CompanyAnthropic8 recent entries10 Aug 2026Same physical state, different collective dynamics: state encodings select synchronization outcomes in language-model agentsarXiv:2608.06968v1 Announce Type: cross Abstract: Language-model agents act on state encodings of their environment, yet these are treated as interchangeable interfaces. Using pretrained language mode→13 Aug 2026Mechanist: AI as a Scientific Instrument for Discovering the Mechanisms of IntelligencearXiv:2608.12036v1 Announce Type: new Abstract: AI models have achieved remarkable success across diverse domains, yet the mechanisms underlying their capabilities and the risks they may pose remain p
CompanyOpenAI8 recent entries24 Aug 2026Rigorous Evaluation of Large Language Models for Malaria Drug Discovery: Trade-offs in Performance, Scale, and Resource UtilityarXiv:2608.20418v1 Announce Type: cross Abstract: We introduce Malaria-Instruct, a curated instruction-following dataset derived from the ChEMBL Legacy Malaria corpus for Malaria virtual screening, an→28 Aug 2026The BS-meter: Detecting Politics and Labour through ChatGPT's LanguagearXiv:2411.15129v3 Announce Type: replace-cross Abstract: What can we learn about language from studying how it is used by ChatGPT and other large language model (LLM)-based chatbots? In this paper, w→4 Sept 2026Investigating the Ability of Large Language Models to Analyze Recipes for DiabetesarXiv:2609.03967v1 Announce Type: cross Abstract: Several studies have evaluated the ability of Large Language Models (LLMs) for meal planning, yielding positive outcomes. These models can process nat→11 Sept 2026BruNet: A Cross-Domain Transfer Framework for Bruise SegmentationarXiv:2609.11463v1 Announce Type: new Abstract: Segmenting bruises is a challenging task in medical imaging due to limited data and annotations, diffuse boundaries, and highly variable appearance. In →17 Sept 2026Disentangling Algorithmic Bias from Archival Artifacts: A Controlled Audit of Vision-Language Model Valuation in Metropolitan Museum ArchivesarXiv:2609.17572v1 Announce Type: new Abstract: Auditing vision-language models (VLMs) for societal bias requires distinguishing direct algorithmic valuation disparities from confounders embedded with→5 Oct 2026Mitigating Private Data Leakage in LLMs with WhiteoutarXiv:2610.02418v1 Announce Type: cross Abstract: Modern large language models (LLMs) are trained on massive, largely unfiltered datasets, including content scraped from nearly every accessible websit→7 Oct 2026Semantic Capability Acquisition and Specialization During Vision-Language Model Fine-TuningarXiv:2610.07385v1 Announce Type: new Abstract: Fine-tuning vision-language models (VLMs) is typically evaluated at a single downstream checkpoint, obscuring whether a semantic capability was never ac→7 Oct 2026No Transformer Beats Six Covariates: Long-Horizon Prediction of Depressive Symptoms from Childhood EssaysarXiv:2610.07764v1 Announce Type: cross Abstract: Natural language processing (NLP) models can detect depression-related language in text written near the time symptoms are measured, but whether pretr
CompanyGoogle8 recent entries20 Aug 2026CL4D: Contrastive Language-4D Pretraining for Vision-Language Reasoning in Dynamic ScenesarXiv:2608.18734v1 Announce Type: new Abstract: 4D understanding and reasoning is a fundamental capability for embodied AI agents operating in dynamic physical environments. However, existing vision e→24 Aug 2026Rigorous Evaluation of Large Language Models for Malaria Drug Discovery: Trade-offs in Performance, Scale, and Resource UtilityarXiv:2608.20418v1 Announce Type: cross Abstract: We introduce Malaria-Instruct, a curated instruction-following dataset derived from the ChEMBL Legacy Malaria corpus for Malaria virtual screening, an→9 Sept 2026TrojanWorld: Backdooring World-Model Agents via Imagination SteeringarXiv:2609.07051v1 Announce Type: new Abstract: World models increasingly serve as the predictive core of model-based reinforcement learning agents, enabling them to simulate future dynamics and reaso→10 Sept 2026Alignment Whack-a-Mole : Finetuning Activates Verbatim Recall of Copyrighted Books in Large Language ModelsarXiv:2603.20957v4 Announce Type: replace-cross Abstract: Frontier LLM companies have repeatedly assured courts and regulators that their models do not store copies of training data. They further rely→25 Sept 2026Robo-Harness K1: Harnessing Robot-Use Agents via Perception AugmentationarXiv:2609.29389v1 Announce Type: new Abstract: Foundation vision-language models (VLMs) understand objects, instructions, and spatial relations, yet translating this capability into robotic manipulat→28 Sept 2026StarWM: Self-Supervised Trained Attention Routing for Robust World ModelsarXiv: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→30 Sept 2026Who Warmed the Archives? LLMs Overestimate Historical WarmtharXiv:2609.37499v1 Announce Type: new Abstract: Historical archives are an under-used source for extending the instrumental climate record backward in time, and LLMs offer a way to extract the indices→30 Sept 2026AutoMark: Enabling Autoresearch to Discover Better LLM WatermarksarXiv:2609.37310v1 Announce Type: cross Abstract: With LLM watermarking being deployed commercially and now required by regulations, improving its reliability and effectiveness has become crucial. Yet
CompanyMeta8 recent entries6 Oct 2026Robust Parameter-Efficient LLM Adaptation on Analog HardwarearXiv:2610.05318v1 Announce Type: new Abstract: Analog in-memory computing is a promising platform for on-device execution of large language models because it performs matrix--vector multiplications (→6 Oct 2026Clean: Second-order LLM Training at Linear Memory Cost via Nystrom SketchingarXiv:2610.04204v1 Announce Type: new Abstract: Training large language models (LLMs) entails a fundamental trade-off: memory-efficient optimizers such as Adam discard cross-parameter curvature, where→7 Oct 2026Principled Under Pressure: Post-Training Decides Whether LLMs Act on Their Own Moral JudgmentarXiv:2610.08670v1 Announce Type: cross Abstract: Language models increasingly act as agents. An agent that says an action is wrong and then takes it anyway is a different failure from one that does n→8 Oct 2026LRCC: Generalizing Low-Rank Compression with Conditional ComputationarXiv:2610.08858v1 Announce Type: new Abstract: Low-rank compression reduces the cost of pretrained language models by replacing linear transformations with low-rank factorizations. However, conventio→9 Oct 2026Rounding in Preconditioner Space: Redesigning 4-bit AdamW Optimizer-State QuantizationarXiv:2610.12444v1 Announce Type: new Abstract: Quantizing AdamW's optimizer states reduces persistent storage, but quantization errors propagate through the moment recurrences and perturb subsequent →9 Oct 2026Amortized Off-Policy Evaluation for LLMsarXiv:2610.10848v1 Announce Type: new Abstract: Accurate evaluation is central to selecting which LLM to deploy, yet testing a candidate on live traffic exposes real users to an unvetted model. Teams →12 Oct 2026Weight Clustering Reveals the Importance of Relative Rank in Large Language ModelsarXiv:2603.17917v2 Announce Type: replace-cross Abstract: Large language models (LLMs) contain billions of parameters, but many exact weight values are not essential. We apply scalar K-means clusterin→12 Oct 2026Byzantine-Resilient Federated Large Language Model via Hypergraph Message PassingarXiv:2610.12711v1 Announce Type: new Abstract: Federated Large Language Models (FedLLMs) are promising to deliver privacy-preserving, collaborative intelligence. The decentralized training process is
CompanyMistral8 recent entries23 Jul 2026The Blessing of Dimensionality: How Near-Orthogonality in High-Dimensional Spaces Explains Temporal PortabilityarXiv:2607.20301v1 Announce Type: cross Abstract: Fine-tuning has been widely used to adapt large language models (LLMs) for domain-specific tasks. Parameter efficient fine-tuning (PEFT) methods such →24 Aug 2026Rigorous Evaluation of Large Language Models for Malaria Drug Discovery: Trade-offs in Performance, Scale, and Resource UtilityarXiv:2608.20418v1 Announce Type: cross Abstract: We introduce Malaria-Instruct, a curated instruction-following dataset derived from the ChEMBL Legacy Malaria corpus for Malaria virtual screening, an→1 Sept 2026Test-Time Training with Next-Token PredictionarXiv:2606.21803v2 Announce Type: replace Abstract: Next-token prediction is the self-supervised signal that trains language models, and every observed prompt token provides the same signal at test ti→4 Sept 2026Investigating the Ability of Large Language Models to Analyze Recipes for DiabetesarXiv:2609.03967v1 Announce Type: cross Abstract: Several studies have evaluated the ability of Large Language Models (LLMs) for meal planning, yielding positive outcomes. These models can process nat→15 Sept 2026Towards a Mechanistic Understanding of Propositional Logical Reasoning in Large Language ModelsarXiv:2601.04260v2 Announce Type: replace Abstract: Understanding how Large Language Models (LLMs) perform logical reasoning internally remains a fundamental challenge. While prior mechanistic studies→21 Sept 2026Stiefel-AdamW: Geometry-Aware AdamW for Linear Factorization BlocksarXiv:2609.21039v1 Announce Type: new Abstract: A pervasive structural pattern in modern deep learning is the linear factorization block: a submodule of the form W = BA in which two parameter matrices→29 Sept 2026From Attention Sensitivity to Layer Role: Revisiting Mixed-Precision Quantization of TransformersarXiv:2609.34866v1 Announce Type: new Abstract: Most post-training quantization pipelines fit each weight matrix to its pretrained counterpart, one matrix at a time. Whether that proxy tracks what an →1 Oct 2026DataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language ModelsarXiv: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
CompanyxAI5 recent entries28 Apr 2026DenoGrad: A Gradient-Based Framework for Data Refinement in Tabular and Time-Series LearningarXiv:2511.10161v2 Announce Type: replace Abstract: In the Data-Centric Artificial Intelligence (AI) paradigm, improving data quality is essential for robust machine learning. However, many denoising →16 Jul 2026Anatomically Faithful but Temporally Blind: Auditing Attribution for Left-Ventricular Ejection-Fraction Estimation from EchocardiographyarXiv:2607.13738v1 Announce Type: cross Abstract: Background and Objective: Deep video models estimate left-ventricular ejection fraction (EF) from echocardiography with near-expert accuracy, and post→30 Jul 2026Comparing the Performance of Foundation Model Derived Embeddings with Traditional Approaches for Distant Metastasis Prediction in Head and Neck CancerarXiv:2607.26276v1 Announce Type: new Abstract: Background: Early prediction of distant metastasis (DM) risk in head and neck cancer (HNC) can enable timely interventions that may improve treatment ou→10 Aug 2026Post-Grokking Collapse at the Representation-Readout Interface in Muon-Trained TransformersarXiv:2608.07436v1 Announce Type: new Abstract: Under the standard split, Muon gets hidden matrices and AdamW embeddings/output head. Muon groks modular addition faster, but its solutions do not hold.→19 Aug 2026AppendiGrade: An XAI-Enhanced Deep Learning Framework for Grading Appendicitis in Ultrasound with Gaussian Blur and Grad-CAMarXiv:2608.17923v1 Announce Type: new Abstract: Appendicitis is one of the most common abdominal emergencies worldwide and requires prompt diagnosis and treatment to prevent life-threatening condition
CompanyDeepSeek8 recent entries2 Sept 2026LLMPEDIA: Browsing, Verifying, and Comparing the Parametric Encyclopedic Knowledge of LLMsarXiv:2609.01182v1 Announce Type: new Abstract: Flagship language models appear saturated on benchmarks like MMLU (Hendrycks et al., 2021), scoring above 90% - yet benchmarks test only what the experi→10 Sept 2026Alignment Whack-a-Mole : Finetuning Activates Verbatim Recall of Copyrighted Books in Large Language ModelsarXiv:2603.20957v4 Announce Type: replace-cross Abstract: Frontier LLM companies have repeatedly assured courts and regulators that their models do not store copies of training data. They further rely→15 Sept 2026MoARa: Module-Aware Rank Allocation and Structure-Preserving Decomposition for Low-Rank LLM Pre-trainingarXiv:2609.15037v1 Announce Type: cross Abstract: Low-rank gradient projection reduces the optimizer-state memory cost of large language model (LLM) pretraining, but the steps and wall-clock time need→18 Sept 2026DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache CompressionarXiv:2609.19969v1 Announce Type: new Abstract: The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost→22 Sept 2026An Unexpected Robot Policy: Early Evaluations of GPT-6 Astra on RoboDojo and BeyondarXiv:2609.24170v1 Announce Type: new Abstract: Embodied AI systems are often organized into System 1 and System 2. System 1 is typically a pretrained policy that generates actions at high frequency, →30 Sept 2026Who Warmed the Archives? LLMs Overestimate Historical WarmtharXiv:2609.37499v1 Announce Type: new Abstract: Historical archives are an under-used source for extending the instrumental climate record backward in time, and LLMs offer a way to extract the indices→2 Oct 2026Match the Distribution, Not the Compute: Post-Training Multi-Token Prediction HeadsarXiv:2610.00888v1 Announce Type: cross Abstract: Multi-token prediction (MTP) improves the throughput of autoregressive generation by enabling the language model to draft multiple next tokens per for→12 Oct 2026Knowing Is Not Telling: Identity Ossification in LLM Representations of Trans-border CommunitiesarXiv:2610.12610v1 Announce Type: cross Abstract: Ethnic and cultural identities rarely align with political borders, a tension made sharper by migration and long-standing cross-border ties. Communiti
CompanyNVIDIA8 recent entries29 Sept 2026Approximating Softmax in Pretrained LLMs: Model Sensitivity and Kernel AccelerationarXiv:2609.33586v1 Announce Type: new Abstract: On NVIDIA Blackwell B200, tensor-core throughput outpaces special-function exponential throughput by more than two orders of magnitude, exposing exponen→1 Oct 2026Sparse-WAM: Accelerating World Action Models via Action-Guided Sparse ImaginationarXiv:2609.38984v1 Announce Type: cross Abstract: World-action models (WAMs) leverage pretrained video models to improve generalization in robot control by jointly predicting future visual states and →1 Oct 2026DiffWAM: A Fast and Efficient Navigation World Action ModelarXiv:2609.39763v1 Announce Type: cross Abstract: Pretrained video foundation models encode rich semantic and spatiotemporal priors for embodied navigation, yet converting these priors into UAV motion→2 Oct 2026Learning Rate Transfer for Hybrid Transformer-SSM ArchitecturesarXiv:2610.01172v1 Announce Type: new Abstract: We study learning rate (LR) scaling for hybrid architectures combining Transformer and State-Space Model (SSM) blocks, a class adopted by several recent→2 Oct 2026Faynt: Scaling and Optimizing Policies for Competitive MeleearXiv:2610.02144v1 Announce Type: new Abstract: We introduce Faynt, a family of 10M- and 75M-parameter Transformer policies for Super Smash Bros. Melee, each controlling all 26 characters with a singl→2 Oct 2026Fast Polynomial Transcendentals for LLMsarXiv:2610.00049v1 Announce Type: new Abstract: Graphics processing unit (GPU) generations scale matrix, special-function, and memory pipelines at different rates, so kernel bottlenecks move as hardwa→6 Oct 2026Vision Transformer Ensembles for Panoramic Street SegmentationarXiv:2610.06063v1 Announce Type: new Abstract: Semantic segmentation of street panoramas can support detailed descriptions of urban environments, yet small datasets and unequal training costs make mo→7 Oct 2026Enhancing Diffusion Language Models with Autoregressive Post-Training WeightsarXiv:2610.08108v1 Announce Type: new Abstract: Diffusion language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) language models, offering flexible token-update orders