Search: “llm”

HuGo: LLMs as Whole-Body Policy Code Designers for Humanoid Loco-Manipulation

arXiv:2609.30594v1 Announce Type: new Abstract: For humanoids to be useful in everyday environments, they must perform a wide range of tasks that couple locomotion and manipulation. Existing approache

safetyarxiv-cs-ro

28 Sept 2026

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Research

Improving Visual Sensitivity of LLMs on Multimodal Machine Translation with Metric-based Loss Weighting

DGX agent

arXiv:2609.31169v1 Announce Type: cross Abstract: Multimodal Machine Translation aims to incorporate additional signal from non-textual modalities to improve translations by resolving ambiguities. Whi

researcharxiv-cs-ai

28 Sept 2026

Delay-of-Gratification as a Multi-Agent Survival Micro-benchmark for Long-Horizon LLMs: Social Exposure, Personas, and Tool Use Budgets

arXiv:2609.29509v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as multi-turn agents that must sustain goals, use tools, and adapt to other agents over extended

25 Sept 2026

Generating Interesting Scientific Ideas using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders

arXiv:2405.17044v4 Announce Type: replace Abstract: The rapid growth of scientific literature makes it increasingly challenging for researchers to identify novel and impactful ideas, especially across

25 Sept 2026

Who Is Behind the Harness? Fingerprinting LLMs through Agentic Behavior

arXiv:2609.28559v1 Announce Type: cross Abstract: LLMs increasingly operate through coding-agent harnesses that inspect repositories, invoke tools, and modify files. Substituting the model behind such

agentsarxiv-cs-ai

25 Sept 2026

Evolving Inspectable O-RAN Slicing xApps with LLMs

arXiv:2609.27337v1 Announce Type: cross Abstract: Open RAN (O-RAN) slicing xApps must adapt resource allocations to changing channel conditions and traffic demands while meeting service-level agreemen

24 Sept 2026

Offline A/B Testing of Slate Recommendation Systems with LLMs: Reducing the Dependency on Pre-Collected User Interaction Data

arXiv:2511.04541v2 Announce Type: replace-cross Abstract: Slate recommender systems (RecSys) present users with ordered sets of interacting items (e.g., playlists). We investigate whether large langua

safetyarxiv-cs-ai

24 Sept 2026

Repurposing Pre-trained LLMs as High Fidelity Continuous Text Autoencoders

model-releasesarxiv-cs-lg

arXiv:2609.27248v1 Announce Type: new Abstract: Next-token prediction has enabled highly fluent autoregressive language models, but it represents global structure only indirectly through sequential fa

24 Sept 2026

Dynamic Deep Prompt Optimization for Defending Against Jailbreak Attacks on LLMs

arXiv:2609.26185v1 Announce Type: cross Abstract: Large Language Models (LLMs) demonstrate impressive capabilities across many applications but remain vulnerable to jailbreak attacks, which elicit har

safetyarxiv-cs-cl

23 Sept 2026

Optimizing Denoising Trajectories in dLLMs: A Lightweight Evolutionary Heuristic Approach

arXiv:2609.26052v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to conventional Auto-Regressive (AR) Large Language Models (LLM

23 Sept 2026

BreakFun: Jailbreaking LLMs via Object Instantiation under Simulated Code Execution

arXiv:2510.17904v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are widely used because they process structures, syntax and code well, but this same ability also makes them para

safetyarxiv-cs-cl

22 Sept 2026

Connect the Dots: Training LLMs for Long-Lifecycle Agents with Cross-Domain Generalization Via Reinforcement Learning

arXiv:2606.20002v2 Announce Type: replace-cross Abstract: This work presents a general framework for training large language models (LLMs) to 'Connect the Dots' (CoD), a meta-capability required by lo

agentsarxiv-cs-cl

22 Sept 2026

Privacy Personalization Trade offs in LLMs: The Impact of Stylometric Signal Reduction on User-Specific Text Generation

arXiv:2609.22112v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated the ability to generate user-specific text with high stylistic fidelity. However, the personal data that

22 Sept 2026

Local Ai

TierKV: Long-Context On-Device LLMs via Predictive Multi-Tier KV Caching

arXiv:2609.21172v1 Announce Type: new Abstract: Large language models (LLMs) are moving onto mobile devices for increasingly diverse workloads over text, images, video, and audio. These applications o

local-aiarxiv-cs-lg

21 Sept 2026

No Usable Linear 'Capitulation Direction' in Two Small LLMs: A Validation Protocol for Activation-Steering Claims, and a Cross-Family Behavioral Study of Sycophancy Under Pushback

arXiv:2609.17550v1 Announce Type: new Abstract: Language models frequently abandon correct answers when users push back. We study this in two small instruction-tuned models from different families, Qw

PentestChain: A Cost-Aware, MCP-Orchestrated Framework for Automated Penetration Testing with Free-Tier LLMs

arXiv:2609.18120v1 Announce Type: cross Abstract: AI-driven penetration testing has been demonstrated with premium frontier models such as GPT-4, but the per-engagement token cost makes continuous, au

Relationally Guided Use Case Modeling with LLMs

arXiv:2609.18291v1 Announce Type: cross Abstract: Use case flows are important elements of use case modeling because they support downstream software engineering activities, including requirements ana

researcharxiv-cs-cl

When Cognitive Graphs Meet LLMs: BDEI Cognitive Pathways for Panic Emotional Arousal Prediction

arXiv:2606.15121v3 Announce Type: replace Abstract: Predicting the timing of individual and collective panic emotional arousal before manifestation is essential for timely emergency intervention. Exis

Early-Bird Decoding: Accelerating Diffusion LLMs with Learnable Block Sizes and Parallel Sampling

arXiv:2609.16450v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer a promising parallel decoding paradigm as an alternative to autoregressive generation through iterative

16 Sept 2026

Applications

Finding Common Mistakes In Modelling With Mathematical Formalisms Using LLMs

arXiv:2609.17111v1 Announce Type: cross Abstract: Modelling with mathematical formalisms like logical formulas, mathematical equations, or regular expressions is an important yet challenging task for

applicationsarxiv-cs-ai

16 Sept 2026

When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control

arXiv:2609.17516v1 Announce Type: cross Abstract: Large language models can produce fluent answers when their factual support is weak. This paper introduces Chain-of-Self-Questioning (CoSQ), a prompt-

16 Sept 2026

Empathy Is Steerable but Multi-Axial: Mechanism Geometry and Persona Effects in LLMs

arXiv:2609.15654v1 Announce Type: new Abstract: Activation steering has been used to control traits such as honesty, refusal, and sycophancy, yet supportive empathy is evaluated along multiple dimensi

researcharxiv-cs-cl

HypoEvolve: Genetic Algorithms Enable Multi-Agent LLMs to Discover Scientific Hypotheses

arXiv:2609.15938v1 Announce Type: new Abstract: Scientific agents contribute to hypothesis discovery by synthesizing evidence, assessing proposals, and developing new explanations. Recent systems comb

agentsarxiv-cs-cl

Is my model perplexed for the right reason? Contrasting LLMs' Benchmark Behavior with Token-Level Perplexity

arXiv:2603.29396v2 Announce Type: replace Abstract: Standard evaluations of Large language models (LLMs) focus on task performance, offering limited insight into whether correct behavior reflects appr

Applications

Medical Knowledge Simplification for Patients in the Era of LLMs: A Case Study on Diabetes

arXiv:2609.15129v1 Announce Type: new Abstract: Complex medical information is often difficult for patients to understand, making effective medical knowledge simplification essential for improving pat

applicationsarxiv-cs-ai

One Example Is Enough to Pass Fairness Benchmarks: Rethinking Fairness Evaluation for Aligned LLMs

arXiv:2609.14860v1 Announce Type: cross Abstract: Warning: This submission studies stereotypes and biases, and contains toxic and offensive examples, used for illustration purposes only. Fairness benc

Surprising Effectiveness of Self-Demonstrations in Enhancing Schema-Ontology Mapping with LLMs

arXiv:2609.13776v1 Announce Type: new Abstract: Integrating heterogeneous relational databases into a centralized ontology remains a persistent challenge in enterprise knowledge representation, primar

To See is Not to Master: Teaching LLMs to Use Private Libraries for Code Generation

arXiv:2603.15159v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown strong potential for code generation, yet they remain limited in private-library-oriented code generat

When Bias Pretends to Be Truth: How Spurious Correlations Undermine Hallucination Detection in LLMs

arXiv:2511.07318v3 Announce Type: replace Abstract: Despite substantial advances, large language models (LLMs) continue to exhibit hallucinations, generating plausible yet incorrect responses. In this

14 Sept 2026

Can LLMs Follow Medical Expert Logic? A Benchmark for Hierarchical Logical Consistency in Risk-of-Bias Assessment

arXiv:2609.11185v1 Announce Type: new Abstract: Evidence-based medicine demands strict logical consistency, yet current evaluations of large language models (LLMs) prioritize superficial label matchin

How LLMs Follow Instructions: Skillful Coordination, Not a Universal Mechanism

arXiv:2604.06015v2 Announce Type: replace Abstract: Instruction tuning is commonly assumed to endow language models with a domain-general ability to follow instructions, yet the underlying mechanism r

LLMs as Post-hoc Auditors of Physiological Plausibility in Symbolic Regression: A Clinician-Evaluated Case Study

arXiv:2609.11431v1 Announce Type: new Abstract: Genetic Programming and its variants, such as grammatical evolution, are widely used in Symbolic Regression to derive mathematical expressions from mult

agentsarxiv-cs-ai

NovGauge: A Fine-Grained Benchmark for Diagnosing LLMs' Capability in Paper Novelty Assessment

arXiv:2609.11234v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in peer review at major AI conferences, yet novelty remains a persistent weak point. Existing benchma

EMMI: Edge Multi-Modal Intelligence for Communication-Efficient MLLM Inference via Fused Representation Compression

model-releasesarxiv-cs-lg

arXiv:2609.11058v1 Announce Type: new Abstract: Recent advances in multimodal large language mod- els (MLLMs) have opened new opportunities for edge intelligence by enabling reasoning across heterogen

11 Sept 2026

Reference-Based Bias Detection in LLMs via Relative Representations of Hidden States

arXiv:2609.10060v1 Announce Type: new Abstract: Existing bias auditing methods typically rely on model outputs, requiring costly benchmarks or judge models and potentially missing internal shifts that

11 Sept 2026

Bait-and-Recover: Poisoning Internal Refusal Signals to Defend LLMs against White-Box Editing Jailbreaks

arXiv:2609.05794v1 Announce Type: cross Abstract: Open-weight large language models face a low-cost white-box threat from representation engineering attacks. Attackers can estimate refusal directions

safetyarxiv-cs-ai

Local Ai

Do LLMs Make More Mistakes If They Do Not Believe the Input Data?

arXiv:2609.09363v1 Announce Type: new Abstract: Large language models (LLMs) are prone to hallucinating or misinterpreting facts, which impairs their usability in retrieval-augmented generation or dat

local-aiarxiv-cs-cl

Evaluating and Improving Evidence-Grounded Fact-Checking in LLMs via Multi-Round Evidence Ablation

arXiv:2609.08943v1 Announce Type: cross Abstract: Automatic fact-checking systems assess the veracity of claims given evidence from relevant documents. Large Language Models (LLMs) have demonstrated s

ProcArena: A Multi-Scenario Benchmark for LLMs on Direct and Interactive PL/SQL Development from Natural Language

arXiv:2609.06527v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong potential for translating natural-language (NL) requirements into PL/SQL programs, attracting increasin

Who Laughs with Whom? Disentangling Influential Factors in Humor Preferences across User Clusters and LLMs

arXiv:2601.03103v2 Announce Type: replace-cross Abstract: Humor preferences vary widely across individuals and cultures, complicating the evaluation of humor using large language models (LLMs). In thi

Applications

Data Quality Rule Generation with LLMs

arXiv:2609.06053v1 Announce Type: new Abstract: The validation of data, such as customer and employee data, is an important task in many organizations. Errors in data can have severe consequences. For

applicationsarxiv-cs-lg

Local Ai

From Coordinates to Candidate Regions: Temporal Change Localization via Region Selection in Remote Sensing Multimodal LLMs

arXiv:2609.08391v1 Announce Type: new Abstract: Remote sensing multimodal large language models (RS-MLLMs) have advanced scene understanding and visual question answering over satellite imagery, yet l

local-aiarxiv-cs-cv

Introducing HALC: A general pipeline for the systematic and reliable construction of prompts for automated coding with LLMs in the computational social sciences

arXiv:2507.21831v2 Announce Type: replace-cross Abstract: LLMs are seeing widespread use for task automation, including automated coding in the social sciences. However, even though researchers have p

researcharxiv-cs-lg

SAFIRE: Safety-Critical Benchmark for Fine-grained Fire and Smoke Understanding in Multimodal LLMs

model-releasesarxiv-cs-cv

arXiv:2609.07823v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) show strong progress on vision-language tasks, yet their reliability in safety-critical settings remains undere

Can Large Language Models Anticipate Behavioral Responses to Social Policies? A Case of Pension Enrollment Prediction among China's Flexible Workers

arXiv:2609.05189v1 Announce Type: new Abstract: Assessing the impacts of social policy changes is a widely acknowledged challenge for policymakers. Econometric methods can be unreliable when extrapola

7 Sept 2026

Scalable Context Orchestration for Serving LLMs Over Voice

arXiv:2609.04288v1 Announce Type: cross Abstract: Voice AI applications are gaining popularity as advances in large language models (LLMs) enable more natural and accessible spoken interactions. Servi

safetyarxiv-cs-ai

7 Sept 2026

A Circuit for Plural Reference: How LLMs Represent and Retrieve Singular and Plural Entities

arXiv:2609.03687v1 Announce Type: new Abstract: Coreference resolution is an important task in contextual reasoning. In this paper, we investigate the mechanism for representing and retrieving singula

researcharxiv-cs-cl

4 Sept 2026

Hardware

Flip, Don't Shuffle: Watermarking LLMs at the Speed of Inference