Search: “llm”

CuraLight: Debate-Guided Data Curation for LLM-Centered Traffic Signal Control

arXiv:2604.05663v2 Announce Type: replace Abstract: Traffic signal control (TSC) is a core component of intelligent transportation systems (ITS), aiming to reduce congestion, emissions, and travel tim

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Local Ai

Fixed: IPEX-LLM + modern Ollama models (qwen3, gemma4) on Intel Arc 140V Lunar Lake Windows 11 — undocumented solution

DGX agent

This Reddit post from r/ollama documents a community-discovered, undocumented workaround for getting IPEX-LLM to successfully run modern Ollama models — specifically Qwen3 and Gemma4 — on systems powe

local-air-ollama

14 Apr 2026

FM-Agent: Scaling Formal Methods to Large Systems via LLM-Based Hoare-Style Reasoning

arXiv:2604.11556v1 Announce Type: cross Abstract: LLM-assisted software development has become increasingly prevalent, and can generate large-scale systems, such as compilers. It becomes crucial to st

Network Effects and Agreement Drift in LLM Debates

arXiv:2604.11312v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated an unprecedented ability to simulate human-like social behaviors, making them useful tools for simulati

Local Ai

RPA-Check: A Multi-Stage Automated Framework for Evaluating Dynamic LLM-based Role-Playing Agents

arXiv:2604.11655v1 Announce Type: cross Abstract: The rapid adoption of Large Language Models (LLMs) in interactive systems has enabled the creation of dynamic, open-ended Role-Playing Agents (RPAs).

local-aiarxiv-cs-ai

Scalable Stewardship of an LLM-Assisted Clinical Benchmark with Physician Oversight

arXiv:2512.19691v3 Announce Type: replace Abstract: Reference labels for machine-learning benchmarks are increasingly synthesized with LLM assistance, but their reliability remains underexamined. We a

Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents

arXiv:2604.10674v1 Announce Type: cross Abstract: Reinforcement learning (RL) has been widely used to train LLM agents for multi-turn interactive tasks, but its sample efficiency is severely limited b

Adaptive Simulation Experiment for LLM Policy Optimization

model-releasesarxiv-cs-lg

arXiv:2604.08779v1 Announce Type: new Abstract: Large language models (LLMs) have significant potential to improve operational efficiency in operations management. Deploying these models requires spec

Automated Standardization of Legacy Biomedical Metadata Using an Ontology-Constrained LLM Agent

arXiv:2604.08552v1 Announce Type: cross Abstract: Scientific metadata are often incomplete and noncompliant with community standards, limiting dataset findability, interoperability, and reuse. When re

BERT-as-a-Judge: A Robust Alternative to Lexical Methods for Efficient Reference-Based LLM Evaluation

arXiv:2604.09497v1 Announce Type: cross Abstract: Accurate evaluation is central to the large language model (LLM) ecosystem, guiding model selection and downstream adoption across diverse use cases.

Can We Still Hear the Accent? Investigating the Resilience of Native Language Signals in the LLM Era

arXiv:2604.08568v1 Announce Type: cross Abstract: The evolution of writing assistance tools from machine translation to large language models (LLMs) has changed how researchers write. This study inves

Chain-in-Tree: Back to Sequential Reasoning in LLM Tree Search

arXiv:2509.25835v4 Announce Type: replace Abstract: Test-time scaling improves large language models (LLMs) on long-horizon reasoning tasks by allocating more compute at inference. LLM inference via t

Overstating Attitudes, Ignoring Networks: LLM Biases in Simulating Misinformation Susceptibility

arXiv:2602.04674v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used as proxies for human judgment in computational social science, yet their ability to reprodu

Applications

ADOPT: Adaptive Dependency-Guided Joint Prompt Optimization for Multi-Step LLM Pipelines

arXiv:2512.24933v2 Announce Type: replace-cross Abstract: Multi-step LLM pipelines can solve complex tasks, but jointly optimizing prompts across steps remains challenging due to missing step-level su

applicationsarxiv-cs-lg

Beyond Surface Judgments: Human-Grounded Risk Evaluation of LLM-Generated Disinformation

arXiv:2604.06820v1 Announce Type: new Abstract: Large language models (LLMs) can generate persuasive narratives at scale, raising concerns about their potential use in disinformation campaigns. Assess

Fighting AI with AI: AI-Agent Augmented DNS Blocking of LLM Services during Student Evaluations

arXiv:2604.02360v1 Announce Type: cross Abstract: The transformative potential of large language models (LLMs) in education, such as improving accessibility and personalized learning, is being eclipse

Open-Ended Instruction Realization with LLM-Enabled Multi-Planner Scheduling in Autonomous Vehicles

model-releasesarxiv-cs-cv

arXiv:2604.08031v1 Announce Type: cross Abstract: Most Human-Machine Interaction (HMI) research overlooks the maneuvering needs of passengers in autonomous driving (AD). Natural language offers an int

The AI Skills Shift: Mapping Skill Obsolescence, Emergence, and Transition Pathways in the LLM Era

arXiv:2604.06906v1 Announce Type: cross Abstract: As Large Language Models reshape the global labor market, policymakers and workers need empirical data on which occupational skills may be most suscep

TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectories

arXiv:2604.07223v1 Announce Type: cross Abstract: As large language models (LLMs) evolve from static chatbots into autonomous agents, the primary vulnerability surface shifts from final outputs to int

I looked at their prompts, It's complete bs They are literally providing all of the insight to the LLM upfront > Are there any security vuln…

I looked at their prompts, It's complete bs They are literally providing all of the insight to the LLM upfront > Are there any security vulnerabilities in this code? Consider the behavior of the SEQ_L

researchyann-lecun--x

9 Apr 2026

[P] Building a LLM from scratch with Mary Shelley's 'Frankenstein' (on Kaggle)

researchr-machinelearning

A beginner-friendly tutorial demonstrating how to build a ~3.2M parameter LLM from scratch using Mary Shelley's *Frankenstein* as the sole training corpus, designed to run on Kaggle's free GPU in u...

8 Apr 2026

AutoOnboarder: Autonomously Onboarding LLM Agents

arXiv:2610.21218v1 Announce Type: new Abstract: Agentic large language models have been documented to lose reasoning capability when placed inside a harness, the software that mediates their observati

Benchmarking Code LLM-as-a-Judge via Constrained Perturbation and Calibration

arXiv:2512.20159v2 Announce Type: replace-cross Abstract: Code from LLM-assisted development may contain defects like API hallucinations, motivating the development of code evaluation metrics to asses

CausalEvo: Causal Skill Self-Evolution for Health LLM Agents

arXiv:2610.21016v1 Announce Type: new Abstract: Large language model (LLM) agents for health can accumulate reusable textual skills from task feedback, such as calculation criteria, measurement time p

researcharxiv-cs-lg

Decentralized LLM Workflows via Sequential Auctions

arXiv:2610.22201v1 Announce Type: cross Abstract: We present a scalable framework for solving multi-step tasks through a decentralized market of self-interested agents. Prior theoretical work demonstr

Decomposing Brain-LLM Alignment with Interpretable Semantic Representations

arXiv:2610.21508v1 Announce Type: new Abstract: Large language models (LLMs) predict neural responses during naturalistic language comprehension, but the linguistic information underlying this brain a

MemComposer: Learning to Assemble Memories for LLM Agents via Reinforcement Learning

arXiv:2610.21091v1 Announce Type: new Abstract: Agent memory enables large language model (LLM) agents to reuse past experiences without updating their parameters. However, semantic similarity alone d

AutoSec-RAN: Specification-Grounded LLM Fine-Tuning for Automated Security Analysis of O-RAN

arXiv:2610.15995v1 Announce Type: cross Abstract: This paper presents AutoSec-RAN, a specification-grounded large language model (LLM) framework for automated security triage of Open Radio Access Netw

Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents

arXiv:2610.17544v1 Announce Type: cross Abstract: Thought templates, skill libraries, agent memories and caches turn many reasoning steps into one. We call this reasoning contraction. A contracted mem

Old Ideas, Novel Problems: The Instability of LLM-Based Novelty Evaluation

arXiv:2610.02022v2 Announce Type: replace Abstract: AI-based ideation systems are often evaluated on the novelty of their ideas, and that judgment is increasingly delegated to large language models. S

researcharxiv-cs-cl

Local Ai

RA^2: Retain-Anchored Attraction Preserves Forget-Adjacent Utility in LLM Unlearning

arXiv:2610.19385v1 Announce Type: new Abstract: Large Language Model (LLM) unlearning is often evaluated primarily by target suppression and clean retain utility. These metrics can miss a local failur

local-aiarxiv-cs-lg

RadixFlow: Filling Execution Bubbles in Prefix-Cached Hybrid LLM Serving

arXiv:2610.18143v1 Announce Type: new Abstract: Prefix reuse improves LLM inference by sharing the KV cache of common prompt prefixes. This helps only if the scheduler can execute a matched branch in

RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory

arXiv:2508.04903v4 Announce Type: replace-cross Abstract: Large language model (LLM) agents are increasingly organized into multi-agent systems for complex reasoning and decision-making tasks. However

RetailAgent: Structured Adverse Timing in Self-Conditioned Multimodal LLM Trading Agents

arXiv:2608.28399v2 Announce Type: replace Abstract: In financial markets, a sequential policy that reacts systematically to price movements may become predictable to other market participants. This pa

Same Attack, Different Judge: A First Controlled Adversarial Comparison of Jev, LLM Judges, and Purpose-Built Safety Classifiers

arXiv:2610.17820v1 Announce Type: cross Abstract: TypeSafe AI's Jev is a new kind of safety judge. It generates no text and no label: it answers questions the developer defines in advance and returns

Hardware

Scaling LLM Inference via Parallelism: Throughput-Latency Trade-off

arXiv:2610.19405v1 Announce Type: cross Abstract: Large Language Model (LLM) serving systems must balance latency and throughput while efficiently utilizing distributed GPU resources. This challenge a

hardwarearxiv-cs-lg

SpectrumQuant: Spectrum Alignment for LLM Quantization

model-releasesarxiv-cs-lg

arXiv:2610.20303v1 Announce Type: new Abstract: Orthogonal transforms have emerged as an effective tool for low-bit quantization of large language models (LLMs), as they reshape activation distributio

Upstream Influence, Downstream Re-Anchoring in LLM Agent Chains

arXiv:2610.17723v1 Announce Type: new Abstract: People act on evaluative judgments produced by LLM agents, and in multi-agent systems those judgments pass through several agents before reaching a pers

Visible but Not Avoided: State-Dependent Re-Proposal of Rejected Candidates in Catalog-Based LLM Optimization Loops

arXiv:2610.15738v1 Announce Type: new Abstract: LLM optimization loops often show the proposer the candidates it has already tried and rejected, but leave it to the proposer to avoid them. We ask whet

Where Do LLM Ensemble Gains Come From? Decomposing Candidate Value, Diversity, and Aggregation

arXiv:2610.18786v1 Announce Type: cross Abstract: LLM ensemble gains are often measured against a single baseline, but the value attributed to additional candidates can change with the comparison used

Banger paper from Stanford, Together AI, and colleagues. Why is this an interesting paper? Today's LLM agents must write out every reasoning…

Banger paper from Stanford, Together AI, and colleagues. Why is this an interesting paper? Today's LLM agents must write out every reasoning step as text before they can search, run code, or take acti

agentsdair-ai--x

Co-DSL: Human-LLM Co-design of Reusable Spatial Concepts for Layout Generation

arXiv:2610.15290v1 Announce Type: cross Abstract: We present Co-DSL, a framework in which users and LLMs co-design reusable spatial concepts for layout generation. In the first stage, our system allow

researcharxiv-cs-cv

Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Serving

arXiv:2610.14571v1 Announce Type: cross Abstract: Large Language Model (LLM) inference is driving a growing share of datacenter carbon emissions. Although carbon-aware scheduling can reduce these emis

safetyarxiv-cs-lg

Encoding Context into LLM Weights: What Makes It Context?

arXiv:2610.17686v1 Announce Type: cross Abstract: Longer contexts allow large language models (LLMs) to condition their predictions on more information, but inference costs grow prohibitively with con

researcharxiv-cs-lg

From Acoustic Sensing to Physical Actions: A Comprehensive Survey of Security Risks in Edge-Deployed Audio LLM-Based Agents

arXiv:2610.15390v1 Announce Type: cross Abstract: Audio Large Language Models (Audio LLMs) are embedded in agents for smart homes, vehicles, wearables, and embodied systems, where acoustic inputs can

agentsarxiv-cs-cv

From LLM Plans to Authorized Motion: Execution-Bound Authorization for Excavators

arXiv:2610.14245v1 Announce Type: new Abstract: An LLM-directed excavator can execute mechanically feasible motion whose destination or authority no longer matches the operator's approval. We propose

researcharxiv-cs-ro

GPU Goldmines: Tunable WebGPU Kernels for Edge LLM Inference

model-releasesarxiv-cs-lg

arXiv:2610.17321v1 Announce Type: cross Abstract: As demand for LLM inference grows and consumer GPUs grow more capable, edge deployment is becoming increasingly attractive. WebGPU is compelling in th

Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction

arXiv:2610.15864v1 Announce Type: cross Abstract: Large-scale personalization is shifting from embedding-table retrieval to LLM-based recommenders that generate the Semantic ID (SID) of the item to re

researcharxiv-cs-lg