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IronLLM: Forging Compact Edge-Native Language Models for Real-Time Embodied Intelligence

arXiv:2609.36860v1 Announce Type: new Abstract: We present IronLLM-0.6B, a 654M-parameter language model designed for efficient on-device inference. IronLLM-0.6B combines a hybrid attention architectu

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Is Agent Code Less Maintainable Than Human Code?

DGX agent

arXiv:2606.21804v2 Announce Type: replace-cross Abstract: Maintainability is a core dimension of software engineering, shaping how code is written, reviewed, and developed over time. While coding agen

agentsarxiv-cs-ai

30 Sept 2026

Is Human-Readable Text Necessary for Effective LLM Fine-Tuning?

arXiv:2609.35868v1 Announce Type: new Abstract: Is human readability necessary for effective fine-tuning of large language models? We investigate whether model-conditioned training representations can

Is manual software optimization a thing of the past?

arXiv:2609.37849v1 Announce Type: cross Abstract: Scientific software is increasingly required to process larger datasets while maintaining acceptable execution times. Software optimization traditiona

agentsarxiv-cs-ai

Iterative Exact Discrete Guidance for Energy-Based Sampling

arXiv:2609.37043v1 Announce Type: new Abstract: Sampling from unnormalized distributions over large discrete state spaces becomes difficult when a multimodal target is far from a tractable reference.

tutorialsarxiv-cs-lg

It's All Training: A Fully Synthetic Single-Stage Recipe for LLMs

arXiv:2609.37891v1 Announce Type: new Abstract: Current pre-training datasets are derived from web crawls, with all their issues, and were not designed to support mid- and post-training pipelines--for

researcharxiv-cs-cl

It's Not What the Image Shows: Irrelevant Context Destabilises VLM Judges Without Informing Them

arXiv:2609.37863v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used in place of human annotators, making it important that substitutability tests reflect the model ra

Jaxolotl: A Unified High-Performance Benchmark Suite for LTL-Based Multi-Task RL

arXiv:2609.38065v1 Announce Type: cross Abstract: Training agents to follow arbitrary instructions is an important goal of multi-task reinforcement learning (RL). Linear temporal logic (LTL) provides

JEV-as-a-Judge: Accept When Confident, Escalate When Unsure

arXiv:2609.26550v3 Announce Type: replace Abstract: LLM-as-a-judge scales evaluation, but reasoning judges are slow and costly. We study JEV-as-a-Judge: evaluation with JEV, a decision-only judge that

JointEdit3D: Feed-Forward 3D Scene Editing in a Unified Latent Space

model-releasesarxiv-cs-cv

arXiv:2606.13345v2 Announce Type: replace Abstract: Existing 3D scene editing methods typically rely on per-scene optimization over explicit 3D representations or cascaded edit-and-reconstruct pipelin

JudgeCast: Time Series Forecasting with Experience-Informed Covariate Judgements

arXiv:2609.36966v1 Announce Type: cross Abstract: Covariate effects vary across contexts and shift over time, requiring forecasters to assess how to use them for each forecasting context. As forecasti

tutorialsarxiv-cs-ai

JudgeProfile: Understanding and Steering Subjectivity in LLM Judges

arXiv:2609.36705v1 Announce Type: new Abstract: LLM judges are inherently subjective, often favoring different responses in pairwise comparison when neither option is objectively wrong. To study this

tutorialsarxiv-cs-ai

Just Initialize: A Training-Free Initialization Component for Large-Scale Routing Optimization

arXiv:2609.35443v2 Announce Type: replace Abstract: Large-scale routing problems are difficult to solve efficiently as their search spaces grow rapidly with problem size. Existing approaches primarily

KernelOnet: An Interpretable Neural Operator Based on Kernel Functions

arXiv:2609.35938v1 Announce Type: new Abstract: This paper proposes an interpretable neural operator framework, the Kernel Operator Network (KernelOnet), which incorporates kernel functions explicitly

tutorialsarxiv-cs-lg

Kinematic Nonlinear Spatio-Temporal Trajectory Warping for Contact-Rich Dexterous Manipulation Demonstrations

arXiv:2609.36676v1 Announce Type: new Abstract: We present a straightforward but effective method for repurposing existing contact-rich dexterous manipulation demonstrations. Starting from inputs of h

researcharxiv-cs-ro

Know Thyself, Teach Thyself: Internal Information Flow for Selective Self-Distillation

arXiv:2609.36695v1 Announce Type: new Abstract: Self-distillation turns knowledge distillation into a closed learning loop and offers a path toward recursive self-improvement. Without an external teac

safetyarxiv-cs-lg

Knowing Is Not Choosing: What Explicit Verification Adds Beyond Generative Preference

model-releasesarxiv-cs-cl

arXiv:2609.33142v2 Announce Type: replace Abstract: Generating a correct answer does not mean that a language model will select it. We separate factual recall into three steps: generating a correct ca

Koa-action: Fast and Consistent Structured Decision Making with Generative LLMs

model-releasesarxiv-cs-lg

arXiv:2609.36115v1 Announce Type: new Abstract: Industry applications often demand low-latency classification, yet current large language model (LLM) approaches remain poorly suited for latency-critic

Kolmogorov-Arnold Classifier Systems as Universal Approximators

model-releasesarxiv-cs-lg

arXiv:2609.37958v1 Announce Type: new Abstract: As the input dimension n grows, rule-based machine learning, such as Learning Classifier Systems (LCSs), faces a fundamental scalability bottleneck for

KPI: A Promptable Kernel for Physical Interaction on Humanoids

arXiv:2609.36151v1 Announce Type: new Abstract: Humanoids now walk, balance and reach with remarkable generality: one whole-body tracking policy follows references from a human, or from an end-to-end

safetyarxiv-cs-ro

KT-EGO: A Knowledge Transfer Assisted Efficient Global Optimization Algorithm for Solving High-Dimensional Expensive Black-Box Problems

model-releasesarxiv-cs-lg

arXiv:2609.37473v1 Announce Type: cross Abstract: Many engineering problems involve optimizing a high-dimensional expensive black-box (HEB) design space. To solve such problems efficiently, we propose

KUPAS MASTER: Distilling the Tacit Expertise of Master Practitioners into Agent-Ready Experience Corpora

arXiv:2609.37673v1 Announce Type: new Abstract: Experienced professionals know more than just facts and conclusions. They know which cues matter, why a judgment is reasonable, and which action to take

Local Ai

KV-Kaizen: Learning Context-Adaptive Cache Compression Choices

arXiv:2609.37988v1 Announce Type: new Abstract: As the context size of text processed with an LLM grows, the size of KV caches can outstrip the memory allocated for the original model weights. This im

local-aiarxiv-cs-ai

Hardware

Label Less, Learn More: Resource-Efficient Active Semi-Supervised Learning for Onboard Satellite Image Annotation

arXiv:2609.37481v1 Announce Type: new Abstract: Large-scale pervasive sensing increasingly relies on high-resolution satellite imagery, yet task-specific onboard vision is constrained by costly annota

hardwarearxiv-cs-cv

Language as the Interface: Foundation-Model Contrastive Learning Links Transcriptomes and Electrophysiology

arXiv:2609.37024v1 Announce Type: new Abstract: Integrating transcriptomic and electrophysiological data is essential for building multimodal foundation models for neuroscience. Patch-seq provides pai

Language Models Are 'Insecure' Reporters

arXiv:2609.36139v1 Announce Type: cross Abstract: As large language models are deployed in increasingly autonomous long-horizon tasks, manually auditing and verifying the actions, artifacts, and outpu

Large Language Models Exhibit Human-Like Bayesian Hypocrisy

model-releasesarxiv-cs-cl

arXiv:2609.35779v1 Announce Type: cross Abstract: Given recent achievements of large language models (LLMs), frontier models are expected to perform well on Bayesian reasoning tasks, at least as well

Large-scale factor analysis shows machine intelligence is only partially interpretable

arXiv:2609.36515v1 Announce Type: cross Abstract: A common assumption in language model development is that cognitive abilities are organized around a general, domain-free intelligence factor, like fl

Larry Caused the Car to Stop, But the Model Didn't Notice: Transformer Blindness to the M-Heuristic

model-releasesarxiv-cs-cl

arXiv:2609.37497v1 Announce Type: new Abstract: Modern transformer models excel at capturing semantic relationships through sentence embeddings, yet their ability to perform pragmatic reasoning remain

Last night, we hosted our monthly @daytonaio AI Builders Demo Night at the @WorkOS office in San Francisco! Great demos, conversations, and …

Last night, we hosted our monthly @daytonaio AI Builders Demo Night at the @WorkOS office in San Francisco! Great demos, conversations, and energy from the AI builder community. Huge thanks to @llama_

agentsjerry-liu--x

Industry

Last Week in AI #345 - 5 new models, 9 misalignment incidents, some Dots

Last Week in AI #345 notes that Anthropic and OpenAI are in a close race to launch more capable but cheaper models this month: Anthropic unveiled Claude Opus 5.5 with stricter cybersecurity safeguards

industrylast-week-in-ai

LatCom: Cross-Agent Latent Compression for Efficient Multi-Agent Collaboration

arXiv:2609.37017v1 Announce Type: new Abstract: LLM-based multi-agent systems (MAS) increasingly use latent collaboration to avoid the information loss and repeated encoding-decoding overhead of natur

agentsarxiv-cs-cl

Latent Inference-Time Guidance of Time Series Foundation Models

arXiv:2609.38058v1 Announce Type: cross Abstract: Time Series Foundation Models (TSFMs) currently provide state-of-the-art results in forecasting tasks. They are available out-of-the-box and rely on i

researcharxiv-cs-lg

LatentSift: Policy-State Filtering for Token-Efficient Verification of Software Engineering Agents

arXiv:2609.36371v1 Announce Type: cross Abstract: Test-time scaling improves software engineering agents by generating multiple candidate trajectories and selecting the best one. Verifying and selecti

Applications

LAURA: Knowledge Distillation for Interpretable Ambiguous Clause Identification in Legal Contracts

arXiv:2609.36707v1 Announce Type: new Abstract: Legal contracts contain ambiguities that expose enterprises to financial and legal risks. Some ambiguities allow flexible interpretation without trigger

applicationsarxiv-cs-cl

Layer-Informed Fine-Tuning via Three-Stage Functional Segmentation of LLMs

arXiv:2609.38027v1 Announce Type: new Abstract: In recent years, the performance of large language models (LLMs) on reasoning tasks has been remarkable, even surpassing human capabilities on various b

researcharxiv-cs-cl

LazySloth: Bounded LLM-based Lazy Tree Search for Fast Long Video Comprehension

arXiv:2609.37426v1 Announce Type: cross Abstract: Modern vision-language models (VLMs) have shown promising results in long-video understanding due to the rich semantic information they can capture. H

LDM-is-AE: Latent Diffusion Model is an Auto-Encoder for End-to-End Image Generation

arXiv:2609.37080v1 Announce Type: new Abstract: Latent Diffusion Models (LDMs) typically adopt a two-stage pipeline: an auto-encoder (AE) is first pre-trained to define a latent space, then a diffusio

researcharxiv-cs-cv

LeapQuant: Efficient Linear Attention with Accurate Recurrent State Quantization

arXiv:2609.38166v1 Announce Type: cross Abstract: Recent LLMs increasingly adopt hybrid designs that replace standard attention with linear attention, such as Gated DeltaNet (GDN) and Kimi Delta Atten

Learn from the Gap: Differential-Aware Advantage Pruning with Adaptive Rollout Sampling for GRPO

arXiv:2609.36932v1 Announce Type: new Abstract: Recently, Group Relative Policy Optimization (GRPO) and its variants have been developed for policy optimization and demonstrated notable performance ga

Learn more ↓ https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/

model-releasesgoogle-ai--x

Google announced **Gemini 4 Argon**, described as the next generation of frontier intelligence in a September 30, 2026 blog post on Google AI’s site. The model represents an evolution of the Gemini se

Learn Now, Use Next, Trust Later: Prequential Test-Time Learning for LLM Agents

arXiv:2609.35911v1 Announce Type: cross Abstract: Adapting large language model agents during deployment requires not only retaining past experience, but also turning new observations into timely guid

Local Ai

Learned Compression of SAR Phase-History Data: A Rate-Honest Feasibility Study on GOTCHA

arXiv:2609.35848v1 Announce Type: new Abstract: On-board compression of synthetic aperture radar (SAR) phase history is bandwidth-critical, and block-adaptive quantization (BAQ) remains the operationa

local-aiarxiv-cs-lg

Learned Queries and Keys Are All You Need: Replacing the Value Projection with Structured Transforms

arXiv:2609.36698v1 Announce Type: new Abstract: To reduce the number of parameters and cache memory requirements of transformers we introduce dual-headed transformers instead of three heads. We studie

researcharxiv-cs-lg

Learned Reporting Preferences in RLVR Can Conflict with the Current Request

arXiv:2609.36587v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a prominent approach for improving language-model performance on reasoning tasks using

safetyarxiv-cs-lg

Learning as Deepfakes Evolve: RF-Prompt for Continual Audio Deepfake Detection

arXiv:2609.37586v1 Announce Type: cross Abstract: Continual audio deepfake detection requires learning newly emerging deepfake methods while retaining discrimination of previously encountered speech.

Learning Beyond What You Sample: Off-Policy-Aware Cross-Model Trajectory Exchange for RLVR

arXiv:2609.37868v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) methods such as GRPO rely on successful self-generated trajectories, but finite rollout budgets

Learning Causal Normalizing Flows from Incomplete Data via Observed-Data Likelihood

arXiv:2609.37664v1 Announce Type: new Abstract: Causal Normalizing Flows (CNFs) enable causal inference from observational data given the causal structure, but they assume fully observed training data

researcharxiv-cs-lg