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After the Hugging Face incident, we committed to conducting a much broader review of actions taken by our models during training and evaluat…

After the Hugging Face incident, we committed to conducting a much broader review of actions taken by our models during training and evaluation and to being transparent about our findings. This is an

model-releasesopenai--x

25 Sept 2026

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Model Releases

Baseline Shape Decides the Verdict: A Controlled Re-Examination of Ternary Language Models at 60K Parameters

DGX agent

arXiv:2609.29397v1 Announce Type: new Abstract: Ternary (1.58-bit) weights are attractive for microcontroller-class language models, but the sub-1M-parameter regime rests mainly on isolated, single-se

model-releasesarxiv-cs-cl

25 Sept 2026

Do World Models Make Better Robots? A Survey of Evaluation Benchmarks for Predictive Embodied Intelligence

arXiv:2609.29669v1 Announce Type: cross Abstract: Robot learning now advances along two tracks that rarely meet. On one side, direct Vision-Language-Action (VLA) policies map observations to actions a

safetyarxiv-cs-ai

25 Sept 2026

A JEPA Recipe for Tabular Foundation Models

arXiv:2609.25541v1 Announce Type: new Abstract: Tabular foundation models learn to predict cell values in context, whereas world-model self-supervision asks for prediction in representation space (LeC

23 Sept 2026

Construting Reverse Thinking: Developing Large Language Models' Reverse Thingking Ability

arXiv:2609.24760v1 Announce Type: new Abstract: When facing complex problems, humans tend to try various ideas for different issues. Human thinking patterns exhibit remarkable flexibility in adapting

23 Sept 2026

Anthropic releases Claude Opus 5.5 and OpenAI counters with two cheaper GPT-6 models

model-releasessiliconangle

Despite rampant worries about runaway artificial intelligence, the two big AI model makers aren’t yet slowing down: Anthropic PBC released Claude Opus 5.5 today and cut its price 20%, and minutes late

IMPLICIT-Bench: Measuring Implicit Bias in Text-to-Image Models under Neutral Prompts

arXiv:2609.24228v1 Announce Type: new Abstract: Text-to-image (T2I) models are typically evaluated for bias using slot-based templates such as ``a photo of a [profession]''. Such templates probe only

Optimizers for Diffusion Models: A Controlled Benchmark

arXiv:2609.23055v1 Announce Type: new Abstract: Discrete diffusion models now match autoregressive language models on several benchmarks, while the question of how best to train them has received far

Q-DEQ: Discrete Solving and Quantization for Deep Equilibrium Models in Time Series Forecasting under Edge Deployment Coding Constraints

arXiv:2609.24042v1 Announce Type: new Abstract: Edge deployment motivates forecasting models with compact parameter storage and low-bit representations. Deep equilibrium models (DEQs) obtain implicit

SPHQuant: Efficient extreme low bit weight quantization for Vision-Language Models

arXiv:2609.24875v1 Announce Type: new Abstract: Recent foundation models are moving toward native multimodal Vision-Language Models (VLMs), making VLMs a central form of next-generation foundation mod

The Role of Dataset Linguistic Structure in the Cultural Awareness of Large Language Models

model-releasesarxiv-cs-cl

arXiv:2602.01161v2 Announce Type: replace Abstract: The global deployment of large language models (LLMs) has raised concerns about cultural misalignment, yet the linguistic properties of fine-tuning

for some harder tests (this was insect identification), hosted models spread over 23 pts. Qwen 4B + Glance (0.96) was within a point of the …

model-releasesyohei-nakajima--x

On September 21 2026, Yohei reported results from a hard “insect‑identification” benchmark where model accuracy spanned 23 points across the evaluated systems. The Qwen 4B + Glance model scored 0.96,

21 Sept 2026

HERMES: A Holistic End-to-End Risk-Aware Multimodal Embodied System with Vision-Language Models for Long-Tail Autonomous Driving

arXiv:2602.00993v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving models increasingly benefit from large vision-language models for semantic understanding, yet safe and reliable

21 Sept 2026

Intervention Granularity Matters: Coherent Treatment Bundles in Counterfactual Simulation with Clinical World Models

arXiv:2609.21906v1 Announce Type: new Abstract: Counterfactual simulation with a clinical world model means fixing a patient's history, changing the treatment, and reading off the predicted response.

21 Sept 2026

Beyond Depth Truncation: Controlled Evaluation of Depth Utilization in Recursive Language Models

arXiv:2609.19934v1 Announce Type: new Abstract: Depth-recurrent language models iteratively apply a small layer stack, decoupling per-token compute from distinct parameter count. To determine whether

It’s time for our end-of-week recap 👇 — Gemini 3.8 Live and 3.8 Live Extended Thinking, our most advanced live dialogue audio models yet — …

model-releasesgoogle-ai--x

It’s time for our end-of-week recap 👇 — Gemini 3.8 Live and 3.8 Live Extended Thinking, our most advanced live dialogue audio models yet — Dreambeans, an experiment from @GoogleLabs that curates a dai

Riemannian--Lorentz Fusion of Vision Transformers and State-Space Models

arXiv:2609.19384v1 Announce Type: cross Abstract: Scaling deep learning faces critical bottlenecks: data exhaustion, exponential training costs, and resource concentration. Model merging combines pre-

When Perplexity Lies: Generation-Focused Distillation of Hybrid Sequence Models

arXiv:2603.26556v3 Announce Type: replace-cross Abstract: Converting a pretrained Transformer into a more efficient hybrid model through distillation offers a promising approach to reducing inference

An Empirical Evaluation of Cost-Efficient Large Language Models on Algorithmic Programming Tasks

arXiv:2609.18052v1 Announce Type: cross Abstract: This study empirically evaluates whether cost-efficient Large Language Models (LLMs) can be trusted to generate enterprise code to a written specifica

CSWAM: Better Causal Semantic Representations for Out-of-Distribution Generalization in World Action Models

arXiv:2609.18462v1 Announce Type: cross Abstract: FastWAM-style world action models enable efficient action-only inference, but generalize poorly under visual distribution shifts. Their reconstruction

researcharxiv-cs-ai

Decodable but Misrouted: Sparse Features Uncover a Readout Gap in Vision-Language Models for Harmful Meme Detection

arXiv:2609.18860v1 Announce Type: cross Abstract: When a large vision-language model misclassifies a harmful meme, the failure may reflect missing internal evidence or an inability to route represente

On the Identifiability of Mixed Ordinal and Exponential Family Causal DAGs under Linear Parametric Models

arXiv:2609.17942v1 Announce Type: new Abstract: The problem of identifiability in linear parametric models (LPMs) whose nodes follow either an ordered logit model or a regular one-parameter exponentia

Hardware

Banger paper from NVIDIA. It's on the topic of choosing which models go into a multi-agent system. The team compared eight selection strateg…

Banger paper from NVIDIA. It's on the topic of choosing which models go into a multi-agent system. The team compared eight selection strategies, based on size, accuracy, answer diversity and error div

hardwaredair-ai--x

Easy to Catch a Liar, Hard to Clear an Honest One: Language Models Diagnosing a Corrupted Reward Channel from a Verified Record

arXiv:2609.17226v1 Announce Type: cross Abstract: An agent that learns from rewards has to trust whatever reports those rewards. When the reports suddenly change, either the world changed or the repor

Efficient Reasoning Distillation: Small Video-Language Models via Synthetic CoT and Difficulty-Aware Fine-Tuning

arXiv:2609.16255v1 Announce Type: cross Abstract: We present an efficient method to distill reasoning capabilities into compact video-language models (VLMs) for video question answering (VideoQA). Our

EgoPathBench: Evaluating Zero-Shot Egocentric Waypoint Decision-Making in Vision-Language Models

arXiv:2609.16610v1 Announce Type: new Abstract: Zero-shot waypoint navigation requires vision-language models to select, from the current first-person observation, a sequence of spatial actions that i

Permutation-Based Stegomalware in Large Language Models: Threats and Countermeasures

arXiv:2609.16193v1 Announce Type: cross Abstract: The difficulty of training large language models (LLMs), together with their ubiquity, raises the threat of stegomalware, where malicious payloads are

researcharxiv-cs-ai

Safe Error Correction for Language Models: Frozen-Base Adjustment with Capability Preservation

arXiv:2609.16145v1 Announce Type: new Abstract: We study a practical question: can a small correction module fix errors in a frozen language model's outputs without degrading its base capabilities? We

What Does Layer-Importance Reveal About Transformers and State-Space Models?

arXiv:2609.16537v1 Announce Type: cross Abstract: Transformers and state-space models (SSMs) are the two dominant families of sequence models, and a central open question is how far the analytical kno

safetyarxiv-cs-ai

Tutorials

A Chosen Future Can Still Be Rewritten: Causal Writability in Video Models

arXiv:2609.15980v1 Announce Type: cross Abstract: When a video model generates physically incorrect motion, did it fail to learn the correct motion, or did it learn it but fail to use it? We show the

tutorialsarxiv-cs-cv

A Ticket from Marginals to Joints: Coupled-Noise Distillation for One-Step Block Generation in Diffusion Language Models

arXiv:2609.06324v2 Announce Type: replace Abstract: Diffusion language models (dLLMs) predict all tokens of a block in parallel, but a single forward pass samples each position from its own marginal d

researcharxiv-cs-cl

Beyond Accuracy: Robustness, Cost, and Governance Trade-offs for Vision-Language Models in Templated Document Extraction

arXiv:2609.15706v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly used to extract structured fields from business documents, yet most evaluations report accuracy on clean

Building Legal Reward Models for Grounding and Abstention

arXiv:2609.14739v1 Announce Type: cross Abstract: Large language models are increasingly used in high-stakes domains such as law, where systems must ground their reasoning in retrieved evidence and ab

Applications

DIDO: Distilling Interaction-Centric Dynamics into One-Step Denoising for World Action Models

arXiv:2609.15570v1 Announce Type: new Abstract: World Action Models (WAMs) use video generation models to predict future visual dynamics for robotic manipulation, but iterative denoising introduces ad

applicationsarxiv-cs-ro

EEG-Xplain: Decoding Neural Black-Boxes of EEG Foundation Models

arXiv:2609.15687v1 Announce Type: new Abstract: EEG foundation models such as BIOT, LaBraM, and EEGMamba have achieved remarkable performance in neural signal decoding, but their black-box nature limi

Agents

Enabling Streaming User Transcription in Full-Duplex Speech-to-Speech Models

arXiv:2609.15759v1 Announce Type: new Abstract: Full-duplex speech-to-speech (S2S) models enable natural conversational AI by allowing simultaneous listening and speaking. However, these models typica

agentsarxiv-cs-cl

Hallucination in Multimodal Foundation Models: A Survey on Causes, Corrections, and Evaluations

arXiv:2410.15359v2 Announce Type: replace Abstract: Multimodal Foundation Models represent a significant leap in artificial intelligence. Among them, Large Vision-Language Models (LVLMs) serve as the

researcharxiv-cs-ai

Introducing our most advanced Gemini Audio models yet 🗣 Gemini 3.8 Live and 3.8 Live Extended Thinking let you speak, collaborate, and exec…

model-releasesgoogle-ai--x

Introducing our most advanced Gemini Audio models yet 🗣 Gemini 3.8 Live and 3.8 Live Extended Thinking let you speak, collaborate, and execute tasks seamlessly, meaning conversing with AI just got a l

K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations

arXiv:2609.15855v1 Announce Type: cross Abstract: % !TEX root = ../main.tex People increasingly use large language models (LLMs) for mental health support, yet their safety in evolving, high-risk conv

Lightning Weave: Improving the Accuracy-Efficiency Frontier of Reasoning Models through Capability Composition

arXiv:2609.14708v1 Announce Type: new Abstract: A core goal of efficient reasoning is to improve the accuracy-efficiency frontier. However, jointly improving reasoning accuracy and inference efficienc

Signatures of Steerability in Activation Space of Language Models

arXiv:2609.14151v1 Announce Type: cross Abstract: Steering language models using a set of contrastive representations has been a canonical and computationally efficient method for controlling model be

safetyarxiv-cs-ai

Which AI models are most likely to cheat when given the chance? To find out, we built CheatBench [https://cheatbench.ai] : a benchmark that …

model-releasesdan-hendrycks--x

Which AI models are most likely to cheat when given the chance? To find out, we built CheatBench [https://cheatbench.ai] : a benchmark that tests whether agents attempt to cheat when given difficult t

Tutorials

Diffusion Models and Concept Formation

arXiv:2609.13047v1 Announce Type: cross Abstract: Humans organize knowledge into a taxonomy of concepts with nested levels of abstraction and a basic level at which people recognize and name objects w

tutorialsarxiv-cs-lg

Direct Topology Tracking in Continuous Implicit Models

arXiv:2609.12157v1 Announce Type: cross Abstract: We present a framework for tracking topological features directly within continuous implicit models. Such models, including implicit neural representa

researcharxiv-cs-lg

MARE: Multimodal Alignment and Reinforcement for Explainable Deepfake Detection via Vision-Language Models

arXiv:2601.20433v4 Announce Type: replace Abstract: Deepfake detection is a widely researched topic that is crucial for combating the spread of malicious content, with existing methods mainly modeling

safetyarxiv-cs-cv

Physics as the label for measuring and correcting materials reasoning in multimodal models

arXiv:2609.12181v1 Announce Type: new Abstract: Vision-language and language models increasingly interpret materials data, yet benchmarks report that they hallucinate invalid properties and violate ph

Zipbench: Low-Cost Framework for Compressing Comprehensive Benchmarks of Large Language Models

model-releasesarxiv-cs-cl

arXiv:2609.12475v1 Announce Type: new Abstract: Comprehensive benchmark suites are essential for improving large language models (LLMs), but many widely used benchmarks are redundant, making evaluatio

Detectable Only Where It Is Confounded: What Verified Duplication Counts Say About Membership Evidence in Language Models