Search: “llm”

Persistent LLM Orchestration of Heterogeneous Robot Teams via Partial Delegation to Multi-Agent Protocols

arXiv:2610.15224v1 Announce Type: cross Abstract: Natural language specifications let non-expert users task robot teams, and large language models (LLMs) have recently proven effective at grounding su

agentsarxiv-cs-ro

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Research

Probe and Skip: Self-Predictive Token Skipping for Efficient Long-Context LLM Inference

DGX agent

arXiv:2601.13155v3 Announce Type: replace-cross Abstract: Long-context inference enhances the reasoning capability of Large Language Models (LLMs), but incurs significant computational overhead. Token

researcharxiv-cs-lg

14 Oct 2026

Rethinking LLM-Guided Visual Question Answering: Does Deeper Knowledge Integration Really Help?

model-releasesarxiv-cs-cv

arXiv:2610.17079v1 Announce Type: new Abstract: Knowledge-based Visual Question Answering (VQA) requires reasoning that extends beyond visible image content, demanding external commonsense and factual

SERQ: Saliency-Aware Low-Rank Error Reconstruction for LLM Quantization

arXiv:2603.08185v2 Announce Type: replace Abstract: Post-training quantization (PTQ) has emerged as a prevailing technique for deploying large language models (LLMs) efficiently in terms of both memor

TLA-Verus: Generating and Solving Temporal Proof Obligations with LLM Agents

arXiv:2610.14923v1 Announce Type: new Abstract: The TLA+ (temporal logic of actions) formal specification toolkit has seen widespread adoption in modeling modern distributed software systems including

safetyarxiv-cs-lg

When LLM Agents See a Conflict but Still Proceed: Cross-Domain Dependencies in Network Slicing

arXiv:2610.17118v1 Announce Type: cross Abstract: Future AI-native networks are being designed to apply autonomous agents built on large language models (LLMs) in network management through intents. W

agentsarxiv-cs-lg

A Multi-Agent LLM System for Automated Tuning of Specialized Neural Network Models

arXiv:2610.13663v1 Announce Type: new Abstract: Many machine learning approaches are two-level: an expert selects a class of methods, within this class the choice of key components is critical and usu

agentsarxiv-cs-ai

AMLworld-Compact: Importance-Weighted Downsampling for Cost-Effective LLM Evaluation and Error Diagnosis

arXiv:2610.13627v1 Announce Type: new Abstract: Evaluating large language models (LLMs) on AMLworld's smallest test splits is costly because each of over a million targets requires a separate graph pr

Evaluating LLM Creativity through Long-Tail Performance

arXiv:2610.12998v1 Announce Type: new Abstract: Creativity benchmarks for large language models (LLMs) vary widely in task and metric design. This raises the question of whether they capture a common

GeoFitBench-LLM: A Benchmark for Assessing Large Language Models' Capabilities in Geospatial Data Requirements Elicitation for Geo-Analytic Tasks

arXiv:2610.12669v1 Announce Type: cross Abstract: Large language models are becoming central to GeoAI systems, yet their evaluation rarely considers the elicitation of geospatial data requirements. Th

Structural and Representational Agency in LLM-Driven Timbre Generation and Interpolation

arXiv:2610.12532v1 Announce Type: cross Abstract: Generative creative interfaces routinely report the user's position within a space of possibilities. Such displays imply that the quantities shown wer

Surfing the Activation Wave: Analyzing the Information and Geometry of LLM Hidden States for Speculative Decoding

arXiv:2610.12939v1 Announce Type: cross Abstract: Decoder hidden states from different layers of large language models (LLMs) often encode complementary information. However, existing speculative deco

Taking Tokenization off the Host for Agentic LLM Serving

arXiv:2610.13325v1 Announce Type: cross Abstract: Agentic workloads resend long contexts that barely change between calls. Prefix caching now skips most of their prefill, yet every request must still

U4RASD at KnowledgeGraphEval-2026: Ontology-Constrained Encoder and LLM Systems for Arabic Relation Extraction

arXiv:2610.12742v1 Announce Type: new Abstract: This paper details our submission to KnowledgeGraphEval 2026 Subtask 2 on directed Arabic relation extraction. We explored encoder-based and large langu

Beyond Euclidean Clipping: Overcoming Exploration Collapse in LLM RL via Riemannian Isometric Policy Optimization

arXiv:2607.10169v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a dominant paradigm for enhancing LLMs' reasoning capabilities. However, RL algorithms with PPO-Clip ar

Beyond Sequences: Distilling Structured Decision Memory for LLM Recommendation

arXiv:2610.11501v1 Announce Type: new Abstract: Despite the adoption of large language models (LLMs) in recommendation systems, prevailing approaches mostly model single-type behaviors (e.g., views or

researcharxiv-cs-cl

From Investigation Failures to Reliable SOC Agents: Understanding and Improving LLM-Based Alert Triage

arXiv:2610.10608v1 Announce Type: cross Abstract: Security operations centers (SOCs) must triage large volumes of alerts, most of which are benign, while missed attacks can remain uninvestigated. Tool

Has LLM Screening Performance Stalled in Software Engineering Systematic Reviews?

arXiv:2610.10633v1 Announce Type: cross Abstract: Screening in systematic reviews (SRs) is manual and time-consuming. Prior work has explored large language models (LLMs) for automating this step, but

Natural Language to First-Order Logic LLM-based Autoformalization

arXiv:2610.12030v1 Announce Type: new Abstract: Large Language Models (LLMs) have renewed interest in autoformalization. Yet, when First-Order Logic (FOL) is considered as the target formalism, the fi

researcharxiv-cs-cl

PaReGTA: A Temporally Aware LLM-Based Patient Representation Framework for EHR Analytics

arXiv:2602.19661v3 Announce Type: replace Abstract: Temporal information in structured electronic health records (EHRs) is often lost in sparse one-hot or count-based representations, while sequence m

Same Outcome, Different Evidence: Intent Recovery in LLM Safety Evaluation

arXiv:2610.11766v1 Announce Type: cross Abstract: Safety evaluations of large language models commonly summarize harmful-output behavior with attack success rate (ASR). Yet the same non-harmful outcom

Advancing LLM-based phoneme-to-grapheme for multilingual speech recognition

arXiv:2603.29217v3 Announce Type: replace-cross Abstract: Phoneme-based ASR factorizes recognition into speech-to-phoneme (S2P) and phoneme-to-grapheme (P2G), enabling cross-lingual acoustic sharing w

Cache the Encoder Within:Compact, Reusable Memory across LLM Queries

arXiv:2610.10058v1 Announce Type: new Abstract: Repeated queries over shared documents incur redundant encoding, while caching model states introduces persistent storage costs. Building on CoMem's int

Phoneme-Guided Initialization for LLM-based Speech Recognition

arXiv:2610.08994v1 Announce Type: cross Abstract: Speech large language models (speech LLMs) perform well on automatic speech recognition (ASR) when sufficient paired speech-text data is available, bu

researcharxiv-cs-cl

RT-DETR-World: Transferring Rich LLM Semantics to Real-Time Open-Vocabulary Detection

model-releasesarxiv-cs-cv

arXiv:2610.09502v1 Announce Type: new Abstract: Open-vocabulary detection (OVD) recognizes categories unseen during training through textual category queries, yet achieving strong generalization with

TiTok: Audio-Visual LLM for Multi-Segment Temporal Grounding

arXiv:2610.09408v1 Announce Type: new Abstract: Audio-visual multi-segment grounding (AV-MSG) in untrimmed videos, reasoning over audio-visual evidence and predicting multiple segments for a query, is

safetyarxiv-cs-cv

Activation Denoising: A Robustness View on Parallel vs Sequential LLM Quantization

arXiv:2610.07522v1 Announce Type: new Abstract: Post-training quantization is a powerful tool for compressing large language models. The most scalable methods quantize every layer in parallel, but qua

LeanPlan: Optimal Planning with LLM-Generated Heuristics and Admissibility Proofs

arXiv:2610.08246v1 Announce Type: new Abstract: Frontier large language models (LLMs) can generate heuristic functions that guide search to achieve state-of-the-art performance in satisficing planning

Local Ai

Looking for 10–20 people for a coordinated LLM test

Thursday, October 8 — 15:00–19:00 UTC Access is free and the API is OpenAI-compatible. During those 4 hours, everyone will be asked to use it actively at the same time—coding, chatting, agents, long c

local-air-ollama

RadOnc-Agent: An LLM-Orchestrated Framework for AI Workflows Across the Radiotherapy Care Pathway

arXiv:2610.06923v1 Announce Type: new Abstract: Artificial intelligence has advanced individual radiotherapy tasks, yet these capabilities remain separated across clinical stages, software environment

agentsarxiv-cs-ai

Unbiased Reward Modeling from Implicit Feedback for LLM Alignment

arXiv:2603.23184v2 Announce Type: replace-cross Abstract: Despite the success of reinforcement learning from human feedback (RLHF), existing reward modeling methods largely rely on explicit feedback,

Local Ai

Beyond LLM Serving: Characterizing Vision-Language-Action Workloads for Embodied AI System Design

arXiv:2610.05062v1 Announce Type: cross Abstract: Vision-language-action (VLA) models translate multimodal observations into low-level robot actions. During robot operation, each control period sets a

local-aiarxiv-cs-ro

LocusRL: Diagnosing LLM Reward and Policy Interventions in Competitive Games

model-releasesarxiv-cs-lg

arXiv:2610.04441v1 Announce Type: new Abstract: Large language models can intervene in reinforcement learning through both reward design and action selection, yet aggregate performance offers an incom

REFLEX: Reflective Evolution from LLM Experience

arXiv:2606.16496v2 Announce Type: replace-cross Abstract: Large multimodal language models (MLLMs) have emerged as powerful tools for guiding evolutionary search toward interpretable programmatic poli

Understanding the Weight Averaging Mechanism in LLM Training for Post-Training Quantization

arXiv:2610.05329v1 Announce Type: new Abstract: Large language models (LLMs) are typically pretrained in high precision but increasingly deployed with low-precision post-training quantization (PTQ). R

Hardware

Batched Speech Decisions Without Decoding: Single-Token Supervision Lets a Frozen LLM Hear Beyond the Transcript

arXiv:2610.02638v1 Announce Type: new Abstract: Full-duplex voice agents make many small, closed decisions, which current systems answer by slow autoregressive decoding. We propose DuplexJev, which fe

hardwarearxiv-cs-ai

Applications

BioMol-MQA: A Multi-Modal Question Answering Dataset For LLM Reasoning Over Bio-Molecular Interactions

arXiv:2506.05766v2 Announce Type: replace Abstract: Retrieval augmented generation (RAG) has shown great power in improving Large Language Models (LLMs). However, most existing RAG-based LLMs are dedi

applicationsarxiv-cs-cl

CONTRA: Discovering and Qualifying Behavior-Changing Questions for Selective Clarification in LLM Code Generation

arXiv:2610.01769v1 Announce Type: cross Abstract: Coding agents can generate code that appears correct but implements behavior the user never intended. This mismatch can arise when an agent silently r

DataWeave: Deploying Human-LLM Analytics for Exploratory Structured Data Analysis

arXiv:2610.02679v1 Announce Type: new Abstract: Data journalism, the practice of using data analysis to surface newsworthy stories, depends increasingly on the ability of reporters and investigative j

agentsarxiv-cs-ai

Finding the Move Is Not Winning the Game: XiangqiBench for Closed-Loop Evaluation of LLM Agents

arXiv:2610.02425v1 Announce Type: new Abstract: Static evaluations credit a language model for naming the right move, but an agent must carry a plan through to a verified outcome while an opponent res

NeutronGym: Physics-Graded Neutron Instrument Design for LLM Agents

arXiv:2610.03631v1 Announce Type: new Abstract: Designing a scientific instrument tests whether language-model agents can do physics rather than recall it, provided the grading cannot be argued with.

Not Until the Evidence Says So: Teaching LLM Investigators When to Close a Case

arXiv:2610.03190v1 Announce Type: cross Abstract: Accident, defect and outage investigations end with a decision that ordinary question answering never faces: whether the evidence gathered so far is e

PLCWorld: Benchmarking LLM-Generated PLC Programs in Closed-Loop Plant Simulation

arXiv:2610.02982v1 Announce Type: new Abstract: Programmable logic controllers (PLCs) coordinate industrial equipment by reading sensor inputs and issuing control commands. Evaluating whether large la

Asynchronous LLM Post-Training: Group-Mass Capping and Convergence Analysis

arXiv:2610.01896v1 Announce Type: cross Abstract: Asynchronous reinforcement learning (RL) improves the efficiency of large language model post-training but introduces stale rollouts generated by earl

Hardware

Denoising Surface: Modeling and Predicting Inference Cost for Diffusion LLM Serving

arXiv:2610.00499v1 Announce Type: new Abstract: As diffusion large language models (dLLMs) become more capable, they are moving from research settings to real-world extit{serving}, where request manag

hardwarearxiv-cs-lg

Distilling LLM Reasoning into Graph of Concept Predictors

arXiv:2602.03006v3 Announce Type: replace Abstract: Deploying Large Language Models (LLMs) for discriminative workloads is often limited by inference latency, compute, and API costs at scale. Active d

researcharxiv-cs-ai

GPEC: Efficient Pre-LLM Gaussian Process Embedding Correction for Cardiac Video Caption Generation

model-releasesarxiv-cs-cv

arXiv:2610.00196v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown strong potential for video understanding and caption generation, but their performance may decline i

LLM-Assisted Discovery of Typed Semantic Links for Ontology Network Construction

arXiv:2610.01393v1 Announce Type: cross Abstract: Constructing typed, justified semantic links between ontologies is essential for enabling interoperability across heterogeneous and interdisciplinary

researcharxiv-cs-ai