model releases

So… are we actually getting something big today? 👀

Tibo said Tuesday, GPT-6 Sol is suddenly everywhere, and now Claude Opus 5.5 leaks are blowing up too. Yet… nothing so far. 😂 At this point I’m just refreshing X like it’s a live event. What do you th

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Model Releases

SocioVerse2: A Longitudinal Dynamic Social Simulation Framework under a Human-AI Co-evolutionary Paradigm

DGX agent

arXiv:2609.24911v1 Announce Type: new Abstract: Social simulation offers the social sciences an experimental instrument that the real world cannot supply, and generative agents have transformed it by

model-releasesarxiv-cs-cl

22 Sept 2026

SolarFlowRefiner: Refinement-Aware Flow Matching for Surface Solar Radiation Downscaling

arXiv:2609.22126v1 Announce Type: cross Abstract: High-resolution surface solar radiation (SSR) is important for solar forecasting and grid operation. However, physically consistent reanalysis product

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

STA-TFM: Spatio-Temporal Aggregation Across Views TransForMer for Pose Estimation

arXiv:2609.24482v1 Announce Type: new Abstract: Monocular 3D human pose estimation (HPE) remains challenging due to depth ambiguity, occlu- sions, and the need for temporal consistency. While multi-vi

STAR: Scene- and Task-Aware 4D Radar Preprocessing Towards End-to-End Cognitive Radar

arXiv:2609.24151v1 Announce Type: new Abstract: Four-dimensional (4D) Radar has emerged as a key sensor for environmental perception, providing range, azimuth, elevation, and Doppler measurements whil

STEVE: Stabilizing Textual Gradient-Based Prompt Optimization via Error-Driven Refinement and Regularized Verification

arXiv:2609.23716v1 Announce Type: new Abstract: Textual-gradient methods automate prompt optimization through natural-language feedback, but their iterative updates can be unstable. We identify two so

Stochastic Reconfiguration as Statistical Filtering for Overparameterized Neural Quantum States

arXiv:2609.23334v1 Announce Type: cross Abstract: Stochastic reconfiguration (SR) is the standard optimizer for neural quantum states (NQS), but modern NQS often have far more parameters than Monte Ca

STOP: Structured On-Policy Pruning of Long-Form Reasoning in Low-Data Regimes

arXiv:2605.13165v2 Announce Type: replace Abstract: Long chain-of-thought (Long CoT) reasoning improves performance on multi-step problems, but it also induces overthinking. This inefficiency is espec

Streaming Deep Reinforcement Learning Finally Works

arXiv:2410.14606v3 Announce Type: replace Abstract: Learning from a stream of experience as it arrives, also known as streaming learning, is a core part of natural learning. However, reliable streamin

Subgoal Search For Complex Reasoning Tasks

arXiv:2108.11204v4 Announce Type: replace-cross Abstract: Humans excel in solving complex reasoning tasks through a mental process of moving from one idea to a related one. Inspired by this, we propos

Swiss-Knife: A Framework for Reconfigurable Externalised Multi-Objective Alignment at Decode Time

arXiv:2609.22226v1 Announce Type: new Abstract: Decode-time alignment methods steer a frozen language model by scoring candidate continuations with an external reward and selecting the maximiser. We a

t_0: A Time-Series Foundation Model for Forecasting with Context

arXiv:2609.24559v1 Announce Type: new Abstract: We present t_0, a family of open-weights foundation models for forecasting with multivariate context. We release its first two members: exttt{t0-alpha}

TACIT: Tactile Contact Supervision for Spatial Attention in Dexterous Manipulation

model-releasesarxiv-cs-ro

arXiv:2609.24507v1 Announce Type: new Abstract: Visuomotor policies trained from a few demonstrations may reproduce demonstrated trajectories without reliably following changes in object position. Exi

Taramandal-GPT: Enhancing Astrodynamics Problem-Solving with Knowledge Retrieval and Structured Thinking

arXiv:2609.24246v1 Announce Type: new Abstract: Large language models (LLMs) have shown remarkable progress in natural language understanding, yet their effectiveness in specialized fields like astron

TARGet: Topology-Aware Fusion-based Radio Frequency Circuit Functional Modeling using Graph Neural Networks

arXiv:2609.22165v1 Announce Type: new Abstract: Automatic synthesis of analog and Radio Frequency (RF) circuits is an emerging area that requires an efficient circuit modeling method. In recent years,

Team DArgk at the 2026 ELOQUENT lab for evaluating generative language model quality: Residuals of Humanity: AI Detection Evasion via GRPO Fine-Tuning

arXiv:2609.22221v1 Announce Type: new Abstract: Large language models (LLMs) can generate fluent and coherent text that is increasingly difficult to distinguish from human writing, motivating the deve

Temporal Generalization and Explanation Stability of Control Flow Graph Neural Networks for Malware Detection

arXiv:2609.24280v1 Announce Type: cross Abstract: Malware detection is a critical task in cybersecurity, and graph neural networks over control flow graphs have shown promising results for it. However

The BD-LSC Dataset: Facilitating the Benchmarking of Models for Lexical Semantic Change Detection in Slang and Standard Usage

arXiv:2606.16560v2 Announce Type: replace Abstract: Automatic semantic change detection aims to identify how word meanings shift over time, offering insights into both linguistic and societal change.

The Effect of Quantization on Clinical Benchmarks: Accuracy and Safety Across Model Families

arXiv:2609.22216v1 Announce Type: cross Abstract: Quantization enables deployment of large language models on resource-constrained clinical edge devices, but its effect on clinical accuracy and safety

The fastest PDF-to-Markdown parser just got faster. ⚡️ With LiteParse v2.14.6, text-based PDFs parse about 25% faster. Across realistic docu…

model-releasesjerry-liu--x

The fastest PDF-to-Markdown parser just got faster. ⚡️ With LiteParse v2.14.6, text-based PDFs parse about 25% faster. Across realistic documents, LiteParse processed pages at 2.8ms/page and 1.5× fast

The Price of Self-Calibration: Exact Evidence Budgets and Manufactured Blind Sets in Adaptive Monitoring

arXiv:2609.23254v1 Announce Type: new Abstract: Self-calibrating monitors adapt their threshold online to guarantee a prescribed long-run false-alarm rate under arbitrary drift. We compute the price o

The Role of AI in Online Reviews

arXiv:2609.22198v1 Announce Type: new Abstract: The rapid adoption of large language models (LLMs) creates new opportunities for strategic content generation on online platforms, including potentially

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

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

The Situated Identity Test: Distinguishing Persistent Cognitive Identity from Persona Imitation

arXiv:2609.22195v1 Announce Type: new Abstract: Large language models can convincingly adopt personas, recall past dialogues, and weave rich autobiographies. Yet this conversational eloquence conceals

The Ups and Downs of Backprop Weights

arXiv:2609.22554v1 Announce Type: new Abstract: Backpropagation (BP) has driven the remarkable success of modern deep learning by enabling large hierarchical networks to learn complex functions end-to

The Visual Target Matters: Learning across the Visual Hierarchy for Brain-to-Image Retrieval

arXiv:2609.24136v1 Announce Type: new Abstract: Brain-to-image retrieval seeks to identify the visual stimulus that elicited a non-invasive neural response. Candidate images are typically represented

This is why people who call themselves decent vote for indecent policies decade after decade after decade. Because it never hurts them. Thei…

model-releasesanthropic--x

This is why people who call themselves decent vote for indecent policies decade after decade after decade. Because it never hurts them. Theirs is a monstrous indifference, because empathy is for the l

this-that-model-1.0: A typed decision model that decides in 30 ms, for a millionth of a cent

arXiv:2609.23886v1 Announce Type: new Abstract: Software delegates more of its branches to models every year: which queue a ticket enters, whether a command is safe to run, whether a claim clears with

Time-Incremental Continued Pretraining of LLMs: Knowledge Updates Without Catastrophic Forgetting

arXiv:2609.23916v1 Announce Type: new Abstract: Large language models (LLMs) drift out of date the moment their pretraining ends, yet retraining from scratch is prohibitively expensive. Continued pret

To Consolidate or not to Consolidate? Evaluating the Impact of Consolidation in Multi-Reference Training using Peer Reviews

arXiv:2609.22805v1 Announce Type: new Abstract: Natural language generation (NLG) tasks span the spectrum of conditional entropy, ranging from highly constrained machine translation to open-ended dial

Tool-Augmented On-Policy Distillation for LLM Domain Adaptation in Sequence-Based Omics Tasks

arXiv:2609.23435v1 Announce Type: cross Abstract: Multi-omics sequences contain complex biological patterns, yet deciphering their mechanisms for automated scientific discovery remains challenging. As

Toollery: Scaling LLM Agents to Thousands of Skills and Tools

arXiv:2609.22218v1 Announce Type: cross Abstract: As LLM agents are exposed to hundreds to tens of thousands of skills, tools, and API functions, full-library prompting becomes costly, slow, and less

Toward Human-in-the-Loop Robot Failure Recovery: Bridging Communication Gaps in Human-Robot Collaboration

model-releasesarxiv-cs-ro

arXiv:2609.24055v1 Announce Type: new Abstract: Robots can recover from failures by asking bystanders for help, but effective human-in-the-loop recovery requires communication that accounts for differ

Towards Robust Classroom Attendance: A Comprehensive Evaluation of Face Detection and Recognition Models

arXiv:2609.22750v1 Announce Type: new Abstract: Manual attendance methods, such as paper or register-based systems, take a lot of time, can lead to errors, and are easy to falsify. Face recognition is

TRACE: Tractable Routing Autoencoder for Clinical ECG

arXiv:2609.23460v1 Announce Type: new Abstract: Deep learning has advanced automated electrocardiogram (ECG) diagnosis, but the field's most accurate models, foundation models pretrained on millions o

Training-Free Spectral Transductive Refinement for Cross-Domain Few-Shot Classification

arXiv:2609.23758v1 Announce Type: new Abstract: Few-shot recognition with frozen visual features is especially fragile under domain shift and one-shot supervision, where a single labelled image is an

Triggers and Diagnostics for LLM-Based Interpretability Failures in Active Inference Agents

arXiv:2609.23215v1 Announce Type: new Abstract: LLM explainers are increasingly attached to autonomous agents as runtime oversight, with operators reading a generated account of the agent's beliefs an

Tsallis Entropy Regularization for Linear Quadratic Regulator and Kullback-Leibler Control

arXiv:2403.01805v2 Announce Type: replace-cross Abstract: Shannon entropy regularization is widely adopted in optimal control due to its ability to promote exploration and enhance robustness, e.g., ma

TTSE: A Two-Track Online Self-Evolution Framework

arXiv:2609.24289v1 Announce Type: new Abstract: As Large Language Model (LLM) agents are applied in continuously interactive environments, driving the evolution of their own capabilities becomes a cor

TWIG: A Time-Causal Wavelet Operator for Autoregressive Forecasting on Irregular Graphs

arXiv:2609.22585v1 Announce Type: new Abstract: We introduce TWIG (Time-Causal Wavelet Operator for Irregular Graphs), a graph-native neural operator for autoregressive surrogate modeling on static ir

U-PEN Mamba: Progressive Expansion with Selective State-Space Modeling for Efficient Retinal Vessel Segmentation

arXiv:2609.24049v1 Announce Type: new Abstract: Accurate retinal vessel segmentation is important for computer-aided ophthalmic analysis, yet thin vessels, low contrast, and severe foreground-backgrou

UK-PRBENCH: A Paragraph-Level Precedent Retrieval Benchmark for United Kingdom Case Law

arXiv:2609.24613v1 Announce Type: cross Abstract: Prior case retrieval (PCR) aims to identify precedent cases relevant to a given query case. Existing PCR benchmarks and methods predominantly operate

UniK: Universal Knowledge Perception for Digital and Physical AI

arXiv:2609.23971v1 Announce Type: cross Abstract: Two transformative classes of AI systems are reshaping how organizations operate: extit{digital AI}, which reasons over enterprise knowledge to power

URA-NER: A Unified Retrieval-Augmented Framework with Retrieval Alignment and Uncertainty Reduction for Low-Resource NER

arXiv:2609.24372v1 Announce Type: new Abstract: In-context learning (ICL) based on large language models (LLMs) has shown promising potential in alleviating performance bottlenecks caused by the limit

Used, Mentioned, or Condemned? A Controlled Contrast-Set Diagnostic for the Use-Mention Distinction in Code-Mixed Hinglish Misogyny Detection

arXiv:2609.22261v1 Announce Type: new Abstract: Lexicon-driven misogyny detectors cannot, by construction, distinguish a slur used against a woman from the same slur mentioned in counter-speech ('don'

Validating, Not Sampling: Region-Level Robustness of Vision-Language and Vision-Language-Action Models

arXiv:2609.22293v1 Announce Type: new Abstract: Vision-language models (VLMs) and vision-language-action models (VLAs) are increasingly deployed in real-world applications. There, a small perturbation

Vast Data launches confidential computing service for sensitive workloads

model-releasessiliconangle

Vast Data Inc. today introduced DataEnclave, a confidential computing environment designed to let companies run advanced artificial intelligence models against sensitive data without exposing either t