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arXiv:2610.14106v1 Announce Type: new Abstract: Large language models can translate natural-language instructions into plausible manipulation plans, but robotic execution requires physically grounded

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Kairos: Transactional Security Enforcement at the System Boundary for LLM Agents

DGX agent

arXiv:2610.18457v1 Announce Type: cross Abstract: Large language model (LLM) agents are becoming an integral part of the software development lifecycle, and the capabilities granted to them allow an a

safetyarxiv-cs-lg

14 Oct 2026

LLM Agents for Automated Post-Training via Structured Recipe Optimization

arXiv:2610.18572v1 Announce Type: cross Abstract: Automated post-training transforms pre-trained large language models (LLMs) into models capable of handling diverse domains by automatically construct

Tutorials

LLM-to-Flow Distillation: Teaching Natural Language Goals to Scientific Generators

arXiv:2610.17246v1 Announce Type: new Abstract: Generative discovery in science requires coupling high-level scientific reasoning with the ability to generate samples respecting the structure of a des

tutorialsarxiv-cs-lg

Measuring all the noises of LLM Evals

arXiv:2512.21326v3 Announce Type: replace Abstract: Applying well-established statistical methods effectively to LLM evals requires consideration of model correlations and prediction noises. Leading e

researcharxiv-cs-lg

Applications

RetroPlanner: Multi-Route Expansion and Multi-Objective Reasoning for LLM-Based Retrosynthesis Planning

arXiv:2610.16391v1 Announce Type: cross Abstract: Multi-step retrosynthesis planning is often considered solved once a route to purchasable starting materials is found, but such a route should also be

applicationsarxiv-cs-lg

SlideDP: Scaling Host-Resident LLM Fine-Tuning Across Multiple GPUs

arXiv:2609.34162v1 Announce Type: cross Abstract: Host-resident layer streaming enables full-parameter LLM fine-tuning beyond GPU memory, but data-parallel ranks compete for shared host resources. Rep

StructZO: Structured Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning

arXiv:2610.16451v1 Announce Type: new Abstract: Fine-tuning large language models at scale is increasingly limited by the memory cost of backpropagation. Zeroth-Order (ZO) optimizers reduce this burde

researcharxiv-cs-lg

VH-GPO: Verified Handoff for LLM Agent Reinforcement Learning

arXiv:2610.14000v1 Announce Type: new Abstract: Group-relative reinforcement learning methods such as GRPO train large language model (LLM) agents by comparing rollouts for the same task. When every t

safetyarxiv-cs-lg

A short horror video I made with Minimax h3, Kroma, Vibevoice and LLM magic

This was fun. The base images are made with Kroma, the videos with Minimax h3 and the text-to-speech with Microsoft Vibevoice. The text message screen recording thing is made by an LLM agent (GLM 5.3)

agentsr-stablediffusion

Industry

Fun addition to transformers serve: you can now turn any of the 250 LLM & VLM model into a JEV-like decision model. /v1/systemone scores a s…

Fun addition to transformers serve: you can now turn any of the 250 LLM & VLM model into a JEV-like decision model. /v1/systemone scores a set of decision with the model's logits. No need to generate

industryclem-delangue--x

Tutorials

Host-Aware Web Crawling for LLM Pretraining Data

arXiv:2610.13321v1 Announce Type: new Abstract: Web crawling is a major source of large language model (LLM) pretraining data, yet data-selection pipelines discard a large share of crawled pages. We i

tutorialsarxiv-cs-cl

Human and LLM Facilitation in Collaborative Design Meetings: A Computational Behavioral Comparison

arXiv:2610.13082v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being explored as facilitators of group discussion and collaborative work, but the behavioral differences

SynAct: Synergistic Active Feedback Learning for LLM Agents

arXiv:2610.12651v1 Announce Type: new Abstract: LLM agents executing multi-step workflows fail in two fundamentally different ways: recoverable slips, which further attempts can repair, and knowledge

agentsarxiv-cs-lg

The ultimate LLM benchmark: Chilean Spanish. 80% of AI models fail to recognize Chilean Spanish and insist on speaking like an Argentine

model-releasescristobal-valenzuela--x

The ultimate LLM benchmark: Chilean Spanish. 80% of AI models fail to recognize Chilean Spanish and insist on speaking like an Argentine 80% de modelos de IA no logra reconocer el español chileno e in

10 Oct 2026

A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimization

arXiv:2610.12183v1 Announce Type: cross Abstract: Black-box optimization (BBO) arises in many scientific and engineering problems where objective evaluations are expensive and limited. Recent large la

All Verdicts are Not Equal: Rethinking LLM Judge Reliability

arXiv:2610.12083v1 Announce Type: cross Abstract: LLM-as-a-Judge is the standard paradigm for NLP evaluation, yet its systemic reliability remains poorly understood despite being widely treated as a d

Banger paper from Meta Superintelligence Labs on user simulators for agent training. Agent RL setups usually let an assistant LLM play the u…

Banger paper from Meta Superintelligence Labs on user simulators for agent training. Agent RL setups usually let an assistant LLM play the user, so the simulated user is too cooperative and too explic

model-releasesdair-ai--x

Beyond Imitation: A Framework and Benchmark for LLM-Assisted Peer Review

arXiv:2610.11087v1 Announce Type: new Abstract: The rapid growth of scientific publishing has strained peer review, particularly in machine learning, raising concerns about declining review quality an

Tutorials

Curating Always-Loaded Context for LLM Agents: A Capacitated Assortment Model with Censored Feedback

arXiv:2610.11007v1 Announce Type: new Abstract: At the start of every session, LLM agents load a fixed context file, such as exttt{AGENTS.md}. Each loaded token in the file is charged again in every l

tutorialsarxiv-cs-ai

Error-Propagation Modeling for Failure Attribution in LLM-Based Multi-Agent Systems

arXiv:2610.11600v1 Announce Type: new Abstract: LLM-based multi-agent systems (MASs) are increasingly used to solve complex tasks through coordinated reasoning, tool use, and interaction with external

FlowState: Execution State as Memory for Long-Horizon LLM Agents

arXiv:2609.34565v2 Announce Type: replace Abstract: Long-horizon tasks require LLM agents to continually draw on information from earlier interactions. However, retaining the full history increases co

Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving

arXiv:2407.00079v5 Announce Type: replace-cross Abstract: Mooncake is the serving platform for Kimi, a leading LLM service provided by Moonshot AI. It features a KVCache-centric disaggregated architec

Narrow and Deep: An Ontology Tower as the Knowledge of an LLM Agent for an Industrial Equipment System

arXiv:2610.11768v1 Announce Type: cross Abstract: Large language model (LLM) agents are beginning to operate industrial energy equipment, and what they get right depends on what they are told about th

OnTrack: Real-Time Monitoring and Intervention in LLM Agent Trajectories via Streaming Structure-Aware Optimal Transport

arXiv:2610.12375v1 Announce Type: new Abstract: Agents are deployed in applications from trip planners and stock trading to IT incident triage. In most cases, LLM agents work autonomously with minimal

PlurVA-LLM-2026 Shared Task Track-1: Pluralistic Value Alignment in LLMs via Multilingual Fine-Tuning and Threshold Calibration

arXiv:2609.32382v2 Announce Type: replace Abstract: We present our system for the PlurVA-LLM 2026 Shared Task Track-1, which focuses on pluralistic value alignment in the contexts of China, Indonesia,

Poster: A Preliminary Study of LLM Distillation Inference

arXiv:2610.12137v1 Announce Type: cross Abstract: Unauthorized model distillation, in which a model is trained on the outputs of a proprietary large language model (LLM), is a growing threat to model

Local Ai

The Harness as the Only Mutable Surface: Compliance-Bounded Self-Evolution of LLM Agents in Credit Pipelines, with a Measured Admission Gate

arXiv:2610.10629v1 Announce Type: new Abstract: Self-improving LLM agents can adapt a credit pipeline to a changed rule, but an agent that rewrites itself destroys the artefact a supervisor reviews: a

local-aiarxiv-cs-ai

What to Admit and How to Present: Governing Persistent Memory in LLM Agents

arXiv:2610.11188v1 Announce Type: new Abstract: Persistent memory can improve personalization in LLM agents but can also induce sycophancy and cross-domain leakage. We distinguish two governance decis

A Positive Case for Faithfulness: LLM Self-Explanations Help Predict Model Behavior

arXiv:2602.02639v2 Announce Type: replace-cross Abstract: LLM self-explanations are often presented as a promising tool for AI oversight, yet their faithfulness to the model's true reasoning process i

Ask the Expert: LLM-Guided Reinforcement Learning for Autonomous Cyber Defense

arXiv:2610.09337v1 Announce Type: cross Abstract: Policy-based reinforcement learning (RL) approaches have produced promising results for autonomous cyber defense; however, they are sample-inefficient

safetyarxiv-cs-lg

Beyond the Sycophancy Score: How Task, Model, and Pressure Shape LLM Yielding

arXiv:2610.08840v1 Announce Type: new Abstract: Large language models (LLMs) often abandon a correct answer, or endorse a user's position, once the user pushes back. This behavior, called sycophancy,

safetyarxiv-cs-cl

Local Ai

EvoSignal: LLM-Guided Evolutionary Design of Modular Traffic Signal Control Programs

arXiv:2610.09563v1 Announce Type: new Abstract: Effective traffic signal control (TSC) requires policies that respond to changing traffic demand and network conditions while meeting different control

local-aiarxiv-cs-lg

Applications

From Prompts to Trees: Effective LLM-Guided Tree Generation for Few-Shot Tabular Classification

arXiv:2610.10227v1 Announce Type: cross Abstract: While Large Language Models (LLMs) possess rich world knowledge and impressive generalization capabilities, their direct application to tabular data c

applicationsarxiv-cs-cl

From Uncertainty to Action: Learning to Steer LLM Agents

arXiv:2610.09115v1 Announce Type: cross Abstract: Steering an LLM agent means deciding whether to correct it, at which step, and with which mechanism. Uncertainty is often used to decide when to corre

Learning Situation-Conditioned Thinking Policies for Long-Term LLM Agents

model-releasesarxiv-cs-ro

arXiv:2610.09590v1 Announce Type: cross Abstract: Long-running autonomous agents must reuse accumulated reasoning experience without allowing explicit historical memory and LLM context to grow indefin

Leveraging LLM-Generated Explanations for Detecting Emotionally Rewritten Fake News

arXiv:2610.08835v1 Announce Type: new Abstract: The spread of fake news may cause severe social consequences. Existing fake news detection methods mainly focus on stylistic variations or incorporate e

researcharxiv-cs-cl

Reproducible LLM Inference Benchmarking: A Sequential Isolation Protocol for Regression Testing

arXiv:2610.09778v1 Announce Type: cross Abstract: Reproducible benchmarking of Large Language Model (LLM) inference is challenging because repeated measurements can vary with execution and system stat

The Winner's Curse in LLM Self-Improvement Loops: Selection Noise, Lock-in, and Acceptance Rules

arXiv:2610.09239v1 Announce Type: cross Abstract: Self-improving LLM systems propose changes to themselves and keep those that score better on a small evaluation set. We treat this keep-if-better step

Training Advisors for LLM Agents from Task Outcomes

arXiv:2610.09858v1 Announce Type: new Abstract: Large language model agents tackle multi-step tasks by interleaving reasoning and tool calls with observations from the environment. Prior work has show

Cite What You Explore: Budget-Aware LLM Reasoning over Medical KGs with Verifiable Evidence

arXiv:2610.07739v1 Announce Type: new Abstract: Post-discharge risk prediction from electronic health records (EHRs) is difficult because many dependencies that link discharge-time observations to dow

safetyarxiv-cs-lg

Confidence Reasoning Graphs: Structured Confidence Estimation for LLM Agents

arXiv:2610.07948v1 Announce Type: new Abstract: When using an LLM agent in a consequential domain, making an informed decision about whether to trust its output or intervene requires calibrated confid

agentsarxiv-cs-ai

Does Steering Break Your Model? A Multi-Dimensional Evaluation Suite for LLM Steering Methods

arXiv:2610.07722v1 Announce Type: new Abstract: Activation steering provides a lightweight and flexible way to control large language model (LLM) behavior. However, effective steering requires more th

Evaluate the Stack, Not the Layer: Do Deterministic and LLM Gates for Agent Actions Fail Independently?

arXiv:2610.07359v1 Announce Type: new Abstract: Runtime gates for agent tool calls are stacked on the assumption that their errors multiply. We test it on 1,119 labelled agent actions from three corpo

agentsarxiv-cs-ai

I released an LLM plugin for sending prompts to OpenAI's new Jev-clone decision model that's based on GPT-6 Luna https://simonwillison.net/2…

model-releasessimon-willison--x

Simon Willison released the **“llm-openai-decisions”** plugin (version 0.1a0) on October 7 , 2026. The lightweight library lets developers send prompts directly to OpenAI’s new Jev‑clone decision mode

Improving Diversity in LLM Short Story Generation

arXiv:2610.06729v2 Announce Type: replace Abstract: Large language models (LLMs) can generate accurate responses, but these are void of diversity. We attempt to address this for the task of creative s

researcharxiv-cs-cl

MRPilot: Supervising and Intervening LLM-Based Multi-Robot Teams through Mixed Reality

arXiv:2610.07477v1 Announce Type: cross Abstract: Large language models (LLMs) let users direct heterogeneous multi-robot systems (MRS) through natural language, but make task interpretation, robot as

researcharxiv-cs-ro

POLAR: Ontology-Guided Risk Prevention for Tool-Calling LLM Agents

arXiv:2610.08082v1 Announce Type: new Abstract: LLM tool-use agents operate in dynamic environments where many actions carry operational risk. However, most safety mechanisms react only after errors m