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In case you're wondering while this steaming POS is being floated now, it's because SoftBank needs to refinance the $40B bridge loan it used…

In case you're wondering while this steaming POS is being floated now, it's because SoftBank needs to refinance the 40B bridge loan it used to invest 30B into OpenAI before it matures in March 2027...

safetygary-marcus--x

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In-Context Neurofeedback: Can LLMs Control Their Internal Representations through Privileged Access?

DGX agent

arXiv:2609.00904v1 Announce Type: new Abstract: Whether large language models (LLMs) can control their own internal representations matters for both machine metacognition and AI safety. A recent study

safetyarxiv-cs-ai

2 Sept 2026

in other words, generative AI is poised to become a complete clusterfuck.

in other words, generative AI is poised to become a complete clusterfuck. All three pillars of a safety case look about to fall. We are rather likely to have highly capable, poorly monitorable, dubiou

safetygary-marcus--x

InComeS: Integrating Compression and Selection Mechanisms into LLMs for Efficient Model Editing

arXiv:2505.22156v4 Announce Type: replace Abstract: Although existing model editing methods perform well in recalling exact edit facts, they often struggle in complex scenarios that require deeper sem

Incremental Risk Assessment of Progressive Elder Financial Scams via Instruction-Tuned Small Language Models

arXiv:2609.00005v1 Announce Type: new Abstract: Financial scams targeting older adults increasingly occur through text and voice channels such as email, SMS, and phone calls, unfolding over multiple c

Agents

Independent Reinforcement Learning in Discounted Markov Games

arXiv:2609.00504v1 Announce Type: cross Abstract: In this work, we study radically uncoupled learning in discounted general-sum Markov games. Assuming ``mathsf{ETH} for mathsf{PPAD}', we show that, fo

agentsarxiv-cs-ai

Agents

Independent verifiers improve agent output - but frontier verifiers are expensive. So we trained our own. Results on @GoogleDeepMind's FACTS…

Independent verifiers improve agent output - but frontier verifiers are expensive. So we trained our own. Results on @GoogleDeepMind's FACTS-Search: > No verifier: 89.4 (previously published SOTA) > W

agentsai21-labs--x

Applications

Individualized Algorithmic Advice as a Strategic Signal on Competitive Markets

arXiv:2511.09454v2 Announce Type: replace-cross Abstract: As algorithms increasingly mediate competitive decision-making, their influence extends beyond individual outcomes to shaping strategic market

applicationsarxiv-cs-ai

InSight: A Benchmark for Agentic Claim Verification in Interactive Visualizations

arXiv:2609.01383v1 Announce Type: new Abstract: Vision Language Models have demonstrated remarkable proficiency in interpreting static visual artifacts, but modern data analysis is inherently dynamic,

Inspicio: Open-Vocabulary, LLM-Based Sense Retrieval for Historical Languages

arXiv:2609.00998v1 Announce Type: cross Abstract: Word Sense Disambiguation has advanced rapidly for English and a handful of well-resourced modern languages, but it continues to assume the existence

Local Ai

Instance-Guided Report Anchoring for Text-Free 3D Abnormality Segmentation in Chest CT

arXiv:2609.00447v1 Announce Type: new Abstract: Accurate 3D abnormality segmentation in chest CT requires dense spatial supervision, but obtaining expert voxel-level labels is costly. Radiology report

local-aiarxiv-cs-cv

Instella-MoE Technical Report

arXiv:2609.00791v1 Announce Type: cross Abstract: In this work, we introduce Instella-MoE, a fully open Mixture-of-Experts (MoE) language model with 16 billion total parameters and 2.8 billion active

Integrating Traffic Noise Emission Modelling into Variable Speed Limit Control

arXiv:2609.01339v1 Announce Type: new Abstract: Road traffic noise remains a major environmental challenge, yet most speed management strategies are static and do not respond to short-term variations

safetyarxiv-cs-ro

Intelligent Edge Computing

arXiv:2609.00181v1 Announce Type: cross Abstract: The number of edge devices in large-scale edge systems is rapidly increasing. Edge devices have limited processing power, memory, and network bandwidt

InteractBench: Benchmarking LLMs on Competitive Programming under Unrevealed Information

arXiv:2608.29632v1 Announce Type: cross Abstract: Competitive programming is increasingly being used to evaluate the algorithmic reasoning capabilities of large language models (LLMs). However, existi

IntroConformal: Conformal Factuality Guarantees for Large Vision-Language Models via Introspective Signals

arXiv:2609.01375v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved strong multimodal performance, yet ensuring the factual correctness of generated content remains ch

Introducing Gemini 3.8 Flash, another jump in Gemini's agentic + coding capabilities, and our 3rd updated Flash model in only 6 weeks... Thi…

model-releasesfrancois-chollet--x

Introducing Gemini 3.8 Flash, another jump in Gemini's agentic + coding capabilities, and our 3rd updated Flash model in only 6 weeks... This model has been a ton of fun to work with, excited to see w

Invalidation Contracts for Cross-Episode Agent Memory

arXiv:2609.00243v1 Announce Type: new Abstract: LLM agents that cache recovery suggestions from API errors can skip re-derivation in later episodes, spending fewer tokens and fewer model calls on cons

Inverse kinematic solution for generic 3R positional robots using Conformal Geometric Algebra

arXiv:2609.00311v1 Announce Type: new Abstract: The inverse kinematics of generic 3R robots has been investigated through multiple approaches, mainly algebraic methods involving the solution of certai

researcharxiv-cs-ro

Inverse Reconstruction of Shock Time Series from Shock Response Spectrum Curves using Machine Learning

arXiv:2603.03229v3 Announce Type: replace Abstract: The shock response spectrum (SRS) is widely used to characterize the response of single-degree-of-freedom (SDOF) systems to transient accelerations.

researcharxiv-cs-lg

Applications

Inverse Rendering for Modeling with Line Primitives

arXiv:2609.00625v1 Announce Type: cross Abstract: Faithfully capturing diverse real-world objects with fuzzy, anisotropic structures, such as hair, fur, fibers, and textiles, for efficient real-time v

applicationsarxiv-cs-cv

Investigating Assistant Bias in LLM User Simulators Using a Role Vector

arXiv:2609.00608v1 Announce Type: new Abstract: LLM-based user simulators are increasingly used to evaluate autonomous agents at scale, in place of costly human evaluations. Despite this promise, thes

safetyarxiv-cs-cl

Investigating Hyperparameter Optimization and Transferability for ES-HyperNEAT: A TPE Approach

arXiv:2609.00449v1 Announce Type: cross Abstract: Neuroevolution of Augmenting Topologies (NEAT) and its advanced version, Evolvable-Substrate HyperNEAT (ES-HyperNEAT), have shown great potential in d

Investigating Linear Probe Robustness to Linguistic Register, Medical Specialty, and Corpus Shifts in Medical QA

arXiv:2609.01361v1 Announce Type: new Abstract: Linear classifiers trained on hidden states of a large language model (LLM), linear probes, can flag factual errors from a single forward pass. Geometri

iPINN for Broadband CARS Phase Retrieval: A Framework for Function Approximation and Inverse Modeling Problems in Nonlinear Spectroscopy

model-releasesarxiv-cs-lg

arXiv:2609.00883v1 Announce Type: new Abstract: Phase retrieval in broadband coherent anti-Stokes Raman spectroscopy (BCARS) is an ill-posed inverse problem. The Raman-like signal is encoded in the im

Applications

Is Knowledge Distillation Actually Greener? A Case Study in Machine Translation

arXiv:2602.09691v2 Announce Type: replace Abstract: Knowledge distillation (KD) is a technique to compress a larger teacher system into a smaller student. In machine translation, KD is commonly evalua

applicationsarxiv-cs-cl

ISO-RAG: Isoperimetric Noise Control for Retrieval-Augmented Generation

arXiv:2609.00513v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) mitigates large language models (LLMs) hallucinations, yet conventional dense retrieval struggles with the complex

It Takes Two to Match: Co-Evolving Generative Retriever with Reinforcement Learning

arXiv:2609.00638v1 Announce Type: cross Abstract: Retrieval is the first stage of modern search and advertising systems, selecting a candidate set from a large item universe for downstream ranking and

IT-TextFusion: Iterative Text-Image Interaction with Text-Guided Residual Refinement for Degradation-Aware Image Fusion

model-releasesarxiv-cs-cv

arXiv:2609.01092v1 Announce Type: new Abstract: Text-guided image fusion has recently emerged as an effective paradigm for integrating multi-modal information while enabling flexible and task-oriented

Iterative GRPO: Batch-Online Policy Iteration for Multi-Turn RL via Single-Turn RLHF

arXiv:2511.21638v2 Announce Type: replace Abstract: Practical LLM agents often operate over multi-turn conversations where success is determined only after the full interaction ends. Most multi-turn R

safetyarxiv-cs-lg

Jailbreaking Text-to-Image Models Through Cracks: Navigating Heterogeneous Safety Filters via Multi-Agent Debate

arXiv:2609.01168v1 Announce Type: new Abstract: Text-to-image (T2I) models remain vulnerable to jailbreak attacks that elicit Not-Safe-For-Work (NSFW) content, despite increasingly being guarded by he

safetyarxiv-cs-ai

JanusMesh: Fast and Zero-Shot 3D Visual Illusion Generation via Cross-Space Denoising

arXiv:2606.20563v2 Announce Type: replace Abstract: Creating 3D visual illusions, a single 3D mesh that reveals entirely different semantics from various viewing angles, is a fascinating but tough cha

safetyarxiv-cs-cv

Joint Training Is Not Enough: Conditioned Cross-Granularity Training for Multimodal Document Understanding

arXiv:2609.00756v1 Announce Type: new Abstract: The Mutual Reinforcement Effect (MRE) asks whether a fine, span-level and a coarse, document-level task help each other when one model handles both. We

Keep Everyone Happy: Online Fair Division of Numerous Items with Few Copies

arXiv:2408.12845v3 Announce Type: replace-cross Abstract: This paper considers a novel variant of the online fair division problem involving multiple agents in which a learner sequentially observes an

safetyarxiv-cs-ai

KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering

arXiv:2511.04093v2 Announce Type: replace Abstract: Large language models (LLMs) excel at reasoning but struggle with knowledge-intensive questions due to limited context and parametric knowledge. How

KItCAT: Knowledge Injection via Input Corruption for Auto-regressive Training

arXiv:2609.00082v1 Announce Type: cross Abstract: LLMs acquire vast amounts of knowledge during pre-training, but often lack the specialized knowledge needed to answer questions from niche sources suc

Knowing When to Stop: Adaptive Action Chunking via Internal Cross-Attention Dynamics in VLAs

arXiv:2609.00908v1 Announce Type: new Abstract: Action chunking is a standard execution strategy in modern Vision-Language-Action (VLA) frameworks, but fixed execution horizons impose a trade-off betw

safetyarxiv-cs-ro

Knowledge Distillation During Mid-Training Favors Reasoning over Factual Recall

arXiv:2609.01532v1 Announce Type: new Abstract: Logit-based knowledge distillation (KD) is used to train smaller language models (LMs) via supervision from stronger teachers, but whether its benefits

Lagged Coupling: Internal Representations Become Readable Before They Become Causal

arXiv:2609.01048v1 Announce Type: cross Abstract: Across the full Pythia suite (160M-12B, eight checkpoints, four task families), a linear probe can read a target variable from the residual stream as

Lasso Security launches CPU-only AI guardrail, raises $30M

model-releasessiliconangle

Artificial intelligence security startup Lasso Security Ltd. today launched a guardrail for AI applications that runs on ordinary server processors, with no graphics chip required. The product is call

Late Transformer Layers Recode Syntax Canonically: Evidence from Greek Scrambling and Cross-Layer Generalisation

arXiv:2609.00416v1 Announce Type: new Abstract: Probing studies have established that syntactic information is decodable in early and middle transformer layers, but what happens to that information in

Latent Mechanisms of Language Control in Multilingual Language Models

arXiv:2609.00325v1 Announce Type: new Abstract: Multilingual large language models can exhibit unintended code-switching -- unnecessarily alternating between languages during generation. We present a

Latent Recurrent Thoughts: Recurrent Refinement of Proposed Latents for Reasoning with Frozen LLMs

arXiv:2609.01117v1 Announce Type: new Abstract: Chain-of-thought reasoning unfolds in discrete token space: each step is committed as text, errors propagate, and eliciting good traces presupposes trac

Latent-Space No-Arbitrage Geometry of Generative Models for Implied Volatility Surfaces

arXiv:2609.00332v1 Announce Type: cross Abstract: Generative models for implied volatility surfaces must produce outputs that satisfy static no-arbitrage constraints. We study these constraints in lat

LatentPress: Context Compression Beyond Text and Vision

arXiv:2609.01507v1 Announce Type: cross Abstract: Compressed context is usually carried as human-readable text or as rendered images that must be decoded, even when its consumer is a language model. W

LEAP: Likelihood Elicitation and Aggregation for LLM-based Probabilistic Forecasting

arXiv:2609.01337v1 Announce Type: new Abstract: LLM-based forecasting systems have improved on real-world tasks such as financial markets and sports outcomes, largely through stronger search and tool

Learning Sparse Decision Trees via Transformer Variational Auto-Encoders

arXiv:2609.01430v1 Announce Type: cross Abstract: Decision trees are among the most widely used models in machine learning, largely due to their transparent decision logic, making them well-suited for

Learning Task-Specific Antibody Representations via Function-Aware Masking

model-releasesarxiv-cs-lg

arXiv:2609.00518v1 Announce Type: new Abstract: Antibody-specific language models pretrained via masked language modeling (MLM) learn representations that are critical for downstream sequence design a