Search: “llm”

Evaluating Local Language Model Agents for Reproducible Data Engineering: An Empirical Software Engineering Study of Mobility Workflows

arXiv:2610.11482v1 Announce Type: cross Abstract: Context: Large language model (LLM) agents are increasingly used as software and data-engineering assistants, yet evidence about locally deployable op

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Safety

How to post-train on a surrogate: Envelope sampling mitigates reward hacking

DGX agent

arXiv:2610.11281v1 Announce Type: cross Abstract: Large language models (LLMs) are commonly post-trained against LLM judges and other cheap surrogates because the true reward, such as human preference

safetyarxiv-cs-ai

9 Oct 2026

Large Language Model Turnover Undermines Screening for Artificial Intelligence-Assisted Scientific Writing

arXiv:2610.11599v1 Announce Type: cross Abstract: Journals and conferences have begun to screen submitted manuscripts for text written using large language models (LLMs). The reliability of this scree

Large Language Models for Machine Translation Quality Annotation: Humans and Models Are Both Challenged

arXiv:2610.10918v1 Announce Type: new Abstract: Large Language Models (LLMs) are considered to be a more efficient and cost-effective alternative to human judgment for Machine Translation (MT) evaluat

Agents

Memento 3: Model-Based Recursive Self-Improvement through Reflective Rulebooks

arXiv:2610.11794v1 Announce Type: new Abstract: Learning to act in unfamiliar environments requires agents to infer how the world works and revise that understanding as new evidence arrives. Yet limit

agentsarxiv-cs-ai

Predicting Alignment Generalization with Value Representations

arXiv:2610.12410v1 Announce Type: cross Abstract: LLM developers post-train their models to exhibit prosocial values and behavioral traits, which are enumerated in an alignment target. However, while

SAGE: Sink-Aware Guided Emphasis for Visual Grounding in Vision-Language Decoders

arXiv:2610.11469v1 Announce Type: cross Abstract: Recent large vision-language models (VLMs) pair a visual encoder with a large language model (LLM) and perform well on diverse image-text tasks, yet t

safetyarxiv-cs-cl

A Comparative Study of Evaluation Metrics for Long-Document Financial Narrative Summarization with Transformers

arXiv:2610.09529v1 Announce Type: new Abstract: There are more than 2,000 listed companies on the UK's London Stock Exchange, divided into 11 sectors who are required to communicate their financial re

EASE: Entropy-Adaptive Distribution Shaping for Evading AI-generated Text Detectors

arXiv:2610.09976v1 Announce Type: new Abstract: AI-generated text (AIGT) detection can be sensitive to the decoding choices of the source large language model (LLM). We observe that perturbing next-to

Input-Blind Controls Produce Substantial Oracle Headroom for Layer Programs in Multiple-Choice Evaluation

arXiv:2610.10368v1 Announce Type: cross Abstract: Adaptive computation aims to improve language-model inference by tailoring execution to each input. For layer programs, oracle evaluations use known a

On KL-Regularized Policy Optimization

arXiv:2610.08963v1 Announce Type: cross Abstract: Asynchronous reinforcement learning (RL) for large language model (LLM) agents trains one policy on trajectories generated by another: rollouts come f

safetyarxiv-cs-cl

Quad-State Safety Evaluation of Open-Weight Large Language Models on Non-Canonical Inputs

arXiv:2610.09033v1 Announce Type: new Abstract: Standard safety evaluations of large language models assess harmful requests written in canonical plain text, while models in real-world deployment rout

RACER: Reflective Agent Coupling Query Interpretation and Tool-Based Retrieval for Frame Selection in Long Video Understanding

arXiv:2610.08954v1 Announce Type: new Abstract: Video large language models (Vid-LLMs) excel at diverse video-language tasks by reasoning over selected frames. However, frame selection for long videos

local-aiarxiv-cs-cv

Tutorials

The Dichotomy Between Pattern Recognition and Step-by-Step Reasoning

arXiv:2610.09186v1 Announce Type: new Abstract: We argue that pattern recognition and step-by-step reasoning are two ends of a spectrum. A large language model (LLM) learns to reason step-by-step when

tutorialsarxiv-cs-lg

Towards Explaining Query Expansion Performance in Information Retrieval

arXiv:2610.09724v1 Announce Type: cross Abstract: Query Expansion (QE) techniques have long been widely used in Information Retrieval (IR) to address the vocabulary mismatch problem. They remain relev

A Systematic Investigation of Bias in Large Language Models for Advertising Relevance

arXiv:2610.07544v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to judge how well an advertisement matches a query, but the fairness of these judgments has received

A Trust Layer for Agent Evaluation

arXiv:2610.07274v1 Announce Type: new Abstract: Deterministic benchmark scores show that an agent received credit, but not whether that credit was earned, reported honestly, or would hold on a second

ChartBmkAgent: Harness-Governed Multi-Agent Construction of Chart QA Benchmarks from Sparse Error-Taxonomy Specifications

arXiv:2610.08106v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) advance rapidly, while conventional benchmark development lags behind, delaying investigation of newly observed

EMHO: EMbodied Agent Harness Optimization via Experience Traces

arXiv:2610.08432v1 Announce Type: new Abstract: Improving embodied agents often focuses on optimizing the underlying model through training, while the surrounding agent harness that controls planning,

Federated Mixture-of-Experts Alignment on Mobile Edge Networks under Data Heterogeneity

arXiv:2603.21276v2 Announce Type: replace-cross Abstract: The growing demand for on-device large language model (LLM) services on mobile edge devices has driven the adoption of Mixture-of-Experts (MoE

Foresight-over-Graph: Reasoning Beyond Local Horizons for Knowledge Base Question Answering

arXiv:2610.08388v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong capabilities in question answering, yet they still frequently suffer from hallucinations on know

local-aiarxiv-cs-ai

Applications

In With the Old: Enhancing 'Classical' Document Automation with Generative AI

arXiv:2610.07480v1 Announce Type: new Abstract: Software-based legal assistance systems have leveraged many different forms of knowledge representation and reasoning. This article explores how documen

applicationsarxiv-cs-ai

OMIT the Action: Measuring Framing-Invariant Omission Bias under Philosophical Disagreement

arXiv:2610.07847v1 Announce Type: new Abstract: As LLMs increasingly assist in moral reasoning, omission bias, the tendency to prefer inaction even when equivalent framings reverse substantive outcome

Hardware

OSFP4: Joint Optimization of Diagonal Smoothing and Block Scales for NVFP4 Quantization

arXiv:2610.08231v1 Announce Type: new Abstract: NVFP4 is an attractive datatype for large language model (LLM) inference, offering compact storage and native tensor-core acceleration. However, preserv

hardwarearxiv-cs-ai

Personal-Agent Mediated Recommendation with Cross-Platform User History

arXiv:2610.07588v1 Announce Type: new Abstract: Modern recommendation is shifting from platform-centric personalization toward user-governed personalization, where a personal LLM agent can act on the

Quantization Effects on Tool-Failure Recovery Vary Across Prompts and Evaluation Designs

arXiv:2610.07781v1 Announce Type: new Abstract: Post-training quantization reduces the cost of deploying language-model agents, but its effect on recovery from temporary tool failures can depend on ho

Secure Speculative Decoding for Large Language Models

arXiv:2610.08678v1 Announce Type: cross Abstract: Speculative decoding accelerates inference for a large language model (LLM), referred to as the target model, by first using a smaller model, referred

safetyarxiv-cs-ai

Small Language Models for Smart Data Model Classification at the Edge: A Cost-Aware Hybrid Approach

arXiv:2610.07093v1 Announce Type: new Abstract: The rapid proliferation of heterogeneous data sources within the Internet of Things (IoT) across domains such as smart cities, energy management, and en

researcharxiv-cs-ai

TRACE: Rollout-Guided Quantization-Aware Training for FP4 Reinforcement Learning of MoE Language Models

arXiv:2610.07767v1 Announce Type: cross Abstract: Reinforcement learning (RL) for post-training large language models (LLMs) incurs substantial computation and memory overhead during rollout generatio

safetyarxiv-cs-cl

Turnslide: Scalable Multi-Turn Data Synthesis by Walking a Finite-State Machine

arXiv:2610.07070v1 Announce Type: new Abstract: Small language models are inexpensive to serve and can run on private infrastructure, but base models are often not good enough at multi-turn tool calli

WASD: Wasserstein-based Knowledge Distillation for Large Language Models

arXiv:2610.07706v1 Announce Type: cross Abstract: Autoregressive large language models (LLMs) have rapidly advanced in capability, but their increasing scale comes with substantial computational and m

safetyarxiv-cs-ai

World Properties without World Models: Distributional Associations and the Interpretation of Decoding Results from Language Models

arXiv:2603.04317v2 Announce Type: replace-cross Abstract: A growing literature shows that variables can be linearly decoded from the activations of large language models (LLMs). These range from prope

ANT: A Multi-Granularity Network Traffic Dataset and Benchmark for Agents Behavior Auditing

arXiv:2610.06514v1 Announce Type: cross Abstract: The growing adoption of large language model (LLM) agents creates a need for network administrators and security teams to audit agent behavior within

Discovered, Not Designed: Population Evolution for Collaborative and Compute-Intensive Model Discovery

arXiv:2610.05950v1 Announce Type: new Abstract: LLM-driven evolution enables iterative model development, but two practical goals remain underexplored: finding model designs that transfer across relat

local-aiarxiv-cs-lg

Do Small Language Models Learn to Negotiate? A Controlled Scaling Study of RL-Trained Sellers

arXiv:2610.06204v1 Announce Type: cross Abstract: LLM agents are starting to own the full customer experience. Soon, LLMs may be selling and buying on behalf of companies and customers respectively. S

Agents

EvoCast: Reliable Autonomous Research Agents for Iterative Forecasting Architecture Evolution

arXiv:2610.04517v1 Announce Type: cross Abstract: Deep time-series forecasting models have rapidly diversified, yet adapting them to a specific task still requires extensive expert effort in model sel

agentsarxiv-cs-lg

Evolving in Thought Space: Training a Small Model at Test Time Unlocks Better Discoveries

arXiv:2610.06269v1 Announce Type: cross Abstract: Open-ended scientific discovery often requires repeatedly proposing and evaluating candidate solutions. LLM-based systems can support this process by

safetyarxiv-cs-lg

Mistral Large 4

model-releasessimon-willison

My comment on Mistral Large 4 — Hacker News. wren6991: The benchmark is saturated. Frontier models are tested with an armadillo in fishnet tights jaywalking on Mars. OK well I couldn't resist this one

SFlexRCA: Lightweight, Scalable, and Flexible Root Cause Analysis for IIoT Edge Systems

arXiv:2610.04893v1 Announce Type: new Abstract: Industrial Internet of Things (IIoT) systems generate high-dimensional sensor telemetry from interconnected components, where faults can propagate acros

Hardware

Transformers vs RNNs vs SSMs: Where Does Memory Actually Live? [D]

hardwarer-machinelearning

Someone who has always loved looking at the space between different AI techniques, this time I went a little deeper into the memory trade-offs between RNNs, Transformers and SSMs. I found it interesti

Tutorials

What Matters for Latent Reasoning with Flow Matching

arXiv:2610.06666v1 Announce Type: new Abstract: Latent reasoning lets a large language model (LLM) think in a continuous space and verbalize only the answer. We argue that an effective latent thought

tutorialsarxiv-cs-lg

AdaStep: Adaptive Step Credit Weighting for Agentic Reinforcement Learning

arXiv:2610.03223v1 Announce Type: cross Abstract: Long-horizon LLM agents are typically trained with sparse outcome rewards, making trajectory-level objectives too coarse to distinguish the contributi

local-aiarxiv-cs-cl

All Work And No Play Makes Jack a Dull Boy: Understanding and Preventing Catastrophic Strategy Collapse in RLVR

arXiv:2610.02835v1 Announce Type: new Abstract: During post-training of large language models (LLMs) with Reinforcement Learning with Verifiable Rewards (RLVR), GRPO-style algorithms can exhibit sever

Agents

APDMem: Agent-Controlled Progressive Disclosure for Query-Adaptive Long-Term Memory

arXiv:2610.02472v1 Announce Type: cross Abstract: Personalized LLM assistants must recover sparse evidence from long conversation histories across queries of varying complexity. We introduce APDMem (A

agentsarxiv-cs-ai

CVE2AP: Automated Generation of PDDL-Encoded Attack Paths via Large Language Models

arXiv:2610.03383v1 Announce Type: new Abstract: Attack Path (AP) modeling is fundamental to cybersecurity analysis, where the Planning Domain Definition Language (PDDL) has been widely adopted to enco

Does Every User Need a Private LoRA? Decoupling Personalization from Per-User Adaptation

arXiv:2610.02353v1 Announce Type: cross Abstract: Personalized large language models often require a complete adaptation state for each user. However, this paradigm scales poorly as the user populatio

Evaluating and Improving the Robustness of Large Language Models to Input Sequence Variations

arXiv:2610.02432v1 Announce Type: cross Abstract: Large language models (LLMs) in production systems face prompt injections, trojans (backdoors), and manipulation of automatic quality metrics. This th

FinDialogLens: Event Extraction over Multi-Party Dialogue for Missed-Trade Identification in Financial Chatrooms

arXiv:2610.02455v1 Announce Type: cross Abstract: Multi-party financial chatrooms are vital for sales-and-trading professionals, but their complexity makes manual recovery of missed trades infeasible:

researcharxiv-cs-ai