Search: “llm”

Which Knowledge Should LLMs Trust? Uncovering a Generalizable Steering Direction Across Knowledge Conflicts

arXiv:2610.20067v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently encounter conflicts between internal parametric knowledge and external contextual knowledge, yet the mechanisms

15 Oct 2026

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Agents

Agentic Data Engineering for LLMs: System Design and a Controlled Empirical Study

DGX agent

arXiv:2610.14238v1 Announce Type: cross Abstract: LLM data engineering requires iterative experimentation to determine how data selection, rewards, and curricula affect model learning, making it a nat

agentsarxiv-cs-lg

14 Oct 2026

Bifocal: Training-Free Mixed Granularity Visual Token Pruning for Multimodal LLMs

arXiv:2610.18399v1 Announce Type: new Abstract: Visual token pruning for multimodal large language models (MLLMs) appears highly effective, with existing methods retaining over 95% of unpruned perform

researcharxiv-cs-cv

Applications

Embedding Graph Kernel Entropy (EGKE): Fine-grained Hallucination Detection for LLMs from Embeddings

arXiv:2610.14214v1 Announce Type: new Abstract: Hallucination detection is critical for ensuring the reliability of Large Language Models (LLMs) in real-world deployments. While recent works, such as

applicationsarxiv-cs-lg

From Associations to Activations: Comparing Behavioral and Hidden-State Semantic Geometry in LLMs

arXiv:2602.00628v3 Announce Type: replace Abstract: We investigate the extent to which an LLM's hidden-state geometry can be recovered from its behavior in psycholinguistic experiments. Across eight i

How Low Can You Go: Fine-Tuning LLMs with Bayesian Optimization (LlMBO)

arXiv:2610.18504v1 Announce Type: cross Abstract: Recently, Morris et al. introduced TinyLoRA, a strategy for LLM fine-tuning which introduces arbitrarily few parameters, and demonstrated accuracy imp

Pick the Mixture, Not the Samples: Combinatorial Data Selection for LLMs

arXiv:2610.14028v1 Announce Type: new Abstract: Data selection for LLM fine-tuning faces two facts about real training: data pools draw from many distinct domains, and evaluation spans diverse tasks.

researcharxiv-cs-lg

Query-Adaptive Neuron Steering for LLMs

arXiv:2610.14519v1 Announce Type: new Abstract: Effectively eliciting the capabilities of large language models (LLMs) at inference time remains an important challenge. Existing studies address this c

TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings

arXiv:2610.17065v1 Announce Type: new Abstract: Real-world tables are often heterogeneous, combining numerical features with free-form string columns. Table foundation models (TFMs) excel on numerical

Training LLMs for Multi-Agent Collaboration with Verifiable Information Rewards

arXiv:2610.17893v1 Announce Type: cross Abstract: Effective collaboration among large language model (LLM) agents in multi-agent systems requires sharing relevant evidence, requesting missing informat

A Critical Look at Power Sampling when Test Time Sharpening LLMs

arXiv:2610.13653v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has substantially improved LLM reasoning, yet growing evidence suggests it mainly performs distr

researcharxiv-cs-lg

12 Oct 2026

Agents

Do LLMs Learn from Rewards in Context? : Rethinking the role of reward in In-Context Reinforcement Learning

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

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

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

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

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

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

Local Ai

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

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

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

Local Ai

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

Local Ai

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

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

safetyarxiv-cs-cl

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

safetyarxiv-cs-cl

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