Search: “models”

arXiv:2610.04914v1 Announce Type: cross Abstract: Monitoring the chain-of-thought (CoT) of large reasoning models (LRMs) is a common way to detect misbehavior in real-world practice. However, current

safetyarxiv-cs-lg

6 Oct 2026

Tutorials

Pythia: Toward Foundation World Models for Multimodal Time Series

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DGX agent

arXiv:2610.05240v1 Announce Type: new Abstract: Time-series foundation models offer a unified approach to forecasting across heterogeneous domains. Textual context and auxiliary observations provide c

tutorialsarxiv-cs-lg

6 Oct 2026

Bridging Research and Practice: A Systematic Evaluation of Generalist and Dermatology-Specific Models in Clinical Skin Lesion Classification

model-releasesarxiv-cs-cv

arXiv:2610.03193v1 Announce Type: new Abstract: The application of machine learning to dermatology has grown substantially in recent years, moving beyond proof-of-concept studies toward potential appl

Do Large Language Models Know Colombian Law? A Reliability Benchmark for the Colombian Legal System

arXiv:2610.03639v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to support legal practice, education, and research, yet their reliability in national legal systems o

Jumping up and down: Denoiser diffusion models for discrete ordinal data

arXiv:2610.02754v1 Announce Type: new Abstract: Diffusion models are highly developed in continuous spaces for image and video domains. Recently, major advances have been made for discrete diffusion m

researcharxiv-cs-lg

Language Models that Play Chess and Explain Their Moves

arXiv:2610.03695v1 Announce Type: new Abstract: Modern chess engines are silent experts: they play at a superhuman level, but do not offer explanations for their play. On the other hand, language mode

Recursive Self-Improvement in Unified Multimodal Models

arXiv:2610.03002v1 Announce Type: new Abstract: Unified multimodal models (UMMs) understand and generate both text and images, which lets a model produce its own training data. Existing self-improveme

researcharxiv-cs-cl

Understanding Affective Adaptation in Multimodal Foundation Models: Emergent Functional Specialization

model-releasesarxiv-cs-cv

arXiv:2601.15906v2 Announce Type: replace Abstract: Despite rapid progress in multimodal affective foundation models, how affective capabilities emerge within their internal architectures remains poor

Interpreting Reasoning of Large Language Models via Partial Information Decomposition

arXiv:2610.00571v1 Announce Type: cross Abstract: Large reasoning models (LRMs) have achieved substantial improvements in solving complex mathematical problems, but often produce lengthy, repetitive,

Mingbird: A Local-First Agent Harness Enabling Small Open Models to Complete Real Tasks

arXiv:2610.02001v1 Announce Type: new Abstract: Small open-weight models (2-9B) run on ordinary laptops, but under cloud-scale agent harnesses they rarely complete real tasks: tool prefill overflows t

Mixture-Trained Merging for Unified Multi-Objective Models

arXiv:2610.01238v1 Announce Type: new Abstract: Unified language models are increasingly expected to combine heterogeneous capabilities, such as mathematics, code, instruction following, and controlla

Random Recursive Models

arXiv:2610.00541v1 Announce Type: cross Abstract: Recursive models create computational depth through parameter reuse, offering a parameter-efficient alternative to increasing model size. However, mos

DataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language Models

arXiv:2603.26164v2 Announce Type: replace-cross Abstract: Data-centric training has emerged as a promising direction for improving large language models (LLMs) by optimizing not only model parameters

1 Oct 2026

Hardware

One from Infinity: Actualizing Futures from Pretrained World Models into Robot Actions

arXiv:2609.36413v1 Announce Type: new Abstract: A pretrained video world model admits many plausible futures for a scene, but a robot must realize the exact task-conditioned one. To turn world models

hardwarearxiv-cs-ro

30 Sept 2026

Stochastic World Models for Verifying Vision-Based Neural Feedback Systems

arXiv:2609.38120v1 Announce Type: new Abstract: Verifying a vision-based neural feedback system requires a model of the observations its controller acts upon. Such a model must capture the variation t

30 Sept 2026

What You Observe Determines How You Identify Causal Effects: Evaluating Causal Models across Observational Views

arXiv:2609.36881v1 Announce Type: new Abstract: Causal foundation models (CFMs) pre-trained on data generated from various structural causal models (SCMs) have been proposed for estimating causal effe

30 Sept 2026

BOReFT: Manifold Steering of Language Models for Black-box Optimization

arXiv:2609.33722v1 Announce Type: new Abstract: Language models are increasingly used as proposal models for black-box search, from program optimization to molecular design. Existing approaches typica

d-OPD: Future-Aware On-Policy Distillation for Block Diffusion Language Models

arXiv:2609.35362v1 Announce Type: new Abstract: Large language models (LLMs) typically generate text autoregressively (AR), predicting one token at a time. Block diffusion language models (dLLMs) inst

Program-Verified Self-Evolution for Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2609.33855v1 Announce Type: new Abstract: Self-evolving vision-language models train on questions they generate from unlabeled images. Since these questions have no gold answers, prior methods l

Rethinking Personalized Generation: Test-Time Alignment via Factorized Ranking Models

arXiv:2609.35695v1 Announce Type: new Abstract: Aligning large language models (LLMs) to diverse user preferences is fundamentally hindered by standard alignment paradigms that optimize for monolithic

Reuse or Relearn? A Spectral View of Earth Observation Foundation Models

arXiv:2609.32756v1 Announce Type: new Abstract: Foundation models are rarely used as generic, frozen feature extractors; instead, they are fine-tuned for the target downstream application. This practi

Action Forcing: Training World Models on Unsupervised Video by Recovering Underlying Egomotion Bases

arXiv:2609.30595v1 Announce Type: cross Abstract: Synchronised action annotations are needed to train controllable world models and these datasets remain elusive. Existing approaches make use of instr

researcharxiv-cs-ai

Attribution Bias in Large Language Models

arXiv:2604.05224v2 Announce Type: replace Abstract: As Large Language Models (LLMs) are increasingly used to support search and information retrieval, it is critical that they accurately attribute con

BioEVAL: A global, multi-institutional benchmark of large language and multimodal models for bioengineering

arXiv:2609.30489v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated historic breakthroughs in general reasoning with early successes in biomedical science. However, existing

FeatMark: Feature-level Watermark Protection against Mimicry Attacks with Diffusion Models

arXiv:2609.30980v1 Announce Type: cross Abstract: Text-to-image diffusion models enable data-efficient 'mimicry' attacks, wherein adversaries fine-tune the model on a handful of public photos to synth

researcharxiv-cs-cv

HaM-World: Soft-Hamiltonian World Models with Selective Memory for Planning

arXiv:2605.05951v2 Announce Type: replace Abstract: World models support model-based planning through learned latent dynamics, but imagined rollouts can become unstable as the planning horizon grows o

researcharxiv-cs-ai

Intent2Tc: Automated Intent-to-Traffic Control Translation with Language Models

arXiv:2609.31397v1 Announce Type: cross Abstract: Automated and highly usable Quality-of-Service (QoS) enforcement requires translating high-level service intents into deployable traffic-management po

Parameter Estimation for Unnormalized Discrete Models via Empirically Localized Deformed Bregman Divergence

arXiv:2609.30713v1 Announce Type: cross Abstract: Estimation of parameter of probabilistic models is an important task in the field of machine learning.For models of discrete variables, calculation of

Safety

StarWM: Self-Supervised Trained Attention Routing for Robust World Models

arXiv:2609.30667v1 Announce Type: new Abstract: A robust world model must strike the balance between faithfully capturing environmental dynamics and abstracting away from irrelevant content. While rec

safetyarxiv-cs-cv

The Path Matters: Evaluating Small Language Models Beyond Answer Accuracy in KGQA

arXiv:2609.27669v1 Announce Type: cross Abstract: Small language models (SLMs) are increasingly paired with knowledge graphs (KGs), yet end-to-end KG question answering conflates graph access, search,

24 Sept 2026

Capable yet Parsimonious: Extracting and Characterizing Hidden Chain-of-Thought in Frontier Models

arXiv:2609.26637v1 Announce Type: new Abstract: The rapid capability gains of frontier language models are widely attributed to improved reasoning abilities, yet this cannot be verified as raw CoT tra

23 Sept 2026

TSS: Target-Side Sparsification for Speculative Decoding in Domain-Specific Large Language Models

arXiv:2609.26100v1 Announce Type: new Abstract: Speculative decoding accelerates large language model inference through collaboration between a lightweight draft model and a target verifier. Existing

23 Sept 2026

OmniEdu: Open Foundation Models for Learning and Teaching

arXiv:2609.23088v1 Announce Type: new Abstract: Educational foundation models must solve problems, understand curriculum structure, diagnose learner difficulties, and provide appropriate instructional

22 Sept 2026

Vox-Infinity: Benchmarking the Limits of Long-Context Spoken Language Models

arXiv:2609.22452v1 Announce Type: new Abstract: Long-context understanding remains a fundamental challenge for large language models, as excessively long inputs often lead models to forget salient inf

22 Sept 2026

Reading Anxiety or Reading the Label? Comparing Fine-Tuned and Frontier Models for Anxiety Detection on Social Media

arXiv:2609.20847v1 Announce Type: new Abstract: Anxiety is among the most common mental health conditions, and people often write about it online well before seeking clinical help. Practitioners build

21 Sept 2026

Understanding Structural Representation in Foundation Models for Polymers

arXiv:2512.11881v2 Announce Type: replace-cross Abstract: From the relative scarcity of training data to the lack of standardized benchmarks, the creation of effective foundation models for polymers f

21 Sept 2026

Co-VLA: Consensus-based Federated Training for Vision-Language-Action Models

model-releasesarxiv-cs-ro

arXiv:2609.19923v1 Announce Type: new Abstract: Vision-language-action models (VLAs) have emerged as a promising paradigm for general-purpose robot learning, with performance improving as models and d

18 Sept 2026

Local Ai

How Far Can Sub-3B Open Language Models Go in Zero-Shot Essay Scoring on an 8 GB Consumer GPU?

arXiv:2609.20250v1 Announce Type: new Abstract: Zero-shot essay scoring with large language models is usually demonstrated with proprietary API models, yet the settings where automated scoring is most

local-aiarxiv-cs-lg

18 Sept 2026

Bypassing the Rationale: Causal Auditing of Implicit Reasoning in Language Models

arXiv:2602.03994v3 Announce Type: replace-cross Abstract: Chain-of-thought (CoT) prompting is widely used as a reasoning aid and is often treated as a transparency mechanism. Yet behavioral gains unde

17 Sept 2026

Do Frontier Models Seek Safety Evidence Before Acting?

arXiv:2609.17865v1 Announce Type: new Abstract: Frontier models are often evaluated on how they respond to safety information once it is already in context. We study an earlier decision point: whether

17 Sept 2026

EviScope: Paired Counterfactual Evidence Diagnostics for Faithful and Efficient Grounded Language Models

arXiv:2609.17081v1 Announce Type: new Abstract: Grounded language-model systems are often evaluated by final answer accuracy, yet a correct answer can be unsupported, drawn from the wrong source, or p

16 Sept 2026

Japanese Stroke LLM Evaluation: A Conversational Benchmark for Safe Stroke Care in Japanese Using Large Language Models

arXiv:2609.16739v1 Announce Type: new Abstract: Background: Large language models (LLMs) have achieved physician-comparable performance on multiple-choice medical knowledge examinations, but their cap

16 Sept 2026

Do Tabular Foundation Models Still Need Feature Engineering?

arXiv:2609.13202v1 Announce Type: new Abstract: Feature engineering has long been a cornerstone of tabular machine learning. Tabular foundation models (TFMs) are pretrained on a wide range of tabular

LPA-CWM: A Learned Physical Adjudicator for Motion Reasoning with Counterfactual World Models

arXiv:2609.14073v1 Announce Type: cross Abstract: Counterfactual world models (CWM) extract motion from pretrained video predictors by comparing factual and intervened predictions. However, responses

Mind2Dialogue: Training Human-Aware Language Models by Simulating User Mental States

arXiv:2609.15972v1 Announce Type: new Abstract: As language models become more capable, long-term collaboration in learning, reasoning, and decision-making calls for a deeper understanding of the peop

Post-Reasoning: Improving the Performance of Non-Thinking Models at No Cost

arXiv:2605.06165v2 Announce Type: replace Abstract: As the widespread adoption of Large Language Models (LLMs) accelerates, token consumption from intermediate reasoning traces increasingly contribute

Towards a Mechanistic Understanding of Propositional Logical Reasoning in Large Language Models

arXiv:2601.04260v2 Announce Type: replace Abstract: Understanding how Large Language Models (LLMs) perform logical reasoning internally remains a fundamental challenge. While prior mechanistic studies

When the World Lies: Backdoor Attacks on Latent World Models for Downstream Control

arXiv:2609.15781v1 Announce Type: cross Abstract: Pretrained world models, learned simulators that encode an observation into a latent state and predict how it evolves under actions, are beginning to