Search: “models”

arXiv:2610.13113v1 Announce Type: cross Abstract: Continual post-training faces a dual challenge of acquisition and retention: models must learn new capabilities while preserving prior knowledge. Whil

local-aiarxiv-cs-ai

12 Oct 2026

Human

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Model Releases

I made an open-source AI leaderboard that shows the best models your machine can run

DGX agent

Hey everyone, I made whatmodel.sh To use it, Enter your RAM and GPU See what models you can run locally with its rank and the command to run it Compare them with API models on the same score Building

model-releasesr-localllama

12 Oct 2026

Let Large Language Models Judge Each Other: Multi-Agent Peer-Reviewed Reasoning for Medical Question Answering

arXiv:2606.15419v2 Announce Type: replace-cross Abstract: Objective: To enhance the accuracy, interpretability, and robustness of large language models (LLMs) in medical question answering (MedQA). Me

12 Oct 2026

Agents

Meta has some Wonder Full new AI models

Meta is making up for lost time in the AI race. After launching Muse Spark, the first model from its revamped Superintelligence Labs division, and buzzy AI agent Muse, it's now launching two new model

agentsthe-verge-ai

12 Oct 2026

I asked 3 Qwen 3.8 models to solve recent AtCoder problems at 4 thinking efforts

model-releasesr-localllama

I was looking into finalizing my daily driver and it was not clear which one to chose. I have a 5060ti 16GB so I picked the following contenders, which seem to be working at a reasonable speed on my h

10 Oct 2026

Deception by Omission: Language Models Knowingly Hide Their Mistakes

arXiv:2610.11351v1 Announce Type: new Abstract: Large language models (LLMs) increasingly act as agents with little human oversight, so potential mistakes they make can go unnoticed. Users then depend

Internalizer: Portable Context-to-Parameter Mapping for Very Large Language Models

arXiv:2610.11715v1 Announce Type: new Abstract: Hypernetworks that map a context directly to a LoRA adapter let a large language model carry that context in its weights, but prior work has demonstrate

Large Language Models for Machine Translation Quality Annotation: Humans and Models Are Both Challenged

arXiv:2610.10918v1 Announce Type: new Abstract: Large Language Models (LLMs) are considered to be a more efficient and cost-effective alternative to human judgment for Machine Translation (MT) evaluat

researcharxiv-cs-cl

S4oP: Operator-level Pruning of Structured State Space Models for Resource-Constrained Devices

arXiv:2606.18096v2 Announce Type: replace-cross Abstract: Structured State Space Models (SSMs), including the S4 and S4D architectures, have recently emerged as powerful alternatives to attention-base

Safe at One Loop, Risky at Another: Aligning Safety Across Recurrent Depths in Looped Language Models

arXiv:2610.10625v1 Announce Type: cross Abstract: Looped Language Models (LoopLMs) provide a parameter efficient approach to scaling model capabilities through repeated use of shared parameters across

Stepping away from Benchmarks and Code, what models are you using for Creating writing projects

model-releasesr-localllama

All anyone ever talks in this sub is about x new model having x benchmark scores and how well it can code. Or some new tech to increase t/s for qwen. So I wanted to go back to the roots of this sub an

Strategic Governance of AI Models in Earth Science

arXiv:2610.10560v1 Announce Type: cross Abstract: AI foundation models pretrained on weather and climate data are increasingly fine-tuned to Earth science tasks well beyond weather forecasting. Their

Multi-Label Topic Assignment via LLM Distillation: A Comparative Analysis of Generative vs. Discriminative Student Models

arXiv:2610.09063v1 Announce Type: cross Abstract: Multi-label topic assignment for user-generated content (UGC) -- including product reviews and buyer-seller conversations -- poses unique scalability

8 Oct 2026

The Long Road to the Same Answer: Cognitive Bias Under Escalating Reasoning Budgets in Large Language Models

arXiv:2610.10049v1 Announce Type: new Abstract: Reasoning models allocate extra computation at inference time and present their answers as the product of deliberate thought. If this deliberation works

8 Oct 2026

Safety

Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)

arXiv:2510.22954v2 Announce Type: replace Abstract: Language models (LMs) often struggle to generate diverse, human-like creative content, raising concerns about the long-term homogenization of human

safetyarxiv-cs-cl

Claude Haiku 5.5 is now available on OpenDocRouter as one of the cheapest models for document parsing. It is roughly $1.2 / 1k pages and doe…

model-releasesjerry-liu--x

Claude Haiku 5.5 is now available on OpenDocRouter as one of the cheapest models for document parsing. It is roughly $1.2 / 1k pages and does quite well on tables and text reading order compared to ot

Safety

From Local Evidence to Safety Verdicts: Causal Tracing in Vision-Language Models

arXiv:2610.07514v1 Announce Type: new Abstract: A vision-language model may need to combine an image with a prompt to recognize a safety risk that neither reveals alone. Where does this joint safety j

safetyarxiv-cs-ai

Medical Image Alignment Assessment as a Test of Generalist Visual Reasoning in Frontier Multimodal Models

arXiv:2610.06896v1 Announce Type: new Abstract: Frontier multimodal large language models (MLLMs) are increasingly positioned as general purpose visual reasoners as part of the quest for artificial ge

Scale-Invariant Training for Time Series Foundation Models

arXiv:2610.07324v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) are trained on large collections of time series datasets that span various morphologies and domains. This settin

decision models + local ai = ❤️ tried Laya (421M) on llama.cpp in a little apple-picking benchmark, it's almost as good as Qwen 4B at 12x th…

model-releasesgeorgi-gerganov--x

decision models + local ai = ❤️ tried Laya (421M) on llama.cpp in a little apple-picking benchmark, it's almost as good as Qwen 4B at 12x the speed. Media Decision models in llama.cpp are now availabl

H-JEPA: End-to-End Learning of Hierarchical World Models for Visual Planning

arXiv:2610.06805v1 Announce Type: new Abstract: Long-horizon planning with latent world models requires reasoning across timescales and levels of abstraction. Existing task-agnostic JEPA world models

researcharxiv-cs-lg

How Long, Not How Close: A Learned Temporal Metric for Planning in Latent World Models

arXiv:2610.04988v1 Announce Type: new Abstract: Latent world models plan by rolling a frozen predictor forward under candidate action sequences and ranking the candidates by the latent distance betwee

researcharxiv-cs-lg

MEND: RL For Flow Models via Proximal Velocity Matching

arXiv:2610.05954v1 Announce Type: cross Abstract: Reward post-training of flow models either reweights the model's own samples under a KL penalty or a frozen reference, often for thousands of updates,

researcharxiv-cs-cv

Safety

Monitorability Disposition in Large Reasoning 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

Tutorials

Pythia: Toward Foundation World Models for Multimodal Time Series

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

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

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

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

Should we plead opensource labs to still produce great non thinking (instruct) models?

model-releasesr-localllama

The results are in, no thinking / instruct mode for new models degrade performance more than on old models such as 3.6 vs 3.8, where 3.6 takes the lead on several coding benches in instruct mode. I wo

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

From world models to active agents: the next step for physical AI

Generative AI has made remarkable progress in creating increasingly realistic representations of the world. Image models can synthesize photorealistic scenes, video models can generate complex dynamic

hardwarelambda-labs

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

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

28 Sept 2026

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