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Inline Memory Meets Reusable Skills: Memory-centric Framework for Vision-Language-Action Model

arXiv:2609.39794v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown strong promise for general-purpose robotic manipulation, yet adapting them to new tasks and domains remai

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Model Releases

Instruction Retrieval at Inference Time for Small Language Models

DGX agent

arXiv:2510.13935v3 Announce Type: replace-cross Abstract: The facts a language model stores are tied to its parameter count, so small models that fit on edge devices fail on expert problems, which nee

model-releasesarxiv-cs-ai

1 Oct 2026

Introducing Agent Droplets: everything an agent needs, one price, one bill

model-releasesdigitalocean

Last week we launched DigitalOcean Managed Agents in public preview: a managed runtime, governed access to more than 16,000 tools, serverless inference, and persistent memory and storage, all on one c

Introducing the Server Side Cloud Swift SDK

model-releasesgoogle-cloud-ai

For years, Swift was perceived mainly as a UI language tied to Apple client devices. With the release of Swift 6 and strict concurrency checking, Swift has matured into a premier systems with cloud en

Is Weight Tying Still Beneficial for Decoder-Only LLMs in Private Settings Under DP-SGD?

arXiv:2609.40335v1 Announce Type: new Abstract: Differentially Private Stochastic Gradient Descent (DP-SGD) is a leading approach for privacy-preserving fine-tuning of large language models (LLMs). Ma

Jacobian Rank Collapse in Decision-Focused Learning

arXiv:2609.39261v1 Announce Type: new Abstract: Decision-focused learning (DFL) trains predictors through downstream objectives, but a different loss need not provide an independent parameter-update d

Janus: Evidence-Before-Effect Sagas and Offline-Verifiable Provenance for Agentic LLMs

arXiv:2609.38266v1 Announce Type: cross Abstract: Agentic large language models (LLMs) now move money through tools, yet the record of what they did is usually a trace their own process emits beside t

JARQ: Joint Alternating Refinement for Quantization

arXiv:2609.38599v1 Announce Type: new Abstract: Group-wise post-training quantizers for large language models round weights onto a grid that is not refit to the resulting integer codes. We show that t

Jeff-Qwen3.5-0.8B v1.2 + 9 LoRA adapters: put it in front of Qwen3.8-27B for 38× faster decisions and +8.7 points accuracy, for under 2 GB extra memory

model-releasesr-localllama

A few days ago I released Jeff-Qwen3.5-0.8B, a small 'System 1' model that picks between options you define and returns a calibrated probability for each, in one forward pass. Speed was great on my M4

KilometerVision: A New Frontier for Large-Scale Spatial Intelligence in VLMs

arXiv:2609.39588v1 Announce Type: new Abstract: We push the frontier of large-scale spatial intelligence in Vision-Language Models (VLMs) and introduce the first benchmark that probes geographical lay

KlinikeBench: Evaluating Language Models Beyond Diagnostic Accuracy

arXiv:2609.38480v1 Announce Type: cross Abstract: Most clinical benchmarks evaluate language models (LMs) on diagnosis using complete case descriptions. In clinical practice, however, patients present

KVEraser: Learning to Steer KV Cache for Efficient Localized Context Erasing

arXiv:2606.17034v3 Announce Type: replace Abstract: Post-hoc context erasing over the KV cache is challenging because a local edit has a global consequence: once a span has been processed, its influen

LampAttention: Look-Ahead Mixed-Precision FlashAttention for Dedicated Accelerators

arXiv:2609.39361v1 Announce Type: new Abstract: While most attention logits can be computed in low precision without degrading numerical stability, current attention kernels fail to exploit this pheno

Language-Conditioned World Modeling for Visual Navigation

arXiv:2603.26741v2 Announce Type: replace-cross Abstract: Goal-conditioned visual navigation has been a long-standing testbed for embodied AI. We study a natural language-conditioned variant, language

LARC: Low-Rank Adaptive Residual Connections for Learning in Frozen Models

arXiv:2609.40063v1 Announce Type: cross Abstract: Low-Rank Adaptive Residual Connections (LARC) give a frozen model a compact numerical state that can learn from feedback. The map h+BAh adds a low-ran

Large Language Models are Approximate Survival Estimators

arXiv:2609.38181v1 Announce Type: new Abstract: Survival analysis estimates time-to-event outcomes from patient covariates and is widely used for medical risk assessment. Patients seeking prognostic i

LeanPolish: Verified Supervision for Lean Proof Compression

arXiv:2609.38384v1 Announce Type: new Abstract: Verified proof edits offer a natural source of supervision for improving language-model-generated Lean proofs. Yet verification establishes that an edit

Learning Chaos Without Seeing Chaos: Extrapolation of Global Dynamics in Autoregressive Transformers

arXiv:2609.38814v1 Announce Type: new Abstract: Autoregressive models are trained to predict a system's behavior one step at a time, and recursive generation allows the learned dynamics to unfold over

Learning from Research: Toward Lifelong Agent Harness Evolution

arXiv:2609.40169v1 Announce Type: new Abstract: Language agents are expected to solve increasingly complex tasks, creating a growing need for continual improvement. One promising approach is to evolve

Learning from Runtime Feedback through Failure-Bank Self-Evolution for Vision-Language-Action Models

arXiv:2609.39820v1 Announce Type: cross Abstract: Vision-language-action (VLA) models generalize broadly across robotic manipulation tasks, but complex environments require balancing task success with

Learning Functional Subspaces for Neural Network Compression

arXiv:2609.40127v1 Announce Type: cross Abstract: Modern transformers pair impressive capabilities with substantial memory and compute demands. Low-rank weight factorization reduces both while keeping

Learning Reliable GUI Agents under Imperfect Priors

arXiv:2609.39547v1 Announce Type: new Abstract: GUI agents built on large language and vision-language models still struggle on unseen applications and complex multi-step tasks, as completing real GUI

Learning to Build: Autonomous Robotic Assembly of Stable Structures Without Predefined Plans

arXiv:2602.23934v2 Announce Type: replace-cross Abstract: This paper presents a novel autonomous robotic assembly framework for constructing stable structures without relying on predefined architectur

Learning to Route in Visual Space via Multi-Step Embedding Retrieval

arXiv:2609.38743v1 Announce Type: new Abstract: LLM agents rely on retrieval tools to access external knowledge, yet visual agentic search remains severely bottlenecked by standard single-step retriev

Learning Under Forgetting: Statistical Support-Selective Retention in Stochastic Training Dynamics

arXiv:2609.38768v1 Announce Type: cross Abstract: Prior work has shown that neural networks exhibit implicit biases toward low-complexity structure (e.g., spectral bias), memorization dynamics, and co

Lens Flare Removal and Reconstruction

arXiv:2609.39527v1 Announce Type: new Abstract: The presence of lens flares in images can significantly reduce the quality of downstream application results for tasks such as 3D scene reconstruction.

Less Data Approximates More: Earning Faithful Confidence in High-Stakes Domains

arXiv:2604.08454v2 Announce Type: replace Abstract: Large language models are increasingly deployed in high-stakes domains, where confident yet incorrect inferences may cause severe real-world harm, b

LIBERO-Agent: Evaluating General-Purpose Agents for Direct Embodied Manipulation

model-releasesarxiv-cs-ro

arXiv:2609.39507v1 Announce Type: new Abstract: General-purpose agents can plan, use tools, and revise their behavior from feedback, but it remains unclear whether these capabilities transfer from dig

Linguistic Loopholes in LLM Unlearning: From a 174-Language Benchmark to Coverage-Aware Unlearning

arXiv:2609.40286v1 Announce Type: cross Abstract: Unlearning a fact in one language does not guarantee its removal in others as changing the query or even the requested answer language can reopen seem

Listening to the Wise Few: Query-Key Alignment Unlocks Latent Correct Answers in Large Language Models

arXiv:2410.02343v2 Announce Type: replace Abstract: Large language models (LLMs) routinely fail to output the correct option in multiple-choice question answering (MCQA) while encoding the answer inte

LLM Persona Unlearning

arXiv:2609.39882v1 Announce Type: new Abstract: Pre-training equips large language models (LLMs) with a broad repertoire of behavioral patterns associated with roles, styles, values, and goals. Post-t

LOCI: Spatial Linear Memory for Streaming World Models

arXiv:2609.40222v1 Announce Type: new Abstract: When a camera revisits a previously observed region, a video world model should reproduce what was there before. This requires both remembering past obs

LongEmo: Towards Emotion Understanding and Reasoning in Long Videos

arXiv:2609.40079v1 Announce Type: cross Abstract: While recent Multimodal Large Language Models (MLLMs) have shown promise in affective computing, their reasoning capabilities are largely confined to

Looped Diffusion Transformer

arXiv:2609.40305v1 Announce Type: new Abstract: Improving text-to-image models has traditionally relied on increasing model size or the number of denoising steps. In this work, we explore an alternati

Lowest Span Confidence: Zero-Shot Hallucination Detection from a Single LLM Response

arXiv:2601.19918v2 Announce Type: replace Abstract: Hallucinations in Large Language Models (LLMs), i.e., plausible but non-factual generations, pose a significant challenge to reliable deployment in

MADBench: Benchmarking the Security of Multi-Agent Debate

arXiv:2609.39146v1 Announce Type: new Abstract: Multi-agent debate (MAD) can improve large language model (LLM) reasoning by allowing multiple agents to exchange and critique their answers to the same

Make Code as Policy Great Again: Frontier Agents Write, Call, and Evolve Robot Tools

arXiv:2609.39018v1 Announce Type: cross Abstract: Frontier models can control robots, but reasoning through every reach, grasp, and retreat makes manipulation slow and token-intensive. We revisit code

Marginal Response Surface Elicitation for Zero-Label Tabular Learning

arXiv:2609.39639v1 Announce Type: new Abstract: Tabular learning uses structured data to predict target outcomes. Traditionally, this process has relied on labeled data. However, large language models

MASCRDM: Multi-Agent System for Compliance Risk Detection and Mitigation in Training Process of Large Language Models

arXiv:2609.39107v1 Announce Type: new Abstract: Large Language Models (LLMs) have been applied in various fields. However, ensuring compliance and safety of LLMs, such as avoiding discrimination and b

MatLoom: Layered Text-to-Material Generation in a Compact Program Space

arXiv:2609.40322v1 Announce Type: cross Abstract: Material generation should produce not only an appearance, but also the rules that construct it. We introduce MatLoom, a compact, layer-oriented langu

Measuring Structure in Graph Benchmark Datasets Using Graph Invariants

arXiv:2605.06462v2 Announce Type: replace Abstract: Progress in graph learning is hindered by benchmark practices that conflate the contributions of node features and graph structure, making it hard t

MedKIT: Evaluating Knowledge Integration and Generalization in Large Language Models

arXiv:2609.38543v1 Announce Type: new Abstract: Constantly evolving real-world knowledge necessitates models to be updated continuously. Especially in medicine, as clinical evidence changes over time,

MedRECT: A Bilingual Medical Reasoning Benchmark for Error Correction in Clinical Texts

arXiv:2511.00421v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show promise in medical applications, but their ability to detect and correct errors in clinical texts remains un

MetaPersona: Task-Grounded Synthetic Populations from Empirical Social Science

arXiv:2609.38392v1 Announce Type: new Abstract: Personas used to seed LLM social simulations face a cold-start problem: existing methods lack a principled basis for deciding which attributes to includ

Metaview wants to eliminate recruiting grunt work and speed up hiring after raising $60M

model-releasessiliconangle

Artificial intelligence recruiting software startup Metaview Labs Inc. said today the industry is ready for a “Claude Code moment” after it closed on 60 million in a Series C funding. The round was le

Miami-based Doxx.net, whose platform enables serverless P2P calling, messaging, and file transfers for humans and AI agents, raised a $38M Series A led by a16z (Chris Metinko/Axios)

Chris Metinko / Axios: Miami-based Doxx.net, whose platform enables serverless P2P calling, messaging, and file transfers for humans and AI agents, raised a 38M Series A led by a16z — Doxx.net has rai

model-releasestechmeme

Mid-Harness: Scaling Actions Between Model and Harness for Terminal Agents

arXiv:2609.39982v1 Announce Type: cross Abstract: Terminal agents act through stochastic model generations, yet the ability to generate a useful action does not ensure its reliable execution. A poor c

MM-FinEval: A Multi-Task Multimodal Benchmark for Real-World Financial Forecasting

arXiv:2609.38523v1 Announce Type: cross Abstract: Financial forecasting from earnings conference calls requires models to reason over complex corporate disclosures, market expectations, and subtle com