Search: “llm”

Not the Same Protector: Deployment-Dependent Protective Intervention in LLMs

arXiv:2608.29136v1 Announce Type: cross Abstract: We ask whether a model protects a user in the same way when that user speaks rather than types. Using a single distress vignette---a physical injury o

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S3Gym: Can LLMs Turn Self-Testing and Self-Judging into Self-Improvement?

DGX agent

arXiv:2608.31100v1 Announce Type: new Abstract: Large language models (LLMs) increasingly interact with external environments and accumulate substantial behavioral experience, yet existing agent bench

model-releasesarxiv-cs-cl

1 Sept 2026

ScienceArena: Benchmarking LLMs on Latest Scientific Olympiad Competitions

arXiv:2608.30517v1 Announce Type: new Abstract: Benchmark saturation and data contamination increasingly obscure genuine scientific reasoning in frontier LLMs. We introduce extsc{ScienceArena}, an oly

Unknown Unknowns: Do Hidden Intentions in LLMs Evade Detection?

arXiv:2601.18552v2 Announce Type: replace Abstract: LLMs expand accessibility and provide wide-reaching access to information. Yet these interactions also create opportunities to embed subtle, goal-or

applicationsarxiv-cs-cl

Agents

VisLens: Single-Pass Interpretable Visual Search for Multimodal LLMs

arXiv:2608.30705v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) struggle with fine-grained Visual Search, the task of locating small or rare objects in high-resolution images.

agentsarxiv-cs-cv

Learning a Single Token to Replace Long System Prompts in LLMs

arXiv:2511.23271v2 Announce Type: replace Abstract: Long system prompts are widely used to steer Large Language Models (LLMs), but repeatedly processing them at inference time is inefficient and consu

31 Aug 2026

Semantic Head Specialization Guides Hybrid ViT Attention for Multimodal LLMs

arXiv:2608.28383v1 Announce Type: cross Abstract: Hybrid attention dominates frontier LLMs, yet Vision Transformers (ViTs) in multimodal LLMs lack a satisfactory hybrid design, with no consensus on wh

31 Aug 2026

The Calls are Coming from Inside the Model: Investigating Probe-based Detection of Tool-Calling Errors in LLMs

arXiv:2608.27750v1 Announce Type: cross Abstract: The hidden states of large language models (LLMs) are known to capture rich information relating to model knowledge and behavior that can be hard to e

applicationsarxiv-cs-cl

31 Aug 2026

A Multi-Framework Comparison of Outline Stages in Long-Form Generation with LLMs

arXiv:2608.26177v1 Announce Type: cross Abstract: Long-form generation exposes fundamental limitations of large language models. Even 70B-parameter models exhibit length collapse at 16k-token outputs,

Activation Outliers Matter: Robust Recovery for Quantized Multimodal LLMs

arXiv:2608.26581v1 Announce Type: new Abstract: Low-bit quantization offers a promising avenue for reducing the computational and memory demands of Multimodal Large Language Models (MLLMs). Recent har

researcharxiv-cs-lg

Agents

Do LLMs Understand Personality? Rethinking Persona Fidelity Evaluation through Structured Behavioral Inference

arXiv:2608.26674v1 Announce Type: cross Abstract: As large language models are increasingly deployed to simulate diverse human characters, ensuring persona fidelity, defined as the extent to which an

agentsarxiv-cs-ai

From Atomic to Agentic: Towards Interpretable Evaluation of LLMs' Agentic Mathematical Capabilities

arXiv:2608.26950v1 Announce Type: new Abstract: Large Language Models (LLMs) are evolving from performing end-to-end mathematical reasoning to integrating agentic intelligence. However, most existing

Reason in the Words You Speak: Idiolectal Paraphrasing Off-Policy Traces for Reasoning Distillation in VideoLLMs

arXiv:2608.26684v1 Announce Type: new Abstract: Recent large language models achieve strong performance on complex reasoning tasks, where reinforcement learning with Group Relative Policy Optimization

safetyarxiv-cs-cv

Which India Survives Translation? Narrative Homogenisation Across Indian Oral Traditions in LLMs

arXiv:2608.26123v1 Announce Type: new Abstract: Large language models (LLMs) are trained predominantly on English-language internet text that over-represents certain cultural narratives, raising conce

Ban&Pick: Enhancing Performance and Efficiency of MoE-LLMs via Smarter Routing

model-releasesarxiv-cs-lg

arXiv:2509.06346v3 Announce Type: replace Abstract: Sparse Mixture-of-Experts (MoE) has become a key architecture for scaling large language models (LLMs) efficiently. Recent fine-grained MoE designs

From Specialization to Generalization: Instruction-tuned LLMs for Robust Harmful Content Mitigation

arXiv:2608.25605v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate impressive performance across a wide range of general NLP tasks; however, their effectiveness in sensitive doma

Agents

Groundhog Bit-Flip Attack: Seeding Infinite Generation Loops in Mixture-of-Experts LLMs through Bit Flips

arXiv:2608.25276v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures enable scalable and efficient large language models (LLMs) by selectively activating expert sub-networks through

agentsarxiv-cs-cl

Skill Issue: Are Skills Language-Invariant in LLMs?

arXiv:2608.25832v1 Announce Type: new Abstract: Large language models access knowledge inconsistently across languages, but to what extent do they differ in their skill sets when interacting with diff

Names Can Hurt: Spotting Slopsquatting Risks Caused by Package Name Hallucinations in Local Coding LLMs

arXiv:2608.23897v1 Announce Type: cross Abstract: When a code generating language model fabricates a Python package name, an adversary who has pre-registered that name on PyPI can convert that halluci

26 Aug 2026

Can LLMs Truly Forget? Revealing Unlearning Gaps Through Adversarial Evaluation

arXiv:2608.21606v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of targeted training data from a model while preserving its remaining capabilities, but evaluating wheth

Effects of Theory of Mind and Prosocial Beliefs on Steering Human-Aligned Behaviors of LLMs in Ultimatum Games

arXiv:2505.24255v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown potential in simulating human behaviors and performing theory-of-mind (ToM) reasoning, crucial for com

Evaluating the Efficacy of LLMs to Emulate Realistic Human Personalities

arXiv:2402.14879v2 Announce Type: replace-cross Abstract: To enhance immersion and engagement in video games, the design of Affective Non-Player Characters (ANPCs) is a key focus for researchers and p

Local Ai

Role-Specialized Mixture-of-Agents with Open-Weight LLMs for Clinical Prediction

arXiv:2608.22176v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly applied to clinical prediction tasks such as in-hospital mortality and readmission from electronic health

local-aiarxiv-cs-ai

Unveiling the Depth-Performance Dilemma in Split-Federated Fine-tuning of LLMs

arXiv:2608.22188v1 Announce Type: cross Abstract: Split Federated Fine-tuning (SFF) is a promising paradigm for scaling Large Language Models (LLMs) by partitioning model depth between resource-constr

What AstroPT knows about galaxies, and what that can teach us about LLMs

arXiv:2608.22614v1 Announce Type: new Abstract: Interpretability research increasingly asks when concepts emerge during training and whether linear probes recover real structure, but in language model

researcharxiv-cs-lg

Your AI, On a Dial: Controlling Investment Bias in LLMs with a Single Neuron

arXiv:2608.22852v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in investment decision-making, yet prior work shows that they exhibit systematic, model-specific inve

Move by Move: Measuring and Steering How LLMs Conduct Psychotherapy

arXiv:2608.21325v1 Announce Type: new Abstract: Users increasingly turn to large language models for emotional support, yet little is known about how these models actually conduct a psychotherapy inte

24 Aug 2026

Quantization-Aware Healing: A Practical Recipe for Recovering Compressed, 4-Bit LLMs

arXiv:2608.20953v1 Announce Type: cross Abstract: Serving large language models cheaply increasingly means shipping models that are both structurally compressed to a fraction of their parameters and q

24 Aug 2026

From Storage to Access: Verifiable Activation of Parametric Knowledge in LLMs via Explicit Priming and Implicit Reasoning

arXiv:2608.18581v1 Announce Type: cross Abstract: Although Large Language Models (LLMs) encode rich factual knowledge in their parameters, reliably recalling and verifying such knowledge remains a key

20 Aug 2026

Hardware

Beyond FLOPs: Energy-Aware Knowledge Distillation for Sustainable LLMs on Code-Related Task

arXiv:2608.17515v1 Announce Type: cross Abstract: Background: Large Language Models (LLMs) are increasingly being applied to Software Engineering (SE) tasks, achieving high accuracy across problems su

hardwarearxiv-cs-ai

19 Aug 2026

CEDAR-GRPO: Process-Aware Reinforcement Learning for General Abductive Reasoning in LLMs

arXiv:2608.14791v1 Announce Type: new Abstract: Abductive reasoning, often characterized as inference to the best explanation, is central to explanation under uncertainty, from everyday sense-making a

Computational KJ-Ho: An Analyst-Bias-Free Insight Extraction Framework from Large-Scale Qualitative Data Using Domain-Specialized LLMs

arXiv:2608.16467v1 Announce Type: cross Abstract: The qualitative research methodologies that underpin consumer-insight generation - the KJ method, Grounded Theory, and Thematic Analysis - share a str

Do Assessment Instruments Measure the Same Thing for Humans and LLMs? A Latent Structure Analysis

arXiv:2608.15630v1 Announce Type: cross Abstract: The rapid development and growing deployment of large language models (LLMs) have made it increasingly important to understand their capabilities. A c

applicationsarxiv-cs-ai

Do LLMs Know What to Ask and When? Evaluating Multi-Turn Information Seeking

arXiv:2608.14808v1 Announce Type: new Abstract: When a user question is underspecified, a capable model should recognize that its context is insufficient, identify the missing information, ask for it,

DYNASHIELD: A Black-Box Moving Target Defense for LLMs via Dynamic Decoding Customization

arXiv:2412.07672v2 Announce Type: replace-cross Abstract: Large language models (LLMs) remain vulnerable to jailbreak attacks in which adversarial prompts induce harmful outputs. Existing defenses oft

Grounding Healthcare LLMs in a Causal Knowledge Graph: Framework, Metrics, and a Cardiovascular Pilot

arXiv:2608.15382v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly proposed for healthcare decision support, but their evaluations still reward single-answer accuracy rather

applicationsarxiv-cs-ai

HarmProfile: Characterizing Harmful Distributions in Frontier LLMs

arXiv:2608.14577v1 Announce Type: cross Abstract: Frontier large language models (LLMs) safety evaluation has largely treated harmful generation as an attack outcome rather than as an object of analys

Implementation of a Metacognition Framework for Self-Awareness and Self-Regulation in Ensembles of LLMs

arXiv:2608.15400v1 Announce Type: new Abstract: Large Language Models (LLMs) are notorious for struggling with assessing their own uncertainty, detecting knowledge conflicts, or recognizing when probl

RamseyGadgets: A Graph Construction Dataset for LLMs

arXiv:2608.14999v1 Announce Type: cross Abstract: Constructing special graphs is an important task within graph theory and computer science. Many popular graph constructions are the result of a compre

The Machine's Internal Clock: Do LLMs Share Human Temporal Illusions?

arXiv:2608.15394v1 Announce Type: new Abstract: Human perception of time is subjective. Well-documented temporal illusions show that the brain relies on context and relational cues for judging duratio

CangjieBench: Benchmarking LLMs on a Low-Resource General-Purpose Programming Language

arXiv:2603.14501v2 Announce Type: replace-cross Abstract: Large Language Models excel in high-resource programming languages but struggle with low-resource ones. Existing research related to low-resou

14 Aug 2026

Gradual Code-Switching as Inference-Time Cross-Lingual Representational Alignment for LLMs

arXiv:2510.05678v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have achieved notable progress in multilingual settings, their performance remains uneven across languages

14 Aug 2026

Can Frontier LLMs Match Natively Multimodal Embeddings? A Comparison on Hard-Negative Text-to-Image Retrieval

arXiv:2608.11343v1 Announce Type: new Abstract: Multimodal retrieval and classification across different types of media, spanning text, images,video and audio, has traditionally relied on dual-encoder

13 Aug 2026

Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs

arXiv:2608.11624v1 Announce Type: cross Abstract: Persuasion is a core dynamic of natural language communication, shaping how large language models (LLMs) update beliefs, resolve disagreements, and re

13 Aug 2026

Who Would You Vote For? Auditing Political Alignment in LLMs: An Italian Case-Study

arXiv:2608.11649v1 Announce Type: new Abstract: As users increasingly turn to Large Language Models (LLMs) for information and advice on political matters, particularly during election periods, the po

13 Aug 2026

Do LLMs Benefit From Their Own Words?

arXiv:2602.24287v2 Announce Type: replace-cross Abstract: In multi-turn conversations, large language models typically condition on the full conversation history: both past user prompts and assistant

12 Aug 2026

How Robust Are LLMs to Vietnamese Dialects?

arXiv:2608.10414v1 Announce Type: new Abstract: Large Language Models (LLMs) are typically evaluated on standard written Vietnamese, yet everyday communication frequently involves regional dialects th

12 Aug 2026

TrAC: Trace-Conditioned Answer Consistency for Efficient Uncertainty Quantification in LLMs

arXiv:2608.00422v2 Announce Type: replace Abstract: Large language models (LLMs) can generate fluent reasoning traces that nevertheless lead to incorrect answers, making response-level uncertainty est