Search: “models”

A Distributional Optimisation Perspective on Combining Models in Deep Learning

model-releasesarxiv-cs-lg

arXiv:2609.24328v1 Announce Type: new Abstract: Combining predictions from different models can improve performance at machine learning tasks, but the training of the individual models and the rule us

22 Sept 2026

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Model Releases

CAMFT: Conflict-Aware Mergeable Fine-Tuning for Large Language Models

DGX agent

arXiv:2609.22253v1 Announce Type: cross Abstract: Model merging has emerged as a promising paradigm for integrating multiple task-specific capabilities into a single large language model. However, exi

model-releasesarxiv-cs-cl

22 Sept 2026

LIBERO-VPro: Benchmarking Closed-Loop Visual Robustness of Robotic Foundation Models

arXiv:2609.24350v1 Announce Type: cross Abstract: Robotic foundation models achieve impressive performance on standard manipulation benchmarks, yet these evaluations typically assume clean, timely, an

22 Sept 2026

Research

Monocultural Biases: Correlated biases in large language models lead to unequal systemic exclusion rates in hiring

arXiv:2609.22169v1 Announce Type: new Abstract: Employers are increasingly using large language models (LLMs) to automate their hiring process. This paper investigates the risk of monocultural biases,

researcharxiv-cs-cl

22 Sept 2026

Benchmarking World Models for Continual Learning on Compositional Tasks

model-releasesarxiv-cs-lg

arXiv:2609.22055v1 Announce Type: new Abstract: A desirable property of a world model is the ability to learn continually across tasks, adapting to new environments without forgetting what the agent h

21 Sept 2026

Do small language models know what they don't know?

arXiv:2609.20824v1 Announce Type: new Abstract: We explore whether entropy-based confidence signals can be leveraged to improve the accuracy of Small Language Models (SLMs) with fewer than 3 billion p

21 Sept 2026

Geometry of Values: Task Vector Composition for Ethical Preference Alignment in Language Models

arXiv:2609.21094v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in applications that must weigh clashing moral values, yet even strong models exhibit hidden bi

21 Sept 2026

Benchmarking Large Language Models for Biomedical Relation Extraction

arXiv:2609.19071v1 Announce Type: new Abstract: Extracting SNP-phenotype associations from biomedical literature is vital but challenging. We benchmarked diverse NLP models, including MLMs, hybrid arc

GYROval: A Robust Benchmark for Cultural Value Orientation in Large Language Models

arXiv:2609.18384v1 Announce Type: cross Abstract: We present a robust benchmark for measuring cultural value orientation in large language models on the two Inglehart-Welzel axes over several domains

LM Fight Arena: Benchmarking Large Multimodal Models via Game Competition

arXiv:2510.08928v2 Announce Type: replace Abstract: Existing benchmarks for large multimodal models (LMMs) often fail to capture their performance in real-time, adversarial environments. We introduce

Applications

Long-Context Demonstration Selection Using State Space Models

arXiv:2609.17888v1 Announce Type: cross Abstract: We study the problem of demonstration selection, which involves selecting a subset of examples for prepending to a query to a language model. This pro

applicationsarxiv-cs-cl

Research

Made in Hungary: Comments on the performance of generative language models

arXiv:2609.18284v1 Announce Type: new Abstract: In recent years, three initiatives have emerged to develop generative language models in Hungary. The motivation behind them is the same. For Hungarian,

researcharxiv-cs-cl

Crypto Accounting Bench: Evaluating Frontier and Open-Weight Models on Crypto-Asset Accounting Tasks

arXiv:2609.14811v1 Announce Type: new Abstract: We introduce Crypto Accounting Bench (CAB), a benchmark for assessing whether frontier and open-weight language models can reconstruct the complete jour

Diagnosing Faults in Reinforcement Learning Simulators and World Models with Canonical Polynomial Invariants

arXiv:2609.13194v1 Announce Type: cross Abstract: A large literature builds physical structure into learned dynamics on the premise that models respecting the underlying physics predict better. We tes

Local Ai

Drift-Constrained Optimization: Only Direction Matters in Fine-Tuning Instruct Models

arXiv:2609.13680v1 Announce Type: new Abstract: Fine-tuning instruct models often improves target performance while inducing behavioral drift from the reference model, which can degrade existing capab

local-aiarxiv-cs-ai

Research

Generalization Can Emerge in Tabular Foundation Models From a Single Table

arXiv:2511.09665v1 Announce Type: cross Abstract: Deep tabular modelling increasingly relies on in-context learning where, during inference, a model receives a set of (x,y) pairs as context and predic

researcharxiv-cs-ai

Mixture-of-Experts Language Models Can Be Strong and Efficient Retrievers

arXiv:2609.13486v1 Announce Type: cross Abstract: Recent work has shown that fine-tuning decoder-only large language models (LLMs) for retrieval yields strong first-stage retrievers, with effectivenes

Overflip: Repetition-Induced Label Flips in Guardrail Models

arXiv:2609.15013v1 Announce Type: new Abstract: Guardrail models are classifiers deployed to screen malicious prompts and responses in LLM-based services. To meet latency constraints, many lightweight

Task-Aware Federated Fine-Tuning for MoE-based Large Language Models

arXiv:2609.13395v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) has become a widely adopted architecture for Large Language Models (LLMs), as it improves model capacity while limiting compu

Towards Evolving Context Parameterization for Large Language Models

arXiv:2609.14168v1 Announce Type: cross Abstract: Context parameterization enables large language models (LLMs) to internalize contexts into reusable model parameters, avoiding repeated processing acr

SteerDuplex: Steerable Duplex Speech Dialogue Models

arXiv:2609.12623v1 Announce Type: cross Abstract: Full-duplex spoken dialogue models support low-latency turn taking, interruption handling, and backchanneling, yet a key capability remains underexplo

14 Sept 2026

Arbitrary Cipher Attacks Against Large Language Models Do Not Require Fine-Tuning

arXiv:2609.09553v1 Announce Type: cross Abstract: Large language model safety and security research is preoccupied with, among other things, detecting and preventing jailbreak attacks: alignment bypas

safetyarxiv-cs-ai

Domain-Specific Hallucination Detection in Large Language Models

arXiv:2609.11878v1 Announce Type: new Abstract: Large language models generate fluent text that can contain unfaithful claims -- a phenomenon known as hallucination. We present a multi-signal detectio

From Evaluation to Enhancement: Benchmarking and Improving Think-with-Video Reasoning for Video Generative Models

arXiv:2609.11242v1 Announce Type: new Abstract: Video generation has advanced to produce visually compelling and temporally coherent results. Yet, whether these models can genuinely think with video--

Tutorials

World in World: Explore the World with World Models

arXiv:2609.11548v1 Announce Type: new Abstract: Autoregressive video world models enable interactive, long-horizon exploration, but flexible control remains challenging. Exploring a source video from

tutorialsarxiv-cs-cv

BTBR: A Bayesian-Theory-Driven Probabilistic-Fuzzy Framework for Implicit Bias Removal in Large Language Models

arXiv:2408.10608v2 Announce Type: replace Abstract: Large language models (LLMs) may encode biased associations from heterogeneous training corpora that are not immediately visible under ordinary prom

safetyarxiv-cs-cl

Evaluating the Scalability and Adversarial Generalization of GRPO-Trained NLI Models

arXiv:2504.18376v3 Announce Type: replace-cross Abstract: Natural Language Inference (NLI) is a central task in natural language understanding with applications in fact-checking, question answering, a

Explainable Deep Learning for Price-Trade Dynamics: From Black-Box Forecasts to Effective Parametric Models

arXiv:2609.06085v1 Announce Type: cross Abstract: Understanding the joint dynamics of prices and trades is central to market microstructure, where returns and order flow interact through nonlinear and

How AI Models Manage Epistemic Authority: A Taxonomy and Comparative Analysis of Responses to User Disagreement

arXiv:2609.07662v1 Announce Type: cross Abstract: Large language models are increasingly used as sources of advice and information, including in high-stakes settings, yet little is known about how the

LogiScope-VQA: Benchmarking Vision-Language Models for Logistics Hazard Identification in Industrial Scenarios

arXiv:2609.09790v1 Announce Type: cross Abstract: Large Multimodal Models (LMMs) large-scale deployment in industrial warehouse settings specifically necessitates that models exhibit human-expert-leve

Mapping the Emerging Social Science of Large Language Models

arXiv:2609.07598v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly shape communication, learning, work, creativity, and decision-making, yet social-science research on these d

safetyarxiv-cs-ai

MetroLLM-Bench: Evaluating Language Models as Transit Kiosk Runtimes

arXiv:2609.10016v1 Announce Type: cross Abstract: We introduce MetroLLM-Bench, a 955-case benchmark for testing language models as the policy layer of a transit kiosk. It covers six real metro systems

OdysSim: Building Foundation Models for Human Behavior Simulation

model-releasesarxiv-cs-lg

arXiv:2606.14199v2 Announce Type: replace-cross Abstract: Large language models are increasingly deployed as human simulators for interactive evaluation and social simulation. Yet helpfulness-driven p

9 Sept 2026

PhysVidBench: Language-Grounded Evaluation of Physical Commonsense in Text-to-Video Models

arXiv:2507.15824v2 Announce Type: replace Abstract: Text-to-video (T2V) models now produce striking visuals, yet they routinely violate everyday physics; objects float, tools are misused, and causal s

9 Sept 2026

Tri-PvP: Exposing Modality Bias in Omni-Modal Large Language Models through Perceptual-Propositional Evidence Conflicts

arXiv:2609.06011v1 Announce Type: cross Abstract: Omni-modal large language models (OLLMs) jointly process vision, audio, and text, yet their modality bias under cross-modal conflict remains underexpl

9 Sept 2026

Hardware

GPU-Accelerated Astrodynamics World Models for Spacecraft Rendezvous and Proximity Operations

arXiv:2609.03067v1 Announce Type: new Abstract: World models are an emerging paradigm in representation learning in which an agent jointly learns state-action dynamics and observation models from offl

hardwarearxiv-cs-ro

Principia: Relational Physics Tests for Video Models

arXiv:2609.04200v1 Announce Type: new Abstract: Evaluating physical reasoning in video models is difficult because absolute motion measurements depend on frame rate, object scale, and camera calibrati

TAP-Path: Task-Adaptive Structural and Token Pruning for Efficient and Trustworthy Pathology Foundation Models

arXiv:2609.04071v1 Announce Type: cross Abstract: Pathology foundation models improve transferable representation learning for histopathology, but recent gains often rely on encoders with hundreds of

To What Extent Do Large Language Models Understand Bangla Idioms?

arXiv:2609.03410v1 Announce Type: new Abstract: Idiomatic expressions are an integral part of natural language, reflecting cultural nuances and posing unique challenges for computational models, parti

Auditory Illusion Benchmark for Large Audio Language Models

arXiv:2609.02277v1 Announce Type: cross Abstract: Perceptual illusions have long served as crucial probes into human cognition, revealing biases and limitations of perception. In the auditory domain,

Applications

Door-in-the-Face Requests and Refusal Behaviour in Large Language Models

arXiv:2609.02707v1 Announce Type: new Abstract: Does the door-in-the-face technique work on language models? In humans, a large request that is refused makes a smaller follow-up request more likely to

applicationsarxiv-cs-ai

FairLens: Benchmarking Fairness in Vision-Language Models for High-Stakes Decision-Making

arXiv:2609.01691v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly used to make decisions from visual inputs. We introduce FAIRLENS, a benchmark and evaluation framework fo

Gauge dependence and structured-output corruption in sign-branched repetition penalties: measurements across models, inference stacks, and alternative repetition controls

arXiv:2607.09791v2 Announce Type: replace-cross Abstract: The multiplicative repetition penalty shipped across the LLM inference ecosystem (HuggingFace, vLLM, llama.cpp, and a dozen further engines) b

MemeCULT-1K: Benchmarking South Asian Cultural Context and Humor Understanding of Multimodal Models

arXiv:2609.01772v1 Announce Type: new Abstract: Meme understanding goes beyond recognizing visual content or literal text; it requires implicit cultural knowledge and pragmatic inference that most vis

CordisBench: Can Language Models Reason About Component Lifecycles in Dynamic Agent Harnesses?

arXiv:2609.01600v1 Announce Type: cross Abstract: Dynamic agent harnesses let language models change the software that shapes their own execution. This flexibility brings a new reasoning burden: a loc

GUI-CC: Benchmarking Contextual Consistency of GUI World Models as Agent Environments

arXiv:2609.00048v1 Announce Type: cross Abstract: GUI world models are increasingly evaluated as one-step next-screen predictors, yet their intended use is often as multi-step environments for GUI age

Heard but Not Heeded: Paralinguistic Information Encoding and Loss in Audio-Language Models

arXiv:2609.00727v1 Announce Type: cross Abstract: Audio language models are designed to understand speech, yet it remains unclear whether they capture how something is said beyond what is said. We pre

safetyarxiv-cs-ai

SciTrue: Reliable Scientific Claim Validation with Frontier and Open Language Models at the NTCIR SciClaimEval Task

arXiv:2609.00654v1 Announce Type: new Abstract: We describe the SciTrue team's participation in both subtasks of the NTCIR-19 SciClaimEval task~ite{sciclaimeval}, which asks systems to verify scientif