniques for Large Models

arXiv:2606.20295v2 Announce Type: replace-cross Abstract: Large model inference optimization serves as a key foundation for supporting the scalable, low-cost, and highly stable operation of large mode

applicationsarxiv-cs-cl

27 Jul 2026

Chronofy: A Temporal-Logical Decay Architecture for Information Validity in Time-Aware Retrieval-Augmented Generation

model-releasesarxiv-cs-lg

arXiv:2607.20560v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems retrieve and integrate external knowledge to ground large language model (LLM) outputs. However, current RA

Code Monitor Red Teaming for Public-Test-Passing Code

arXiv:2607.20852v1 Announce Type: new Abstract: Visible tests are a common gate for LLM-generated code, but passing them does not certify specification correctness. We study a deployment-like monitori

GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG

arXiv:2607.21324v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems increasingly employ multiple LLM agents. Yet, most prior work optimizes components in isolation rather th

Human-in-the-Loop Large Language Model Framework for Identification of Cutaneous Immune-Related Adverse Events

arXiv:2607.20428v1 Announce Type: new Abstract: This study evaluated a retrieval-augmented, multi-agent large language model (LLM)-driven, human-in-the-loop framework for detecting cutaneous immune-re

agentsarxiv-cs-cl

WebCoach: Self-Evolving Web Agents with Cross-Session Memory Guidance

arXiv:2511.12997v2 Announce Type: replace Abstract: Multimodal LLM-powered agents have recently demonstrated impressive capabilities in web navigation, enabling agents to complete complex browsing tas

ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking

arXiv:2601.06487v3 Announce Type: replace-cross Abstract: Reinforcement learning has substantially improved the performance of LLM agents on tasks with verifiable outcomes, but it still struggles on o

ChannelGuard: Safe Models Do Not Compose into Safe Multi-Agent Systems

arXiv:2607.19430v1 Announce Type: cross Abstract: Multi-agent LLM applications chain a planner, worker agents, a verifier, and a synthesizer, and every hop between agents is an unmonitored channel thr

PRO-LONG: Programmatic Memory Enables Long-Horizon Reasoning

arXiv:2607.20064v1 Announce Type: new Abstract: Long-horizon tasks require sustained perception, reasoning, and exploration, and are a persistent challenge for large language model (LLM) agents. This

Reference-Free Evaluation of Reasoning in Open-Ended Question Answering

arXiv:2607.19678v1 Announce Type: cross Abstract: AI-generated answers in high-stakes domains are often fluent but difficult to verify, especially when they contain multi-step reasoning rather than a