model releases

TESSERA v2: Scaling Pixel-wise Earth Foundation Models

arXiv:2607.03949v1 Announce Type: new Abstract: Pixel-wise Earth-observation (EO) foundation models are now achieving state-of-the-art performance via generated spatial embeddings. However, how these

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TestMate: Test-Time Domain Adaptation Aided by Lightweight Vision Foundation Model

DGX agent

arXiv:2607.03810v1 Announce Type: new Abstract: Test-Time Domain Adaptation (TTDA) aims to adapt Deep Neural Networks to distribution shifts using only streaming, unlabeled test data in real time. Cur

model-releasesarxiv-cs-cv

7 Jul 2026

TexTailor: Inference-Time Textual Guidance Tailoring for Multimodal Diffusion Transformers

arXiv:2601.02211v2 Announce Type: replace Abstract: Recent breakthroughs of transformer-based diffusion models, particularly with Multimodal Diffusion Transformers (MMDiT) driven models like FLUX and

The AI labs desperately need non-engineer Peters, Borises, and Thariqs. Most demos for 'business users' are about replying to emails or to S…

model-releasesallie-k--miller--x

The AI labs desperately need non-engineer Peters, Borises, and Thariqs. Most demos for 'business users' are about replying to emails or to Slack. And yes, that's helpful to manage the cacophonous hell

The global ecosystem has recently learned what sovereign AI means: access to critical infrastructure that can't be revoked overnight. This i…

The global ecosystem has recently learned what sovereign AI means: access to critical infrastructure that can't be revoked overnight. This is what Cohere was built to solve: your data, your weights, a

model-releasescohere--x

The Good, the Bad, and the Brittle: Benchmarking Robustness and Generalisation of Histopathology Foundation Models

arXiv:2607.04401v1 Announce Type: new Abstract: How robust and generalisable are pathology foundation models and have their scaling limites been reached? We benchmarked twelve pathology foundation mod

The Map Behind the Flow: Finite-Step Gradient Descent as a Dynamical System

arXiv:2607.04993v1 Announce Type: cross Abstract: Many phenomena of deep learning are dynamical: they concern not only which minima exist, but how gradient descent reaches, avoids, or selects among th

The Method of Gaps: Exact Expressions for the Generalization Error of Supervised Learning Algorithms

arXiv:2411.12030v3 Announce Type: replace Abstract: In this paper, the method of gaps, a technique for deriving closed-form expressions in terms of information measures for the generalization error of

The Moving Target: A Longitudinal Audit of Trustworthiness Drift Across Twelve Checkpoints of Open-Source Chat LLMs

arXiv:2607.02587v1 Announce Type: cross Abstract: Model cards quote trust-benchmark scores without recording when they were measured, and the same number is routinely carried across successive checkpo

The Multipath Blind Spot: K-Agnostic Robust Calibration for Sparse-Anchor Metric Depth from Frozen Foundations

arXiv:2607.04101v1 Announce Type: new Abstract: Monocular depth foundations predict domain-general relative depth but lack absolute scale; a handful of sparse metric anchors from a range sensor can ca

The P^3 Dataset: Pixels, Points and Polygons for Multimodal Building Vectorization

arXiv:2505.15379v2 Announce Type: replace Abstract: We present the P^3 dataset, a large-scale multimodal benchmark for building vectorization, constructed from aerial LiDAR point clouds, high-resoluti

The problem with Anthropic's consciousness paper My last post got more attention than I expected, and the question I keep getting is some ve…

model-releasesgary-marcus--x

The problem with Anthropic's consciousness paper My last post got more attention than I expected, and the question I keep getting is some version of 'okay, so what is actually wrong with the paper?'.

The release of Fable 5 just points to the importance of agent orchestration. You really don't need Fable 5 for most tasks. You can plan with…

The release of Fable 5 just points to the importance of agent orchestration. You really don't need Fable 5 for most tasks. You can plan with Opus 4.8/Fable 5, execute with GPT-5.5, and design with GLM

model-releasesdair-ai--x

The Remarkable Effectiveness of Providing AI Agents with Natural Language Tools: A Replication Study Validating NLT Performance Across 14 Models

arXiv:2607.03953v1 Announce Type: cross Abstract: This study independently replicates and extends the Natural Language Tools (NLT) framework of Johnson et al.~(2025), which questions the use of struct

The Role of Prompt Language and Translation-Theory-Driven Prompts in Large Language Models: A Case Study on Spanish-Chinese Journalistic Translation

arXiv:2607.03160v1 Announce Type: cross Abstract: This study examines how prompt language and translation theory-driven prompt design influence the quality of Spanish-Chinese journalistic translations

The Truncation Blind Spot: How Decoding Strategies Systematically Exclude Human-Like Token Choices

arXiv:2603.18482v2 Announce Type: replace Abstract: Standard decoding strategies for text generation, including top-k, nucleus sampling, and contrastive search, select tokens based on likelihood, rest

Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens

arXiv:2602.13517v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated impressive reasoning capabilities by scaling test-time compute via long Chain-of-Thought (CoT). Howev

This is a good clip that shows a few layouts. I'm pretty happy with this, maybe the slide layout could be a bit different and I'm curious ho…

This is a good clip that shows a few layouts. I'm pretty happy with this, maybe the slide layout could be a bit different and I'm curious how it will alternate between camera cuts. That's all for now,

model-releasesthariq--x

this is a great approach, seeing this more @flymy_ai also does this when you build an agent via their api, they'll build a deterministic reu…

model-releasesyohei-nakajima--x

this is a great approach, seeing this more @flymy_ai also does this when you build an agent via their api, they'll build a deterministic reusable workflow, except for where you need models we built th

This is what stealing your data looks like

This post from Cohere likely illustrates or visualizes data theft methods, showing how personal or organizational data can be compromised or misused by bad actors. It probably demonstrates common data

model-releasescohere--x

Tile-Level Activation Overlap for Efficient LLM Inference

arXiv:2607.02521v1 Announce Type: cross Abstract: SwiGLU is the dominant MLP activation in modern large language models, yet its intermediate tensor materialization costs 9-37% of MLP execution time.

TiROD: Tiny Robotics Dataset and Benchmark for Continual Object Detection

arXiv:2409.16215v4 Announce Type: replace-cross Abstract: Detecting objects with visual sensors is crucial for numerous mobile robotics applications, from autonomous navigation to inspection. However,

TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behavior

arXiv:2512.20757v2 Announce Type: replace Abstract: Tokenizers provide the fundamental basis through which text is represented and processed by language models (LMs). Despite the importance of tokeniz

ToolFailBench: Diagnosing Tool-Use Failures in LLM Agents

arXiv:2607.04686v1 Announce Type: cross Abstract: Tool calling is central to modern language model agents, but aggregate benchmark scores often hide where tool use fails. A model that never calls a ne

Topology-Driven Transferability Estimation for 3D Medical Vision Foundation Models

arXiv:2607.04199v1 Announce Type: new Abstract: The growing number of medical vision foundation models highlights the need for effective model selection. However, mainstream selection methods rely on

Toward Efficient Agents: Memory, Tool learning, and Planning

arXiv:2601.14192v2 Announce Type: replace Abstract: Recent years have witnessed increasing interest in extending large language models into agentic systems. While the effectiveness of agents has conti

Toward Trustworthy Large Language Model Agents in Healthcare

arXiv:2607.05055v1 Announce Type: new Abstract: Healthcare appointment scheduling remains a persistent operational bottleneck, driven by manual coordination, fragmented legacy systems, and high admini

Towards Open-World Referring Expression Comprehension: A Benchmark with Training-free Multi-task Consistency Checker

arXiv:2605.25706v2 Announce Type: replace Abstract: Referring expression comprehension (REC) aims to localize a target object within an image based on a given expression. Although recent advances in v

Towards Realistic Remote Sensing Dataset Distillation with Discriminative Prototype-guided Diffusion

arXiv:2601.15829v2 Announce Type: replace Abstract: Recent years have witnessed the remarkable success of deep learning in remote sensing image interpretation, driven by the availability of large-scal

Towards Reliable Local Security Agents: Verifiable Post-Training for Linux Privilege Escalation

arXiv:2603.17673v2 Announce Type: replace-cross Abstract: LLM agents are becoming increasingly important in the security domain, but leading systems are often closed-source, cloud-based, hard to repro

Towards Standardized Light Field Quality Assessment: Hybrid Subjective Benchmarking and Objective Metric Evaluation

arXiv:2607.03494v1 Announce Type: new Abstract: Benchmarking immersive media coding solutions, especially in the standardization context, requires reliable and reproducible subjective quality assessme

Towards transferable lightweight neuromorphic computing through a model-free temporal-switch framework

arXiv:2607.02608v1 Announce Type: cross Abstract: Lightweight neuromorphic computing offers a promising route to efficient AI, with particular benefits for resource-constrained edge deployments. Howev

TRACE: Capability-Targeted Agentic Training

arXiv:2604.05336v2 Announce Type: replace Abstract: Models often fail to complete agentic tasks because they lack core capabilities required by the target environment. However, mainstream approaches f

Training-Free Model Selection and Domain-Aware Score Calibration for First-Shot Anomalous Sound Detection

arXiv:2607.04526v1 Announce Type: cross Abstract: First-shot anomalous sound detection in DCASE Challenge Task 2 must flag anomalies of unseen machine types with a single threshold, without knowing wh

Training Hybrid Block Diffusion Language Models with Partial Bidirectionality

arXiv:2607.02805v1 Announce Type: cross Abstract: High-throughput long-context generation is one of the central challenges for large language models. Generation is typically memory-bandwidth-bound rat

Transcribe Arabic is built to bring frontier capabilities to the millions of Arabic speakers in business and developer communities. Built to…

Transcribe Arabic is built to bring frontier capabilities to the millions of Arabic speakers in business and developer communities. Built to handle code-switching, multiple dialects, and Arabic-accent

model-releasescohere--x

Transformers with Physics-Informed Encodings and Simulation-Based Inference for Robust Detection of Eccentric Binary Black Holes in Pulsar Timing Array Data

arXiv:2607.03904v1 Announce Type: new Abstract: Pulsar timing arrays (PTAs) provide a unique window into nanohertz gravitational waves (GWs), but extracting astrophysical parameters from noisy, long-b

TREK: Distill to Explore, Reinforce to Refine

arXiv:2607.05339v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) is effective when the current policy already samples useful reasoning trajectories, but it stalls on hard pr

TrendFact: A Benchmark Towards Hotspot Perception in Automatic Fact-Checking

arXiv:2410.15135v5 Announce Type: replace Abstract: With the surge of online misinformation, Large Language Models (LLMs) and Reasoning Large Language Models (RLMs) serving as Automatic Fact-Checking

Triple-Phase Multimodal Knowledge Aggregation Framework for Microbial Keratitis Subtype Diagnosis on Slit-Lamp Photography

arXiv:2607.03740v1 Announce Type: cross Abstract: Microbial keratitis requires rapid pathogen identification to guide treatment, but culture- and PCR-based diagnostics are slow and resource-intensive.

TSP with Predictions: Heatmap to Tour with Provable Guarantees

arXiv:2607.03791v1 Announce Type: cross Abstract: The Traveling Salesperson Problem (TSP) has long served as a benchmark for evaluating the strength of optimization techniques in the classical theory

U-Joint CAAMS: Experimental Evaluation of a Universal-Joint Continuum Manipulator for Aerial Manipulation

model-releasesarxiv-cs-ro

arXiv:2607.03321v1 Announce Type: new Abstract: Continuum manipulators mounted on multi-rotor UAVs enable compliant aerial manipulation, but payloads and propeller downwash amplify out-of-plane bendin

Unbiased Alignment for Large Language Models with Noisy Preferences

arXiv:2607.03248v1 Announce Type: cross Abstract: The alignment of large language models with human preferences is commonly achieved through Reinforcement Learning from Human Feedback or Direct Prefer

Uncertainty-aware damage identification in short-span bridges via physics-informed variational autoencoder

arXiv:2607.05025v1 Announce Type: new Abstract: Vibration-based damage identification in civil infrastructure is a challenging, ill-posed inverse problem due to measurement noise, sparse sensor arrays

Unified Audio Intelligence Without Regressing on Text Intelligence

arXiv:2607.05196v1 Announce Type: cross Abstract: Audio intelligence involves understanding, reasoning about, and generating both audio and speech. In this work, we introduce Nemotron-Labs-Audex-30B-A

UniICL: Systematizing Unified Multimodal In-context Learning through a Capability-Oriented Taxonomy

arXiv:2603.24690v2 Announce Type: replace Abstract: In-context learning (ICL) enables fast task adaptation from demonstrations without per-task parameter updates but remains highly sensitive to exampl

UniVideo: Unified Understanding, Generation, and Editing for Videos

arXiv:2510.08377v4 Announce Type: replace Abstract: Unified multimodal models have shown promising results in multimodal content generation and editing but remain largely limited to the image domain.

Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching

arXiv:2603.27044v3 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) is widely recognized as sample-inefficient, a limitation attributable in part to the high dimensionality and