Search: “llm”

Benchmarking Arabic--Russian Machine Translation: A Comparison of Fine-tuned NMT and Few-shot LLMs under Rich Morphology and Low Lexical Overlap

arXiv:2609.29559v1 Announce Type: new Abstract: Arabic-Russian machine translation (MT) remains under-explored due to the rich morphology of Arabic and low lexical overlap between the two languages. W

25 Sept 2026

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Benchmarking Argumentative Behaviour of LLMs: A Study of Defences Against Character Attacks

DGX agent

arXiv:2609.28673v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed as argumentative agents in persuasive dialogues, necessitating rigorous evaluation of their debat

model-releasesarxiv-cs-cl

25 Sept 2026

Applications

Detecting Data Poisoning in Code Generation LLMs via Black-Box, Vulnerability-Oriented Scanning

arXiv:2603.17174v2 Announce Type: replace-cross Abstract: Code generation large language models (LLMs) are increasingly integrated into modern software development workflows. Recent work has shown tha

applicationsarxiv-cs-ai

25 Sept 2026

Can LLMs Reason About Runtime Behavior? A Repository-Level Dynamic Benchmark

arXiv:2609.28449v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in coding tasks, but their ability to reason about code execution remains unclear. Existing reposit

24 Sept 2026

Math Reasoning in LLMs is Organized by Approach, Not Topic

arXiv:2609.27041v1 Announce Type: new Abstract: Mathematical reasoning benchmarks are typically organized by topic, but language models may organize their internal computation by reusable reasoning ap

24 Sept 2026

Applications

Not What You Meant: Can LLMs Follow a Specified Negation Semantics?

arXiv:2609.27517v1 Announce Type: new Abstract: Negation does not carry a uniform interpretation across domains. In legal, regulatory, and medical reasoning, the intended interpretation depends on the

applicationsarxiv-cs-ai

24 Sept 2026

BigO(Bench): Can LLMs Generate Code with Controlled Time and Space Complexity?

arXiv:2503.15242v3 Announce Type: replace Abstract: We introduce BigO(Bench), a novel coding benchmark designed to evaluate the capabilities of generative language models in understanding and generati

23 Sept 2026

DeceptionAnalyser: A Web-Based AI Tool for Performing Structured Deception Analysis with Argumentation Schemes and LLMs

arXiv:2609.24369v1 Announce Type: cross Abstract: Deception plays a central role in Intelligence operations, yet it remains difficult to analyse systematically without expert knowledge of reasoning pa

23 Sept 2026

Few-Shot Demonstrations Elicit the Use of In-Context World Representations in LLMs

arXiv:2609.24352v1 Announce Type: new Abstract: Large language models (LLMs), when acting as agents, are expected to take observed data in context, infer the latent state space underlying the world, a

agentsarxiv-cs-ai

23 Sept 2026

Beyond Accuracy and Surface Fluency: Risk-Sensitive Evaluation of LLMs for Legal Clause Generation

arXiv:2609.22127v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to draft contractual language, yet conventional accuracy or preference-based evaluations are poorly m

Compound-QA: A Benchmark for Evaluating LLMs on Compound Questions

arXiv:2411.10163v3 Announce Type: replace Abstract: Large language models (LLMs) demonstrate remarkable performance across various tasks, prompting researchers to develop diverse evaluation benchmarks

Ground-Truth Subgraphs for Better Training and Evaluation of Knowledge Graph Augmented LLMs

arXiv:2511.04473v3 Announce Type: replace-cross Abstract: Retrieval of information from graph-structured knowledge bases represents a promising direction for improving the factuality of LLMs. While va

iSDFT: Information-Proximal Self-Distillation for Continual Learning in LLMs

model-releasesarxiv-cs-lg

arXiv:2609.24646v1 Announce Type: new Abstract: On-policy self-distillation fine-tuning (SDFT) learns new skills from demonstrations while reducing forgetting, but it always distils toward the full de

LLMs Anchor on Chief Complaint and Fail to Integrate Evidence in Sequential Clinical Triage

arXiv:2609.22904v1 Announce Type: new Abstract: Triage in the emergency department (ED) is a sequential decision process that unfolds turn by turn. Existing evaluations of large language models (LLMs)

Looking for enterprise-grade CLI alternatives to Codex CLI (maybe Chinese LLMs?) now that Pro 20X is paused

Now that OpenAI has officially paused new sign-ups and upgrades for the ChatGPT Pro 200 (Pro 20X) plan, I’m looking for a solid alternative for enterprise-level development. For those unfamiliar: Code

model-releasesr-chatgpt

What Is The Political Content in LLMs' Pre- and Post-Training Data?

arXiv:2509.22367v3 Announce Type: replace Abstract: Large language models (LLMs) reflect politically-slanted opinions in their generated text. Even though it is widely assumed that model behavior stem

Accelerating Dense LLMs via L0-regularized Mixture-of-Experts

arXiv:2609.21672v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance but suffer from slow and costly inference. Existing acceleration methods often lead to noticeabl

researcharxiv-cs-ai

Local Ai

Detecting Hallucination in LLMs: Tracing the Topological Signatures of Impaired Context Sharing

arXiv:2609.21096v1 Announce Type: new Abstract: In this work, we examine the topology of information flow patterns within attention graphs to effectively distinguish hallucinated from non-hallucinated

local-aiarxiv-cs-ai

Do Personality-Tuned LLMs Make Better Social Agents?

arXiv:2609.21857v1 Announce Type: cross Abstract: LLMs are increasingly used in social simulations for socially interactive agents and robots, offering more flexibility than rule-based systems. Howeve

I built a framework-free prototype learner that lets local LLMs learn and correct facts instantly (1.6x–4x faster than backprop)[R]

model-releasesr-machinelearning

Hey everyone, I wanted to share a project I’ve been working on called Jayce. The whole thing started because I was watching a toddler named learn the names of stuff He didn't need to completely rewire

I'll Keep an Ear Out: Teaching AudioLLMs Proactive Audio Assistance

arXiv:2609.21183v1 Announce Type: cross Abstract: Audio large language models (AudioLLMs) operate reactively, responding only when queried. We introduce proactive audio assistance, where an AudioLLM m

MENASpeechBank: A Reference Voice Bank with Persona-Conditioned Multi-Turn Conversations for AudioLLMs

arXiv:2602.07036v2 Announce Type: replace-cross Abstract: Audio large language models (AudioLLMs) enable instruction following over speech and general audio, but progress is limited by the scarcity of

agentsarxiv-cs-ai

Local Ai

Seeing how differently people prompt LLMs is funny

So my brother and I both use LLMs for coding. I've started using a local GLM 5.3 Flash instance - q4 qat. My brother uses GPT-6-Astra as his daily driver. He has mentioned repeatedly to me that his ap

local-air-localllama

20 Sept 2026

Steer LLMs and Agents at the Token Level: An interactive tool for token visualization & control, model inspection and data annotation.

model-releasesr-localllama

onPanda is designed for geeks, power users, curious minds, and engineers. Its UI is built for deep exploration and efficient data annotation. - The core loop is simple: hover over a token → click an a

19 Sept 2026

Tutorials

Bayesian Optimization with Rich Auxiliary Information via LLMs

arXiv:2609.19437v1 Announce Type: new Abstract: Bayesian Optimization (BO) is widely used for optimizing expensive black-box functions, yet many real-world optimization problems contain substantially

tutorialsarxiv-cs-lg

Compass-v3: Scaling Domain-Specific LLMs for Multilingual E-Commerce in Southeast Asia

arXiv:2509.09121v2 Announce Type: replace Abstract: Large language models (LLMs) excel in general-domain applications, yet their performance often degrades in specialized tasks requiring domain-specif

Reading Emotions in the Token Space: Discriminative Adaptation of SpeechLLMs for Emotion Recognition

arXiv:2609.20081v1 Announce Type: cross Abstract: SpeechLLMs have shown strong potential for emotion recognition, yet they read the predicted emotion off a generative decoder not suited for classifica

researcharxiv-cs-ai

Xeno-Interpretability: Investigating the Alien Minds of LLMs

arXiv:2609.20408v1 Announce Type: cross Abstract: Large language models are usually interpreted through concepts that humans already possess: truthfulness, refusal, deception, personality, harmfulness

Clueing up LLMs with Tool-Augmented Deductive Reasoning

arXiv:2609.18736v1 Announce Type: new Abstract: Despite recent advances in large language models (LLMs), performing logically consistent deductive reasoning over extended interactions remains challeng

17 Sept 2026

Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data

arXiv:2609.18842v1 Announce Type: new Abstract: The scaling laws hold that a language model grows more capable with more parameters and more training data, and Mixture-of-Experts (MoE) architectures h

17 Sept 2026

Industry

LLMs respond differently to harmful prompts when AI watermarking is used

Artificial intelligence platforms are adopting AI watermarking—specifically Google’s open‑source SynthID‑Text—to meet new EU regulations, embedding a secret key that subtly changes word selection and

industryars-technica

17 Sept 2026

Auto-HSI: Personalized human control of a robot swarm on demand by using LLMs for online automatic code generation

arXiv:2609.16346v1 Announce Type: cross Abstract: This paper presents Auto-HSI, a method for generating personalized human-swarm interaction (HSI) interfaces on demand. The objective is to enable untr

agentsarxiv-cs-ai

Do LLMs Have Values? A Quantitative Analysis and Alignment Framework for Values in Large Language Models

arXiv:2609.16589v1 Announce Type: new Abstract: As Large Language Models (LLMs) increasingly handle complex subjective tasks, aligning their intentions and behaviors with human values has become a cri

Fine-Tuning Fixes Mode Collapse and Over-Dispersion in LLMs

arXiv:2609.16454v1 Announce Type: new Abstract: Recent work by Doshi and Hauser (2024), Bisbee et al. (2024), and Xie et al. (2026) raises concerns that outputs from large language models (LLMs) tend

GoBench: Evaluating LLMs on the game of Go [R]

researchr-machinelearning

GoBench evaluates LLMs on 9x9 Go games against a ladder of KataGo opponents, from random to superhuman. It measures general reasoning ability, strongly correlates with ARC-AGI 2 (r=0.83 correlation),

Nameless Tokenization: A Lossless Tokenizer-Level Defense Against Control-Token Forgery in Open-Weight LLMs

arXiv:2609.16984v1 Announce Type: new Abstract: Open-weight language models publish the strings their chat templates use to mark turns, roles and tool results, which the tokenizer maps back to the res

agentsarxiv-cs-cl

Sparse MLLM Anchors, Dense Adaptation: Breaking the Self-Referential Loop in Wild Test-Time Adaptation

arXiv:2609.17040v1 Announce Type: new Abstract: Wild test-time adaptation (WTTA) updates a source model online under small test batches, concurrent distribution shifts, and time-varying class imbalanc

The Pain Axis: LLMs Represent Self-Directed Harm and Act to Relieve It

arXiv:2609.16247v1 Announce Type: new Abstract: Large language models sometimes behave in ways resembling human emotional responses, and recent work has identified internal representations that may ex

VideoMM: Adaptive Macro-Micro Inference for Efficient Video MLLMs

arXiv:2609.16722v1 Announce Type: new Abstract: Scaling Multimodal Large Language Models (MLLMs) to long-form video understanding is bottlenecked by the explosion of visual tokens, which saturates con

researcharxiv-cs-ai

XeBoostLM: Native C++ Local LLMs for Intel NPUs & iGPUs

model-releasesr-localllama

https://reddit.com/link/1wi2zuc/video/rs1a4sx92xph1/player Hey everyone, I’ve been experimenting with local LLMs on Intel hardware, and I wanted a hyper-optimized, standalone CLI tool that didn't rely

AdaVSkip: Adaptive Visual Token Skipping Across Layers For Efficient MLLMs Inference

arXiv:2609.15131v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) require substantial computation to process numerous visual tokens across all transformer layers. Most methods

An Efficient and Modular Framework for Targeted Harm Mitigation in LLMS

arXiv:2609.13624v1 Announce Type: cross Abstract: Large Language Models (LLMs) are powerful zero-shot learners but remain prone to misalignment with human preferences, often producing biased, toxic, o

Artificial entrepreneurial cognition: Locating and causally steering an opportunity recognition dial inside large language models (LLMs)

arXiv:2609.15277v1 Announce Type: new Abstract: Entrepreneurial cognition is a foundation of entrepreneurship research. Yet the growing involvement of large language models (LLMs) in entrepreneurial w

Learning to Solve Hard Problems in RL for LLMs by Never Giving Up

arXiv:2609.13443v1 Announce Type: cross Abstract: We demonstrate that training LLMs with RL does not improve performance equally across a dataset. RL shows large improvements on easy problems that an

Measuring the Creativity of Frontier LLMs in Automated Research

arXiv:2609.14057v1 Announce Type: new Abstract: Frontier LLMs are increasingly capable of conducting automated research, yet their creativity in this setting has not been systematically evaluated. In

Beyond Prompting: Efficient and Robust Contextual Biasing for Speech LLMs via Logit-Space Integration (LOGIC)

arXiv:2601.15397v3 Announce Type: replace-cross Abstract: The rapid emergence of new entities -- driven by cultural shifts, evolving trends, and personalized user data -- poses a significant challenge

11 Sept 2026

Can LLMs Normalize Databases? A Benchmark and Multi-Agent Framework for Schema Normalization

arXiv:2609.11141v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used to generate structured outputs, but their reliability remains unclear when those outputs must satisfy

11 Sept 2026

BRACE: Anchored Bellman-Residual Correction for Stale Critics in Asynchronous RL