Search: “llm”

arXiv:2610.11152v1 Announce Type: cross Abstract: LLM agents increasingly improve at inference time by accumulating experience in context rather than by updating parameters. This process is often desc

agentsarxiv-cs-cl

9 Oct 2026

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SynCo: Data Synthesis Co-Training for Self-Evolving LLMs via Multi-Agent Reinforcement Learning

DGX agent

arXiv:2610.11345v1 Announce Type: new Abstract: Self-evolving LLM agents promise to improve autonomously through continual interaction and learning, reducing their dependence on manually curated super

safetyarxiv-cs-ai

9 Oct 2026

LLM4Impact: Integrating Heterogeneous Information for Scientific Impact Prediction

arXiv:2610.10138v1 Announce Type: new Abstract: Predicting the future impact of a newly published paper is challenging because it must be inferred from heterogeneous evidence available at publication

8 Oct 2026

PHRBench: A Behavioral Evaluation of Post-Hallucination Reasoning in LLMs

arXiv:2610.10455v1 Announce Type: new Abstract: Hallucinated information can propagate through multi-stage LLM systems and become part of the context for subsequent reasoning. Existing studies of post

8 Oct 2026

Tutorials

Educating future engineers about LLMs: A scalable workshop

arXiv:2610.07027v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly integrated into engineering workflows, students require hands-on experience to learn how to collabora

tutorialsarxiv-cs-ro

7 Oct 2026

JEV versus LLMs: Accuracy, Cost and Calibration on Seven Political Science Replications

arXiv:2610.06625v2 Announce Type: replace Abstract: Large language models (LLMs) annotate and scale political text or constructs by generating text tokens. A new class of models, which TypeSafe market

7 Oct 2026

Running an open-source agent platform with no third party in the chain: what each piece needs (Ollama/vLLM, local embeddings, local OCR)

Disclosure first: I work at Vstorm and I build AgenticOS, an open-source (Apache-2.0), self-hosted platform for building AI agents in a browser. This post was drafted with an LLM's help and edited by

model-releasesr-ollama

7 Oct 2026

Tutorials

Anosognosia in LLMs: Probing Self-Awareness of Quantized Computational Substrate

arXiv:2610.06174v1 Announce Type: new Abstract: Can LLMs recognize degradation in their own computational substrate? Inspired by anosognosia, a neurological condition in which patients fail to recogni

tutorialsarxiv-cs-lg

6 Oct 2026

Collaborative Personalized Preference Alignment for LLMs under Data Deficiency

arXiv:2610.05898v1 Announce Type: new Abstract: Real-world users often exhibit highly heterogeneous preferences over multiple objectives for LLM responses. A lightweight aligner can tailor these respo

safetyarxiv-cs-lg

6 Oct 2026

Reflections and Fragments: Securing LLMs Against Sequential Mosaic Attacks

arXiv:2610.05346v1 Announce Type: new Abstract: Self-play red-teaming improves language-model safety by pitting attacker and defender roles against each other in a zero-sum game. However, real adversa

safetyarxiv-cs-lg

6 Oct 2026

Clinical Concept Centers in LLMs

arXiv:2610.02829v1 Announce Type: new Abstract: Large language models are increasingly used in clinical settings. However, research into the reliability and performance of these models has focused alm

5 Oct 2026

MintEval: Do LLMs Implement the Trading Strategy You Asked For? A Behavioural-Equivalence Benchmark for Natural-Language-to-Strategy Code

arXiv:2610.03080v1 Announce Type: cross Abstract: Large language models are moving from producing trading signals to writing the code that executes them. The failure mode of the second role is silent:

5 Oct 2026

Come let your LLMs play World of Warcraft

I hosted my own world of warcraft private server then built a client that you can play in the browser on PC or mobile at https://jankcraft.xyz/ for free. Afterwards, I created a custom MCP and agent h

local-air-localllama

3 Oct 2026

Mathematical Transfer in LLMs Follows Reasoning Approach More Than Topic

arXiv:2610.00331v1 Announce Type: new Abstract: When selecting mathematical training data for LLMs, a natural organizing principle is topic: probability examples for probability targets. An alternativ

researcharxiv-cs-ai

2 Oct 2026

Removing the NEEDLE in the Haystack: Backdoor Removal in LLMs via Weight Orthogonalisation

arXiv:2610.00348v1 Announce Type: cross Abstract: Backdoor attacks can be implanted in Large Language Models (LLMs) during training, causing unwanted behaviour when a trigger appears in the input. Exi

safetyarxiv-cs-lg

2 Oct 2026

E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch

arXiv:2609.38335v1 Announce Type: cross Abstract: Coding agents powered by large language models (LLMs) are evolving from making localized code changes to developing complete software repositories. Ho

1 Oct 2026

When Context Changes: Understanding Update Failures in LLMs

arXiv:2609.38866v1 Announce Type: new Abstract: As preferences, goals, and facts change, LLM agents must use the current state while earlier versions remain in context. Yet they can answer with an old

1 Oct 2026

Agents

LLMs Learn to Evade Latent Monitors from Prior Feedback Alone

arXiv:2609.36490v1 Announce Type: cross Abstract: Latent space monitors aim to detect undesired behaviors in LLM agents by inspecting an agent's internal activations rather than its outputs. However,

agentsarxiv-cs-ai

30 Sept 2026

Teaching LLMs to Generate Challenging MILP Instances via Solver Feedback

arXiv:2609.37356v1 Announce Type: new Abstract: Generating optimization instances that are both feasible and computationally challenging is crucial for benchmarking solvers and training learning-based

30 Sept 2026

When Does Correction Become Repair? Mechanistic Auditing of Internal Interventions in Tool-Using LLMs

arXiv:2609.36138v1 Announce Type: new Abstract: Before invoking external tools, an agentic LLM must select among a K-way action space: executing a call, seeking clarification, answering directly, or d

30 Sept 2026

AnchorRep: Defending LLMs Against Cross-Model Adversarial Transfer via Representation Repulsion

model-releasesarxiv-cs-lg

arXiv:2609.32602v1 Announce Type: new Abstract: Adversarial attacks optimized on a single open-weight LLM can transfer to and jailbreak architecturally different models, allowing an attacker with whit

Cross-Rollout Bellman Closure for Long-Horizon Agentic Reinforcement Learning

arXiv:2609.35082v1 Announce Type: new Abstract: Group-based reinforcement learning such as GRPO trains LLM agents by comparing rollouts sampled for each task, without a learned critic. In long-horizon

local-aiarxiv-cs-lg

LLMs as Adaptive Meta-Solvers: Strategy-Diverse RL for Industrial-Scale Optimization

model-releasesarxiv-cs-lg

arXiv:2609.34427v1 Announce Type: new Abstract: Scaling LLM-based optimization from textbook-scale instances to real-world, industrial tasks remains a critical open challenge. Existing approaches are

Prefill-Free Cross-Family KV Cache Transfer for Heterogeneous Multi-Agent LLMs

model-releasesarxiv-cs-lg

arXiv:2609.32259v1 Announce Type: cross Abstract: Recent multi-agent LLM systems increasingly combine heterogeneous models for specialized agent roles. However, text-based communication requires each

Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations

arXiv:2407.08983v2 Announce Type: replace-cross Abstract: Trustworthiness and interpretability are inextricably linked concepts for LLMs. The more interpretable an LLM is, the more trustworthy it beco

local-aiarxiv-cs-lg

You Only Edit Once: Incentivizing In-Context Capability of LLMs via Local Demonstration Refinement

arXiv:2609.33609v1 Announce Type: new Abstract: In-context learning (ICL) is crucial for boosting the inference performance of large language models (LLMs). However, the effectiveness of ICL in LLMs i

local-aiarxiv-cs-lg

ViSTA: A Simple Bridge Extends Visual Alignment to Clinical Time-Series Understanding in Multimodal LLMs

arXiv:2609.31448v1 Announce Type: cross Abstract: Clinical prediction models estimate risk from patient measurements, while large language models support medical text understanding and question answer

28 Sept 2026

My Reading Library: Evaluating LLMs on Android Tasks

Can LLM agents actually get through a day in the life of a normal user? That question got me reading papers on Android agents and mobile benchmarks over the past few months. A few patterns kept showin

local-air-localllama

26 Sept 2026

Likelihood Ranking doesn't Scale Like Prompting in LLMs

arXiv:2609.29390v1 Announce Type: new Abstract: LLM evaluation is commonly performed either by prompting models to produce answers or by scoring candidate outputs with likelihood-based metrics. In mul

25 Sept 2026

Prefilling the Reasoning Channel: Output-Prefix Attacks on Reasoning LLMs

arXiv:2609.29775v1 Announce Type: cross Abstract: Large Language Models (LLMs) consume and produce a single sequence of text; hence, if text can be added to the beginning of the LLM's response, i.e.,

25 Sept 2026

Combining LLMs and Genetic Search for ARC-AGI-2

arXiv:2609.27242v1 Announce Type: cross Abstract: LLMs can generate programs for ARC-AGI-2 tasks, but the provided compute only allows a small number of attempts to generate, debug and validate soluti

researcharxiv-cs-ai

24 Sept 2026

Exact Feedback Is Not Control: Evaluating Text-based Closed-Loop Revision in LLMs

arXiv:2609.28150v1 Announce Type: new Abstract: Closed-loop revision is increasingly used in large language model (LLM) applications, but failures may reflect incomplete feedback or ineffective respon

24 Sept 2026

Tutorials

ClusterFewshot: Improving Few-shot Optimization for LLMs workflow

arXiv:2609.25939v1 Announce Type: new Abstract: The performance of large language model (LLM) workflows often depends on selecting a small set of in-context demonstrations to guide model behavior on n

tutorialsarxiv-cs-cl

23 Sept 2026

Beyond Similarity: Coverage-Aware Prompt Selection for Time Series Forecasting with LLMs

arXiv:2609.22977v1 Announce Type: cross Abstract: Similarity-based retrieval is the dominant rule for conditioning large language models (LLMs) in in-context learning, retrieval-augmented generation,

22 Sept 2026

onPanda: Efficient Annotation of On-Policy Alignment Data for LLMs and Agents via Token-Level Correction

arXiv:2609.24983v1 Announce Type: new Abstract: We present onPanda, an interactive tool for efficiently annotating LLM alignment data and agent trajectories. onPanda adopts token-level correction as i

22 Sept 2026

Some Dialects Are More Equal Than Others: Non-Prestigious Arabic Dialectal Bias in LLMs

arXiv:2609.23955v1 Announce Type: new Abstract: Previous work on Egyptian Arabic in NLP has focused largely on the prestigious Cairene Egyptian Arabic (CEA) dialect, resulting in a lack of representat

22 Sept 2026

LLMs as Feature Engineers for Text-and-Tabular Prediction

arXiv:2609.21894v1 Announce Type: new Abstract: We introduce an iterative framework that automates the extraction of interpretable, schema-bound categorical features from unstructured text for tabular

researcharxiv-cs-lg

21 Sept 2026

Faking Good and Faking Bad in LLMs: Response Distortion Across Dark Triad Personality Traits

arXiv:2609.17534v1 Announce Type: new Abstract: Social desirability and impression management are pervasive sources of response distortion in human personality assessment, yet their effects on Large L

17 Sept 2026

Agents

One Axis, No Brake: Self-Knowledge Limits the Filtering of Harmful Peer Conformity in LLMs

arXiv:2609.18998v1 Announce Type: cross Abstract: Multi-agent LLM systems are expected to be more reliable because agents can catch each other's mistakes. But peer pressure cuts both ways: the same co

agentsarxiv-cs-cl

17 Sept 2026

Which Demographics do LLMs Default to During Annotation?

arXiv:2410.08820v4 Announce Type: replace Abstract: Demographics and cultural background of annotators influence the labels they assign in text annotation -- for instance, an elderly woman might find

17 Sept 2026

Can LLMs Model Incorrect Student Reasoning? A Case Study on Distractor Generation

arXiv:2603.15547v2 Announce Type: replace-cross Abstract: Modeling student misconceptions in a realistic manner is critical for AI in education. In this work, we examine how large language models (LLM

safetyarxiv-cs-ai

16 Sept 2026

ReMova: Fine-tuning LLMs for English to Belarusian translation

arXiv:2609.16427v1 Announce Type: new Abstract: This paper presents a Belarusian-specific data-cleaning pipeline and fine-tuning for English-Belarusian machine translation. Our cleaning pipeline disti

16 Sept 2026

Script Fragmentation and Format: What Drives the English-Bengali Performance Gap in Open LLMs?

arXiv:2507.23248v2 Announce Type: replace Abstract: Bengali is spoken by more than 230 million people, yet no standardized instrument evaluates large language models (LLMs) on Bengali across the task

16 Sept 2026

Bellman Policy Optimization

arXiv:2609.15987v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models (LLMs). We introduce Bellman Policy

15 Sept 2026

SALUTE: Benchmarking and Adapting LLMs for the Defense Domain

arXiv:2609.15022v1 Announce Type: new Abstract: Defense is a knowledge-intensive domain that requires precise understanding of specialized terminology, doctrinal concepts, operational procedures, and

15 Sept 2026

Validating Hybrid-State Cache Recovery for GLM-5.3-Flash with vLLM and LMCache

arXiv:2609.15030v1 Announce Type: cross Abstract: External cache transfers can succeed while a hybrid language model resumes from an inconsistent state. We examine the full 45-layer GLM-5.3-Flash mode

safetyarxiv-cs-ai

15 Sept 2026

PACIFIC: Can LLMs Discern the Psychometric Traits Influencing Your Preferences? Personality-Driven Preference Alignment in LLMs

arXiv:2602.07181v4 Announce Type: replace Abstract: User preferences are increasingly used to personalize Large Language Model (LLM) responses, yet reliably leveraging preference signals remains under

14 Sept 2026

Component-Aware Differential Privacy for Federated Multilingual Speech-LLMs

arXiv:2609.11762v1 Announce Type: new Abstract: Per-layer differential privacy (DP) clipping improves gradient fidelity in federated learning by allocating per-matrix clipping budgets proportional to